add comments to explain the code better

.github/workflows/pr_test_fetcher.yml

@@ -1,175 +0,0 @@
-name: Fast tests for PRs - Test Fetcher
-
-on:
-  pull_request:
-    branches:
-      - main
-  push:
-    branches:
-      - ci-*
-
-env:
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 4
-  MKL_NUM_THREADS: 4
-  PYTEST_TIMEOUT: 60
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  setup_pr_tests:
-    name: Setup PR Tests
-    runs-on: docker-cpu
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-    defaults:
-      run:
-        shell: bash
-    outputs:
-      matrix: ${{ steps.set_matrix.outputs.matrix }}
-      test_map: ${{ steps.set_matrix.outputs.test_map }}
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-    - name: Install dependencies
-      run: |
-        apt-get update && apt-get install libsndfile1-dev libgl1 -y
-        python -m pip install -e .
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Fetch Tests
-      run: |
-        python utils/tests_fetcher.py | tee test_preparation.txt
-    - name: Report fetched tests
-      uses: actions/upload-artifact@v3
-      with:
-        name: test_fetched
-        path: test_preparation.txt
-    - id: set_matrix
-      name: Create Test Matrix
-      # The `keys` is used as GitHub actions matrix for jobs, i.e. `models`, `pipelines`, etc.
-      # The `test_map` is used to get the actual identified test files under each key.
-      # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail)
-      run: |
-        if [ -f test_map.json ]; then
-            keys=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); d = list(test_map.keys()); print(json.dumps(d))')
-            test_map=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); print(json.dumps(test_map))')
-        else
-            keys=$(python3 -c 'keys = ["dummy"]; print(keys)')
-            test_map=$(python3 -c 'test_map = {"dummy": []}; print(test_map)')
-        fi
-        echo $keys
-        echo $test_map
-        echo "matrix=$keys" >> $GITHUB_OUTPUT
-        echo "test_map=$test_map" >> $GITHUB_OUTPUT
-
-  run_pr_tests:
-    name: Run PR Tests
-    needs: setup_pr_tests
-    if: contains(fromJson(needs.setup_pr_tests.outputs.matrix), 'dummy') != true
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-      matrix:
-        modules: ${{ fromJson(needs.setup_pr_tests.outputs.matrix) }}
-    runs-on: docker-cpu
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        apt-get update && apt-get install libsndfile1-dev libgl1 -y
-        python -m pip install -e .[quality,test]
-        python -m pip install accelerate
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run all selected tests on CPU
-      run: |
-        python -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.modules }}_tests_cpu ${{ fromJson(needs.setup_pr_tests.outputs.test_map)[matrix.modules] }}
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      continue-on-error: true
-      run: |
-        cat reports/${{ matrix.modules }}_tests_cpu_stats.txt
-        cat reports/${{ matrix.modules }}_tests_cpu/failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v3
-      with:
-          name: ${{ matrix.modules }}_test_reports
-          path: reports
-
-  run_staging_tests:
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - name: Hub tests for models, schedulers, and pipelines
-            framework: hub_tests_pytorch
-            runner: docker-cpu
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_hub
-
-    name: ${{ matrix.config.name }}
-    runs-on: ${{ matrix.config.runner }}
-    container:
-      image: ${{ matrix.config.image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-
-    defaults:
-      run:
-        shell: bash
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        apt-get update && apt-get install libsndfile1-dev libgl1 -y
-        python -m pip install -e .[quality,test]
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run Hub tests for models, schedulers, and pipelines on a staging env
-      if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}
-      run: |
-        HUGGINGFACE_CO_STAGING=true python -m pytest \
-          -m "is_staging_test" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v2
-      with:
-        name: pr_${{ matrix.config.report }}_test_reports
-        path: reports

.github/workflows/pr_tests.yml

@@ -115,7 +115,7 @@ jobs:
       run: |
         python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
-          examples
+          examples/test_examples.py
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/push_tests_fast.yml

@@ -5,10 +5,6 @@ on:
     branches:
       - main
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
 env:
   DIFFUSERS_IS_CI: yes
   HF_HOME: /mnt/cache
@@ -100,7 +96,7 @@ jobs:
       run: |
         python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
-          examples
+          examples/test_examples.py 
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/push_tests_mps.yml

@@ -13,10 +13,6 @@ env:
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
 jobs:
   run_fast_tests_apple_m1:
     name: Fast PyTorch MPS tests on MacOS

PHILOSOPHY.md

@@ -82,7 +82,7 @@ Models are designed as configurable toolboxes that are natural extensions of [Py
 The following design principles are followed:
 - Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.
 - All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...
-- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.
+- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modelling files and shows that models do not really follow the single-file policy.
 - Models intend to expose complexity, just like PyTorch's `Module` class, and give clear error messages.
 - Models all inherit from `ModelMixin` and `ConfigMixin`.
 - Models can be optimized for performance when it doesn’t demand major code changes, keep backward compatibility, and give significant memory or compute gain.

docs/source/en/_toctree.yml

@@ -72,8 +72,6 @@
       title: Overview
     - local: using-diffusers/sdxl
       title: Stable Diffusion XL
-    - local: using-diffusers/sdxl_turbo
-      title: SDXL Turbo
     - local: using-diffusers/kandinsky
       title: Kandinsky
     - local: using-diffusers/controlnet
@@ -96,8 +94,6 @@
       title: Latent Consistency Model-LoRA
     - local: using-diffusers/inference_with_lcm
       title: Latent Consistency Model
-    - local: using-diffusers/svd
-      title: Stable Video Diffusion
     title: Specific pipeline examples
   - sections:
     - local: training/overview
@@ -133,8 +129,6 @@
         title: LoRA
       - local: training/custom_diffusion
         title: Custom Diffusion
-      - local: training/lcm_distill
-        title: Latent Consistency Distillation
       - local: training/ddpo
         title: Reinforcement learning training with DDPO
       title: Methods
@@ -284,8 +278,6 @@
       title: Kandinsky 2.1
     - local: api/pipelines/kandinsky_v22
       title: Kandinsky 2.2
-    - local: api/pipelines/kandinsky3
-      title: Kandinsky 3
     - local: api/pipelines/latent_consistency_models
       title: Latent Consistency Models
     - local: api/pipelines/latent_diffusion
@@ -335,14 +327,12 @@
         title: Stable Diffusion 2
       - local: api/pipelines/stable_diffusion/stable_diffusion_xl
         title: Stable Diffusion XL
-      - local: api/pipelines/stable_diffusion/sdxl_turbo
-        title: SDXL Turbo
       - local: api/pipelines/stable_diffusion/latent_upscale
         title: Latent upscaler
       - local: api/pipelines/stable_diffusion/upscale
         title: Super-resolution
       - local: api/pipelines/stable_diffusion/ldm3d_diffusion
-        title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
+        title: LDM3D Text-to-(RGB, Depth)
       - local: api/pipelines/stable_diffusion/adapter
         title: Stable Diffusion T2I-Adapter
       - local: api/pipelines/stable_diffusion/gligen

docs/source/en/api/pipelines/kandinsky3.md

@@ -1,49 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Kandinsky 3
-
-Kandinsky 3 is created by [Vladimir Arkhipkin](https://github.com/oriBetelgeuse),[Anastasia Maltseva](https://github.com/NastyaMittseva),[Igor Pavlov](https://github.com/boomb0om),[Andrei Filatov](https://github.com/anvilarth),[Arseniy Shakhmatov](https://github.com/cene555),[Andrey Kuznetsov](https://github.com/kuznetsoffandrey),[Denis Dimitrov](https://github.com/denndimitrov), [Zein Shaheen](https://github.com/zeinsh)
-
-The description from it's Github page: 
-
-*Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.*
-
-Its architecture includes 3 main components:
-1. [FLAN-UL2](https://huggingface.co/google/flan-ul2), which is an encoder decoder model based on the T5 architecture. 
-2. New U-Net architecture featuring BigGAN-deep blocks doubles depth while maintaining the same number of parameters.
-3. Sber-MoVQGAN is a decoder proven to have superior results in image restoration.
-
-
-
-The original codebase can be found at [ai-forever/Kandinsky-3](https://github.com/ai-forever/Kandinsky-3).
-
-<Tip>
-
-Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.
-
-</Tip>
-
-<Tip>
-
-Make sure to check out the schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## Kandinsky3Pipeline
-
-[[autodoc]] Kandinsky3Pipeline
-	- all
-	- __call__
-
-## Kandinsky3Img2ImgPipeline
-
-[[autodoc]] Kandinsky3Img2ImgPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/overview.md

@@ -51,10 +51,9 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 | [InstructPix2Pix](pix2pix) | image editing |
 | [Kandinsky 2.1](kandinsky) | text2image, image2image, inpainting, interpolation |
 | [Kandinsky 2.2](kandinsky_v22) | text2image, image2image, inpainting |
-| [Kandinsky 3](kandinsky3) | text2image, image2image |
 | [Latent Consistency Models](latent_consistency_models) | text2image |
 | [Latent Diffusion](latent_diffusion) | text2image, super-resolution |
-| [LDM3D](stable_diffusion/ldm3d_diffusion) | text2image, text-to-3D, text-to-pano, upscaling |
+| [LDM3D](stable_diffusion/ldm3d_diffusion) | text2image, text-to-3D |
 | [MultiDiffusion](panorama) | text2image |
 | [MusicLDM](musicldm) | text2audio |
 | [Paint by Example](paint_by_example) | inpainting |

docs/source/en/api/pipelines/pixart.md

@@ -35,112 +35,6 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 
 </Tip>
 
-## Inference with under 8GB GPU VRAM
-
-Run the [`PixArtAlphaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example. 
-
-First, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library:
-
-```bash
-pip install -U bitsandbytes
-```
-
-Then load the text encoder in 8-bit:
-
-```python
-from transformers import T5EncoderModel
-from diffusers import PixArtAlphaPipeline
-import torch
-
-text_encoder = T5EncoderModel.from_pretrained(
-    "PixArt-alpha/PixArt-XL-2-1024-MS",
-    subfolder="text_encoder",
-    load_in_8bit=True,
-    device_map="auto",
-
-)
-pipe = PixArtAlphaPipeline.from_pretrained(
-    "PixArt-alpha/PixArt-XL-2-1024-MS",
-    text_encoder=text_encoder,
-    transformer=None,
-    device_map="auto"
-)
-```
-
-Now, use the `pipe` to encode a prompt:
-
-```python
-with torch.no_grad():
-    prompt = "cute cat"
-    prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt)
-```
-
-Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up som GPU VRAM:
-
-```python
-import gc 
-
-def flush():
-    gc.collect()
-    torch.cuda.empty_cache()
-
-del text_encoder
-del pipe
-flush()
-```
-
-Then compute the latents with the prompt embeddings as inputs:
-
-```python
-pipe = PixArtAlphaPipeline.from_pretrained(
-    "PixArt-alpha/PixArt-XL-2-1024-MS",
-    text_encoder=None,
-    torch_dtype=torch.float16,
-).to("cuda")
-
-latents = pipe(
-    negative_prompt=None, 
-    prompt_embeds=prompt_embeds,
-    negative_prompt_embeds=negative_embeds,
-    prompt_attention_mask=prompt_attention_mask,
-    negative_prompt_attention_mask=negative_prompt_attention_mask,
-    num_images_per_prompt=1,
-    output_type="latent",
-).images
-
-del pipe.transformer
-flush()
-```
-
-<Tip>
-
-Notice that while initializing `pipe`, you're setting `text_encoder` to `None` so that it's not loaded.
-
-</Tip>
-
-Once the latents are computed, pass it off to the VAE to decode into a real image:
-
-```python
-with torch.no_grad():
-    image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
-image = pipe.image_processor.postprocess(image, output_type="pil")[0]
-image.save("cat.png")
-```
-
-By deleting components you aren't using and flushing the GPU VRAM, you should be able to run [`PixArtAlphaPipeline`] with under 8GB GPU VRAM.
-
-![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pixart/8bits_cat.png)
-
-If you want a report of your memory-usage, run this [script](https://gist.github.com/sayakpaul/3ae0f847001d342af27018a96f467e4e).
-
-<Tip warning={true}>
-
-Text embeddings computed in 8-bit can impact the quality of the generated images because of the information loss in the representation space caused by the reduced precision. It's recommended to compare the outputs with and without 8-bit.
-
-</Tip>
-
-While loading the `text_encoder`, you set `load_in_8bit` to `True`. You could also specify `load_in_4bit` to bring your memory requirements down even further to under 7GB.
-
 ## PixArtAlphaPipeline
 
 [[autodoc]] PixArtAlphaPipeline

docs/source/en/api/pipelines/stable_diffusion/ldm3d_diffusion.md

@@ -14,11 +14,6 @@ specific language governing permissions and limitations under the License.
 
 LDM3D was proposed in [LDM3D: Latent Diffusion Model for 3D](https://huggingface.co/papers/2305.10853) by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unlike the existing text-to-image diffusion models such as [Stable Diffusion](./overview) which only generates an image. With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps. 
 
-Two checkpoints are available for use:
-- [ldm3d-original](https://huggingface.co/Intel/ldm3d). The original checkpoint used in the [paper](https://arxiv.org/pdf/2305.10853.pdf)
-- [ldm3d-4c](https://huggingface.co/Intel/ldm3d-4c). The new version of LDM3D using 4 channels inputs instead of 6-channels inputs and finetuned on higher resolution images. 
-
-
 The abstract from the paper is:
 
 *This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at [this url](https://t.ly/tdi2).*
@@ -31,25 +26,12 @@ Make sure to check out the Stable Diffusion [Tips](overview#tips) section to lea
 
 ## StableDiffusionLDM3DPipeline
 
-[[autodoc]] pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d.StableDiffusionLDM3DPipeline
+[[autodoc]] StableDiffusionLDM3DPipeline
 	- all
 	- __call__
 
-
 ## LDM3DPipelineOutput
 
 [[autodoc]] pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d.LDM3DPipelineOutput
 	- all
 	- __call__
-
-# Upscaler
-
-[LDM3D-VR](https://arxiv.org/pdf/2311.03226.pdf) is an extended version of LDM3D. 
-
-The abstract from the paper is:
-*Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite of diffusion models targeting virtual reality development that includes LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD based on textual prompts and the upscaling of low-resolution inputs to high-resolution RGBD, respectively. Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions. Both models are evaluated in comparison to existing related methods*
-
-Two checkpoints are available for use:
-- [ldm3d-pano](https://huggingface.co/Intel/ldm3d-pano). This checkpoint enables the generation of panoramic images and requires the StableDiffusionLDM3DPipeline pipeline to be used.
-- [ldm3d-sr](https://huggingface.co/Intel/ldm3d-sr). This checkpoint enables the upscaling of RGB and depth images. Can be used in cascade after the original LDM3D pipeline using the StableDiffusionUpscaleLDM3DPipeline from communauty pipeline.
-

docs/source/en/api/pipelines/stable_diffusion/overview.md

@@ -121,16 +121,10 @@ The table below summarizes the available Stable Diffusion pipelines, their suppo
             <td class="px-4 py-2 text-gray-700">
             <a href="./ldm3d_diffusion">StableDiffusionLDM3D</a>
             </td>
-            <td class="px-4 py-2 text-gray-700">text-to-rgb, text-to-depth, text-to-pano</td>
+            <td class="px-4 py-2 text-gray-700">text-to-rgb, text-to-depth</td>
             <td class="px-4 py-2"><a href="https://huggingface.co/spaces/r23/ldm3d-space"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
             </td>
         </tr>
-        <tr>
-            <td class="px-4 py-2 text-gray-700">
-            <a href="./ldm3d_diffusion">StableDiffusionUpscaleLDM3D</a>
-            </td>
-            <td class="px-4 py-2 text-gray-700">ldm3d super-resolution</td>
-        </tr>
         </tbody>
     </table>
     </div>

docs/source/en/api/pipelines/stable_diffusion/sdxl_turbo.md

@@ -1,53 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# SDXL Turbo
-
-Stable Diffusion XL (SDXL) Turbo was proposed in [Adversarial Diffusion Distillation](https://stability.ai/research/adversarial-diffusion-distillation) by Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach.
-
-The abstract from the paper is:
-
-*We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1–4 steps while maintaining high image quality. We use score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal in combination with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. Our analyses show that our model clearly outperforms existing few-step methods (GANs,Latent Consistency Models) in a single step and reaches the performance of state-of-the-art diffusion models (SDXL) in only four steps. ADD is the first method to unlock single-step, real-time image synthesis with foundation models.*
-
-## Tips
-
-- SDXL Turbo uses the exact same architecture as [SDXL](./stable_diffusion_xl).
-- SDXL Turbo should disable guidance scale by setting `guidance_scale=0.0`
-- SDXL Turbo should use `timestep_spacing='trailing'` for the scheduler and use between 1 and 4 steps.
-- SDXL Turbo has been trained to generate images of size 512x512.
-- SDXL Turbo is open-access, but not open-source meaning that one might have to buy a model license in order to use it for commercial applications. Make sure to read the [official model card](https://huggingface.co/stabilityai/sdxl-turbo) to learn more.
-
-<Tip>
-
-To learn how to use SDXL Turbo for various tasks, how to optimize performance, and other usage examples, take a look at the [Stable Diffusion XL](../../../using-diffusers/sdxl_turbo) guide.
-
-Check out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the official base and refiner model checkpoints!
-
-</Tip>
-
-## StableDiffusionXLPipeline
-
-[[autodoc]] StableDiffusionXLPipeline
-	- all
-	- __call__
-
-## StableDiffusionXLImg2ImgPipeline
-
-[[autodoc]] StableDiffusionXLImg2ImgPipeline
-	- all
-	- __call__
-
-## StableDiffusionXLInpaintPipeline
-
-[[autodoc]] StableDiffusionXLInpaintPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/text_to_video_zero.md

@@ -92,19 +92,6 @@ imageio.mimsave("video.mp4", result, fps=4)
 ```
 
 
-- #### SDXL Support
-In order to use the SDXL model when generating a video from prompt, use the `TextToVideoZeroSDXLPipeline` pipeline:
-
-```python
-import torch
-from diffusers import TextToVideoZeroSDXLPipeline
-
-model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-pipe = TextToVideoZeroSDXLPipeline.from_pretrained(
-    model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
-```
-
 ### Text-To-Video with Pose Control
 To generate a video from prompt with additional pose control
 
@@ -154,33 +141,7 @@ To generate a video from prompt with additional pose control
     result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images
     imageio.mimsave("video.mp4", result, fps=4)
     ```
-- #### SDXL Support
-	
-	Since our attention processor also works with SDXL, it can be utilized to generate a video from prompt using ControlNet models powered by SDXL:
-	```python
-	import torch
-	from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
-	from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
-	
-	controlnet_model_id = 'thibaud/controlnet-openpose-sdxl-1.0'
-	model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
-	
-	controlnet = ControlNetModel.from_pretrained(controlnet_model_id, torch_dtype=torch.float16)
-	pipe = StableDiffusionControlNetPipeline.from_pretrained(
-		model_id, controlnet=controlnet, torch_dtype=torch.float16
-	).to('cuda')
-	
-	# Set the attention processor
-	pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
-	pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
-	
-	# fix latents for all frames
-	latents = torch.randn((1, 4, 128, 128), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)
-	
-	prompt = "Darth Vader dancing in a desert"
-	result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images
-	imageio.mimsave("video.mp4", result, fps=4)
-	```
+
 
 ### Text-To-Video with Edge Control
 
@@ -292,10 +253,5 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 	- all
 	- __call__
 
-## TextToVideoZeroSDXLPipeline
-[[autodoc]] TextToVideoZeroSDXLPipeline
-	- all
-	- __call__
-
 ## TextToVideoPipelineOutput
 [[autodoc]] pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput

docs/source/en/api/schedulers/score_sde_vp.md

@@ -25,4 +25,4 @@ The abstract from the paper is:
 </Tip>
 
 ## ScoreSdeVpScheduler
-[[autodoc]] schedulers.deprecated.scheduling_sde_vp.ScoreSdeVpScheduler
+[[autodoc]] schedulers.scheduling_sde_vp.ScoreSdeVpScheduler

docs/source/en/api/schedulers/stochastic_karras_ve.md

@@ -18,4 +18,4 @@ specific language governing permissions and limitations under the License.
 [[autodoc]] KarrasVeScheduler
 
 ## KarrasVeOutput
-[[autodoc]] schedulers.deprecated.scheduling_karras_ve.KarrasVeOutput
+[[autodoc]] schedulers.scheduling_karras_ve.KarrasVeOutput

docs/source/en/training/lcm_distill.md

@@ -1,255 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Latent Consistency Distillation
-
-[Latent Consistency Models (LCMs)](https://hf.co/papers/2310.04378) are able to generate high-quality images in just a few steps, representing a big leap forward because many pipelines require at least 25+ steps. LCMs are produced by applying the latent consistency distillation method to any Stable Diffusion model. This method works by applying *one-stage guided distillation* to the latent space, and incorporating a *skipping-step* method to consistently skip timesteps to accelerate the distillation process (refer to section 4.1, 4.2, and 4.3 of the paper for more details).
-
-If you're training on a GPU with limited vRAM, try enabling `gradient_checkpointing`, `gradient_accumulation_steps`, and `mixed_precision` to reduce memory-usage and speedup training. You can reduce your memory-usage even more by enabling memory-efficient attention with [xFormers](../optimization/xformers) and [bitsandbytes'](https://github.com/TimDettmers/bitsandbytes) 8-bit optimizer.
-
-This guide will explore the [train_lcm_distill_sd_wds.py](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_sd_wds.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
-
-Before running the script, make sure you install the library from source:
-
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
-
-```bash
-cd examples/consistency_distillation
-pip install -r requirements.txt
-```
-
-<Tip>
-
-🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.
-
-</Tip>
-
-Initialize an 🤗 Accelerate environment (try enabling `torch.compile` to significantly speedup training):
-
-```bash
-accelerate config
-```
-
-To setup a default 🤗 Accelerate environment without choosing any configurations:
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell, like a notebook, you can use:
-
-```bash
-from accelerate.utils import write_basic_config
-
-write_basic_config()
-```
-
-Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
-
-## Script parameters
-
-<Tip>
-
-The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_sd_wds.py) and let us know if you have any questions or concerns.
-
-</Tip>
-
-The training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L419) function. This function provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.
-
-For example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:
-
-```bash
-accelerate launch train_lcm_distill_sd_wds.py \
-  --mixed_precision="fp16"
-```
-
-Most of the parameters are identical to the parameters in the [Text-to-image](text2image#script-parameters) training guide, so you'll focus on the parameters that are relevant to latent consistency distillation in this guide.
-
-- `--pretrained_teacher_model`: the path to a pretrained latent diffusion model to use as the teacher model
-- `--pretrained_vae_model_name_or_path`: path to a pretrained VAE; the SDXL VAE is known to suffer from numerical instability, so this parameter allows you to specify an alternative VAE (like this [VAE]((https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)) by madebyollin which works in fp16)
-- `--w_min` and `--w_max`: the minimum and maximum guidance scale values for guidance scale sampling
-- `--num_ddim_timesteps`: the number of timesteps for DDIM sampling
-- `--loss_type`: the type of loss (L2 or Huber) to calculate for latent consistency distillation; Huber loss is generally preferred because it's more robust to outliers
-- `--huber_c`: the Huber loss parameter
-
-## Training script
-
-The training script starts by creating a dataset class - [`Text2ImageDataset`](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L141) - for preprocessing the images and creating a training dataset.
-
-```py
-def transform(example):
-    image = example["image"]
-    image = TF.resize(image, resolution, interpolation=transforms.InterpolationMode.BILINEAR)
-
-    c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
-    image = TF.crop(image, c_top, c_left, resolution, resolution)
-    image = TF.to_tensor(image)
-    image = TF.normalize(image, [0.5], [0.5])
-
-    example["image"] = image
-    return example
-```
-
-For improved performance on reading and writing large datasets stored in the cloud, this script uses the [WebDataset](https://github.com/webdataset/webdataset) format to create a preprocessing pipeline to apply transforms and create a dataset and dataloader for training. Images are processed and fed to the training loop without having to download the full dataset first.
-
-```py
-processing_pipeline = [
-    wds.decode("pil", handler=wds.ignore_and_continue),
-    wds.rename(image="jpg;png;jpeg;webp", text="text;txt;caption", handler=wds.warn_and_continue),
-    wds.map(filter_keys({"image", "text"})),
-    wds.map(transform),
-    wds.to_tuple("image", "text"),
-]
-```
-
-In the [`main()`](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L768) function, all the necessary components like the noise scheduler, tokenizers, text encoders, and VAE are loaded. The teacher UNet is also loaded here and then you can create a student UNet from the teacher UNet. The student UNet is updated by the optimizer during training.
-
-```py
-teacher_unet = UNet2DConditionModel.from_pretrained(
-    args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
-)
-
-unet = UNet2DConditionModel(**teacher_unet.config)
-unet.load_state_dict(teacher_unet.state_dict(), strict=False)
-unet.train()
-```
-
-Now you can create the [optimizer](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L979) to update the UNet parameters:
-
-```py
-optimizer = optimizer_class(
-    unet.parameters(),
-    lr=args.learning_rate,
-    betas=(args.adam_beta1, args.adam_beta2),
-    weight_decay=args.adam_weight_decay,
-    eps=args.adam_epsilon,
-)
-```
-
-Create the [dataset](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L994):
-
-```py
-dataset = Text2ImageDataset(
-    train_shards_path_or_url=args.train_shards_path_or_url,
-    num_train_examples=args.max_train_samples,
-    per_gpu_batch_size=args.train_batch_size,
-    global_batch_size=args.train_batch_size * accelerator.num_processes,
-    num_workers=args.dataloader_num_workers,
-    resolution=args.resolution,
-    shuffle_buffer_size=1000,
-    pin_memory=True,
-    persistent_workers=True,
-)
-train_dataloader = dataset.train_dataloader
-```
-
-Next, you're ready to setup the [training loop](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L1049) and implement the latent consistency distillation method (see Algorithm 1 in the paper for more details). This section of the script takes care of adding noise to the latents, sampling and creating a guidance scale embedding, and predicting the original image from the noise.
-
-```py
-pred_x_0 = predicted_origin(
-    noise_pred,
-    start_timesteps,
-    noisy_model_input,
-    noise_scheduler.config.prediction_type,
-    alpha_schedule,
-    sigma_schedule,
-)
-
-model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0
-```
-
-It gets the [teacher model predictions](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L1172) and the [LCM predictions](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L1209) next, calculates the loss, and then backpropagates it to the LCM.
-
-```py
-if args.loss_type == "l2":
-    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-elif args.loss_type == "huber":
-    loss = torch.mean(
-        torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c
-    )
-```
-
-If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers tutorial](../using-diffusers/write_own_pipeline) which breaks down the basic pattern of the denoising process.
-
-## Launch the script
-
-Now you're ready to launch the training script and start distilling!
-
-For this guide, you'll use the `--train_shards_path_or_url` to specify the path to the [Conceptual Captions 12M](https://github.com/google-research-datasets/conceptual-12m) dataset stored on the Hub [here](https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset). Set the `MODEL_DIR` environment variable to the name of the teacher model and `OUTPUT_DIR` to where you want to save the model.
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path/to/saved/model"
-
-accelerate launch train_lcm_distill_sd_wds.py \
-    --pretrained_teacher_model=$MODEL_DIR \
-    --output_dir=$OUTPUT_DIR \
-    --mixed_precision=fp16 \
-    --resolution=512 \
-    --learning_rate=1e-6 --loss_type="huber" --ema_decay=0.95 --adam_weight_decay=0.0 \
-    --max_train_steps=1000 \
-    --max_train_samples=4000000 \
-    --dataloader_num_workers=8 \
-    --train_shards_path_or_url="pipe:curl -L -s https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset/resolve/main/data/{00000..01099}.tar?download=true" \
-    --validation_steps=200 \
-    --checkpointing_steps=200 --checkpoints_total_limit=10 \
-    --train_batch_size=12 \
-    --gradient_checkpointing --enable_xformers_memory_efficient_attention \
-    --gradient_accumulation_steps=1 \
-    --use_8bit_adam \
-    --resume_from_checkpoint=latest \
-    --report_to=wandb \
-    --seed=453645634 \
-    --push_to_hub
-```
-
-Once training is complete, you can use your new LCM for inference.
-
-```py
-from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler
-import torch
-
-unet = UNet2DConditionModel.from_pretrained("your-username/your-model", torch_dtype=torch.float16, variant="fp16")
-pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", unet=unet, torch_dtype=torch.float16, variant="fp16")
-
-pipeline.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-pipeline.to("cuda")
-
-prompt = "sushi rolls in the form of panda heads, sushi platter"
-
-image = pipeline(prompt, num_inference_steps=4, guidance_scale=1.0).images[0]
-```
-
-## LoRA
-
-LoRA is a training technique for significantly reducing the number of trainable parameters. As a result, training is faster and it is easier to store the resulting weights because they are a lot smaller (~100MBs). Use the [train_lcm_distill_lora_sd_wds.py](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py) or [train_lcm_distill_lora_sdxl.wds.py](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py) script to train with LoRA.
-
-The LoRA training script is discussed in more detail in the [LoRA training](lora) guide.
-
-## Stable Diffusion XL
-
-Stable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the [train_lcm_distill_sdxl_wds.py](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py) script to train a SDXL model with LoRA.
-
-The SDXL training script is discussed in more detail in the [SDXL training](sdxl) guide.
-
-## Next steps
-
-Congratulations on distilling a LCM model! To learn more about LCM, the following may be helpful:
-
-- Learn how to use [LCMs for inference](../using-diffusers/lcm) for text-to-image, image-to-image, and with LoRA checkpoints.
-- Read the [SDXL in 4 steps with Latent Consistency LoRAs](https://huggingface.co/blog/lcm_lora) blog post to learn more about SDXL LCM-LoRA's for super fast inference, quality comparisons, benchmarks, and more.

docs/source/en/using-diffusers/loading_adapters.md

@@ -307,331 +307,3 @@ prompt = "a house by william eggleston, sunrays, beautiful, sunlight, sunrays, b
 image = pipeline(prompt=prompt).images[0]
 image
 ```
-
-## IP-Adapter 
-
-[IP-Adapter](https://ip-adapter.github.io/) is an effective and lightweight adapter that adds image prompting capabilities to a diffusion model. This adapter works by decoupling the cross-attention layers of the image and text features. All the other model components are frozen and only the embedded image features in the UNet are trained. As a result, IP-Adapter files are typically only ~100MBs.
-
-IP-Adapter works with most of our pipelines, including Stable Diffusion, Stable Diffusion XL (SDXL), ControlNet, T2I-Adapter, AnimateDiff.  And you can use any custom models finetuned from the same base models. It also works with LCM-Lora out of box.
-
-
-<Tip>
-
-You can find official IP-Adapter checkpoints in [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter).
-
-IP-Adapter was contributed by [okotaku](https://github.com/okotaku).
-
-</Tip>
-
-Let's first create a Stable Diffusion Pipeline.
-
-```py
-from diffusers import AutoPipelineForText2Image
-import torch
-from diffusers.utils import load_image
-
-
-pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
-```
-
-Now load the [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) weights with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method. 
-
-```py
-pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
-```
-
-<Tip>
-IP-Adapter relies on an image encoder to generate the image features, if your IP-Adapter weights folder contains a "image_encoder" subfolder, the image encoder will be automatically loaded and registered to the pipeline. Otherwise you can so load a [`~transformers.CLIPVisionModelWithProjection`] model and  pass it to a Stable Diffusion pipeline when you create it.
-
-```py
-from diffusers import AutoPipelineForText2Image, CLIPVisionModelWithProjection
-import torch
-
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(
-    "h94/IP-Adapter", 
-    subfolder="models/image_encoder",
-    torch_dtype=torch.float16,
-).to("cuda")
-
-pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, torch_dtype=torch.float16).to("cuda")
-```
-</Tip>
-
-IP-Adapter allows you to use both image and text to condition the image generation process. For example, let's use the bear image from the [Textual Inversion](#textual-inversion) section as the image prompt (`ip_adapter_image`) along with a text prompt to add "sunglasses". 😎
-
-```py
-pipeline.set_ip_adapter_scale(0.6)
-image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png")
-generator = torch.Generator(device="cpu").manual_seed(33)
-images = pipeline(
-    prompt='best quality, high quality, wearing sunglasses', 
-    ip_adapter_image=image,
-    negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", 
-    num_inference_steps=50,
-    generator=generator,
-).images
-images[0]
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip-bear.png" />
-</div>
-
-<Tip>
-
-You can use the [`~loaders.IPAdapterMixin.set_ip_adapter_scale`] method to adjust the text prompt and image prompt condition ratio.  If you're only using the image prompt, you should set the scale to `1.0`. You can lower the scale to get more generation diversity, but it'll be less aligned with the prompt.
-`scale=0.5` can achieve good results in most cases when you use both text and image prompts.
-</Tip>
-
-IP-Adapter also works great with Image-to-Image and Inpainting pipelines. See below examples of how you can use it with Image-to-Image and Inpaint.
-
-<hfoptions id="tasks">
-<hfoption id="image-to-image">
-
-```py
-from diffusers import AutoPipelineForImage2Image
-import torch
-from diffusers.utils import load_image
-
-pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
-
-image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg")
-ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/river.png")
-
-pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
-generator = torch.Generator(device="cpu").manual_seed(33)
-images = pipeline(
-    prompt='best quality, high quality', 
-    image = image,
-    ip_adapter_image=ip_image,
-    num_inference_steps=50,
-    generator=generator,
-    strength=0.6,
-).images
-images[0]
-```
-
-</hfoption>
-<hfoption id="inpaint">
-
-```py
-from diffusers import AutoPipelineForInpaint
-import torch
-from diffusers.utils import load_image
-
-pipeline = AutoPipelineForInpaint.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float).to("cuda")
-
-image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/inpaint_image.png")
-mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/mask.png")
-ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/girl.png")
-
-image = image.resize((512, 768))
-mask = mask.resize((512, 768))
-
-pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
-
-generator = torch.Generator(device="cpu").manual_seed(33)
-images = pipeline(
-    prompt='best quality, high quality', 
-    image = image,
-    mask_image = mask,
-    ip_adapter_image=ip_image,
-    negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", 
-    num_inference_steps=50,
-    generator=generator,
-    strength=0.5,
-).images
-images[0]
-```
-</hfoption>
-</hfoptions>
-
-
-IP-Adapters can also be used with [SDXL](../api/pipelines/stable_diffusion/stable_diffusion_xl.md)
-
-```python
-from diffusers import AutoPipelineForText2Image
-from diffusers.utils import load_image
-import torch
-
-pipeline = AutoPipelineForText2Image.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    torch_dtype=torch.float16
-).to("cuda")
-
-image = load_image("https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/watercolor_painting.jpeg")
-
-pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
-
-generator = torch.Generator(device="cpu").manual_seed(33)
-image = pipeline(
-    prompt="best quality, high quality", 
-    ip_adapter_image=image,
-    negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", 
-    num_inference_steps=25,
-    generator=generator,
-).images[0]
-image.save("sdxl_t2i.png")
-```
-
-<div class="flex flex-row gap-4">
-  <div class="flex-1">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/watercolor_painting.jpeg"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">input image</figcaption>
-  </div>
-  <div class="flex-1">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/sdxl_t2i.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">adapted image</figcaption>
-  </div>
-</div>
-
-
-### LCM-Lora
-
-You can use IP-Adapter with LCM-Lora to achieve "instant fine-tune" with custom images. Note that you need to load IP-Adapter weights before loading the LCM-Lora weights.
-
-```py
-from diffusers import DiffusionPipeline, LCMScheduler
-import torch
-from diffusers.utils import load_image
-
-model_id =  "sd-dreambooth-library/herge-style"
-lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
-
-pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-
-pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
-pipe.load_lora_weights(lcm_lora_id)
-pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-
-prompt = "best quality, high quality"
-image = load_image("https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png")
-images = pipe(
-    prompt=prompt,
-    ip_adapter_image=image,
-    num_inference_steps=4,
-    guidance_scale=1,
-).images[0]
-```
-
-### Other pipelines
-
-IP-Adapter is compatible with any pipeline that (1) uses a text prompt and (2) uses Stable Diffusion or Stable Diffusion XL checkpoint. To use IP-Adapter with a different pipeline, all you need to do is to run `load_ip_adapter()` method after you create the pipeline, and then pass your image to the pipeline as `ip_adapter_image`
-
-<Tip>
-
-🤗 Diffusers currently only supports using IP-Adapter with some of the most popular pipelines, feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you have a cool use-case and require integrating IP-adapters with a pipeline that does not support it yet!
-
-</Tip>
-
-You can find below examples on how to use IP-Adapter with ControlNet and AnimateDiff. 
-
-<hfoptions id="model">
-<hfoption id="ControlNet">
-
-```
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-from diffusers.utils import load_image
-
-controlnet_model_path = "lllyasviel/control_v11f1p_sd15_depth"
-controlnet = ControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.float16)
-
-pipeline = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16)
-pipeline.to("cuda")
-
-image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/statue.png")
-depth_map = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/depth.png")
-
-pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
-
-generator = torch.Generator(device="cpu").manual_seed(33)
-images = pipeline(
-    prompt='best quality, high quality', 
-    image=depth_map,
-    ip_adapter_image=image,
-    negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", 
-    num_inference_steps=50,
-    generator=generator,
-).images
-images[0]
-```
-<div class="flex flex-row gap-4">
-  <div class="flex-1">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/statue.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">input image</figcaption>
-  </div>
-  <div class="flex-1">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ipa-controlnet-out.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">adapted image</figcaption>
-  </div>
-</div>
-
-</hfoption>
-<hfoption id="AnimateDiff">
-
-```py
-# animate diff + ip adapter
-import torch
-from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
-from diffusers.utils import export_to_gif, load_image
-
-# Load the motion adapter
-adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
-# load SD 1.5 based finetuned model
-model_id = "Lykon/DreamShaper"
-pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
-
-# scheduler
-scheduler = DDIMScheduler(
-    clip_sample=False,
-    beta_start=0.00085,
-    beta_end=0.012,
-    beta_schedule="linear",
-    timestep_spacing="trailing",
-    steps_offset=1
-)
-pipe.scheduler = scheduler
-
-# enable memory savings
-pipe.enable_vae_slicing()
-pipe.enable_model_cpu_offload()
-
-# load ip_adapter
-pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
-
-# load motion adapters
-pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out")
-pipe.load_lora_weights("guoyww/animatediff-motion-lora-tilt-up", adapter_name="tilt-up")
-pipe.load_lora_weights("guoyww/animatediff-motion-lora-pan-left", adapter_name="pan-left")
-
-seed = 42
-image = load_image("https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png")
-images = [image] * 3
-prompts = ["best quality, high quality"] * 3
-negative_prompt = "bad quality, worst quality"
-adapter_weights = [[0.75, 0.0, 0.0], [0.0, 0.0, 0.75], [0.0, 0.75, 0.75]]
-
-# generate
-output_frames = []
-for prompt, image, adapter_weight in zip(prompts, images, adapter_weights):
-    pipe.set_adapters(["zoom-out", "tilt-up", "pan-left"], adapter_weights=adapter_weight)
-    output = pipe(
-      prompt= prompt,
-      num_frames=16,
-      guidance_scale=7.5,
-      num_inference_steps=30,
-      ip_adapter_image = image,
-      generator=torch.Generator("cpu").manual_seed(seed),
-    )
-    frames = output.frames[0]
-    output_frames.extend(frames)
-
-export_to_gif(output_frames, "test_out_animation.gif") 
-```
-
-</hfoption>
-</hfoptions>
-

docs/source/en/using-diffusers/sdxl_turbo.md

@@ -1,116 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Stable Diffusion XL Turbo
-
-[[open-in-colab]]
-
-SDXL Turbo is an adversarial time-distilled [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (SDXL) model capable
-of running inference in as little as 1 step.
-
-This guide will show you how to use SDXL-Turbo for text-to-image and image-to-image.
-
-Before you begin, make sure you have the following libraries installed:
-
-```py
-# uncomment to install the necessary libraries in Colab
-#!pip install -q diffusers transformers accelerate omegaconf
-```
-
-## Load model checkpoints
-
-Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~StableDiffusionXLPipeline.from_pretrained`] method:
-
-```py
-from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
-import torch
-
-pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
-pipeline = pipeline.to("cuda")
-```
-
-You can also use the [`~StableDiffusionXLPipeline.from_single_file`] method to load a model checkpoint stored in a single file format (`.ckpt` or `.safetensors`) from the Hub or locally:
-
-```py
-from diffusers import StableDiffusionXLPipeline
-import torch
-
-pipeline = StableDiffusionXLPipeline.from_single_file(
-    "https://huggingface.co/stabilityai/sdxl-turbo/blob/main/sd_xl_turbo_1.0_fp16.safetensors", torch_dtype=torch.float16)
-pipeline = pipeline.to("cuda")
-```
-
-## Text-to-image
-
-For text-to-image, pass a text prompt. By default, SDXL Turbo generates a 512x512 image, and that resolution gives the best results. You can try setting the `height` and `width` parameters to 768x768 or 1024x1024, but you should expect quality degradations when doing so.
-
-Make sure to set `guidance_scale` to 0.0 to disable, as the model was trained without it. A single inference step is enough to generate high quality images. 
-Increasing the number of steps to 2, 3 or 4 should improve image quality.
-
-```py
-from diffusers import AutoPipelineForText2Image
-import torch
-
-pipeline_text2image = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
-pipeline_text2image = pipeline_text2image.to("cuda")
-
-prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
-
-image = pipeline_text2image(prompt=prompt, guidance_scale=0.0, num_inference_steps=1).images[0]
-image
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sdxl-turbo-text2img.png" alt="generated image of a racoon in a robe"/>
-</div>
-
-## Image-to-image
-
-For image-to-image generation, make sure that `num_inference_steps * strength` is larger or equal to 1. 
-The image-to-image pipeline will run for `int(num_inference_steps * strength)` steps, e.g. `0.5 * 2.0 = 1` step in
-our example below.
-
-```py
-from diffusers import AutoPipelineForImage2Image
-from diffusers.utils import load_image, make_image_grid
-
-# use from_pipe to avoid consuming additional memory when loading a checkpoint
-pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
-
-init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
-init_image = init_image.resize((512, 512))
-
-prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"
-
-image = pipeline(prompt, image=init_image, strength=0.5, guidance_scale=0.0, num_inference_steps=2).images[0]
-make_image_grid([init_image, image], rows=1, cols=2)
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sdxl-turbo-img2img.png" alt="Image-to-image generation sample using SDXL Turbo"/>
-</div>
-
-## Speed-up SDXL Turbo even more
-
-- Compile the UNet if you are using PyTorch version 2 or better. The first inference run will be very slow, but subsequent ones will be much faster.
-
-```py
-pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-```
-
-- When using the default VAE, keep it in `float32` to avoid costly `dtype` conversions before and after each generation. You only need to do this one before your first generation:
-
-```py
-pipe.upcast_vae()
-```
-
-As an alternative, you can also use a [16-bit VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) created by community member [`@madebyollin`](https://huggingface.co/madebyollin) that does not need to be upcasted to `float32`.

docs/source/en/using-diffusers/svd.md

@@ -1,133 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Stable Video Diffusion
-
-[[open-in-colab]]
-
-[Stable Video Diffusion](https://static1.squarespace.com/static/6213c340453c3f502425776e/t/655ce779b9d47d342a93c890/1700587395994/stable_video_diffusion.pdf) is a powerful image-to-video generation model that can generate high resolution (576x1024) 2-4 second videos conditioned on the input image.
-
-This guide will show you how to use SVD to short generate videos from images.
-
-Before you begin, make sure you have the following libraries installed:
-
-```py
-!pip install -q -U diffusers transformers accelerate 
-```
-
-## Image to Video Generation
-
-The are two variants of SVD. [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) 
-and [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt). The svd checkpoint is trained to generate 14 frames and the svd-xt checkpoint is further 
-finetuned to generate 25 frames.
-
-We will use the `svd-xt` checkpoint for this guide.
-
-```python
-import torch
-
-from diffusers import StableVideoDiffusionPipeline
-from diffusers.utils import load_image, export_to_video
-
-pipe = StableVideoDiffusionPipeline.from_pretrained(
-    "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
-)
-pipe.enable_model_cpu_offload()
-
-# Load the conditioning image
-image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true")
-image = image.resize((1024, 576))
-
-generator = torch.manual_seed(42)
-frames = pipe(image, decode_chunk_size=8, generator=generator).frames[0]
-
-export_to_video(frames, "generated.mp4", fps=7)
-```
-
-<video width="1024" height="576" controls>
-  <source src="https://i.imgur.com/jJzVDKw.mp4" type="video/mp4">
-</video>
-
-<Tip>
-Since generating videos is more memory intensive we can use the `decode_chunk_size` argument to control how many frames are decoded at once. This will reduce the memory usage. It's recommended to tweak this value based on your GPU memory.
-Setting `decode_chunk_size=1` will decode one frame at a time and will use the least amount of memory but the video might have some flickering.
-
-Additionally, we also use [model cpu offloading](../../optimization/memory#model-offloading) to reduce the memory usage.
-</Tip>
-
-
-### Torch.compile
-
-You can achieve a 20-25% speed-up at the expense of slightly increased memory by compiling the UNet as follows:
-
-```diff
-- pipe.enable_model_cpu_offload()
-+ pipe.to("cuda")
-+ pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-```
-
-### Low-memory
-
-Video generation is very memory intensive as we have to essentially generate `num_frames` all at once. The mechanism is very comparable to text-to-image generation with a high batch size. To reduce the memory requirement you have multiple options. The following options trade inference speed against lower memory requirement:
-- enable model offloading: Each component of the pipeline is offloaded to CPU once it's not needed anymore.
-- enable feed-forward chunking: The feed-forward layer runs in a loop instead of running with a single huge feed-forward batch size
-- reduce `decode_chunk_size`: This means that the VAE decodes frames in chunks instead of decoding them all together. **Note**: In addition to leading to a small slowdown, this method also slightly leads to video quality deterioration
-
-You can enable them as follows:
-
-```diff
--pipe.enable_model_cpu_offload()
--frames = pipe(image, decode_chunk_size=8, generator=generator).frames[0]
-+pipe.enable_model_cpu_offload()
-+pipe.unet.enable_forward_chunking()
-+frames = pipe(image, decode_chunk_size=2, generator=generator, num_frames=25).frames[0]
-```
-
-
-Including all these tricks should lower the memory requirement to less than 8GB VRAM.
-
-### Micro-conditioning
-
-Along with conditioning image Stable Diffusion Video also allows providing micro-conditioning that allows more control over the generated video.
-It accepts the following arguments:
-
-- `fps`: The frames per second of the generated video.
-- `motion_bucket_id`: The motion bucket id to use for the generated video. This can be used to control the motion of the generated video. Increasing the motion bucket id will increase the motion of the generated video.
-- `noise_aug_strength`: The amount of noise added to the conditioning image. The higher the values the less the video will resemble the conditioning image. Increasing this value will also increase the motion of the generated video.
-
-Here is an example of using micro-conditioning to generate a video with more motion.
-
-
-```python
-import torch
-
-from diffusers import StableVideoDiffusionPipeline
-from diffusers.utils import load_image, export_to_video
-
-pipe = StableVideoDiffusionPipeline.from_pretrained(
-  "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
-)
-pipe.enable_model_cpu_offload()
-
-# Load the conditioning image
-image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true")
-image = image.resize((1024, 576))
-
-generator = torch.manual_seed(42)
-frames = pipe(image, decode_chunk_size=8, generator=generator, motion_bucket_id=180, noise_aug_strength=0.1).frames[0]
-export_to_video(frames, "generated.mp4", fps=7)
-```
-
-<video width="1024" height="576" controls>
-  <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket_generated_motion.mp4" type="video/mp4">
-</video>
-

docs/source/en/using-diffusers/unconditional_image_generation.md

@@ -14,41 +14,54 @@ specific language governing permissions and limitations under the License.
 
 [[open-in-colab]]
 
-Unconditional image generation generates images that look like a random sample from the training data the model was trained on because the denoising process is not guided by any additional context like text or image.
+Unconditional image generation is a relatively straightforward task. The model only generates images - without any additional context like text or an image - resembling the training data it was trained on.
 
-To get started, use the [`DiffusionPipeline`] to load the [anton-l/ddpm-butterflies-128](https://huggingface.co/anton-l/ddpm-butterflies-128) checkpoint to generate images of butterflies. The [`DiffusionPipeline`] downloads and caches all the model components required to generate an image.
+The [`DiffusionPipeline`] is the easiest way to use a pre-trained diffusion system for inference.
 
-```py
+Start by creating an instance of [`DiffusionPipeline`] and specify which pipeline checkpoint you would like to download.
+You can use any of the 🧨 Diffusers [checkpoints](https://huggingface.co/models?library=diffusers&sort=downloads) from the Hub (the checkpoint you'll use generates images of butterflies).
+
+<Tip>
+
+💡 Want to train your own unconditional image generation model? Take a look at the training [guide](../training/unconditional_training) to learn how to generate your own images.
+
+</Tip>
+
+In this guide, you'll use [`DiffusionPipeline`] for unconditional image generation with [DDPM](https://arxiv.org/abs/2006.11239):
+
+```python
 from diffusers import DiffusionPipeline
 
-generator = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128").to("cuda")
+generator = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128", use_safetensors=True)
+```
+
+The [`DiffusionPipeline`] downloads and caches all modeling, tokenization, and scheduling components.
+Because the model consists of roughly 1.4 billion parameters, we strongly recommend running it on a GPU.
+You can move the generator object to a GPU, just like you would in PyTorch:
+
+```python
+generator.to("cuda")
+```
+
+Now you can use the `generator` to generate an image:
+
+```python
 image = generator().images[0]
 image
 ```
 
-<Tip>
+The output is by default wrapped into a [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) object.
 
-Want to generate images of something else? Take a look at the training [guide](../training/unconditional_training) to learn how to train a model to generate your own images.
+You can save the image by calling:
 
-</Tip>
-
-The output image is a [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) object that can be saved:
-
-```py
+```python
 image.save("generated_image.png")
 ```
 
-You can also try experimenting with the `num_inference_steps` parameter, which controls the number of denoising steps. More denoising steps typically produce higher quality images, but it'll take longer to generate. Feel free to play around with this parameter to see how it affects the image quality.
-
-```py
-image = generator(num_inference_steps=100).images[0]
-image
-```
-
-Try out the Space below to generate an image of a butterfly!
+Try out the Spaces below, and feel free to play around with the inference steps parameter to see how it affects the image quality!
 
 <iframe
-	src="https://stevhliu-unconditional-image-generation.hf.space"
+	src="https://stevhliu-ddpm-butterflies-128.hf.space"
 	frameborder="0"
 	width="850"
 	height="500"

docs/source/ja/index.md

@@ -96,4 +96,3 @@ specific language governing permissions and limitations under the License.
 | [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation |
 | [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |
 | [stable_diffusion_ldm3d](./api/pipelines/stable_diffusion/ldm3d_diffusion) | [LDM3D: Latent Diffusion Model for 3D](https://arxiv.org/abs/2305.10853) | Text to Image and Depth Generation |
-| [stable_diffusion_upscaler_ldm3d](./api/pipelines/stable_diffusion/ldm3d_diffusion) | [LDM3D-VR: Latent Diffusion Model for 3D VR](https://arxiv.org/pdf/2311.03226) | Image and Depth Upscaling |

examples/community/README.md

@@ -48,8 +48,7 @@ prompt-to-prompt | change parts of a prompt and retain image structure (see [pap
 |   Latent Consistency Pipeline                                                                                                    | Implementation of [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Latent Consistency Pipeline](#latent-consistency-pipeline)      | - |              [Simian Luo](https://github.com/luosiallen) |
 |   Latent Consistency Img2img Pipeline                                                                                                    | Img2img pipeline for Latent Consistency Models                                                                                                                                                                                                                                                                                                                                                                                                                                    | [Latent Consistency Img2Img Pipeline](#latent-consistency-img2img-pipeline)      | - |              [Logan Zoellner](https://github.com/nagolinc) |
 |   Latent Consistency Interpolation Pipeline                                                                                                    | Interpolate the latent space of Latent Consistency Models with multiple prompts                                                                                                                                                                                                                                                                                                                                                                                                                                    | [Latent Consistency Interpolation Pipeline](#latent-consistency-interpolation-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pK3NrLWJSiJsBynLns1K1-IDTW9zbPvl?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
-| LDM3D-sr (LDM3D upscaler)                                                                                                             | Upscale low resolution RGB and depth inputs to high resolution                                                                                                                                                                                                                                                                                                                                                                                                                              | [StableDiffusionUpscaleLDM3D Pipeline](https://github.com/estelleafl/diffusers/tree/ldm3d_upscaler_community/examples/community#stablediffusionupscaleldm3d-pipeline)                                                                             | -                                                                                                                                                                                                             |                                                        [Estelle Aflalo](https://github.com/estelleafl) |
-|
+
 
 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
 ```py
@@ -2344,324 +2343,3 @@ images = pipe(
 
 assert len(images) == (len(prompts) - 1) * num_interpolation_steps
 ```
-
-###  StableDiffusionUpscaleLDM3D Pipeline
-[LDM3D-VR](https://arxiv.org/pdf/2311.03226.pdf) is an extended version of LDM3D. 
-
-The abstract from the paper is:
-*Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite of diffusion models targeting virtual reality development that includes LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD based on textual prompts and the upscaling of low-resolution inputs to high-resolution RGBD, respectively. Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions. Both models are evaluated in comparison to existing related methods*
-
-Two checkpoints are available for use:
-- [ldm3d-pano](https://huggingface.co/Intel/ldm3d-pano). This checkpoint enables the generation of panoramic images and requires the StableDiffusionLDM3DPipeline pipeline to be used.
-- [ldm3d-sr](https://huggingface.co/Intel/ldm3d-sr). This checkpoint enables the upscaling of RGB and depth images. Can be used in cascade after the original LDM3D pipeline using the StableDiffusionUpscaleLDM3DPipeline pipeline.
-
-'''py
-from PIL import Image
-import os
-import torch
-from diffusers import StableDiffusionLDM3DPipeline, DiffusionPipeline
-
-#Generate a rgb/depth output from LDM3D
-pipe_ldm3d = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d-4c")
-pipe_ldm3d.to("cuda")
-
-prompt =f"A picture of some lemons on a table"
-output = pipe_ldm3d(prompt)
-rgb_image, depth_image = output.rgb, output.depth
-rgb_image[0].save(f"lemons_ldm3d_rgb.jpg")
-depth_image[0].save(f"lemons_ldm3d_depth.png")
-
-
-#Upscale the previous output to a resolution of (1024, 1024)
-pipe_ldm3d_upscale = DiffusionPipeline.from_pretrained("Intel/ldm3d-sr", custom_pipeline="pipeline_stable_diffusion_upscale_ldm3d")
-
-pipe_ldm3d_upscale.to("cuda")
-
-low_res_img = Image.open(f"lemons_ldm3d_rgb.jpg").convert("RGB")
-low_res_depth = Image.open(f"lemons_ldm3d_depth.png").convert("L")
-outputs = pipe_ldm3d_upscale(prompt="high quality high resolution uhd 4k image", rgb=low_res_img, depth=low_res_depth, num_inference_steps=50, target_res=[1024, 1024])
-
-upscaled_rgb, upscaled_depth =outputs.rgb[0], outputs.depth[0]
-upscaled_rgb.save(f"upscaled_lemons_rgb.png")
-upscaled_depth.save(f"upscaled_lemons_depth.png")
-'''
-
-### ControlNet + T2I Adapter Pipeline
-This pipelines combines both ControlNet and T2IAdapter into a single pipeline, where the forward pass is executed once. 
-It receives `control_image` and `adapter_image`, as well as `controlnet_conditioning_scale` and `adapter_conditioning_scale`, for the ControlNet and Adapter modules, respectively. Whenever `adapter_conditioning_scale = 0` or `controlnet_conditioning_scale = 0`, it will act as a full ControlNet module or as a full T2IAdapter module, respectively. 
-
-```py
-import cv2
-import numpy as np
-import torch
-from controlnet_aux.midas import MidasDetector
-from PIL import Image
-
-from diffusers import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
-from diffusers.utils import load_image
-from examples.community.pipeline_stable_diffusion_xl_controlnet_adapter import (
-    StableDiffusionXLControlNetAdapterPipeline,
-)
-
-controlnet_depth = ControlNetModel.from_pretrained(
-    "diffusers/controlnet-depth-sdxl-1.0",
-    torch_dtype=torch.float16,
-    variant="fp16",
-    use_safetensors=True
-)
-adapter_depth = T2IAdapter.from_pretrained(
-  "TencentARC/t2i-adapter-depth-midas-sdxl-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
-
-pipe = StableDiffusionXLControlNetAdapterPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    controlnet=controlnet_depth,
-    adapter=adapter_depth,
-    vae=vae,
-    variant="fp16",
-    use_safetensors=True,
-    torch_dtype=torch.float16,
-)
-pipe = pipe.to("cuda")
-pipe.enable_xformers_memory_efficient_attention()
-# pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
-midas_depth = MidasDetector.from_pretrained(
-  "valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
-).to("cuda")
-
-prompt = "a tiger sitting on a park bench"
-img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-
-image = load_image(img_url).resize((1024, 1024))
-
-depth_image = midas_depth(
-  image, detect_resolution=512, image_resolution=1024
-)
-
-strength = 0.5
-
-images = pipe(
-    prompt,
-    control_image=depth_image,
-    adapter_image=depth_image,
-    num_inference_steps=30,
-    controlnet_conditioning_scale=strength,
-    adapter_conditioning_scale=strength,
-).images
-images[0].save("controlnet_and_adapter.png")
-
-```
-
-### ControlNet + T2I Adapter + Inpainting Pipeline
-```py
-import cv2
-import numpy as np
-import torch
-from controlnet_aux.midas import MidasDetector
-from PIL import Image
-
-from diffusers import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter
-from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
-from diffusers.utils import load_image
-from examples.community.pipeline_stable_diffusion_xl_controlnet_adapter_inpaint import (
-    StableDiffusionXLControlNetAdapterInpaintPipeline,
-)
-
-controlnet_depth = ControlNetModel.from_pretrained(
-    "diffusers/controlnet-depth-sdxl-1.0",
-    torch_dtype=torch.float16,
-    variant="fp16",
-    use_safetensors=True
-)
-adapter_depth = T2IAdapter.from_pretrained(
-  "TencentARC/t2i-adapter-depth-midas-sdxl-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
-
-pipe = StableDiffusionXLControlNetAdapterInpaintPipeline.from_pretrained(
-    "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
-    controlnet=controlnet_depth,
-    adapter=adapter_depth,
-    vae=vae,
-    variant="fp16",
-    use_safetensors=True,
-    torch_dtype=torch.float16,
-)
-pipe = pipe.to("cuda")
-pipe.enable_xformers_memory_efficient_attention()
-# pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
-midas_depth = MidasDetector.from_pretrained(
-  "valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
-).to("cuda")
-
-prompt = "a tiger sitting on a park bench"
-img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-
-image = load_image(img_url).resize((1024, 1024))
-mask_image = load_image(mask_url).resize((1024, 1024))
-
-depth_image = midas_depth(
-  image, detect_resolution=512, image_resolution=1024
-)
-
-strength = 0.4
-
-images = pipe(
-    prompt,
-    image=image,
-    mask_image=mask_image,
-    control_image=depth_image,
-    adapter_image=depth_image,
-    num_inference_steps=30,
-    controlnet_conditioning_scale=strength,
-    adapter_conditioning_scale=strength,
-    strength=0.7,
-).images
-images[0].save("controlnet_and_adapter_inpaint.png")
-
-```
-
-## Diffusion Posterior Sampling Pipeline
-* Reference paper
-    ```
-    @article{chung2022diffusion,
-    title={Diffusion posterior sampling for general noisy inverse problems},
-    author={Chung, Hyungjin and Kim, Jeongsol and Mccann, Michael T and Klasky, Marc L and Ye, Jong Chul},
-    journal={arXiv preprint arXiv:2209.14687},
-    year={2022}
-    }
-    ```
-* This pipeline allows zero-shot conditional sampling from the posterior distribution $p(x|y)$, given observation on $y$, unconditional generative model $p(x)$ and differentiable operator $y=f(x)$.
-* For example, $f(.)$ can be downsample operator, then $y$ is a downsampled image, and the pipeline becomes a super-resolution pipeline.
-* To use this pipeline, you need to know your operator $f(.)$ and corrupted image $y$, and pass them during the call. For example, as in the main function of dps_pipeline.py, you need to first define the Gaussian blurring operator $f(.)$. The operator should be a callable nn.Module, with all the parameter gradient disabled:
-    ```python
-    import torch.nn.functional as F
-    import scipy
-    from torch import nn
-
-    # define the Gaussian blurring operator first
-    class GaussialBlurOperator(nn.Module):
-        def __init__(self, kernel_size, intensity):
-            super().__init__()
-
-            class Blurkernel(nn.Module):
-                def __init__(self, blur_type='gaussian', kernel_size=31, std=3.0):
-                    super().__init__()
-                    self.blur_type = blur_type
-                    self.kernel_size = kernel_size
-                    self.std = std
-                    self.seq = nn.Sequential(
-                        nn.ReflectionPad2d(self.kernel_size//2),
-                        nn.Conv2d(3, 3, self.kernel_size, stride=1, padding=0, bias=False, groups=3)
-                    )
-                    self.weights_init()
-
-                def forward(self, x):
-                    return self.seq(x)
-
-                def weights_init(self):
-                    if self.blur_type == "gaussian":
-                        n = np.zeros((self.kernel_size, self.kernel_size))
-                        n[self.kernel_size // 2,self.kernel_size // 2] = 1
-                        k = scipy.ndimage.gaussian_filter(n, sigma=self.std)
-                        k = torch.from_numpy(k)
-                        self.k = k
-                        for name, f in self.named_parameters():
-                            f.data.copy_(k)
-                    elif self.blur_type == "motion":
-                        k = Kernel(size=(self.kernel_size, self.kernel_size), intensity=self.std).kernelMatrix
-                        k = torch.from_numpy(k)
-                        self.k = k
-                        for name, f in self.named_parameters():
-                            f.data.copy_(k)
-
-                def update_weights(self, k):
-                    if not torch.is_tensor(k):
-                        k = torch.from_numpy(k)
-                    for name, f in self.named_parameters():
-                        f.data.copy_(k)
-
-                def get_kernel(self):
-                    return self.k
-                
-            self.kernel_size = kernel_size
-            self.conv = Blurkernel(blur_type='gaussian',
-                                kernel_size=kernel_size,
-                                std=intensity)
-            self.kernel = self.conv.get_kernel()
-            self.conv.update_weights(self.kernel.type(torch.float32))
-
-            for param in self.parameters():
-                param.requires_grad=False
-
-        def forward(self, data, **kwargs):
-            return self.conv(data)
-
-        def transpose(self, data, **kwargs):
-            return data
-
-        def get_kernel(self):
-            return self.kernel.view(1, 1, self.kernel_size, self.kernel_size)
-    ```
-* Next, you should obtain the corrupted image $y$ by the operator. In this example, we generate $y$ from the source image $x$. However in practice, having the operator $f(.)$ and corrupted image $y$ is enough:
-    ```python
-    # set up source image
-    src = Image.open('sample.png')
-    # read image into [1,3,H,W]
-    src = torch.from_numpy(np.array(src, dtype=np.float32)).permute(2,0,1)[None]
-    # normalize image to [-1,1]
-    src = (src / 127.5) - 1.0
-    src = src.to("cuda")
-
-    # set up operator and measurement
-    operator = GaussialBlurOperator(kernel_size=61, intensity=3.0).to("cuda")
-    measurement = operator(src)
-
-    # save the source and corrupted images
-    save_image((src+1.0)/2.0, "dps_src.png")
-    save_image((measurement+1.0)/2.0, "dps_mea.png")
-    ```
-* We provide an example pair of saved source and corrupted images, using the Gaussian blur operator above
-    * Source image:
-    * ![sample](https://github.com/tongdaxu/Images/assets/22267548/4d2a1216-08d1-4aeb-9ce3-7a2d87561d65)
-    * Gaussian blurred image:
-    * ![ddpm_generated_image](https://github.com/tongdaxu/Images/assets/22267548/65076258-344b-4ed8-b704-a04edaade8ae)
-    * You can download those image to run the example on your own.
-* Next, we need to define a loss function used for diffusion posterior sample. For most of the cases, the RMSE is fine:
-    ```python
-    def RMSELoss(yhat, y):
-        return torch.sqrt(torch.sum((yhat-y)**2))
-    ```
-* And next, as any other diffusion models, we need the score estimator and scheduler. As we are working with $256x256$ face images, we use ddmp-celebahq-256:
-    ```python
-    # set up scheduler
-    scheduler = DDPMScheduler.from_pretrained("google/ddpm-celebahq-256")
-    scheduler.set_timesteps(1000)
-
-    # set up model
-    model = UNet2DModel.from_pretrained("google/ddpm-celebahq-256").to("cuda")
-    ```
-* And finally, run the pipeline:
-    ```python
-    # finally, the pipeline
-    dpspipe = DPSPipeline(model, scheduler)
-    image = dpspipe(
-        measurement = measurement,
-        operator = operator,
-        loss_fn = RMSELoss,
-        zeta = 1.0,
-    ).images[0]
-    image.save("dps_generated_image.png")
-    ```
-* The zeta is a hyperparameter that is in range of $[0,1]$. It need to be tuned for best effect. By setting zeta=1, you should be able to have the reconstructed result:
-    * Reconstructed image:
-    * ![sample](https://github.com/tongdaxu/Images/assets/22267548/0ceb5575-d42e-4f0b-99c0-50e69c982209)
-* The reconstruction is perceptually similar to the source image, but different in details.
-* In dps_pipeline.py, we also provide a super-resolution example, which should produce:
-    * Downsampled image: 
-    * ![dps_mea](https://github.com/tongdaxu/Images/assets/22267548/ff6a33d6-26f0-42aa-88ce-f8a76ba45a13)
-    * Reconstructed image:
-    * ![dps_generated_image](https://github.com/tongdaxu/Images/assets/22267548/b74f084d-93f4-4845-83d8-44c0fa758a5f)

examples/community/dps_pipeline.py

@@ -1,466 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from math import pi
-from typing import Callable, List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-from PIL import Image
-
-from diffusers import DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DModel
-from diffusers.utils.torch_utils import randn_tensor
-
-
-class DPSPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for Diffusion Posterior Sampling.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Parameters:
-        unet ([`UNet2DModel`]):
-            A `UNet2DModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
-            [`DDPMScheduler`], or [`DDIMScheduler`].
-    """
-
-    model_cpu_offload_seq = "unet"
-
-    def __init__(self, unet, scheduler):
-        super().__init__()
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        measurement: torch.Tensor,
-        operator: torch.nn.Module,
-        loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
-        batch_size: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        num_inference_steps: int = 1000,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        zeta: float = 0.3,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            measurement (`torch.Tensor`, *required*):
-                A 'torch.Tensor', the corrupted image
-            operator (`torch.nn.Module`, *required*):
-                A 'torch.nn.Module', the operator generating the corrupted image
-            loss_fn (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *required*):
-                A 'Callable[[torch.Tensor, torch.Tensor], torch.Tensor]', the loss function used
-                between the measurements, for most of the cases using RMSE is fine.
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`torch.Generator`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            num_inference_steps (`int`, *optional*, defaults to 1000):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Example:
-
-        ```py
-        >>> from diffusers import DDPMPipeline
-
-        >>> # load model and scheduler
-        >>> pipe = DDPMPipeline.from_pretrained("google/ddpm-cat-256")
-
-        >>> # run pipeline in inference (sample random noise and denoise)
-        >>> image = pipe().images[0]
-
-        >>> # save image
-        >>> image.save("ddpm_generated_image.png")
-        ```
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images
-        """
-        # Sample gaussian noise to begin loop
-        if isinstance(self.unet.config.sample_size, int):
-            image_shape = (
-                batch_size,
-                self.unet.config.in_channels,
-                self.unet.config.sample_size,
-                self.unet.config.sample_size,
-            )
-        else:
-            image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
-
-        if self.device.type == "mps":
-            # randn does not work reproducibly on mps
-            image = randn_tensor(image_shape, generator=generator)
-            image = image.to(self.device)
-        else:
-            image = randn_tensor(image_shape, generator=generator, device=self.device)
-
-        # set step values
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        for t in self.progress_bar(self.scheduler.timesteps):
-            with torch.enable_grad():
-                # 1. predict noise model_output
-                image = image.requires_grad_()
-                model_output = self.unet(image, t).sample
-
-                # 2. compute previous image x'_{t-1} and original prediction x0_{t}
-                scheduler_out = self.scheduler.step(model_output, t, image, generator=generator)
-                image_pred, origi_pred = scheduler_out.prev_sample, scheduler_out.pred_original_sample
-
-                # 3. compute y'_t = f(x0_{t})
-                measurement_pred = operator(origi_pred)
-
-                # 4. compute loss = d(y, y'_t-1)
-                loss = loss_fn(measurement, measurement_pred)
-                loss.backward()
-
-                print("distance: {0:.4f}".format(loss.item()))
-
-                with torch.no_grad():
-                    image_pred = image_pred - zeta * image.grad
-                    image = image_pred.detach()
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
-
-
-if __name__ == "__main__":
-    import scipy
-    from torch import nn
-    from torchvision.utils import save_image
-
-    # defining the operators f(.) of y = f(x)
-    # super-resolution operator
-    class SuperResolutionOperator(nn.Module):
-        def __init__(self, in_shape, scale_factor):
-            super().__init__()
-
-            # Resizer local class, do not use outiside the SR operator class
-            class Resizer(nn.Module):
-                def __init__(self, in_shape, scale_factor=None, output_shape=None, kernel=None, antialiasing=True):
-                    super(Resizer, self).__init__()
-
-                    # First standardize values and fill missing arguments (if needed) by deriving scale from output shape or vice versa
-                    scale_factor, output_shape = self.fix_scale_and_size(in_shape, output_shape, scale_factor)
-
-                    # Choose interpolation method, each method has the matching kernel size
-                    def cubic(x):
-                        absx = np.abs(x)
-                        absx2 = absx**2
-                        absx3 = absx**3
-                        return (1.5 * absx3 - 2.5 * absx2 + 1) * (absx <= 1) + (
-                            -0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2
-                        ) * ((1 < absx) & (absx <= 2))
-
-                    def lanczos2(x):
-                        return (
-                            (np.sin(pi * x) * np.sin(pi * x / 2) + np.finfo(np.float32).eps)
-                            / ((pi**2 * x**2 / 2) + np.finfo(np.float32).eps)
-                        ) * (abs(x) < 2)
-
-                    def box(x):
-                        return ((-0.5 <= x) & (x < 0.5)) * 1.0
-
-                    def lanczos3(x):
-                        return (
-                            (np.sin(pi * x) * np.sin(pi * x / 3) + np.finfo(np.float32).eps)
-                            / ((pi**2 * x**2 / 3) + np.finfo(np.float32).eps)
-                        ) * (abs(x) < 3)
-
-                    def linear(x):
-                        return (x + 1) * ((-1 <= x) & (x < 0)) + (1 - x) * ((0 <= x) & (x <= 1))
-
-                    method, kernel_width = {
-                        "cubic": (cubic, 4.0),
-                        "lanczos2": (lanczos2, 4.0),
-                        "lanczos3": (lanczos3, 6.0),
-                        "box": (box, 1.0),
-                        "linear": (linear, 2.0),
-                        None: (cubic, 4.0),  # set default interpolation method as cubic
-                    }.get(kernel)
-
-                    # Antialiasing is only used when downscaling
-                    antialiasing *= np.any(np.array(scale_factor) < 1)
-
-                    # Sort indices of dimensions according to scale of each dimension. since we are going dim by dim this is efficient
-                    sorted_dims = np.argsort(np.array(scale_factor))
-                    self.sorted_dims = [int(dim) for dim in sorted_dims if scale_factor[dim] != 1]
-
-                    # Iterate over dimensions to calculate local weights for resizing and resize each time in one direction
-                    field_of_view_list = []
-                    weights_list = []
-                    for dim in self.sorted_dims:
-                        # for each coordinate (along 1 dim), calculate which coordinates in the input image affect its result and the
-                        # weights that multiply the values there to get its result.
-                        weights, field_of_view = self.contributions(
-                            in_shape[dim], output_shape[dim], scale_factor[dim], method, kernel_width, antialiasing
-                        )
-
-                        # convert to torch tensor
-                        weights = torch.tensor(weights.T, dtype=torch.float32)
-
-                        # We add singleton dimensions to the weight matrix so we can multiply it with the big tensor we get for
-                        # tmp_im[field_of_view.T], (bsxfun style)
-                        weights_list.append(
-                            nn.Parameter(
-                                torch.reshape(weights, list(weights.shape) + (len(scale_factor) - 1) * [1]),
-                                requires_grad=False,
-                            )
-                        )
-                        field_of_view_list.append(
-                            nn.Parameter(
-                                torch.tensor(field_of_view.T.astype(np.int32), dtype=torch.long), requires_grad=False
-                            )
-                        )
-
-                    self.field_of_view = nn.ParameterList(field_of_view_list)
-                    self.weights = nn.ParameterList(weights_list)
-
-                def forward(self, in_tensor):
-                    x = in_tensor
-
-                    # Use the affecting position values and the set of weights to calculate the result of resizing along this 1 dim
-                    for dim, fov, w in zip(self.sorted_dims, self.field_of_view, self.weights):
-                        # To be able to act on each dim, we swap so that dim 0 is the wanted dim to resize
-                        x = torch.transpose(x, dim, 0)
-
-                        # This is a bit of a complicated multiplication: x[field_of_view.T] is a tensor of order image_dims+1.
-                        # for each pixel in the output-image it matches the positions the influence it from the input image (along 1 dim
-                        # only, this is why it only adds 1 dim to 5the shape). We then multiply, for each pixel, its set of positions with
-                        # the matching set of weights. we do this by this big tensor element-wise multiplication (MATLAB bsxfun style:
-                        # matching dims are multiplied element-wise while singletons mean that the matching dim is all multiplied by the
-                        # same number
-                        x = torch.sum(x[fov] * w, dim=0)
-
-                        # Finally we swap back the axes to the original order
-                        x = torch.transpose(x, dim, 0)
-
-                    return x
-
-                def fix_scale_and_size(self, input_shape, output_shape, scale_factor):
-                    # First fixing the scale-factor (if given) to be standardized the function expects (a list of scale factors in the
-                    # same size as the number of input dimensions)
-                    if scale_factor is not None:
-                        # By default, if scale-factor is a scalar we assume 2d resizing and duplicate it.
-                        if np.isscalar(scale_factor) and len(input_shape) > 1:
-                            scale_factor = [scale_factor, scale_factor]
-
-                        # We extend the size of scale-factor list to the size of the input by assigning 1 to all the unspecified scales
-                        scale_factor = list(scale_factor)
-                        scale_factor = [1] * (len(input_shape) - len(scale_factor)) + scale_factor
-
-                    # Fixing output-shape (if given): extending it to the size of the input-shape, by assigning the original input-size
-                    # to all the unspecified dimensions
-                    if output_shape is not None:
-                        output_shape = list(input_shape[len(output_shape) :]) + list(np.uint(np.array(output_shape)))
-
-                    # Dealing with the case of non-give scale-factor, calculating according to output-shape. note that this is
-                    # sub-optimal, because there can be different scales to the same output-shape.
-                    if scale_factor is None:
-                        scale_factor = 1.0 * np.array(output_shape) / np.array(input_shape)
-
-                    # Dealing with missing output-shape. calculating according to scale-factor
-                    if output_shape is None:
-                        output_shape = np.uint(np.ceil(np.array(input_shape) * np.array(scale_factor)))
-
-                    return scale_factor, output_shape
-
-                def contributions(self, in_length, out_length, scale, kernel, kernel_width, antialiasing):
-                    # This function calculates a set of 'filters' and a set of field_of_view that will later on be applied
-                    # such that each position from the field_of_view will be multiplied with a matching filter from the
-                    # 'weights' based on the interpolation method and the distance of the sub-pixel location from the pixel centers
-                    # around it. This is only done for one dimension of the image.
-
-                    # When anti-aliasing is activated (default and only for downscaling) the receptive field is stretched to size of
-                    # 1/sf. this means filtering is more 'low-pass filter'.
-                    fixed_kernel = (lambda arg: scale * kernel(scale * arg)) if antialiasing else kernel
-                    kernel_width *= 1.0 / scale if antialiasing else 1.0
-
-                    # These are the coordinates of the output image
-                    out_coordinates = np.arange(1, out_length + 1)
-
-                    # since both scale-factor and output size can be provided simulatneously, perserving the center of the image requires shifting
-                    # the output coordinates. the deviation is because out_length doesn't necesary equal in_length*scale.
-                    # to keep the center we need to subtract half of this deivation so that we get equal margins for boths sides and center is preserved.
-                    shifted_out_coordinates = out_coordinates - (out_length - in_length * scale) / 2
-
-                    # These are the matching positions of the output-coordinates on the input image coordinates.
-                    # Best explained by example: say we have 4 horizontal pixels for HR and we downscale by SF=2 and get 2 pixels:
-                    # [1,2,3,4] -> [1,2]. Remember each pixel number is the middle of the pixel.
-                    # The scaling is done between the distances and not pixel numbers (the right boundary of pixel 4 is transformed to
-                    # the right boundary of pixel 2. pixel 1 in the small image matches the boundary between pixels 1 and 2 in the big
-                    # one and not to pixel 2. This means the position is not just multiplication of the old pos by scale-factor).
-                    # So if we measure distance from the left border, middle of pixel 1 is at distance d=0.5, border between 1 and 2 is
-                    # at d=1, and so on (d = p - 0.5).  we calculate (d_new = d_old / sf) which means:
-                    # (p_new-0.5 = (p_old-0.5) / sf)     ->          p_new = p_old/sf + 0.5 * (1-1/sf)
-                    match_coordinates = shifted_out_coordinates / scale + 0.5 * (1 - 1 / scale)
-
-                    # This is the left boundary to start multiplying the filter from, it depends on the size of the filter
-                    left_boundary = np.floor(match_coordinates - kernel_width / 2)
-
-                    # Kernel width needs to be enlarged because when covering has sub-pixel borders, it must 'see' the pixel centers
-                    # of the pixels it only covered a part from. So we add one pixel at each side to consider (weights can zeroize them)
-                    expanded_kernel_width = np.ceil(kernel_width) + 2
-
-                    # Determine a set of field_of_view for each each output position, these are the pixels in the input image
-                    # that the pixel in the output image 'sees'. We get a matrix whos horizontal dim is the output pixels (big) and the
-                    # vertical dim is the pixels it 'sees' (kernel_size + 2)
-                    field_of_view = np.squeeze(
-                        np.int16(np.expand_dims(left_boundary, axis=1) + np.arange(expanded_kernel_width) - 1)
-                    )
-
-                    # Assign weight to each pixel in the field of view. A matrix whos horizontal dim is the output pixels and the
-                    # vertical dim is a list of weights matching to the pixel in the field of view (that are specified in
-                    # 'field_of_view')
-                    weights = fixed_kernel(1.0 * np.expand_dims(match_coordinates, axis=1) - field_of_view - 1)
-
-                    # Normalize weights to sum up to 1. be careful from dividing by 0
-                    sum_weights = np.sum(weights, axis=1)
-                    sum_weights[sum_weights == 0] = 1.0
-                    weights = 1.0 * weights / np.expand_dims(sum_weights, axis=1)
-
-                    # We use this mirror structure as a trick for reflection padding at the boundaries
-                    mirror = np.uint(np.concatenate((np.arange(in_length), np.arange(in_length - 1, -1, step=-1))))
-                    field_of_view = mirror[np.mod(field_of_view, mirror.shape[0])]
-
-                    # Get rid of  weights and pixel positions that are of zero weight
-                    non_zero_out_pixels = np.nonzero(np.any(weights, axis=0))
-                    weights = np.squeeze(weights[:, non_zero_out_pixels])
-                    field_of_view = np.squeeze(field_of_view[:, non_zero_out_pixels])
-
-                    # Final products are the relative positions and the matching weights, both are output_size X fixed_kernel_size
-                    return weights, field_of_view
-
-            self.down_sample = Resizer(in_shape, 1 / scale_factor)
-            for param in self.parameters():
-                param.requires_grad = False
-
-        def forward(self, data, **kwargs):
-            return self.down_sample(data)
-
-    # Gaussian blurring operator
-    class GaussialBlurOperator(nn.Module):
-        def __init__(self, kernel_size, intensity):
-            super().__init__()
-
-            class Blurkernel(nn.Module):
-                def __init__(self, blur_type="gaussian", kernel_size=31, std=3.0):
-                    super().__init__()
-                    self.blur_type = blur_type
-                    self.kernel_size = kernel_size
-                    self.std = std
-                    self.seq = nn.Sequential(
-                        nn.ReflectionPad2d(self.kernel_size // 2),
-                        nn.Conv2d(3, 3, self.kernel_size, stride=1, padding=0, bias=False, groups=3),
-                    )
-                    self.weights_init()
-
-                def forward(self, x):
-                    return self.seq(x)
-
-                def weights_init(self):
-                    if self.blur_type == "gaussian":
-                        n = np.zeros((self.kernel_size, self.kernel_size))
-                        n[self.kernel_size // 2, self.kernel_size // 2] = 1
-                        k = scipy.ndimage.gaussian_filter(n, sigma=self.std)
-                        k = torch.from_numpy(k)
-                        self.k = k
-                        for name, f in self.named_parameters():
-                            f.data.copy_(k)
-
-                def update_weights(self, k):
-                    if not torch.is_tensor(k):
-                        k = torch.from_numpy(k)
-                    for name, f in self.named_parameters():
-                        f.data.copy_(k)
-
-                def get_kernel(self):
-                    return self.k
-
-            self.kernel_size = kernel_size
-            self.conv = Blurkernel(blur_type="gaussian", kernel_size=kernel_size, std=intensity)
-            self.kernel = self.conv.get_kernel()
-            self.conv.update_weights(self.kernel.type(torch.float32))
-
-            for param in self.parameters():
-                param.requires_grad = False
-
-        def forward(self, data, **kwargs):
-            return self.conv(data)
-
-        def transpose(self, data, **kwargs):
-            return data
-
-        def get_kernel(self):
-            return self.kernel.view(1, 1, self.kernel_size, self.kernel_size)
-
-    # assuming the forward process y = f(x) is polluted by Gaussian noise, use l2 norm
-    def RMSELoss(yhat, y):
-        return torch.sqrt(torch.sum((yhat - y) ** 2))
-
-    # set up source image
-    src = Image.open("sample.png")
-    # read image into [1,3,H,W]
-    src = torch.from_numpy(np.array(src, dtype=np.float32)).permute(2, 0, 1)[None]
-    # normalize image to [-1,1]
-    src = (src / 127.5) - 1.0
-    src = src.to("cuda")
-
-    # set up operator and measurement
-    # operator = SuperResolutionOperator(in_shape=src.shape, scale_factor=4).to("cuda")
-    operator = GaussialBlurOperator(kernel_size=61, intensity=3.0).to("cuda")
-    measurement = operator(src)
-
-    # set up scheduler
-    scheduler = DDPMScheduler.from_pretrained("google/ddpm-celebahq-256")
-    scheduler.set_timesteps(1000)
-
-    # set up model
-    model = UNet2DModel.from_pretrained("google/ddpm-celebahq-256").to("cuda")
-
-    save_image((src + 1.0) / 2.0, "dps_src.png")
-    save_image((measurement + 1.0) / 2.0, "dps_mea.png")
-
-    # finally, the pipeline
-    dpspipe = DPSPipeline(model, scheduler)
-    image = dpspipe(
-        measurement=measurement,
-        operator=operator,
-        loss_fn=RMSELoss,
-        zeta=1.0,
-    ).images[0]
-
-    image.save("dps_generated_image.png")

examples/community/lpw_stable_diffusion_xl.py

@@ -250,7 +250,6 @@ def get_weighted_text_embeddings_sdxl(
     neg_prompt: str = "",
     neg_prompt_2: str = None,
     num_images_per_prompt: int = 1,
-    device: Optional[torch.device] = None,
 ):
     """
     This function can process long prompt with weights, no length limitation
@@ -263,13 +262,10 @@ def get_weighted_text_embeddings_sdxl(
         neg_prompt (str)
         neg_prompt_2 (str)
         num_images_per_prompt (int)
-        device (torch.device)
     Returns:
         prompt_embeds (torch.Tensor)
         neg_prompt_embeds (torch.Tensor)
     """
-    device = device or pipe._execution_device
-
     if prompt_2:
         prompt = f"{prompt} {prompt_2}"
 
@@ -334,17 +330,17 @@ def get_weighted_text_embeddings_sdxl(
     # get prompt embeddings one by one is not working.
     for i in range(len(prompt_token_groups)):
         # get positive prompt embeddings with weights
-        token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=device)
-        weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=device)
+        token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
+        weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
 
-        token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=device)
+        token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
 
         # use first text encoder
-        prompt_embeds_1 = pipe.text_encoder(token_tensor.to(device), output_hidden_states=True)
+        prompt_embeds_1 = pipe.text_encoder(token_tensor.to(pipe.device), output_hidden_states=True)
         prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2]
 
         # use second text encoder
-        prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(device), output_hidden_states=True)
+        prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(pipe.device), output_hidden_states=True)
         prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2]
         pooled_prompt_embeds = prompt_embeds_2[0]
 
@@ -361,16 +357,16 @@ def get_weighted_text_embeddings_sdxl(
         embeds.append(token_embedding)
 
         # get negative prompt embeddings with weights
-        neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=device)
-        neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=device)
-        neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=device)
+        neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
+        neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
+        neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
 
         # use first text encoder
-        neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(device), output_hidden_states=True)
+        neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(pipe.device), output_hidden_states=True)
         neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2]
 
         # use second text encoder
-        neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(device), output_hidden_states=True)
+        neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(pipe.device), output_hidden_states=True)
         neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2]
         negative_pooled_prompt_embeds = neg_prompt_embeds_2[0]
 

examples/community/pipeline_stable_diffusion_upscale_ldm3d.py

@@ -1,772 +0,0 @@
-# Copyright 2023 The Intel Labs Team Authors and the HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import DiffusionPipeline
-from diffusers.image_processor import PipelineDepthInput, PipelineImageInput, VaeImageProcessorLDM3D
-from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.models.lora import adjust_lora_scale_text_encoder
-from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d import LDM3DPipelineOutput
-from diffusers.schedulers import DDPMScheduler, KarrasDiffusionSchedulers
-from diffusers.utils import (
-    USE_PEFT_BACKEND,
-    deprecate,
-    logging,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from diffusers.utils.torch_utils import randn_tensor
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```python
-        >>> from diffusers import StableDiffusionUpscaleLDM3DPipeline
-        >>> from PIL import Image
-        >>> from io import BytesIO
-        >>> import requests
-
-        >>> pipe = StableDiffusionUpscaleLDM3DPipeline.from_pretrained("Intel/ldm3d-sr")
-        >>> pipe = pipe.to("cuda")
-        >>> rgb_path = "https://huggingface.co/Intel/ldm3d-sr/resolve/main/lemons_ldm3d_rgb.jpg"
-        >>> depth_path = "https://huggingface.co/Intel/ldm3d-sr/resolve/main/lemons_ldm3d_depth.png"
-        >>> low_res_rgb = Image.open(BytesIO(requests.get(rgb_path).content)).convert("RGB")
-        >>> low_res_depth = Image.open(BytesIO(requests.get(depth_path).content)).convert("L")
-        >>> output = pipe(
-        ...     prompt="high quality high resolution uhd 4k image",
-        ...     rgb=low_res_rgb,
-        ...     depth=low_res_depth,
-        ...     num_inference_steps=50,
-        ...     target_res=[1024, 1024],
-        ... )
-        >>> rgb_image, depth_image = output.rgb, output.depth
-        >>> rgb_image[0].save("hr_ldm3d_rgb.jpg")
-        >>> depth_image[0].save("hr_ldm3d_depth.png")
-        ```
-"""
-
-
-class StableDiffusionUpscaleLDM3DPipeline(
-    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
-):
-    r"""
-    Pipeline for text-to-image and 3D generation using LDM3D.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    The pipeline also inherits the following loading methods:
-        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
-        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
-        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
-        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-        text_encoder ([`~transformers.CLIPTextModel`]):
-            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
-        tokenizer ([`~transformers.CLIPTokenizer`]):
-            A `CLIPTokenizer` to tokenize text.
-        unet ([`UNet2DConditionModel`]):
-            A `UNet2DConditionModel` to denoise the encoded image latents.
-        low_res_scheduler ([`SchedulerMixin`]):
-            A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of
-            [`DDPMScheduler`].
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
-        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
-    """
-
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        low_res_scheduler: DDPMScheduler,
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-        watermarker: Optional[Any] = None,
-        max_noise_level: int = 350,
-    ):
-        super().__init__()
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            low_res_scheduler=low_res_scheduler,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            watermarker=watermarker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessorLDM3D(vae_scale_factor=self.vae_scale_factor, resample="bilinear")
-        # self.register_to_config(requires_safety_checker=requires_safety_checker)
-        self.register_to_config(max_noise_level=max_noise_level)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d.StableDiffusionLDM3DPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-        **kwargs,
-    ):
-        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
-        deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
-
-        prompt_embeds_tuple = self.encode_prompt(
-            prompt=prompt,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=lora_scale,
-            **kwargs,
-        )
-
-        # concatenate for backwards comp
-        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
-
-        return prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d.StableDiffusionLDM3DPipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-        clip_skip: Optional[int] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            lora_scale (`float`, *optional*):
-                A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-            clip_skip (`int`, *optional*):
-                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
-                the output of the pre-final layer will be used for computing the prompt embeddings.
-        """
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-            # dynamically adjust the LoRA scale
-            if not USE_PEFT_BACKEND:
-                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
-            else:
-                scale_lora_layers(self.text_encoder, lora_scale)
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            if clip_skip is None:
-                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
-                prompt_embeds = prompt_embeds[0]
-            else:
-                prompt_embeds = self.text_encoder(
-                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
-                )
-                # Access the `hidden_states` first, that contains a tuple of
-                # all the hidden states from the encoder layers. Then index into
-                # the tuple to access the hidden states from the desired layer.
-                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
-                # We also need to apply the final LayerNorm here to not mess with the
-                # representations. The `last_hidden_states` that we typically use for
-                # obtaining the final prompt representations passes through the LayerNorm
-                # layer.
-                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
-
-        if self.text_encoder is not None:
-            prompt_embeds_dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            prompt_embeds_dtype = self.unet.dtype
-        else:
-            prompt_embeds_dtype = prompt_embeds.dtype
-
-        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
-            # Retrieve the original scale by scaling back the LoRA layers
-            unscale_lora_layers(self.text_encoder, lora_scale)
-
-        return prompt_embeds, negative_prompt_embeds
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            rgb_feature_extractor_input = feature_extractor_input[0]
-            safety_checker_input = self.feature_extractor(rgb_feature_extractor_input, return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(
-        self,
-        prompt,
-        image,
-        noise_level,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        target_res=None,
-    ):
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-        if (
-            not isinstance(image, torch.Tensor)
-            and not isinstance(image, PIL.Image.Image)
-            and not isinstance(image, np.ndarray)
-            and not isinstance(image, list)
-        ):
-            raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `np.ndarray`, `PIL.Image.Image` or `list` but is {type(image)}"
-            )
-
-        # verify batch size of prompt and image are same if image is a list or tensor or numpy array
-        if isinstance(image, list) or isinstance(image, torch.Tensor) or isinstance(image, np.ndarray):
-            if prompt is not None and isinstance(prompt, str):
-                batch_size = 1
-            elif prompt is not None and isinstance(prompt, list):
-                batch_size = len(prompt)
-            else:
-                batch_size = prompt_embeds.shape[0]
-
-            if isinstance(image, list):
-                image_batch_size = len(image)
-            else:
-                image_batch_size = image.shape[0]
-            if batch_size != image_batch_size:
-                raise ValueError(
-                    f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}."
-                    " Please make sure that passed `prompt` matches the batch size of `image`."
-                )
-
-        # check noise level
-        if noise_level > self.config.max_noise_level:
-            raise ValueError(f"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height, width)
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    # def upcast_vae(self):
-    #     dtype = self.vae.dtype
-    #     self.vae.to(dtype=torch.float32)
-    #     use_torch_2_0_or_xformers = isinstance(
-    #         self.vae.decoder.mid_block.attentions[0].processor,
-    #         (
-    #             AttnProcessor2_0,
-    #             XFormersAttnProcessor,
-    #             LoRAXFormersAttnProcessor,
-    #             LoRAAttnProcessor2_0,
-    #         ),
-    #     )
-    #     # if xformers or torch_2_0 is used attention block does not need
-    #     # to be in float32 which can save lots of memory
-    #     if use_torch_2_0_or_xformers:
-    #         self.vae.post_quant_conv.to(dtype)
-    #         self.vae.decoder.conv_in.to(dtype)
-    #         self.vae.decoder.mid_block.to(dtype)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        rgb: PipelineImageInput = None,
-        depth: PipelineDepthInput = None,
-        num_inference_steps: int = 75,
-        guidance_scale: float = 9.0,
-        noise_level: int = 20,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        target_res: Optional[List[int]] = [1024, 1024],
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
-            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
-                `Image` or tensor representing an image batch to be upscaled.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 5.0):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
-                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
-                provided, text embeddings are generated from the `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
-                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
-                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images and the
-                second element is a list of `bool`s indicating whether the corresponding generated image contains
-                "not-safe-for-work" (nsfw) content.
-        """
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            rgb,
-            noise_level,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-        )
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-        )
-        # For classifier free guidance, we need to do two forward passes.
-        # Here we concatenate the unconditional and text embeddings into a single batch
-        # to avoid doing two forward passes
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        # 4. Preprocess image
-        rgb, depth = self.image_processor.preprocess(rgb, depth, target_res=target_res)
-        rgb = rgb.to(dtype=prompt_embeds.dtype, device=device)
-        depth = depth.to(dtype=prompt_embeds.dtype, device=device)
-
-        # 5. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 6. Encode low resolutiom image to latent space
-        image = torch.cat([rgb, depth], axis=1)
-        latent_space_image = self.vae.encode(image).latent_dist.sample(generator)
-        latent_space_image *= self.vae.scaling_factor
-        noise_level = torch.tensor([noise_level], dtype=torch.long, device=device)
-        # noise_rgb = randn_tensor(rgb.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
-        # rgb = self.low_res_scheduler.add_noise(rgb, noise_rgb, noise_level)
-        # noise_depth = randn_tensor(depth.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
-        # depth = self.low_res_scheduler.add_noise(depth, noise_depth, noise_level)
-
-        batch_multiplier = 2 if do_classifier_free_guidance else 1
-        latent_space_image = torch.cat([latent_space_image] * batch_multiplier * num_images_per_prompt)
-        noise_level = torch.cat([noise_level] * latent_space_image.shape[0])
-
-        # 7. Prepare latent variables
-        height, width = latent_space_image.shape[2:]
-        num_channels_latents = self.vae.config.latent_channels
-
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 8. Check that sizes of image and latents match
-        num_channels_image = latent_space_image.shape[1]
-        if num_channels_latents + num_channels_image != self.unet.config.in_channels:
-            raise ValueError(
-                f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
-                f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
-                f" `num_channels_image`: {num_channels_image} "
-                f" = {num_channels_latents+num_channels_image}. Please verify the config of"
-                " `pipeline.unet` or your `image` input."
-            )
-
-        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 10. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-                # concat latents, mask, masked_image_latents in the channel dimension
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                latent_model_input = torch.cat([latent_model_input, latent_space_image], dim=1)
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    class_labels=noise_level,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        if not output_type == "latent":
-            # make sure the VAE is in float32 mode, as it overflows in float16
-            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
-
-            if needs_upcasting:
-                self.upcast_vae()
-                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
-
-            image = self.vae.decode(latents / self.vae.scaling_factor, return_dict=False)[0]
-
-            # cast back to fp16 if needed
-            if needs_upcasting:
-                self.vae.to(dtype=torch.float16)
-
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-        else:
-            image = latents
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        rgb, depth = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-
-        # 11. Apply watermark
-        if output_type == "pil" and self.watermarker is not None:
-            rgb = self.watermarker.apply_watermark(rgb)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return ((rgb, depth), has_nsfw_concept)
-
-        return LDM3DPipelineOutput(rgb=rgb, depth=depth, nsfw_content_detected=has_nsfw_concept)

examples/community/stable_diffusion_ipex.py

@@ -21,7 +21,7 @@ from packaging import version
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from diffusers.configuration_utils import FrozenDict
-from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.loaders import TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
@@ -62,7 +62,7 @@ EXAMPLE_DOC_STRING = """
 """
 
 
-class StableDiffusionIPEXPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
+class StableDiffusionIPEXPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion on IPEX.
 

examples/consistency_distillation/train_lcm_distill_sd_wds.py

@@ -657,15 +657,6 @@ def parse_args():
         default=0.001,
         help="The huber loss parameter. Only used if `--loss_type=huber`.",
     )
-    parser.add_argument(
-        "--unet_time_cond_proj_dim",
-        type=int,
-        default=256,
-        help=(
-            "The dimension of the guidance scale embedding in the U-Net, which will be used if the teacher U-Net"
-            " does not have `time_cond_proj_dim` set."
-        ),
-    )
     # ----Exponential Moving Average (EMA)----
     parser.add_argument(
         "--ema_decay",
@@ -1147,7 +1138,7 @@ def main(args):
 
                 # 20.4.6. Sample a random guidance scale w from U[w_min, w_max] and embed it
                 w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
-                w_embedding = guidance_scale_embedding(w, embedding_dim=unet.config.time_cond_proj_dim)
+                w_embedding = guidance_scale_embedding(w, embedding_dim=args.unet_time_cond_proj_dim)
                 w = w.reshape(bsz, 1, 1, 1)
                 # Move to U-Net device and dtype
                 w = w.to(device=latents.device, dtype=latents.dtype)

examples/consistency_distillation/train_lcm_distill_sdxl_wds.py

@@ -677,15 +677,6 @@ def parse_args():
         default=0.001,
         help="The huber loss parameter. Only used if `--loss_type=huber`.",
     )
-    parser.add_argument(
-        "--unet_time_cond_proj_dim",
-        type=int,
-        default=256,
-        help=(
-            "The dimension of the guidance scale embedding in the U-Net, which will be used if the teacher U-Net"
-            " does not have `time_cond_proj_dim` set."
-        ),
-    )
     # ----Exponential Moving Average (EMA)----
     parser.add_argument(
         "--ema_decay",
@@ -1242,7 +1233,6 @@ def main(args):
 
                 # 20.4.6. Sample a random guidance scale w from U[w_min, w_max] and embed it
                 w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
-                w_embedding = guidance_scale_embedding(w, embedding_dim=unet.config.time_cond_proj_dim)
                 w = w.reshape(bsz, 1, 1, 1)
                 w = w.to(device=latents.device, dtype=latents.dtype)
 
@@ -1253,7 +1243,7 @@ def main(args):
                 noise_pred = unet(
                     noisy_model_input,
                     start_timesteps,
-                    timestep_cond=w_embedding,
+                    timestep_cond=None,
                     encoder_hidden_states=prompt_embeds.float(),
                     added_cond_kwargs=encoded_text,
                 ).sample
@@ -1318,7 +1308,7 @@ def main(args):
                         target_noise_pred = target_unet(
                             x_prev.float(),
                             timesteps,
-                            timestep_cond=w_embedding,
+                            timestep_cond=None,
                             encoder_hidden_states=prompt_embeds.float(),
                             added_cond_kwargs=encoded_text,
                         ).sample

examples/controlnet/test_controlnet.py

@@ -1,120 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-import tempfile
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class ControlNet(ExamplesTestsAccelerate):
-    def test_controlnet_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/controlnet/train_controlnet.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --dataset_name=hf-internal-testing/fill10
-            --output_dir={tmpdir}
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=6
-            --checkpoints_total_limit=2
-            --checkpointing_steps=2
-            --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_controlnet_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/controlnet/train_controlnet.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --dataset_name=hf-internal-testing/fill10
-            --output_dir={tmpdir}
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-            examples/controlnet/train_controlnet.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --dataset_name=hf-internal-testing/fill10
-            --output_dir={tmpdir}
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
-            --max_train_steps=11
-            --checkpointing_steps=2
-            --resume_from_checkpoint=checkpoint-8
-            --checkpoints_total_limit=3
-            """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-8", "checkpoint-10", "checkpoint-12"},
-            )
-
-
-class ControlNetSDXL(ExamplesTestsAccelerate):
-    def test_controlnet_sdxl(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/controlnet/train_controlnet_sdxl.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
-            --dataset_name=hf-internal-testing/fill10
-            --output_dir={tmpdir}
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet-sdxl
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors")))

examples/controlnet/train_controlnet.py

@@ -86,7 +86,6 @@ def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args, acceler
         controlnet=controlnet,
         safety_checker=None,
         revision=args.revision,
-        variant=args.variant,
         torch_dtype=weight_dtype,
     )
     pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
@@ -250,13 +249,10 @@ def parse_args(input_args=None):
         type=str,
         default=None,
         required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
+        help=(
+            "Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
+            " float32 precision."
+        ),
     )
     parser.add_argument(
         "--tokenizer_name",
@@ -771,13 +767,11 @@ def main(args):
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder = text_encoder_cls.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
-    )
-    vae = AutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     if args.controlnet_model_name_or_path:

examples/controlnet/train_controlnet_sdxl.py

@@ -74,7 +74,6 @@ def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step)
         unet=unet,
         controlnet=controlnet,
         revision=args.revision,
-        variant=args.variant,
         torch_dtype=weight_dtype,
     )
     pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
@@ -244,18 +243,15 @@ def parse_args(input_args=None):
         help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
         " If not specified controlnet weights are initialized from unet.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--revision",
         type=str,
         default=None,
         required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
+        help=(
+            "Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
+            " float32 precision."
+        ),
     )
     parser.add_argument(
         "--tokenizer_name",
@@ -797,16 +793,10 @@ def main(args):
 
     # Load the tokenizers
     tokenizer_one = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
     )
     tokenizer_two = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer_2",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
     )
 
     # import correct text encoder classes
@@ -820,10 +810,10 @@ def main(args):
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder_one = text_encoder_cls_one.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
     text_encoder_two = text_encoder_cls_two.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
     )
     vae_path = (
         args.pretrained_model_name_or_path
@@ -834,10 +824,9 @@ def main(args):
         vae_path,
         subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
         revision=args.revision,
-        variant=args.variant,
     )
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     if args.controlnet_model_name_or_path:

examples/custom_diffusion/test_custom_diffusion.py

@@ -1,130 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-import tempfile
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class CustomDiffusion(ExamplesTestsAccelerate):
-    def test_custom_diffusion(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/custom_diffusion/train_custom_diffusion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt <new1>
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 1.0e-05
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --modifier_token <new1>
-                --no_safe_serialization
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_custom_diffusion_weights.bin")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "<new1>.bin")))
-
-    def test_custom_diffusion_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/custom_diffusion/train_custom_diffusion.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=<new1>
-            --resolution=64
-            --train_batch_size=1
-            --modifier_token=<new1>
-            --dataloader_num_workers=0
-            --max_train_steps=6
-            --checkpoints_total_limit=2
-            --checkpointing_steps=2
-            --no_safe_serialization
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_custom_diffusion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/custom_diffusion/train_custom_diffusion.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=<new1>
-            --resolution=64
-            --train_batch_size=1
-            --modifier_token=<new1>
-            --dataloader_num_workers=0
-            --max_train_steps=9
-            --checkpointing_steps=2
-            --no_safe_serialization
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-            examples/custom_diffusion/train_custom_diffusion.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=<new1>
-            --resolution=64
-            --train_batch_size=1
-            --modifier_token=<new1>
-            --dataloader_num_workers=0
-            --max_train_steps=11
-            --checkpointing_steps=2
-            --resume_from_checkpoint=checkpoint-8
-            --checkpoints_total_limit=3
-            --no_safe_serialization
-            """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )

examples/custom_diffusion/train_custom_diffusion.py

@@ -332,12 +332,6 @@ def parse_args(input_args=None):
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--tokenizer_name",
         type=str,
@@ -746,7 +740,6 @@ def main(args):
                         torch_dtype=torch_dtype,
                         safety_checker=None,
                         revision=args.revision,
-                        variant=args.variant,
                     )
                     pipeline.set_progress_bar_config(disable=True)
 
@@ -808,13 +801,11 @@ def main(args):
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder = text_encoder_cls.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
-    )
-    vae = AutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     # Adding a modifier token which is optimized ####
@@ -1238,7 +1229,6 @@ def main(args):
                         text_encoder=accelerator.unwrap_model(text_encoder),
                         tokenizer=tokenizer,
                         revision=args.revision,
-                        variant=args.variant,
                         torch_dtype=weight_dtype,
                     )
                     pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
@@ -1288,7 +1278,7 @@ def main(args):
         # Final inference
         # Load previous pipeline
         pipeline = DiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype
+            args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype
         )
         pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
         pipeline = pipeline.to(accelerator.device)

examples/dreambooth/test_dreambooth.py

@@ -1,230 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import shutil
-import sys
-import tempfile
-
-from diffusers import DiffusionPipeline, UNet2DConditionModel
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class DreamBooth(ExamplesTestsAccelerate):
-    def test_dreambooth(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
-
-    def test_dreambooth_if(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-if-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --pre_compute_text_embeddings
-                --tokenizer_max_length=77
-                --text_encoder_use_attention_mask
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
-
-    def test_dreambooth_checkpointing(self):
-        instance_prompt = "photo"
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 5, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4
-
-            initial_run_args = f"""
-                examples/dreambooth/train_dreambooth.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt {instance_prompt}
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 5
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            # check can run the original fully trained output pipeline
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(instance_prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
-            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
-
-            # check can run an intermediate checkpoint
-            unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
-            pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
-            pipe(instance_prompt, num_inference_steps=2)
-
-            # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
-            shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
-
-            # Run training script for 7 total steps resuming from checkpoint 4
-
-            resume_run_args = f"""
-                examples/dreambooth/train_dreambooth.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt {instance_prompt}
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-4
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check can run new fully trained pipeline
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(instance_prompt, num_inference_steps=2)
-
-            # check old checkpoints do not exist
-            self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
-
-            # check new checkpoints exist
-            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
-            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
-
-    def test_dreambooth_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/dreambooth/train_dreambooth.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=6
-            --checkpoints_total_limit=2
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_dreambooth_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/dreambooth/train_dreambooth.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-            examples/dreambooth/train_dreambooth.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=11
-            --checkpointing_steps=2
-            --resume_from_checkpoint=checkpoint-8
-            --checkpoints_total_limit=3
-            """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )

examples/dreambooth/test_dreambooth_lora.py

@@ -1,388 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-import tempfile
-
-import safetensors
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-from diffusers import DiffusionPipeline  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class DreamBoothLoRA(ExamplesTestsAccelerate):
-    def test_dreambooth_lora(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"unet"` in their names.
-            starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
-            self.assertTrue(starts_with_unet)
-
-    def test_dreambooth_lora_with_text_encoder(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --train_text_encoder
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # check `text_encoder` is present at all.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            keys = lora_state_dict.keys()
-            is_text_encoder_present = any(k.startswith("text_encoder") for k in keys)
-            self.assertTrue(is_text_encoder_present)
-
-            # the names of the keys of the state dict should either start with `unet`
-            # or `text_encoder`.
-            is_correct_naming = all(k.startswith("unet") or k.startswith("text_encoder") for k in keys)
-            self.assertTrue(is_correct_naming)
-
-    def test_dreambooth_lora_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/dreambooth/train_dreambooth_lora.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=6
-            --checkpoints_total_limit=2
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_dreambooth_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/dreambooth/train_dreambooth_lora.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-            examples/dreambooth/train_dreambooth_lora.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=11
-            --checkpointing_steps=2
-            --resume_from_checkpoint=checkpoint-8
-            --checkpoints_total_limit=3
-            """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )
-
-    def test_dreambooth_lora_if_model(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-if-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --pre_compute_text_embeddings
-                --tokenizer_max_length=77
-                --text_encoder_use_attention_mask
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"unet"` in their names.
-            starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
-            self.assertTrue(starts_with_unet)
-
-
-class DreamBoothLoRASDXL(ExamplesTestsAccelerate):
-    def test_dreambooth_lora_sdxl(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora_sdxl.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"unet"` in their names.
-            starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
-            self.assertTrue(starts_with_unet)
-
-    def test_dreambooth_lora_sdxl_with_text_encoder(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora_sdxl.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --train_text_encoder
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"unet"` or `"text_encoder"` or `"text_encoder_2"` in their names.
-            keys = lora_state_dict.keys()
-            starts_with_unet = all(
-                k.startswith("unet") or k.startswith("text_encoder") or k.startswith("text_encoder_2") for k in keys
-            )
-            self.assertTrue(starts_with_unet)
-
-    def test_dreambooth_lora_sdxl_custom_captions(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora_sdxl.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --caption_column text
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-    def test_dreambooth_lora_sdxl_text_encoder_custom_captions(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora_sdxl.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --caption_column text
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --train_text_encoder
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-    def test_dreambooth_lora_sdxl_checkpointing_checkpoints_total_limit(self):
-        pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora_sdxl.py
-                --pretrained_model_name_or_path {pipeline_path}
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            pipe = DiffusionPipeline.from_pretrained(pipeline_path)
-            pipe.load_lora_weights(tmpdir)
-            pipe("a prompt", num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_dreambooth_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit(self):
-        pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora_sdxl.py
-                --pretrained_model_name_or_path {pipeline_path}
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                --train_text_encoder
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            pipe = DiffusionPipeline.from_pretrained(pipeline_path)
-            pipe.load_lora_weights(tmpdir)
-            pipe("a prompt", num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )

examples/dreambooth/train_dreambooth.py

@@ -139,7 +139,6 @@ def log_validation(
         text_encoder=text_encoder,
         unet=accelerator.unwrap_model(unet),
         revision=args.revision,
-        variant=args.variant,
         torch_dtype=weight_dtype,
         **pipeline_args,
     )
@@ -240,13 +239,10 @@ def parse_args(input_args=None):
         type=str,
         default=None,
         required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
+        help=(
+            "Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
+            " float32 precision."
+        ),
     )
     parser.add_argument(
         "--tokenizer_name",
@@ -300,7 +296,7 @@ def parse_args(input_args=None):
     parser.add_argument(
         "--output_dir",
         type=str,
-        default="dreambooth-model",
+        default="text-inversion-model",
         help="The output directory where the model predictions and checkpoints will be written.",
     )
     parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
@@ -863,7 +859,6 @@ def main(args):
                 torch_dtype=torch_dtype,
                 safety_checker=None,
                 revision=args.revision,
-                variant=args.variant,
             )
             pipeline.set_progress_bar_config(disable=True)
 
@@ -917,18 +912,18 @@ def main(args):
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder = text_encoder_cls.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
 
     if model_has_vae(args):
         vae = AutoencoderKL.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
+            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
         )
     else:
         vae = None
 
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
@@ -1384,7 +1379,6 @@ def main(args):
             args.pretrained_model_name_or_path,
             unet=accelerator.unwrap_model(unet),
             revision=args.revision,
-            variant=args.variant,
             **pipeline_args,
         )
 

examples/dreambooth/train_dreambooth_flax.py

@@ -460,10 +460,7 @@ def main():
 
     # Load models and create wrapper for stable diffusion
     text_encoder = FlaxCLIPTextModel.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="text_encoder",
-        dtype=weight_dtype,
-        revision=args.revision,
+        args.pretrained_model_name_or_path, subfolder="text_encoder", dtype=weight_dtype, revision=args.revision
     )
     vae, vae_params = FlaxAutoencoderKL.from_pretrained(
         vae_arg,
@@ -471,10 +468,7 @@ def main():
         **vae_kwargs,
     )
     unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="unet",
-        dtype=weight_dtype,
-        revision=args.revision,
+        args.pretrained_model_name_or_path, subfolder="unet", dtype=weight_dtype, revision=args.revision
     )
 
     # Optimization

examples/dreambooth/train_dreambooth_lora.py

@@ -57,7 +57,7 @@ from diffusers.models.attention_processor import (
     AttnAddedKVProcessor2_0,
     SlicedAttnAddedKVProcessor,
 )
-from diffusers.models.lora import LoRALinearLayer
+from diffusers.models.lora import LoRALinearLayer, text_encoder_lora_state_dict
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import unet_lora_state_dict
 from diffusers.utils import check_min_version, is_wandb_available
@@ -70,39 +70,6 @@ check_min_version("0.24.0.dev0")
 logger = get_logger(__name__)
 
 
-# TODO: This function should be removed once training scripts are rewritten in PEFT
-def text_encoder_lora_state_dict(text_encoder):
-    state_dict = {}
-
-    def text_encoder_attn_modules(text_encoder):
-        from transformers import CLIPTextModel, CLIPTextModelWithProjection
-
-        attn_modules = []
-
-        if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
-            for i, layer in enumerate(text_encoder.text_model.encoder.layers):
-                name = f"text_model.encoder.layers.{i}.self_attn"
-                mod = layer.self_attn
-                attn_modules.append((name, mod))
-
-        return attn_modules
-
-    for name, module in text_encoder_attn_modules(text_encoder):
-        for k, v in module.q_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.k_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.v_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.out_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v
-
-    return state_dict
-
-
 def save_model_card(
     repo_id: str,
     images=None,
@@ -183,12 +150,6 @@ def parse_args(input_args=None):
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--tokenizer_name",
         type=str,
@@ -756,7 +717,6 @@ def main(args):
                 torch_dtype=torch_dtype,
                 safety_checker=None,
                 revision=args.revision,
-                variant=args.variant,
             )
             pipeline.set_progress_bar_config(disable=True)
 
@@ -810,11 +770,11 @@ def main(args):
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder = text_encoder_cls.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
     try:
         vae = AutoencoderKL.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
+            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
         )
     except OSError:
         # IF does not have a VAE so let's just set it to None
@@ -822,7 +782,7 @@ def main(args):
         vae = None
 
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     # We only train the additional adapter LoRA layers
@@ -1317,7 +1277,6 @@ def main(args):
                     unet=accelerator.unwrap_model(unet),
                     text_encoder=None if args.pre_compute_text_embeddings else accelerator.unwrap_model(text_encoder),
                     revision=args.revision,
-                    variant=args.variant,
                     torch_dtype=weight_dtype,
                 )
 
@@ -1403,7 +1362,7 @@ def main(args):
         # Final inference
         # Load previous pipeline
         pipeline = DiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype
+            args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype
         )
 
         # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -50,9 +50,9 @@ from diffusers import (
     UNet2DConditionModel,
 )
 from diffusers.loaders import LoraLoaderMixin
-from diffusers.models.lora import LoRALinearLayer
+from diffusers.models.lora import LoRALinearLayer, text_encoder_lora_state_dict
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import compute_snr, unet_lora_state_dict
+from diffusers.training_utils import unet_lora_state_dict
 from diffusers.utils import check_min_version, is_wandb_available
 from diffusers.utils.import_utils import is_xformers_available
 
@@ -63,100 +63,37 @@ check_min_version("0.24.0.dev0")
 logger = get_logger(__name__)
 
 
-# TODO: This function should be removed once training scripts are rewritten in PEFT
-def text_encoder_lora_state_dict(text_encoder):
-    state_dict = {}
-
-    def text_encoder_attn_modules(text_encoder):
-        from transformers import CLIPTextModel, CLIPTextModelWithProjection
-
-        attn_modules = []
-
-        if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
-            for i, layer in enumerate(text_encoder.text_model.encoder.layers):
-                name = f"text_model.encoder.layers.{i}.self_attn"
-                mod = layer.self_attn
-                attn_modules.append((name, mod))
-
-        return attn_modules
-
-    for name, module in text_encoder_attn_modules(text_encoder):
-        for k, v in module.q_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.k_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.v_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.out_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v
-
-    return state_dict
-
-
 def save_model_card(
-    repo_id: str,
-    images=None,
-    base_model=str,
-    train_text_encoder=False,
-    instance_prompt=str,
-    validation_prompt=str,
-    repo_folder=None,
-    vae_path=None,
+    repo_id: str, images=None, base_model=str, train_text_encoder=False, prompt=str, repo_folder=None, vae_path=None
 ):
-    img_str = "widget:\n" if images else ""
+    img_str = ""
     for i, image in enumerate(images):
         image.save(os.path.join(repo_folder, f"image_{i}.png"))
-        img_str += f"""
-        - text: '{validation_prompt if validation_prompt else ' ' }'
-          output:
-            url:
-                "image_{i}.png"
-        """
+        img_str += f"![img_{i}](./image_{i}.png)\n"
 
     yaml = f"""
 ---
+license: openrail++
+base_model: {base_model}
+instance_prompt: {prompt}
 tags:
 - stable-diffusion-xl
 - stable-diffusion-xl-diffusers
 - text-to-image
 - diffusers
 - lora
-- template:sd-lora
-{img_str}
-base_model: {base_model}
-instance_prompt: {instance_prompt}
-license: openrail++
+inference: true
 ---
     """
-
     model_card = f"""
-# SDXL LoRA DreamBooth - {repo_id}
+# LoRA DreamBooth - {repo_id}
 
-<Gallery />
-
-## Model description
-
-These are {repo_id} LoRA adaption weights for {base_model}.
-
-The weights were trained  using [DreamBooth](https://dreambooth.github.io/).
+These are LoRA adaption weights for {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. \n
+{img_str}
 
 LoRA for the text encoder was enabled: {train_text_encoder}.
 
 Special VAE used for training: {vae_path}.
-
-## Trigger words
-
-You should use {instance_prompt} to trigger the image generation.
-
-## Download model
-
-Weights for this model are available in Safetensors format.
-
-[Download]({repo_id}/tree/main) them in the Files & versions tab.
-
 """
     with open(os.path.join(repo_folder, "README.md"), "w") as f:
         f.write(yaml + model_card)
@@ -204,59 +141,13 @@ def parse_args(input_args=None):
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default=None,
-        help=(
-            "The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,"
-            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
-            " or to a folder containing files that 🤗 Datasets can understand."
-        ),
-    )
-    parser.add_argument(
-        "--dataset_config_name",
-        type=str,
-        default=None,
-        help="The config of the Dataset, leave as None if there's only one config.",
-    )
     parser.add_argument(
         "--instance_data_dir",
         type=str,
         default=None,
-        help=("A folder containing the training data. "),
+        required=True,
+        help="A folder containing the training data of instance images.",
     )
-
-    parser.add_argument(
-        "--cache_dir",
-        type=str,
-        default=None,
-        help="The directory where the downloaded models and datasets will be stored.",
-    )
-
-    parser.add_argument(
-        "--image_column",
-        type=str,
-        default="image",
-        help="The column of the dataset containing the target image. By "
-        "default, the standard Image Dataset maps out 'file_name' "
-        "to 'image'.",
-    )
-    parser.add_argument(
-        "--caption_column",
-        type=str,
-        default=None,
-        help="The column of the dataset containing the instance prompt for each image",
-    )
-
-    parser.add_argument("--repeats", type=int, default=1, help="How many times to repeat the training data.")
-
     parser.add_argument(
         "--class_data_dir",
         type=str,
@@ -269,7 +160,7 @@ def parse_args(input_args=None):
         type=str,
         default=None,
         required=True,
-        help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'",
+        help="The prompt with identifier specifying the instance",
     )
     parser.add_argument(
         "--class_prompt",
@@ -408,16 +299,9 @@ def parse_args(input_args=None):
     parser.add_argument(
         "--learning_rate",
         type=float,
-        default=1e-4,
+        default=5e-4,
         help="Initial learning rate (after the potential warmup period) to use.",
     )
-
-    parser.add_argument(
-        "--text_encoder_lr",
-        type=float,
-        default=5e-6,
-        help="Text encoder learning rate to use.",
-    )
     parser.add_argument(
         "--scale_lr",
         action="store_true",
@@ -433,14 +317,6 @@ def parse_args(input_args=None):
             ' "constant", "constant_with_warmup"]'
         ),
     )
-
-    parser.add_argument(
-        "--snr_gamma",
-        type=float,
-        default=None,
-        help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
-        "More details here: https://arxiv.org/abs/2303.09556.",
-    )
     parser.add_argument(
         "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
     )
@@ -459,59 +335,13 @@ def parse_args(input_args=None):
             "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
         ),
     )
-
     parser.add_argument(
-        "--optimizer",
-        type=str,
-        default="AdamW",
-        help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'),
-    )
-
-    parser.add_argument(
-        "--use_8bit_adam",
-        action="store_true",
-        help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW",
-    )
-
-    parser.add_argument(
-        "--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers."
-    )
-    parser.add_argument(
-        "--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam and Prodigy optimizers."
-    )
-    parser.add_argument(
-        "--prodigy_beta3",
-        type=float,
-        default=None,
-        help="coefficients for computing the Prodidy stepsize using running averages. If set to None, "
-        "uses the value of square root of beta2. Ignored if optimizer is adamW",
-    )
-    parser.add_argument("--prodigy_decouple", type=bool, default=True, help="Use AdamW style decoupled weight decay")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params")
-    parser.add_argument(
-        "--adam_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder"
-    )
-
-    parser.add_argument(
-        "--adam_epsilon",
-        type=float,
-        default=1e-08,
-        help="Epsilon value for the Adam optimizer and Prodigy optimizers.",
-    )
-
-    parser.add_argument(
-        "--prodigy_use_bias_correction",
-        type=bool,
-        default=True,
-        help="Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW",
-    )
-    parser.add_argument(
-        "--prodigy_safeguard_warmup",
-        type=bool,
-        default=True,
-        help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. "
-        "Ignored if optimizer is adamW",
+        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
     )
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
     parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
     parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
     parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
@@ -584,12 +414,6 @@ def parse_args(input_args=None):
     else:
         args = parser.parse_args()
 
-    if args.dataset_name is None and args.instance_data_dir is None:
-        raise ValueError("Specify either `--dataset_name` or `--instance_data_dir`")
-
-    if args.dataset_name is not None and args.instance_data_dir is not None:
-        raise ValueError("Specify only one of `--dataset_name` or `--instance_data_dir`")
-
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
     if env_local_rank != -1 and env_local_rank != args.local_rank:
         args.local_rank = env_local_rank
@@ -618,84 +442,20 @@ class DreamBoothDataset(Dataset):
     def __init__(
         self,
         instance_data_root,
-        instance_prompt,
-        class_prompt,
         class_data_root=None,
         class_num=None,
         size=1024,
-        repeats=1,
         center_crop=False,
     ):
         self.size = size
         self.center_crop = center_crop
 
-        self.instance_prompt = instance_prompt
-        self.custom_instance_prompts = None
-        self.class_prompt = class_prompt
+        self.instance_data_root = Path(instance_data_root)
+        if not self.instance_data_root.exists():
+            raise ValueError("Instance images root doesn't exists.")
 
-        # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,
-        # we load the training data using load_dataset
-        if args.dataset_name is not None:
-            try:
-                from datasets import load_dataset
-            except ImportError:
-                raise ImportError(
-                    "You are trying to load your data using the datasets library. If you wish to train using custom "
-                    "captions please install the datasets library: `pip install datasets`. If you wish to load a "
-                    "local folder containing images only, specify --instance_data_dir instead."
-                )
-            # Downloading and loading a dataset from the hub.
-            # See more about loading custom images at
-            # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
-            dataset = load_dataset(
-                args.dataset_name,
-                args.dataset_config_name,
-                cache_dir=args.cache_dir,
-            )
-            # Preprocessing the datasets.
-            column_names = dataset["train"].column_names
-
-            # 6. Get the column names for input/target.
-            if args.image_column is None:
-                image_column = column_names[0]
-                logger.info(f"image column defaulting to {image_column}")
-            else:
-                image_column = args.image_column
-                if image_column not in column_names:
-                    raise ValueError(
-                        f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-                    )
-            instance_images = dataset["train"][image_column]
-
-            if args.caption_column is None:
-                logger.info(
-                    "No caption column provided, defaulting to instance_prompt for all images. If your dataset "
-                    "contains captions/prompts for the images, make sure to specify the "
-                    "column as --caption_column"
-                )
-                self.custom_instance_prompts = None
-            else:
-                if args.caption_column not in column_names:
-                    raise ValueError(
-                        f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-                    )
-                custom_instance_prompts = dataset["train"][args.caption_column]
-                # create final list of captions according to --repeats
-                self.custom_instance_prompts = []
-                for caption in custom_instance_prompts:
-                    self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))
-        else:
-            self.instance_data_root = Path(instance_data_root)
-            if not self.instance_data_root.exists():
-                raise ValueError("Instance images root doesn't exists.")
-
-            instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]
-            self.custom_instance_prompts = None
-
-        self.instance_images = []
-        for img in instance_images:
-            self.instance_images.extend(itertools.repeat(img, repeats))
-        self.num_instance_images = len(self.instance_images)
+        self.instance_images_path = list(Path(instance_data_root).iterdir())
+        self.num_instance_images = len(self.instance_images_path)
         self._length = self.num_instance_images
 
         if class_data_root is not None:
@@ -724,23 +484,13 @@ class DreamBoothDataset(Dataset):
 
     def __getitem__(self, index):
         example = {}
-        instance_image = self.instance_images[index % self.num_instance_images]
+        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
         instance_image = exif_transpose(instance_image)
 
         if not instance_image.mode == "RGB":
             instance_image = instance_image.convert("RGB")
         example["instance_images"] = self.image_transforms(instance_image)
 
-        if self.custom_instance_prompts:
-            caption = self.custom_instance_prompts[index % self.num_instance_images]
-            if caption:
-                example["instance_prompt"] = caption
-            else:
-                example["instance_prompt"] = self.instance_prompt
-
-        else:  # costum prompts were provided, but length does not match size of image dataset
-            example["instance_prompt"] = self.instance_prompt
-
         if self.class_data_root:
             class_image = Image.open(self.class_images_path[index % self.num_class_images])
             class_image = exif_transpose(class_image)
@@ -748,25 +498,22 @@ class DreamBoothDataset(Dataset):
             if not class_image.mode == "RGB":
                 class_image = class_image.convert("RGB")
             example["class_images"] = self.image_transforms(class_image)
-            example["class_prompt"] = self.class_prompt
 
         return example
 
 
 def collate_fn(examples, with_prior_preservation=False):
     pixel_values = [example["instance_images"] for example in examples]
-    prompts = [example["instance_prompt"] for example in examples]
 
     # Concat class and instance examples for prior preservation.
     # We do this to avoid doing two forward passes.
     if with_prior_preservation:
         pixel_values += [example["class_images"] for example in examples]
-        prompts += [example["class_prompt"] for example in examples]
 
     pixel_values = torch.stack(pixel_values)
     pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
 
-    batch = {"pixel_values": pixel_values, "prompts": prompts}
+    batch = {"pixel_values": pixel_values}
     return batch
 
 
@@ -883,7 +630,6 @@ def main(args):
                 args.pretrained_model_name_or_path,
                 torch_dtype=torch_dtype,
                 revision=args.revision,
-                variant=args.variant,
             )
             pipeline.set_progress_bar_config(disable=True)
 
@@ -922,16 +668,10 @@ def main(args):
 
     # Load the tokenizers
     tokenizer_one = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
     )
     tokenizer_two = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer_2",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
     )
 
     # import correct text encoder classes
@@ -945,10 +685,10 @@ def main(args):
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder_one = text_encoder_cls_one.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
     text_encoder_two = text_encoder_cls_two.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
     )
     vae_path = (
         args.pretrained_model_name_or_path
@@ -956,13 +696,10 @@ def main(args):
         else args.pretrained_vae_model_name_or_path
     )
     vae = AutoencoderKL.from_pretrained(
-        vae_path,
-        subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
-        revision=args.revision,
-        variant=args.variant,
+        vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision
     )
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     # We only train the additional adapter LoRA layers
@@ -995,8 +732,7 @@ def main(args):
             xformers_version = version.parse(xformers.__version__)
             if xformers_version == version.parse("0.0.16"):
                 logger.warn(
-                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, "
-                    "please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                 )
             unet.enable_xformers_memory_efficient_attention()
         else:
@@ -1130,119 +866,35 @@ def main(args):
             args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
         )
 
-    # Optimization parameters
-    unet_lora_parameters_with_lr = {"params": unet_lora_parameters, "lr": args.learning_rate}
-    if args.train_text_encoder:
-        # different learning rate for text encoder and unet
-        text_lora_parameters_one_with_lr = {
-            "params": text_lora_parameters_one,
-            "weight_decay": args.adam_weight_decay_text_encoder,
-            "lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,
-        }
-        text_lora_parameters_two_with_lr = {
-            "params": text_lora_parameters_two,
-            "weight_decay": args.adam_weight_decay_text_encoder,
-            "lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,
-        }
-        params_to_optimize = [
-            unet_lora_parameters_with_lr,
-            text_lora_parameters_one_with_lr,
-            text_lora_parameters_two_with_lr,
-        ]
+    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
+    if args.use_8bit_adam:
+        try:
+            import bitsandbytes as bnb
+        except ImportError:
+            raise ImportError(
+                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
+            )
+
+        optimizer_class = bnb.optim.AdamW8bit
     else:
-        params_to_optimize = [unet_lora_parameters_with_lr]
+        optimizer_class = torch.optim.AdamW
 
     # Optimizer creation
-    if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"):
-        logger.warn(
-            f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]."
-            "Defaulting to adamW"
-        )
-        args.optimizer = "adamw"
-
-    if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
-        logger.warn(
-            f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
-            f"set to {args.optimizer.lower()}"
-        )
-
-    if args.optimizer.lower() == "adamw":
-        if args.use_8bit_adam:
-            try:
-                import bitsandbytes as bnb
-            except ImportError:
-                raise ImportError(
-                    "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
-                )
-
-            optimizer_class = bnb.optim.AdamW8bit
-        else:
-            optimizer_class = torch.optim.AdamW
-
-        optimizer = optimizer_class(
-            params_to_optimize,
-            betas=(args.adam_beta1, args.adam_beta2),
-            weight_decay=args.adam_weight_decay,
-            eps=args.adam_epsilon,
-        )
-
-    if args.optimizer.lower() == "prodigy":
-        try:
-            import prodigyopt
-        except ImportError:
-            raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
-
-        optimizer_class = prodigyopt.Prodigy
-
-        if args.learning_rate <= 0.1:
-            logger.warn(
-                "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
-            )
-        if args.train_text_encoder and args.text_encoder_lr:
-            logger.warn(
-                f"Learning rates were provided both for the unet and the text encoder- e.g. text_encoder_lr:"
-                f" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. "
-                f"When using prodigy only learning_rate is used as the initial learning rate."
-            )
-            # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be
-            # --learning_rate
-            params_to_optimize[1]["lr"] = args.learning_rate
-            params_to_optimize[2]["lr"] = args.learning_rate
-
-        optimizer = optimizer_class(
-            params_to_optimize,
-            lr=args.learning_rate,
-            betas=(args.adam_beta1, args.adam_beta2),
-            beta3=args.prodigy_beta3,
-            weight_decay=args.adam_weight_decay,
-            eps=args.adam_epsilon,
-            decouple=args.prodigy_decouple,
-            use_bias_correction=args.prodigy_use_bias_correction,
-            safeguard_warmup=args.prodigy_safeguard_warmup,
-        )
-
-    # Dataset and DataLoaders creation:
-    train_dataset = DreamBoothDataset(
-        instance_data_root=args.instance_data_dir,
-        instance_prompt=args.instance_prompt,
-        class_prompt=args.class_prompt,
-        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
-        class_num=args.num_class_images,
-        size=args.resolution,
-        repeats=args.repeats,
-        center_crop=args.center_crop,
+    params_to_optimize = (
+        itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)
+        if args.train_text_encoder
+        else unet_lora_parameters
     )
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        batch_size=args.train_batch_size,
-        shuffle=True,
-        collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
-        num_workers=args.dataloader_num_workers,
+    optimizer = optimizer_class(
+        params_to_optimize,
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
     )
 
     # Computes additional embeddings/ids required by the SDXL UNet.
-    # regular text embeddings (when `train_text_encoder` is not True)
+    # regular text emebddings (when `train_text_encoder` is not True)
     # pooled text embeddings
     # time ids
 
@@ -1269,11 +921,7 @@ def main(args):
 
     # Handle instance prompt.
     instance_time_ids = compute_time_ids()
-
-    # If no type of tuning is done on the text_encoder and custom instance prompts are NOT
-    # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid
-    # the redundant encoding.
-    if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
+    if not args.train_text_encoder:
         instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings(
             args.instance_prompt, text_encoders, tokenizers
         )
@@ -1286,36 +934,49 @@ def main(args):
                 args.class_prompt, text_encoders, tokenizers
             )
 
-    # Clear the memory here
-    if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
+    # Clear the memory here.
+    if not args.train_text_encoder:
         del tokenizers, text_encoders
         gc.collect()
         torch.cuda.empty_cache()
 
-    # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
-    # pack the statically computed variables appropriately here. This is so that we don't
+    # Pack the statically computed variables appropriately. This is so that we don't
     # have to pass them to the dataloader.
     add_time_ids = instance_time_ids
     if args.with_prior_preservation:
         add_time_ids = torch.cat([add_time_ids, class_time_ids], dim=0)
 
-    if not train_dataset.custom_instance_prompts:
-        if not args.train_text_encoder:
-            prompt_embeds = instance_prompt_hidden_states
-            unet_add_text_embeds = instance_pooled_prompt_embeds
-            if args.with_prior_preservation:
-                prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
-                unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0)
-        # if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the
-        # batch prompts on all training steps
-        else:
-            tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt)
-            tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt)
-            if args.with_prior_preservation:
-                class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt)
-                class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt)
-                tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
-                tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
+    if not args.train_text_encoder:
+        prompt_embeds = instance_prompt_hidden_states
+        unet_add_text_embeds = instance_pooled_prompt_embeds
+        if args.with_prior_preservation:
+            prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
+            unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0)
+    else:
+        tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt)
+        tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt)
+        if args.with_prior_preservation:
+            class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt)
+            class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt)
+            tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
+            tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
+
+    # Dataset and DataLoaders creation:
+    train_dataset = DreamBoothDataset(
+        instance_data_root=args.instance_data_dir,
+        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
+        class_num=args.num_class_images,
+        size=args.resolution,
+        center_crop=args.center_crop,
+    )
+
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_size=args.train_batch_size,
+        shuffle=True,
+        collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
+        num_workers=args.dataloader_num_workers,
+    )
 
     # Scheduler and math around the number of training steps.
     overrode_max_train_steps = False
@@ -1418,17 +1079,6 @@ def main(args):
         for step, batch in enumerate(train_dataloader):
             with accelerator.accumulate(unet):
                 pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
-                prompts = batch["prompts"]
-
-                # encode batch prompts when custom prompts are provided for each image -
-                if train_dataset.custom_instance_prompts:
-                    if not args.train_text_encoder:
-                        prompt_embeds, unet_add_text_embeds = compute_text_embeddings(
-                            prompts, text_encoders, tokenizers
-                        )
-                    else:
-                        tokens_one = tokenize_prompt(tokenizer_one, prompts)
-                        tokens_two = tokenize_prompt(tokenizer_two, prompts)
 
                 # Convert images to latent space
                 model_input = vae.encode(pixel_values).latent_dist.sample()
@@ -1449,21 +1099,16 @@ def main(args):
                 # (this is the forward diffusion process)
                 noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
 
-                # Calculate the elements to repeat depending on the use of prior-preservation and custom captions.
-                if not train_dataset.custom_instance_prompts:
-                    elems_to_repeat_text_embeds = bsz // 2 if args.with_prior_preservation else bsz
-                    elems_to_repeat_time_ids = bsz // 2 if args.with_prior_preservation else bsz
-                else:
-                    elems_to_repeat_text_embeds = 1
-                    elems_to_repeat_time_ids = bsz // 2 if args.with_prior_preservation else bsz
+                # Calculate the elements to repeat depending on the use of prior-preservation.
+                elems_to_repeat = bsz // 2 if args.with_prior_preservation else bsz
 
                 # Predict the noise residual
                 if not args.train_text_encoder:
                     unet_added_conditions = {
-                        "time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1),
-                        "text_embeds": unet_add_text_embeds.repeat(elems_to_repeat_text_embeds, 1),
+                        "time_ids": add_time_ids.repeat(elems_to_repeat, 1),
+                        "text_embeds": unet_add_text_embeds.repeat(elems_to_repeat, 1),
                     }
-                    prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
+                    prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat, 1, 1)
                     model_pred = unet(
                         noisy_model_input,
                         timesteps,
@@ -1471,17 +1116,15 @@ def main(args):
                         added_cond_kwargs=unet_added_conditions,
                     ).sample
                 else:
-                    unet_added_conditions = {"time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1)}
+                    unet_added_conditions = {"time_ids": add_time_ids.repeat(elems_to_repeat, 1)}
                     prompt_embeds, pooled_prompt_embeds = encode_prompt(
                         text_encoders=[text_encoder_one, text_encoder_two],
                         tokenizers=None,
                         prompt=None,
                         text_input_ids_list=[tokens_one, tokens_two],
                     )
-                    unet_added_conditions.update(
-                        {"text_embeds": pooled_prompt_embeds.repeat(elems_to_repeat_text_embeds, 1)}
-                    )
-                    prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
+                    unet_added_conditions.update({"text_embeds": pooled_prompt_embeds.repeat(elems_to_repeat, 1)})
+                    prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat, 1, 1)
                     model_pred = unet(
                         noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions
                     ).sample
@@ -1499,34 +1142,16 @@ def main(args):
                     model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
                     target, target_prior = torch.chunk(target, 2, dim=0)
 
+                    # Compute instance loss
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
                     # Compute prior loss
                     prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
 
-                if args.snr_gamma is None:
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-                else:
-                    # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
-                    # Since we predict the noise instead of x_0, the original formulation is slightly changed.
-                    # This is discussed in Section 4.2 of the same paper.
-                    snr = compute_snr(noise_scheduler, timesteps)
-                    base_weight = (
-                        torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
-                    )
-
-                    if noise_scheduler.config.prediction_type == "v_prediction":
-                        # Velocity objective needs to be floored to an SNR weight of one.
-                        mse_loss_weights = base_weight + 1
-                    else:
-                        # Epsilon and sample both use the same loss weights.
-                        mse_loss_weights = base_weight
-
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
-                    loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
-                    loss = loss.mean()
-
-                if args.with_prior_preservation:
                     # Add the prior loss to the instance loss.
                     loss = loss + args.prior_loss_weight * prior_loss
+                else:
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
 
                 accelerator.backward(loss)
                 if accelerator.sync_gradients:
@@ -1587,16 +1212,10 @@ def main(args):
                 # create pipeline
                 if not args.train_text_encoder:
                     text_encoder_one = text_encoder_cls_one.from_pretrained(
-                        args.pretrained_model_name_or_path,
-                        subfolder="text_encoder",
-                        revision=args.revision,
-                        variant=args.variant,
+                        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
                     )
                     text_encoder_two = text_encoder_cls_two.from_pretrained(
-                        args.pretrained_model_name_or_path,
-                        subfolder="text_encoder_2",
-                        revision=args.revision,
-                        variant=args.variant,
+                        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
                     )
                 pipeline = StableDiffusionXLPipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
@@ -1605,7 +1224,6 @@ def main(args):
                     text_encoder_2=accelerator.unwrap_model(text_encoder_two),
                     unet=accelerator.unwrap_model(unet),
                     revision=args.revision,
-                    variant=args.variant,
                     torch_dtype=weight_dtype,
                 )
 
@@ -1683,15 +1301,10 @@ def main(args):
             vae_path,
             subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
             revision=args.revision,
-            variant=args.variant,
             torch_dtype=weight_dtype,
         )
         pipeline = StableDiffusionXLPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            vae=vae,
-            revision=args.revision,
-            variant=args.variant,
-            torch_dtype=weight_dtype,
+            args.pretrained_model_name_or_path, vae=vae, revision=args.revision, torch_dtype=weight_dtype
         )
 
         # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
@@ -1740,8 +1353,7 @@ def main(args):
                 images=images,
                 base_model=args.pretrained_model_name_or_path,
                 train_text_encoder=args.train_text_encoder,
-                instance_prompt=args.instance_prompt,
-                validation_prompt=args.validation_prompt,
+                prompt=args.instance_prompt,
                 repo_folder=args.output_dir,
                 vae_path=args.pretrained_vae_model_name_or_path,
             )

examples/instruct_pix2pix/test_instruct_pix2pix.py

@@ -1,101 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-import tempfile
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class InstructPix2Pix(ExamplesTestsAccelerate):
-    def test_instruct_pix2pix_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/instruct_pix2pix/train_instruct_pix2pix.py
-                --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-                --dataset_name=hf-internal-testing/instructpix2pix-10-samples
-                --resolution=64
-                --random_flip
-                --train_batch_size=1
-                --max_train_steps=7
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                --output_dir {tmpdir}
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_instruct_pix2pix_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/instruct_pix2pix/train_instruct_pix2pix.py
-                --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-                --dataset_name=hf-internal-testing/instructpix2pix-10-samples
-                --resolution=64
-                --random_flip
-                --train_batch_size=1
-                --max_train_steps=9
-                --checkpointing_steps=2
-                --output_dir {tmpdir}
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-                examples/instruct_pix2pix/train_instruct_pix2pix.py
-                --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-                --dataset_name=hf-internal-testing/instructpix2pix-10-samples
-                --resolution=64
-                --random_flip
-                --train_batch_size=1
-                --max_train_steps=11
-                --checkpointing_steps=2
-                --output_dir {tmpdir}
-                --seed=0
-                --resume_from_checkpoint=checkpoint-8
-                --checkpoints_total_limit=3
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )

examples/instruct_pix2pix/train_instruct_pix2pix.py

@@ -78,12 +78,6 @@ def parse_args():
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--dataset_name",
         type=str,
@@ -441,11 +435,9 @@ def main():
         args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
     )
     text_encoder = CLIPTextModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
-    )
-    vae = AutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
     unet = UNet2DConditionModel.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision
     )
@@ -923,7 +915,6 @@ def main():
                     text_encoder=accelerator.unwrap_model(text_encoder),
                     vae=accelerator.unwrap_model(vae),
                     revision=args.revision,
-                    variant=args.variant,
                     torch_dtype=weight_dtype,
                 )
                 pipeline = pipeline.to(accelerator.device)
@@ -975,7 +966,6 @@ def main():
             vae=accelerator.unwrap_model(vae),
             unet=unet,
             revision=args.revision,
-            variant=args.variant,
         )
         pipeline.save_pretrained(args.output_dir)
 

examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py

@@ -118,12 +118,6 @@ def parse_args():
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--dataset_name",
         type=str,
@@ -490,10 +484,9 @@ def main():
         vae_path,
         subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
         revision=args.revision,
-        variant=args.variant,
     )
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     # InstructPix2Pix uses an additional image for conditioning. To accommodate that,
@@ -702,16 +695,10 @@ def main():
 
     # Load scheduler, tokenizer and models.
     tokenizer_1 = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
     )
     tokenizer_2 = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer_2",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
     )
     text_encoder_cls_1 = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
     text_encoder_cls_2 = import_model_class_from_model_name_or_path(
@@ -721,10 +708,10 @@ def main():
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder_1 = text_encoder_cls_1.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
     text_encoder_2 = text_encoder_cls_2.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
     )
 
     # We ALWAYS pre-compute the additional condition embeddings needed for SDXL
@@ -1122,7 +1109,6 @@ def main():
                         tokenizer_2=tokenizer_2,
                         vae=vae,
                         revision=args.revision,
-                        variant=args.variant,
                         torch_dtype=weight_dtype,
                     )
                     pipeline = pipeline.to(accelerator.device)
@@ -1190,7 +1176,6 @@ def main():
             vae=vae,
             unet=unet,
             revision=args.revision,
-            variant=args.variant,
         )
         pipeline.save_pretrained(args.output_dir)
 

examples/t2i_adapter/test_t2i_adapter.py

@@ -1,51 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-import tempfile
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class T2IAdapter(ExamplesTestsAccelerate):
-    def test_t2i_adapter_sdxl(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/t2i_adapter/train_t2i_adapter_sdxl.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
-            --adapter_model_name_or_path=hf-internal-testing/tiny-adapter
-            --dataset_name=hf-internal-testing/fill10
-            --output_dir={tmpdir}
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors")))

examples/t2i_adapter/train_t2i_adapter_sdxl.py

@@ -85,7 +85,6 @@ def log_validation(vae, unet, adapter, args, accelerator, weight_dtype, step):
         unet=unet,
         adapter=adapter,
         revision=args.revision,
-        variant=args.variant,
         torch_dtype=weight_dtype,
     )
     pipeline = pipeline.to(accelerator.device)
@@ -263,12 +262,6 @@ def parse_args(input_args=None):
             " float32 precision."
         ),
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--tokenizer_name",
         type=str,
@@ -819,16 +812,10 @@ def main(args):
 
     # Load the tokenizers
     tokenizer_one = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
     )
     tokenizer_two = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer_2",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
     )
 
     # import correct text encoder classes
@@ -842,10 +829,10 @@ def main(args):
     # Load scheduler and models
     noise_scheduler = EulerDiscreteScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder_one = text_encoder_cls_one.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
     text_encoder_two = text_encoder_cls_two.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
     )
     vae_path = (
         args.pretrained_model_name_or_path
@@ -856,10 +843,9 @@ def main(args):
         vae_path,
         subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
         revision=args.revision,
-        variant=args.variant,
     )
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     if args.adapter_model_name_or_path:

examples/test_examples_utils.py

@@ -1,61 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import shutil
-import subprocess
-import tempfile
-import unittest
-from typing import List
-
-from accelerate.utils import write_basic_config
-
-
-# These utils relate to ensuring the right error message is received when running scripts
-class SubprocessCallException(Exception):
-    pass
-
-
-def run_command(command: List[str], return_stdout=False):
-    """
-    Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture
-    if an error occurred while running `command`
-    """
-    try:
-        output = subprocess.check_output(command, stderr=subprocess.STDOUT)
-        if return_stdout:
-            if hasattr(output, "decode"):
-                output = output.decode("utf-8")
-            return output
-    except subprocess.CalledProcessError as e:
-        raise SubprocessCallException(
-            f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
-        ) from e
-
-
-class ExamplesTestsAccelerate(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        super().setUpClass()
-        cls._tmpdir = tempfile.mkdtemp()
-        cls.configPath = os.path.join(cls._tmpdir, "default_config.yml")
-
-        write_basic_config(save_location=cls.configPath)
-        cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]
-
-    @classmethod
-    def tearDownClass(cls):
-        super().tearDownClass()
-        shutil.rmtree(cls._tmpdir)

examples/text_to_image/test_text_to_image.py

@@ -1,373 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import shutil
-import sys
-import tempfile
-
-from diffusers import DiffusionPipeline, UNet2DConditionModel  # noqa: E402
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class TextToImage(ExamplesTestsAccelerate):
-    def test_text_to_image(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
-
-    def test_text_to_image_checkpointing(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 5, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 5
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4"},
-            )
-
-            # check can run an intermediate checkpoint
-            unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
-            pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
-            shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
-
-            # Run training script for 7 total steps resuming from checkpoint 4
-
-            resume_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-4
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check can run new fully trained pipeline
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {
-                    # no checkpoint-2 -> check old checkpoints do not exist
-                    # check new checkpoints exist
-                    "checkpoint-4",
-                    "checkpoint-6",
-                },
-            )
-
-    def test_text_to_image_checkpointing_use_ema(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 5, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 5
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --use_ema
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4"},
-            )
-
-            # check can run an intermediate checkpoint
-            unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
-            pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
-            shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
-
-            # Run training script for 7 total steps resuming from checkpoint 4
-
-            resume_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-4
-                --use_ema
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check can run new fully trained pipeline
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {
-                    # no checkpoint-2 -> check old checkpoints do not exist
-                    # check new checkpoints exist
-                    "checkpoint-4",
-                    "checkpoint-6",
-                },
-            )
-
-    def test_text_to_image_checkpointing_checkpoints_total_limit(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
-            # Should create checkpoints at steps 2, 4, 6
-            # with checkpoint at step 2 deleted
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_text_to_image_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 9, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4, 6, 8
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 9
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            # resume and we should try to checkpoint at 10, where we'll have to remove
-            # checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint
-
-            resume_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 11
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-8
-                --checkpoints_total_limit=3
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )
-
-
-class TextToImageSDXL(ExamplesTestsAccelerate):
-    def test_text_to_image_sdxl(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/text_to_image/train_text_to_image_sdxl.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))

examples/text_to_image/test_text_to_image_lora.py

@@ -1,308 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-import tempfile
-
-import safetensors
-
-from diffusers import DiffusionPipeline  # noqa: E402
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class TextToImageLoRA(ExamplesTestsAccelerate):
-    def test_text_to_image_lora_sdxl_checkpointing_checkpoints_total_limit(self):
-        prompt = "a prompt"
-        pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
-            # Should create checkpoints at steps 2, 4, 6
-            # with checkpoint at step 2 deleted
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image_lora_sdxl.py
-                --pretrained_model_name_or_path {pipeline_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(pipeline_path)
-            pipe.load_lora_weights(tmpdir)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_text_to_image_lora_checkpointing_checkpoints_total_limit(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
-            # Should create checkpoints at steps 2, 4, 6
-            # with checkpoint at step 2 deleted
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image_lora.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                --seed=0
-                --num_validation_images=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
-            )
-            pipe.load_lora_weights(tmpdir)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_text_to_image_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 9, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4, 6, 8
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image_lora.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 9
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --seed=0
-                --num_validation_images=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
-            )
-            pipe.load_lora_weights(tmpdir)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            # resume and we should try to checkpoint at 10, where we'll have to remove
-            # checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint
-
-            resume_run_args = f"""
-                examples/text_to_image/train_text_to_image_lora.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 11
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-8
-                --checkpoints_total_limit=3
-                --seed=0
-                --num_validation_images=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
-            )
-            pipe.load_lora_weights(tmpdir)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )
-
-
-class TextToImageLoRASDXL(ExamplesTestsAccelerate):
-    def test_text_to_image_lora_sdxl(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/text_to_image/train_text_to_image_lora_sdxl.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-    def test_text_to_image_lora_sdxl_with_text_encoder(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/text_to_image/train_text_to_image_lora_sdxl.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --train_text_encoder
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"unet"` or `"text_encoder"` or `"text_encoder_2"` in their names.
-            keys = lora_state_dict.keys()
-            starts_with_unet = all(
-                k.startswith("unet") or k.startswith("text_encoder") or k.startswith("text_encoder_2") for k in keys
-            )
-            self.assertTrue(starts_with_unet)
-
-    def test_text_to_image_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit(self):
-        prompt = "a prompt"
-        pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
-            # Should create checkpoints at steps 2, 4, 6
-            # with checkpoint at step 2 deleted
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image_lora_sdxl.py
-                --pretrained_model_name_or_path {pipeline_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --train_text_encoder
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(pipeline_path)
-            pipe.load_lora_weights(tmpdir)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )

examples/text_to_image/train_text_to_image.py

@@ -148,7 +148,6 @@ def log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight
         unet=accelerator.unwrap_model(unet),
         safety_checker=None,
         revision=args.revision,
-        variant=args.variant,
         torch_dtype=weight_dtype,
     )
     pipeline = pipeline.to(accelerator.device)
@@ -210,12 +209,6 @@ def parse_args():
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--dataset_name",
         type=str,
@@ -574,10 +567,10 @@ def main():
     # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.
     with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
         text_encoder = CLIPTextModel.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
+            args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
         )
         vae = AutoencoderKL.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
+            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
         )
 
     unet = UNet2DConditionModel.from_pretrained(
@@ -592,7 +585,7 @@ def main():
     # Create EMA for the unet.
     if args.use_ema:
         ema_unet = UNet2DConditionModel.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+            args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
         )
         ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)
 
@@ -1033,7 +1026,6 @@ def main():
             vae=vae,
             unet=unet,
             revision=args.revision,
-            variant=args.variant,
         )
         pipeline.save_pretrained(args.output_dir)
 

examples/text_to_image/train_text_to_image_flax.py

@@ -54,12 +54,6 @@ def parse_args():
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--dataset_name",
         type=str,

examples/text_to_image/train_text_to_image_lora.py

@@ -40,7 +40,8 @@ from transformers import CLIPTextModel, CLIPTokenizer
 
 import diffusers
 from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
-from diffusers.models.lora import LoRALinearLayer
+from diffusers.loaders import AttnProcsLayers
+from diffusers.models.attention_processor import LoRAAttnProcessor
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import compute_snr
 from diffusers.utils import check_min_version, is_wandb_available
@@ -53,39 +54,6 @@ check_min_version("0.24.0.dev0")
 logger = get_logger(__name__, log_level="INFO")
 
 
-# TODO: This function should be removed once training scripts are rewritten in PEFT
-def text_encoder_lora_state_dict(text_encoder):
-    state_dict = {}
-
-    def text_encoder_attn_modules(text_encoder):
-        from transformers import CLIPTextModel, CLIPTextModelWithProjection
-
-        attn_modules = []
-
-        if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
-            for i, layer in enumerate(text_encoder.text_model.encoder.layers):
-                name = f"text_model.encoder.layers.{i}.self_attn"
-                mod = layer.self_attn
-                attn_modules.append((name, mod))
-
-        return attn_modules
-
-    for name, module in text_encoder_attn_modules(text_encoder):
-        for k, v in module.q_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.k_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.v_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.out_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v
-
-    return state_dict
-
-
 def save_model_card(repo_id: str, images=None, base_model=str, dataset_name=str, repo_folder=None):
     img_str = ""
     for i, image in enumerate(images):
@@ -130,12 +98,6 @@ def parse_args():
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--dataset_name",
         type=str,
@@ -460,11 +422,9 @@ def main():
     text_encoder = CLIPTextModel.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
-    vae = AutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
-    )
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
     # freeze parameters of models to save more memory
     unet.requires_grad_(False)
@@ -498,43 +458,25 @@ def main():
     # => 32 layers
 
     # Set correct lora layers
-    unet_lora_parameters = []
-    for attn_processor_name, attn_processor in unet.attn_processors.items():
-        # Parse the attention module.
-        attn_module = unet
-        for n in attn_processor_name.split(".")[:-1]:
-            attn_module = getattr(attn_module, n)
+    lora_attn_procs = {}
+    for name in unet.attn_processors.keys():
+        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
 
-        # Set the `lora_layer` attribute of the attention-related matrices.
-        attn_module.to_q.set_lora_layer(
-            LoRALinearLayer(
-                in_features=attn_module.to_q.in_features, out_features=attn_module.to_q.out_features, rank=args.rank
-            )
-        )
-        attn_module.to_k.set_lora_layer(
-            LoRALinearLayer(
-                in_features=attn_module.to_k.in_features, out_features=attn_module.to_k.out_features, rank=args.rank
-            )
+        lora_attn_procs[name] = LoRAAttnProcessor(
+            hidden_size=hidden_size,
+            cross_attention_dim=cross_attention_dim,
+            rank=args.rank,
         )
 
-        attn_module.to_v.set_lora_layer(
-            LoRALinearLayer(
-                in_features=attn_module.to_v.in_features, out_features=attn_module.to_v.out_features, rank=args.rank
-            )
-        )
-        attn_module.to_out[0].set_lora_layer(
-            LoRALinearLayer(
-                in_features=attn_module.to_out[0].in_features,
-                out_features=attn_module.to_out[0].out_features,
-                rank=args.rank,
-            )
-        )
-
-        # Accumulate the LoRA params to optimize.
-        unet_lora_parameters.extend(attn_module.to_q.lora_layer.parameters())
-        unet_lora_parameters.extend(attn_module.to_k.lora_layer.parameters())
-        unet_lora_parameters.extend(attn_module.to_v.lora_layer.parameters())
-        unet_lora_parameters.extend(attn_module.to_out[0].lora_layer.parameters())
+    unet.set_attn_processor(lora_attn_procs)
 
     if args.enable_xformers_memory_efficient_attention:
         if is_xformers_available():
@@ -549,6 +491,8 @@ def main():
         else:
             raise ValueError("xformers is not available. Make sure it is installed correctly")
 
+    lora_layers = AttnProcsLayers(unet.attn_processors)
+
     # Enable TF32 for faster training on Ampere GPUs,
     # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
     if args.allow_tf32:
@@ -573,7 +517,7 @@ def main():
         optimizer_cls = torch.optim.AdamW
 
     optimizer = optimizer_cls(
-        unet_lora_parameters,
+        lora_layers.parameters(),
         lr=args.learning_rate,
         betas=(args.adam_beta1, args.adam_beta2),
         weight_decay=args.adam_weight_decay,
@@ -700,8 +644,8 @@ def main():
     )
 
     # Prepare everything with our `accelerator`.
-    unet_lora_parameters, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet_lora_parameters, optimizer, train_dataloader, lr_scheduler
+    lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        lora_layers, optimizer, train_dataloader, lr_scheduler
     )
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
@@ -833,7 +777,7 @@ def main():
                 # Backpropagate
                 accelerator.backward(loss)
                 if accelerator.sync_gradients:
-                    params_to_clip = unet_lora_parameters
+                    params_to_clip = lora_layers.parameters()
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
                 optimizer.step()
                 lr_scheduler.step()
@@ -889,7 +833,6 @@ def main():
                     args.pretrained_model_name_or_path,
                     unet=accelerator.unwrap_model(unet),
                     revision=args.revision,
-                    variant=args.variant,
                     torch_dtype=weight_dtype,
                 )
                 pipeline = pipeline.to(accelerator.device)
@@ -946,7 +889,7 @@ def main():
     # Final inference
     # Load previous pipeline
     pipeline = DiffusionPipeline.from_pretrained(
-        args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype
+        args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype
     )
     pipeline = pipeline.to(accelerator.device)
 

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -50,7 +50,7 @@ from diffusers import (
     UNet2DConditionModel,
 )
 from diffusers.loaders import LoraLoaderMixin
-from diffusers.models.lora import LoRALinearLayer
+from diffusers.models.lora import LoRALinearLayer, text_encoder_lora_state_dict
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import compute_snr
 from diffusers.utils import check_min_version, is_wandb_available
@@ -63,39 +63,6 @@ check_min_version("0.24.0.dev0")
 logger = get_logger(__name__)
 
 
-# TODO: This function should be removed once training scripts are rewritten in PEFT
-def text_encoder_lora_state_dict(text_encoder):
-    state_dict = {}
-
-    def text_encoder_attn_modules(text_encoder):
-        from transformers import CLIPTextModel, CLIPTextModelWithProjection
-
-        attn_modules = []
-
-        if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
-            for i, layer in enumerate(text_encoder.text_model.encoder.layers):
-                name = f"text_model.encoder.layers.{i}.self_attn"
-                mod = layer.self_attn
-                attn_modules.append((name, mod))
-
-        return attn_modules
-
-    for name, module in text_encoder_attn_modules(text_encoder):
-        for k, v in module.q_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.k_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.v_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v
-
-        for k, v in module.out_proj.lora_linear_layer.state_dict().items():
-            state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v
-
-    return state_dict
-
-
 def save_model_card(
     repo_id: str,
     images=None,
@@ -180,12 +147,6 @@ def parse_args(input_args=None):
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--dataset_name",
         type=str,
@@ -576,16 +537,10 @@ def main(args):
 
     # Load the tokenizers
     tokenizer_one = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
     )
     tokenizer_two = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer_2",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
     )
 
     # import correct text encoder classes
@@ -599,10 +554,10 @@ def main(args):
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder_one = text_encoder_cls_one.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
     text_encoder_two = text_encoder_cls_two.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
     )
     vae_path = (
         args.pretrained_model_name_or_path
@@ -610,13 +565,10 @@ def main(args):
         else args.pretrained_vae_model_name_or_path
     )
     vae = AutoencoderKL.from_pretrained(
-        vae_path,
-        subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
-        revision=args.revision,
-        variant=args.variant,
+        vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision
     )
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     # We only train the additional adapter LoRA layers
@@ -1191,7 +1143,6 @@ def main(args):
                     text_encoder_2=accelerator.unwrap_model(text_encoder_two),
                     unet=accelerator.unwrap_model(unet),
                     revision=args.revision,
-                    variant=args.variant,
                     torch_dtype=weight_dtype,
                 )
 
@@ -1257,11 +1208,7 @@ def main(args):
         # Final inference
         # Load previous pipeline
         pipeline = StableDiffusionXLPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            vae=vae,
-            revision=args.revision,
-            variant=args.variant,
-            torch_dtype=weight_dtype,
+            args.pretrained_model_name_or_path, vae=vae, revision=args.revision, torch_dtype=weight_dtype
         )
         pipeline = pipeline.to(accelerator.device)
 

examples/text_to_image/train_text_to_image_sdxl.py

@@ -148,12 +148,6 @@ def parse_args(input_args=None):
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--dataset_name",
         type=str,
@@ -624,16 +618,10 @@ def main(args):
 
     # Load the tokenizers
     tokenizer_one = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
     )
     tokenizer_two = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="tokenizer_2",
-        revision=args.revision,
-        use_fast=False,
+        args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
     )
 
     # import correct text encoder classes
@@ -648,10 +636,10 @@ def main(args):
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     # Check for terminal SNR in combination with SNR Gamma
     text_encoder_one = text_encoder_cls_one.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
     text_encoder_two = text_encoder_cls_two.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
     )
     vae_path = (
         args.pretrained_model_name_or_path
@@ -659,13 +647,10 @@ def main(args):
         else args.pretrained_vae_model_name_or_path
     )
     vae = AutoencoderKL.from_pretrained(
-        vae_path,
-        subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
-        revision=args.revision,
-        variant=args.variant,
+        vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision
     )
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     # Freeze vae and text encoders.
@@ -692,7 +677,7 @@ def main(args):
     # Create EMA for the unet.
     if args.use_ema:
         ema_unet = UNet2DConditionModel.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+            args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
         )
         ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)
 
@@ -1160,14 +1145,12 @@ def main(args):
                     vae_path,
                     subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
                     revision=args.revision,
-                    variant=args.variant,
                 )
                 pipeline = StableDiffusionXLPipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
                     vae=vae,
                     unet=accelerator.unwrap_model(unet),
                     revision=args.revision,
-                    variant=args.variant,
                     torch_dtype=weight_dtype,
                 )
                 if args.prediction_type is not None:
@@ -1215,16 +1198,10 @@ def main(args):
             vae_path,
             subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
             revision=args.revision,
-            variant=args.variant,
             torch_dtype=weight_dtype,
         )
         pipeline = StableDiffusionXLPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=unet,
-            vae=vae,
-            revision=args.revision,
-            variant=args.variant,
-            torch_dtype=weight_dtype,
+            args.pretrained_model_name_or_path, unet=unet, vae=vae, revision=args.revision, torch_dtype=weight_dtype
         )
         if args.prediction_type is not None:
             scheduler_args = {"prediction_type": args.prediction_type}

examples/textual_inversion/test_textual_inversion.py

@@ -1,160 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-import tempfile
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class TextualInversion(ExamplesTestsAccelerate):
-    def test_textual_inversion(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --train_data_dir docs/source/en/imgs
-                --learnable_property object
-                --placeholder_token <cat-toy>
-                --initializer_token a
-                --validation_prompt <cat-toy>
-                --validation_steps 1
-                --save_steps 1
-                --num_vectors 2
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "learned_embeds.safetensors")))
-
-    def test_textual_inversion_checkpointing(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --train_data_dir docs/source/en/imgs
-                --learnable_property object
-                --placeholder_token <cat-toy>
-                --initializer_token a
-                --validation_prompt <cat-toy>
-                --validation_steps 1
-                --save_steps 1
-                --num_vectors 2
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 3
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=1
-                --checkpoints_total_limit=2
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-3"},
-            )
-
-    def test_textual_inversion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --train_data_dir docs/source/en/imgs
-                --learnable_property object
-                --placeholder_token <cat-toy>
-                --initializer_token a
-                --validation_prompt <cat-toy>
-                --validation_steps 1
-                --save_steps 1
-                --num_vectors 2
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 3
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=1
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-1", "checkpoint-2", "checkpoint-3"},
-            )
-
-            resume_run_args = f"""
-                examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --train_data_dir docs/source/en/imgs
-                --learnable_property object
-                --placeholder_token <cat-toy>
-                --initializer_token a
-                --validation_prompt <cat-toy>
-                --validation_steps 1
-                --save_steps 1
-                --num_vectors 2
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 4
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=1
-                --resume_from_checkpoint=checkpoint-3
-                --checkpoints_total_limit=2
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-3", "checkpoint-4"},
-            )

examples/textual_inversion/textual_inversion.py

@@ -126,7 +126,6 @@ def log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight
         vae=vae,
         safety_checker=None,
         revision=args.revision,
-        variant=args.variant,
         torch_dtype=weight_dtype,
     )
     pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
@@ -207,12 +206,6 @@ def parse_args():
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
-    parser.add_argument(
-        "--variant",
-        type=str,
-        default=None,
-        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
-    )
     parser.add_argument(
         "--tokenizer_name",
         type=str,
@@ -631,11 +624,9 @@ def main():
     text_encoder = CLIPTextModel.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
     )
-    vae = AutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
-    )
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     # Add the placeholder token in tokenizer
@@ -761,7 +752,6 @@ def main():
         num_cycles=args.lr_num_cycles,
     )
 
-    text_encoder.train()
     # Prepare everything with our `accelerator`.
     text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
         text_encoder, optimizer, train_dataloader, lr_scheduler

examples/unconditional_image_generation/test_unconditional.py

@@ -1,130 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-import tempfile
-
-
-sys.path.append("..")
-from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class Unconditional(ExamplesTestsAccelerate):
-    def test_train_unconditional(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/unconditional_image_generation/train_unconditional.py
-                --dataset_name hf-internal-testing/dummy_image_class_data
-                --model_config_name_or_path diffusers/ddpm_dummy
-                --resolution 64
-                --output_dir {tmpdir}
-                --train_batch_size 2
-                --num_epochs 1
-                --gradient_accumulation_steps 1
-                --ddpm_num_inference_steps 2
-                --learning_rate 1e-3
-                --lr_warmup_steps 5
-                """.split()
-
-            run_command(self._launch_args + test_args, return_stdout=True)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
-
-    def test_unconditional_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            initial_run_args = f"""
-                examples/unconditional_image_generation/train_unconditional.py
-                --dataset_name hf-internal-testing/dummy_image_class_data
-                --model_config_name_or_path diffusers/ddpm_dummy
-                --resolution 64
-                --output_dir {tmpdir}
-                --train_batch_size 1
-                --num_epochs 1
-                --gradient_accumulation_steps 1
-                --ddpm_num_inference_steps 2
-                --learning_rate 1e-3
-                --lr_warmup_steps 5
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_unconditional_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            initial_run_args = f"""
-                examples/unconditional_image_generation/train_unconditional.py
-                --dataset_name hf-internal-testing/dummy_image_class_data
-                --model_config_name_or_path diffusers/ddpm_dummy
-                --resolution 64
-                --output_dir {tmpdir}
-                --train_batch_size 1
-                --num_epochs 1
-                --gradient_accumulation_steps 1
-                --ddpm_num_inference_steps 2
-                --learning_rate 1e-3
-                --lr_warmup_steps 5
-                --checkpointing_steps=1
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-1", "checkpoint-2", "checkpoint-3", "checkpoint-4", "checkpoint-5", "checkpoint-6"},
-            )
-
-            resume_run_args = f"""
-                examples/unconditional_image_generation/train_unconditional.py
-                --dataset_name hf-internal-testing/dummy_image_class_data
-                --model_config_name_or_path diffusers/ddpm_dummy
-                --resolution 64
-                --output_dir {tmpdir}
-                --train_batch_size 1
-                --num_epochs 2
-                --gradient_accumulation_steps 1
-                --ddpm_num_inference_steps 2
-                --learning_rate 1e-3
-                --lr_warmup_steps 5
-                --resume_from_checkpoint=checkpoint-6
-                --checkpointing_steps=2
-                --checkpoints_total_limit=3
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-8", "checkpoint-10", "checkpoint-12"},
-            )

examples/wuerstchen/text_to_image/requirements.txt

@@ -5,4 +5,3 @@ wandb
 huggingface-cli
 bitsandbytes
 deepspeed
-peft>=0.6.0

examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py

@@ -31,14 +31,14 @@ from accelerate.utils import ProjectConfiguration, set_seed
 from datasets import load_dataset
 from huggingface_hub import create_repo, hf_hub_download, upload_folder
 from modeling_efficient_net_encoder import EfficientNetEncoder
-from peft import LoraConfig
-from peft.utils import get_peft_model_state_dict
 from torchvision import transforms
 from tqdm import tqdm
 from transformers import CLIPTextModel, PreTrainedTokenizerFast
 from transformers.utils import ContextManagers
 
 from diffusers import AutoPipelineForText2Image, DDPMWuerstchenScheduler, WuerstchenPriorPipeline
+from diffusers.loaders import AttnProcsLayers
+from diffusers.models.attention_processor import LoRAAttnProcessor
 from diffusers.optimization import get_scheduler
 from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS, WuerstchenPrior
 from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
@@ -139,17 +139,17 @@ More information on all the CLI arguments and the environment are available on y
         f.write(yaml + model_card)
 
 
-def log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dtype, epoch):
+def log_validation(text_encoder, tokenizer, attn_processors, args, accelerator, weight_dtype, epoch):
     logger.info("Running validation... ")
 
     pipeline = AutoPipelineForText2Image.from_pretrained(
         args.pretrained_decoder_model_name_or_path,
-        prior=accelerator.unwrap_model(prior),
         prior_text_encoder=accelerator.unwrap_model(text_encoder),
         prior_tokenizer=tokenizer,
         torch_dtype=weight_dtype,
     )
     pipeline = pipeline.to(accelerator.device)
+    pipeline.prior_prior.set_attn_processor(attn_processors)
     pipeline.set_progress_bar_config(disable=True)
 
     if args.seed is None:
@@ -159,7 +159,7 @@ def log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dty
 
     images = []
     for i in range(len(args.validation_prompts)):
-        with torch.cuda.amp.autocast():
+        with torch.autocast("cuda"):
             image = pipeline(
                 args.validation_prompts[i],
                 prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
@@ -167,6 +167,7 @@ def log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dty
                 height=args.resolution,
                 width=args.resolution,
             ).images[0]
+
         images.append(image)
 
     for tracker in accelerator.trackers:
@@ -526,50 +527,11 @@ def main():
     prior.to(accelerator.device, dtype=weight_dtype)
 
     # lora attn processor
-    prior_lora_config = LoraConfig(
-        r=args.rank, target_modules=["to_k", "to_q", "to_v", "to_out.0", "add_k_proj", "add_v_proj"]
-    )
-    prior.add_adapter(prior_lora_config)
-
-    # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
-    def save_model_hook(models, weights, output_dir):
-        if accelerator.is_main_process:
-            prior_lora_layers_to_save = None
-
-            for model in models:
-                if isinstance(model, type(accelerator.unwrap_model(prior))):
-                    prior_lora_layers_to_save = get_peft_model_state_dict(model)
-                else:
-                    raise ValueError(f"unexpected save model: {model.__class__}")
-
-                # make sure to pop weight so that corresponding model is not saved again
-                weights.pop()
-
-            WuerstchenPriorPipeline.save_lora_weights(
-                output_dir,
-                unet_lora_layers=prior_lora_layers_to_save,
-            )
-
-    def load_model_hook(models, input_dir):
-        prior_ = None
-
-        while len(models) > 0:
-            model = models.pop()
-
-            if isinstance(model, type(accelerator.unwrap_model(prior))):
-                prior_ = model
-            else:
-                raise ValueError(f"unexpected save model: {model.__class__}")
-
-        lora_state_dict, network_alphas = WuerstchenPriorPipeline.lora_state_dict(input_dir)
-        WuerstchenPriorPipeline.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=prior_)
-        WuerstchenPriorPipeline.load_lora_into_text_encoder(
-            lora_state_dict,
-            network_alphas=network_alphas,
-        )
-
-    accelerator.register_save_state_pre_hook(save_model_hook)
-    accelerator.register_load_state_pre_hook(load_model_hook)
+    lora_attn_procs = {}
+    for name in prior.attn_processors.keys():
+        lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=prior.config["c"], rank=args.rank)
+    prior.set_attn_processor(lora_attn_procs)
+    lora_layers = AttnProcsLayers(prior.attn_processors)
 
     if args.allow_tf32:
         torch.backends.cuda.matmul.allow_tf32 = True
@@ -585,9 +547,8 @@ def main():
         optimizer_cls = bnb.optim.AdamW8bit
     else:
         optimizer_cls = torch.optim.AdamW
-    params_to_optimize = list(filter(lambda p: p.requires_grad, prior.parameters()))
     optimizer = optimizer_cls(
-        params_to_optimize,
+        lora_layers.parameters(),
         lr=args.learning_rate,
         betas=(args.adam_beta1, args.adam_beta2),
         weight_decay=args.adam_weight_decay,
@@ -713,8 +674,8 @@ def main():
         num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
     )
 
-    prior, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        prior, optimizer, train_dataloader, lr_scheduler
+    lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        lora_layers, optimizer, train_dataloader, lr_scheduler
     )
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
@@ -821,7 +782,7 @@ def main():
                 # Backpropagate
                 accelerator.backward(loss)
                 if accelerator.sync_gradients:
-                    accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)
+                    accelerator.clip_grad_norm_(lora_layers.parameters(), args.max_grad_norm)
                 optimizer.step()
                 lr_scheduler.step()
                 optimizer.zero_grad()
@@ -867,19 +828,17 @@ def main():
 
         if accelerator.is_main_process:
             if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
-                log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dtype, global_step)
+                log_validation(
+                    text_encoder, tokenizer, prior.attn_processors, args, accelerator, weight_dtype, global_step
+                )
 
     # Create the pipeline using the trained modules and save it.
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
-        prior = accelerator.unwrap_model(prior)
         prior = prior.to(torch.float32)
-
-        prior_lora_state_dict = get_peft_model_state_dict(prior)
-
         WuerstchenPriorPipeline.save_lora_weights(
-            save_directory=args.output_dir,
-            unet_lora_layers=prior_lora_state_dict,
+            os.path.join(args.output_dir, "prior_lora"),
+            unet_lora_layers=lora_layers,
         )
 
         # Run a final round of inference.
@@ -890,12 +849,11 @@ def main():
                 args.pretrained_decoder_model_name_or_path,
                 prior_text_encoder=accelerator.unwrap_model(text_encoder),
                 prior_tokenizer=tokenizer,
-                torch_dtype=weight_dtype,
             )
-            pipeline = pipeline.to(accelerator.device)
-
+            pipeline = pipeline.to(accelerator.device, torch_dtype=weight_dtype)
             # load lora weights
-            pipeline.prior_pipe.load_lora_weights(args.output_dir, weight_name="pytorch_lora_weights.safetensors")
+            pipeline.prior_pipe.load_lora_weights(os.path.join(args.output_dir, "prior_lora"))
+
             pipeline.set_progress_bar_config(disable=True)
 
             if args.seed is None:
@@ -904,7 +862,7 @@ def main():
                 generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
 
             for i in range(len(args.validation_prompts)):
-                with torch.cuda.amp.autocast():
+                with torch.autocast("cuda"):
                     image = pipeline(
                         args.validation_prompts[i],
                         prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,

scripts/convert_kandinsky3_unet.py

@@ -1,98 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import fnmatch
-
-from safetensors.torch import load_file
-
-from diffusers import Kandinsky3UNet
-
-
-MAPPING = {
-    "to_time_embed.1": "time_embedding.linear_1",
-    "to_time_embed.3": "time_embedding.linear_2",
-    "in_layer": "conv_in",
-    "out_layer.0": "conv_norm_out",
-    "out_layer.2": "conv_out",
-    "down_samples": "down_blocks",
-    "up_samples": "up_blocks",
-    "projection_lin": "encoder_hid_proj.projection_linear",
-    "projection_ln": "encoder_hid_proj.projection_norm",
-    "feature_pooling": "add_time_condition",
-    "to_query": "to_q",
-    "to_key": "to_k",
-    "to_value": "to_v",
-    "output_layer": "to_out.0",
-    "self_attention_block": "attentions.0",
-}
-
-DYNAMIC_MAP = {
-    "resnet_attn_blocks.*.0": "resnets_in.*",
-    "resnet_attn_blocks.*.1": ("attentions.*", 1),
-    "resnet_attn_blocks.*.2": "resnets_out.*",
-}
-# MAPPING = {}
-
-
-def convert_state_dict(unet_state_dict):
-    """
-    Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model.
-    Args:
-        unet_model (torch.nn.Module): The original U-Net model.
-        unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet model to match keys with.
-
-    Returns:
-        OrderedDict: The converted state dictionary.
-    """
-    # Example of renaming logic (this will vary based on your model's architecture)
-    converted_state_dict = {}
-    for key in unet_state_dict:
-        new_key = key
-        for pattern, new_pattern in MAPPING.items():
-            new_key = new_key.replace(pattern, new_pattern)
-
-        for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items():
-            has_matched = False
-            if fnmatch.fnmatch(new_key, f"*.{dyn_pattern}.*") and not has_matched:
-                star = int(new_key.split(dyn_pattern.split(".")[0])[-1].split(".")[1])
-
-                if isinstance(dyn_new_pattern, tuple):
-                    new_star = star + dyn_new_pattern[-1]
-                    dyn_new_pattern = dyn_new_pattern[0]
-                else:
-                    new_star = star
-
-                pattern = dyn_pattern.replace("*", str(star))
-                new_pattern = dyn_new_pattern.replace("*", str(new_star))
-
-                new_key = new_key.replace(pattern, new_pattern)
-                has_matched = True
-
-        converted_state_dict[new_key] = unet_state_dict[key]
-
-    return converted_state_dict
-
-
-def main(model_path, output_path):
-    # Load your original U-Net model
-    unet_state_dict = load_file(model_path)
-
-    # Initialize your Kandinsky3UNet model
-    config = {}
-
-    # Convert the state dict
-    converted_state_dict = convert_state_dict(unet_state_dict)
-
-    unet = Kandinsky3UNet(config)
-    unet.load_state_dict(converted_state_dict)
-
-    unet.save_pretrained(output_path)
-    print(f"Converted model saved to {output_path}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Convert U-Net PyTorch model to Kandinsky3UNet format")
-    parser.add_argument("--model_path", type=str, required=True, help="Path to the original U-Net PyTorch model")
-    parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model")
-
-    args = parser.parse_args()
-    main(args.model_path, args.output_path)

scripts/convert_svd_to_diffusers.py

@@ -1,730 +0,0 @@
-from diffusers.utils import is_accelerate_available, logging
-
-
-if is_accelerate_available():
-    pass
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def create_unet_diffusers_config(original_config, image_size: int, controlnet=False):
-    """
-    Creates a config for the diffusers based on the config of the LDM model.
-    """
-    if controlnet:
-        unet_params = original_config.model.params.control_stage_config.params
-    else:
-        if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None:
-            unet_params = original_config.model.params.unet_config.params
-        else:
-            unet_params = original_config.model.params.network_config.params
-
-    vae_params = original_config.model.params.first_stage_config.params.encoder_config.params
-
-    block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
-
-    down_block_types = []
-    resolution = 1
-    for i in range(len(block_out_channels)):
-        block_type = (
-            "CrossAttnDownBlockSpatioTemporal"
-            if resolution in unet_params.attention_resolutions
-            else "DownBlockSpatioTemporal"
-        )
-        down_block_types.append(block_type)
-        if i != len(block_out_channels) - 1:
-            resolution *= 2
-
-    up_block_types = []
-    for i in range(len(block_out_channels)):
-        block_type = (
-            "CrossAttnUpBlockSpatioTemporal"
-            if resolution in unet_params.attention_resolutions
-            else "UpBlockSpatioTemporal"
-        )
-        up_block_types.append(block_type)
-        resolution //= 2
-
-    if unet_params.transformer_depth is not None:
-        transformer_layers_per_block = (
-            unet_params.transformer_depth
-            if isinstance(unet_params.transformer_depth, int)
-            else list(unet_params.transformer_depth)
-        )
-    else:
-        transformer_layers_per_block = 1
-
-    vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)
-
-    head_dim = unet_params.num_heads if "num_heads" in unet_params else None
-    use_linear_projection = (
-        unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
-    )
-    if use_linear_projection:
-        # stable diffusion 2-base-512 and 2-768
-        if head_dim is None:
-            head_dim_mult = unet_params.model_channels // unet_params.num_head_channels
-            head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)]
-
-    class_embed_type = None
-    addition_embed_type = None
-    addition_time_embed_dim = None
-    projection_class_embeddings_input_dim = None
-    context_dim = None
-
-    if unet_params.context_dim is not None:
-        context_dim = (
-            unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0]
-        )
-
-    if "num_classes" in unet_params:
-        if unet_params.num_classes == "sequential":
-            addition_time_embed_dim = 256
-            assert "adm_in_channels" in unet_params
-            projection_class_embeddings_input_dim = unet_params.adm_in_channels
-
-    config = {
-        "sample_size": image_size // vae_scale_factor,
-        "in_channels": unet_params.in_channels,
-        "down_block_types": tuple(down_block_types),
-        "block_out_channels": tuple(block_out_channels),
-        "layers_per_block": unet_params.num_res_blocks,
-        "cross_attention_dim": context_dim,
-        "attention_head_dim": head_dim,
-        "use_linear_projection": use_linear_projection,
-        "class_embed_type": class_embed_type,
-        "addition_embed_type": addition_embed_type,
-        "addition_time_embed_dim": addition_time_embed_dim,
-        "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
-        "transformer_layers_per_block": transformer_layers_per_block,
-    }
-
-    if "disable_self_attentions" in unet_params:
-        config["only_cross_attention"] = unet_params.disable_self_attentions
-
-    if "num_classes" in unet_params and isinstance(unet_params.num_classes, int):
-        config["num_class_embeds"] = unet_params.num_classes
-
-    if controlnet:
-        config["conditioning_channels"] = unet_params.hint_channels
-    else:
-        config["out_channels"] = unet_params.out_channels
-        config["up_block_types"] = tuple(up_block_types)
-
-    return config
-
-
-def assign_to_checkpoint(
-    paths,
-    checkpoint,
-    old_checkpoint,
-    attention_paths_to_split=None,
-    additional_replacements=None,
-    config=None,
-    mid_block_suffix="",
-):
-    """
-    This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
-    attention layers, and takes into account additional replacements that may arise.
-
-    Assigns the weights to the new checkpoint.
-    """
-    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
-
-    # Splits the attention layers into three variables.
-    if attention_paths_to_split is not None:
-        for path, path_map in attention_paths_to_split.items():
-            old_tensor = old_checkpoint[path]
-            channels = old_tensor.shape[0] // 3
-
-            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
-
-            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
-
-            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
-            query, key, value = old_tensor.split(channels // num_heads, dim=1)
-
-            checkpoint[path_map["query"]] = query.reshape(target_shape)
-            checkpoint[path_map["key"]] = key.reshape(target_shape)
-            checkpoint[path_map["value"]] = value.reshape(target_shape)
-
-    if mid_block_suffix is not None:
-        mid_block_suffix = f".{mid_block_suffix}"
-    else:
-        mid_block_suffix = ""
-
-    for path in paths:
-        new_path = path["new"]
-
-        # These have already been assigned
-        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
-            continue
-
-        # Global renaming happens here
-        new_path = new_path.replace("middle_block.0", f"mid_block.resnets.0{mid_block_suffix}")
-        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
-        new_path = new_path.replace("middle_block.2", f"mid_block.resnets.1{mid_block_suffix}")
-
-        if additional_replacements is not None:
-            for replacement in additional_replacements:
-                new_path = new_path.replace(replacement["old"], replacement["new"])
-
-        if new_path == "mid_block.resnets.0.spatial_res_block.norm1.weight":
-            print("yeyy")
-
-        # proj_attn.weight has to be converted from conv 1D to linear
-        is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path)
-        shape = old_checkpoint[path["old"]].shape
-        if is_attn_weight and len(shape) == 3:
-            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
-        elif is_attn_weight and len(shape) == 4:
-            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0]
-        else:
-            checkpoint[new_path] = old_checkpoint[path["old"]]
-
-
-def renew_attention_paths(old_list, n_shave_prefix_segments=0):
-    """
-    Updates paths inside attentions to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item
-
-        #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
-        #         new_item = new_item.replace('norm.bias', 'group_norm.bias')
-
-        #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
-        #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
-
-        #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-        new_item = new_item.replace("time_stack", "temporal_transformer_blocks")
-
-        new_item = new_item.replace("time_pos_embed.0.bias", "time_pos_embed.linear_1.bias")
-        new_item = new_item.replace("time_pos_embed.0.weight", "time_pos_embed.linear_1.weight")
-        new_item = new_item.replace("time_pos_embed.2.bias", "time_pos_embed.linear_2.bias")
-        new_item = new_item.replace("time_pos_embed.2.weight", "time_pos_embed.linear_2.weight")
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def shave_segments(path, n_shave_prefix_segments=1):
-    """
-    Removes segments. Positive values shave the first segments, negative shave the last segments.
-    """
-    if n_shave_prefix_segments >= 0:
-        return ".".join(path.split(".")[n_shave_prefix_segments:])
-    else:
-        return ".".join(path.split(".")[:n_shave_prefix_segments])
-
-
-def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
-    """
-    Updates paths inside resnets to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item.replace("in_layers.0", "norm1")
-        new_item = new_item.replace("in_layers.2", "conv1")
-
-        new_item = new_item.replace("out_layers.0", "norm2")
-        new_item = new_item.replace("out_layers.3", "conv2")
-
-        new_item = new_item.replace("emb_layers.1", "time_emb_proj")
-        new_item = new_item.replace("skip_connection", "conv_shortcut")
-
-        new_item = new_item.replace("time_stack.", "")
-
-        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def convert_ldm_unet_checkpoint(
-    checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False
-):
-    """
-    Takes a state dict and a config, and returns a converted checkpoint.
-    """
-
-    if skip_extract_state_dict:
-        unet_state_dict = checkpoint
-    else:
-        # extract state_dict for UNet
-        unet_state_dict = {}
-        keys = list(checkpoint.keys())
-
-        unet_key = "model.diffusion_model."
-
-        # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
-        if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
-            logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.")
-            logger.warning(
-                "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
-                " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
-            )
-            for key in keys:
-                if key.startswith("model.diffusion_model"):
-                    flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
-                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
-        else:
-            if sum(k.startswith("model_ema") for k in keys) > 100:
-                logger.warning(
-                    "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
-                    " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
-                )
-
-            for key in keys:
-                if key.startswith(unet_key):
-                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
-
-    new_checkpoint = {}
-
-    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
-    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
-    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
-    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
-
-    if config["class_embed_type"] is None:
-        # No parameters to port
-        ...
-    elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
-        new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
-        new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
-        new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
-        new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
-    else:
-        raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
-
-    # if config["addition_embed_type"] == "text_time":
-    new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
-    new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
-    new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
-    new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
-
-    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
-    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
-
-    new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
-    new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
-    new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
-    new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
-
-    # Retrieves the keys for the input blocks only
-    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
-    input_blocks = {
-        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
-        for layer_id in range(num_input_blocks)
-    }
-
-    # Retrieves the keys for the middle blocks only
-    num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
-    middle_blocks = {
-        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
-        for layer_id in range(num_middle_blocks)
-    }
-
-    # Retrieves the keys for the output blocks only
-    num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
-    output_blocks = {
-        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
-        for layer_id in range(num_output_blocks)
-    }
-
-    for i in range(1, num_input_blocks):
-        block_id = (i - 1) // (config["layers_per_block"] + 1)
-        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
-
-        spatial_resnets = [
-            key
-            for key in input_blocks[i]
-            if f"input_blocks.{i}.0" in key
-            and (
-                f"input_blocks.{i}.0.op" not in key
-                and f"input_blocks.{i}.0.time_stack" not in key
-                and f"input_blocks.{i}.0.time_mixer" not in key
-            )
-        ]
-        temporal_resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0.time_stack" in key]
-        # import ipdb; ipdb.set_trace()
-        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
-
-        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
-            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
-                f"input_blocks.{i}.0.op.weight"
-            )
-            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
-                f"input_blocks.{i}.0.op.bias"
-            )
-
-        paths = renew_resnet_paths(spatial_resnets)
-        meta_path = {
-            "old": f"input_blocks.{i}.0",
-            "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}.spatial_res_block",
-        }
-        assign_to_checkpoint(
-            paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-        )
-
-        paths = renew_resnet_paths(temporal_resnets)
-        meta_path = {
-            "old": f"input_blocks.{i}.0",
-            "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}.temporal_res_block",
-        }
-        assign_to_checkpoint(
-            paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-        )
-
-        # TODO resnet time_mixer.mix_factor
-        if f"input_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict:
-            new_checkpoint[
-                f"down_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"
-            ] = unet_state_dict[f"input_blocks.{i}.0.time_mixer.mix_factor"]
-
-        if len(attentions):
-            paths = renew_attention_paths(attentions)
-            meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
-            # import ipdb; ipdb.set_trace()
-            assign_to_checkpoint(
-                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-            )
-
-    resnet_0 = middle_blocks[0]
-    attentions = middle_blocks[1]
-    resnet_1 = middle_blocks[2]
-
-    resnet_0_spatial = [key for key in resnet_0 if "time_stack" not in key and "time_mixer" not in key]
-    resnet_0_paths = renew_resnet_paths(resnet_0_spatial)
-    # import ipdb; ipdb.set_trace()
-    assign_to_checkpoint(
-        resnet_0_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="spatial_res_block"
-    )
-
-    resnet_0_temporal = [key for key in resnet_0 if "time_stack" in key and "time_mixer" not in key]
-    resnet_0_paths = renew_resnet_paths(resnet_0_temporal)
-    assign_to_checkpoint(
-        resnet_0_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="temporal_res_block"
-    )
-
-    resnet_1_spatial = [key for key in resnet_1 if "time_stack" not in key and "time_mixer" not in key]
-    resnet_1_paths = renew_resnet_paths(resnet_1_spatial)
-    assign_to_checkpoint(
-        resnet_1_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="spatial_res_block"
-    )
-
-    resnet_1_temporal = [key for key in resnet_1 if "time_stack" in key and "time_mixer" not in key]
-    resnet_1_paths = renew_resnet_paths(resnet_1_temporal)
-    assign_to_checkpoint(
-        resnet_1_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="temporal_res_block"
-    )
-
-    new_checkpoint["mid_block.resnets.0.time_mixer.mix_factor"] = unet_state_dict[
-        "middle_block.0.time_mixer.mix_factor"
-    ]
-    new_checkpoint["mid_block.resnets.1.time_mixer.mix_factor"] = unet_state_dict[
-        "middle_block.2.time_mixer.mix_factor"
-    ]
-
-    attentions_paths = renew_attention_paths(attentions)
-    meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(
-        attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-    )
-
-    for i in range(num_output_blocks):
-        block_id = i // (config["layers_per_block"] + 1)
-        layer_in_block_id = i % (config["layers_per_block"] + 1)
-        output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
-        output_block_list = {}
-
-        for layer in output_block_layers:
-            layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
-            if layer_id in output_block_list:
-                output_block_list[layer_id].append(layer_name)
-            else:
-                output_block_list[layer_id] = [layer_name]
-
-        if len(output_block_list) > 1:
-            spatial_resnets = [
-                key
-                for key in output_blocks[i]
-                if f"output_blocks.{i}.0" in key
-                and (f"output_blocks.{i}.0.time_stack" not in key and "time_mixer" not in key)
-            ]
-            # import ipdb; ipdb.set_trace()
-
-            temporal_resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0.time_stack" in key]
-
-            paths = renew_resnet_paths(spatial_resnets)
-            meta_path = {
-                "old": f"output_blocks.{i}.0",
-                "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}.spatial_res_block",
-            }
-            assign_to_checkpoint(
-                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-            )
-
-            paths = renew_resnet_paths(temporal_resnets)
-            meta_path = {
-                "old": f"output_blocks.{i}.0",
-                "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}.temporal_res_block",
-            }
-            assign_to_checkpoint(
-                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-            )
-
-            if f"output_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict:
-                new_checkpoint[
-                    f"up_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"
-                ] = unet_state_dict[f"output_blocks.{i}.0.time_mixer.mix_factor"]
-
-            output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
-            if ["conv.bias", "conv.weight"] in output_block_list.values():
-                index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
-                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
-                    f"output_blocks.{i}.{index}.conv.weight"
-                ]
-                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
-                    f"output_blocks.{i}.{index}.conv.bias"
-                ]
-
-                # Clear attentions as they have been attributed above.
-                if len(attentions) == 2:
-                    attentions = []
-
-            attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key and "conv" not in key]
-            if len(attentions):
-                paths = renew_attention_paths(attentions)
-                # import ipdb; ipdb.set_trace()
-                meta_path = {
-                    "old": f"output_blocks.{i}.1",
-                    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
-                }
-                assign_to_checkpoint(
-                    paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-                )
-        else:
-            spatial_layers = [
-                layer for layer in output_block_layers if "time_stack" not in layer and "time_mixer" not in layer
-            ]
-            resnet_0_paths = renew_resnet_paths(spatial_layers, n_shave_prefix_segments=1)
-            # import ipdb; ipdb.set_trace()
-            for path in resnet_0_paths:
-                old_path = ".".join(["output_blocks", str(i), path["old"]])
-                new_path = ".".join(
-                    ["up_blocks", str(block_id), "resnets", str(layer_in_block_id), "spatial_res_block", path["new"]]
-                )
-
-                new_checkpoint[new_path] = unet_state_dict[old_path]
-
-            temporal_layers = [
-                layer for layer in output_block_layers if "time_stack" in layer and "time_mixer" not in key
-            ]
-            resnet_0_paths = renew_resnet_paths(temporal_layers, n_shave_prefix_segments=1)
-            # import ipdb; ipdb.set_trace()
-            for path in resnet_0_paths:
-                old_path = ".".join(["output_blocks", str(i), path["old"]])
-                new_path = ".".join(
-                    ["up_blocks", str(block_id), "resnets", str(layer_in_block_id), "temporal_res_block", path["new"]]
-                )
-
-                new_checkpoint[new_path] = unet_state_dict[old_path]
-
-            new_checkpoint["up_blocks.0.resnets.0.time_mixer.mix_factor"] = unet_state_dict[
-                f"output_blocks.{str(i)}.0.time_mixer.mix_factor"
-            ]
-
-    return new_checkpoint
-
-
-def conv_attn_to_linear(checkpoint):
-    keys = list(checkpoint.keys())
-    attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"]
-    for key in keys:
-        if ".".join(key.split(".")[-2:]) in attn_keys:
-            if checkpoint[key].ndim > 2:
-                checkpoint[key] = checkpoint[key][:, :, 0, 0]
-        elif "proj_attn.weight" in key:
-            if checkpoint[key].ndim > 2:
-                checkpoint[key] = checkpoint[key][:, :, 0]
-
-
-def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0, is_temporal=False):
-    """
-    Updates paths inside resnets to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item
-
-        # Temporal resnet
-        new_item = old_item.replace("in_layers.0", "norm1")
-        new_item = new_item.replace("in_layers.2", "conv1")
-
-        new_item = new_item.replace("out_layers.0", "norm2")
-        new_item = new_item.replace("out_layers.3", "conv2")
-
-        new_item = new_item.replace("skip_connection", "conv_shortcut")
-
-        new_item = new_item.replace("time_stack.", "temporal_res_block.")
-
-        # Spatial resnet
-        new_item = new_item.replace("conv1", "spatial_res_block.conv1")
-        new_item = new_item.replace("norm1", "spatial_res_block.norm1")
-
-        new_item = new_item.replace("conv2", "spatial_res_block.conv2")
-        new_item = new_item.replace("norm2", "spatial_res_block.norm2")
-
-        new_item = new_item.replace("nin_shortcut", "spatial_res_block.conv_shortcut")
-
-        new_item = new_item.replace("mix_factor", "spatial_res_block.time_mixer.mix_factor")
-
-        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
-    """
-    Updates paths inside attentions to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item
-
-        new_item = new_item.replace("norm.weight", "group_norm.weight")
-        new_item = new_item.replace("norm.bias", "group_norm.bias")
-
-        new_item = new_item.replace("q.weight", "to_q.weight")
-        new_item = new_item.replace("q.bias", "to_q.bias")
-
-        new_item = new_item.replace("k.weight", "to_k.weight")
-        new_item = new_item.replace("k.bias", "to_k.bias")
-
-        new_item = new_item.replace("v.weight", "to_v.weight")
-        new_item = new_item.replace("v.bias", "to_v.bias")
-
-        new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
-        new_item = new_item.replace("proj_out.bias", "to_out.0.bias")
-
-        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def convert_ldm_vae_checkpoint(checkpoint, config):
-    # extract state dict for VAE
-    vae_state_dict = {}
-    keys = list(checkpoint.keys())
-    vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else ""
-    for key in keys:
-        if key.startswith(vae_key):
-            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
-
-    new_checkpoint = {}
-
-    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
-    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
-    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
-    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
-    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
-    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
-
-    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
-    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
-    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
-    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
-    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
-    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
-    new_checkpoint["decoder.time_conv_out.weight"] = vae_state_dict["decoder.time_mix_conv.weight"]
-    new_checkpoint["decoder.time_conv_out.bias"] = vae_state_dict["decoder.time_mix_conv.bias"]
-
-    # new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
-    # new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
-    # new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
-    # new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
-
-    # Retrieves the keys for the encoder down blocks only
-    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
-    down_blocks = {
-        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
-    }
-
-    # Retrieves the keys for the decoder up blocks only
-    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
-    up_blocks = {
-        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
-    }
-
-    for i in range(num_down_blocks):
-        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
-
-        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
-            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
-                f"encoder.down.{i}.downsample.conv.weight"
-            )
-            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
-                f"encoder.down.{i}.downsample.conv.bias"
-            )
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
-    num_mid_res_blocks = 2
-    for i in range(1, num_mid_res_blocks + 1):
-        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
-    paths = renew_vae_attention_paths(mid_attentions)
-    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-    conv_attn_to_linear(new_checkpoint)
-
-    for i in range(num_up_blocks):
-        block_id = num_up_blocks - 1 - i
-
-        resnets = [
-            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
-        ]
-
-        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
-            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
-                f"decoder.up.{block_id}.upsample.conv.weight"
-            ]
-            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
-                f"decoder.up.{block_id}.upsample.conv.bias"
-            ]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
-    num_mid_res_blocks = 2
-    for i in range(1, num_mid_res_blocks + 1):
-        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
-    paths = renew_vae_attention_paths(mid_attentions)
-    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-    conv_attn_to_linear(new_checkpoint)
-    return new_checkpoint

src/diffusers/__init__.py

@@ -76,11 +76,9 @@ else:
         [
             "AsymmetricAutoencoderKL",
             "AutoencoderKL",
-            "AutoencoderKLTemporalDecoder",
             "AutoencoderTiny",
             "ConsistencyDecoderVAE",
             "ControlNetModel",
-            "Kandinsky3UNet",
             "ModelMixin",
             "MotionAdapter",
             "MultiAdapter",
@@ -93,7 +91,6 @@ else:
             "UNet2DModel",
             "UNet3DConditionModel",
             "UNetMotionModel",
-            "UNetSpatioTemporalConditionModel",
             "VQModel",
         ]
     )
@@ -217,8 +214,6 @@ else:
             "IFPipeline",
             "IFSuperResolutionPipeline",
             "ImageTextPipelineOutput",
-            "Kandinsky3Img2ImgPipeline",
-            "Kandinsky3Pipeline",
             "KandinskyCombinedPipeline",
             "KandinskyImg2ImgCombinedPipeline",
             "KandinskyImg2ImgPipeline",
@@ -279,10 +274,8 @@ else:
             "StableDiffusionXLPipeline",
             "StableUnCLIPImg2ImgPipeline",
             "StableUnCLIPPipeline",
-            "StableVideoDiffusionPipeline",
             "TextToVideoSDPipeline",
             "TextToVideoZeroPipeline",
-            "TextToVideoZeroSDXLPipeline",
             "UnCLIPImageVariationPipeline",
             "UnCLIPPipeline",
             "UniDiffuserModel",
@@ -450,11 +443,9 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .models import (
             AsymmetricAutoencoderKL,
             AutoencoderKL,
-            AutoencoderKLTemporalDecoder,
             AutoencoderTiny,
             ConsistencyDecoderVAE,
             ControlNetModel,
-            Kandinsky3UNet,
             ModelMixin,
             MotionAdapter,
             MultiAdapter,
@@ -467,7 +458,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             UNet2DModel,
             UNet3DConditionModel,
             UNetMotionModel,
-            UNetSpatioTemporalConditionModel,
             VQModel,
         )
         from .optimization import (
@@ -570,8 +560,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             IFPipeline,
             IFSuperResolutionPipeline,
             ImageTextPipelineOutput,
-            Kandinsky3Img2ImgPipeline,
-            Kandinsky3Pipeline,
             KandinskyCombinedPipeline,
             KandinskyImg2ImgCombinedPipeline,
             KandinskyImg2ImgPipeline,
@@ -632,10 +620,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionXLPipeline,
             StableUnCLIPImg2ImgPipeline,
             StableUnCLIPPipeline,
-            StableVideoDiffusionPipeline,
             TextToVideoSDPipeline,
             TextToVideoZeroPipeline,
-            TextToVideoZeroSDXLPipeline,
             UnCLIPImageVariationPipeline,
             UnCLIPPipeline,
             UniDiffuserModel,

src/diffusers/image_processor.py

@@ -33,15 +33,6 @@ PipelineImageInput = Union[
     List[torch.FloatTensor],
 ]
 
-PipelineDepthInput = Union[
-    PIL.Image.Image,
-    np.ndarray,
-    torch.FloatTensor,
-    List[PIL.Image.Image],
-    List[np.ndarray],
-    List[torch.FloatTensor],
-]
-
 
 class VaeImageProcessor(ConfigMixin):
     """
@@ -335,7 +326,7 @@ class VaeImageProcessor(ConfigMixin):
 
         # expected range [0,1], normalize to [-1,1]
         do_normalize = self.config.do_normalize
-        if do_normalize and image.min() < 0:
+        if image.min() < 0 and do_normalize:
             warnings.warn(
                 "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
                 f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]",
@@ -450,18 +441,6 @@ class VaeImageProcessorLDM3D(VaeImageProcessor):
 
         return pil_images
 
-    @staticmethod
-    def depth_pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.ndarray:
-        """
-        Convert a PIL image or a list of PIL images to NumPy arrays.
-        """
-        if not isinstance(images, list):
-            images = [images]
-
-        images = [np.array(image).astype(np.float32) / (2**16 - 1) for image in images]
-        images = np.stack(images, axis=0)
-        return images
-
     @staticmethod
     def rgblike_to_depthmap(image: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
         """
@@ -547,102 +526,3 @@ class VaeImageProcessorLDM3D(VaeImageProcessor):
             return self.numpy_to_pil(image), self.numpy_to_depth(image)
         else:
             raise Exception(f"This type {output_type} is not supported")
-
-    def preprocess(
-        self,
-        rgb: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],
-        depth: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        target_res: Optional[int] = None,
-    ) -> torch.Tensor:
-        """
-        Preprocess the image input. Accepted formats are PIL images, NumPy arrays or PyTorch tensors.
-        """
-        supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)
-
-        # Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image
-        if self.config.do_convert_grayscale and isinstance(rgb, (torch.Tensor, np.ndarray)) and rgb.ndim == 3:
-            raise Exception("This is not yet supported")
-
-        if isinstance(rgb, supported_formats):
-            rgb = [rgb]
-            depth = [depth]
-        elif not (isinstance(rgb, list) and all(isinstance(i, supported_formats) for i in rgb)):
-            raise ValueError(
-                f"Input is in incorrect format: {[type(i) for i in rgb]}. Currently, we only support {', '.join(supported_formats)}"
-            )
-
-        if isinstance(rgb[0], PIL.Image.Image):
-            if self.config.do_convert_rgb:
-                raise Exception("This is not yet supported")
-                # rgb = [self.convert_to_rgb(i) for i in rgb]
-                # depth = [self.convert_to_depth(i) for i in depth]  #TODO define convert_to_depth
-            if self.config.do_resize or target_res:
-                height, width = self.get_default_height_width(rgb[0], height, width) if not target_res else target_res
-                rgb = [self.resize(i, height, width) for i in rgb]
-                depth = [self.resize(i, height, width) for i in depth]
-            rgb = self.pil_to_numpy(rgb)  # to np
-            rgb = self.numpy_to_pt(rgb)  # to pt
-
-            depth = self.depth_pil_to_numpy(depth)  # to np
-            depth = self.numpy_to_pt(depth)  # to pt
-
-        elif isinstance(rgb[0], np.ndarray):
-            rgb = np.concatenate(rgb, axis=0) if rgb[0].ndim == 4 else np.stack(rgb, axis=0)
-            rgb = self.numpy_to_pt(rgb)
-            height, width = self.get_default_height_width(rgb, height, width)
-            if self.config.do_resize:
-                rgb = self.resize(rgb, height, width)
-
-            depth = np.concatenate(depth, axis=0) if rgb[0].ndim == 4 else np.stack(depth, axis=0)
-            depth = self.numpy_to_pt(depth)
-            height, width = self.get_default_height_width(depth, height, width)
-            if self.config.do_resize:
-                depth = self.resize(depth, height, width)
-
-        elif isinstance(rgb[0], torch.Tensor):
-            raise Exception("This is not yet supported")
-            # rgb = torch.cat(rgb, axis=0) if rgb[0].ndim == 4 else torch.stack(rgb, axis=0)
-
-            # if self.config.do_convert_grayscale and rgb.ndim == 3:
-            #     rgb = rgb.unsqueeze(1)
-
-            # channel = rgb.shape[1]
-
-            # height, width = self.get_default_height_width(rgb, height, width)
-            # if self.config.do_resize:
-            #     rgb = self.resize(rgb, height, width)
-
-            # depth = torch.cat(depth, axis=0) if depth[0].ndim == 4 else torch.stack(depth, axis=0)
-
-            # if self.config.do_convert_grayscale and depth.ndim == 3:
-            #     depth = depth.unsqueeze(1)
-
-            # channel = depth.shape[1]
-            # # don't need any preprocess if the image is latents
-            # if depth == 4:
-            #     return rgb, depth
-
-            # height, width = self.get_default_height_width(depth, height, width)
-            # if self.config.do_resize:
-            #     depth = self.resize(depth, height, width)
-        # expected range [0,1], normalize to [-1,1]
-        do_normalize = self.config.do_normalize
-        if rgb.min() < 0 and do_normalize:
-            warnings.warn(
-                "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
-                f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{rgb.min()},{rgb.max()}]",
-                FutureWarning,
-            )
-            do_normalize = False
-
-        if do_normalize:
-            rgb = self.normalize(rgb)
-            depth = self.normalize(depth)
-
-        if self.config.do_binarize:
-            rgb = self.binarize(rgb)
-            depth = self.binarize(depth)
-
-        return rgb, depth

src/diffusers/loaders/__init__.py

@@ -8,7 +8,7 @@ def text_encoder_lora_state_dict(text_encoder):
     deprecate(
         "text_encoder_load_state_dict in `models`",
         "0.27.0",
-        "`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.",
+        "`text_encoder_lora_state_dict` has been moved to `diffusers.models.lora`. Please make sure to import it via `from diffusers.models.lora import text_encoder_lora_state_dict`.",
     )
     state_dict = {}
 
@@ -34,7 +34,7 @@ if is_transformers_available():
         deprecate(
             "text_encoder_attn_modules in `models`",
             "0.27.0",
-            "`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.",
+            "`text_encoder_lora_state_dict` has been moved to `diffusers.models.lora`. Please make sure to import it via `from diffusers.models.lora import text_encoder_lora_state_dict`.",
         )
         from transformers import CLIPTextModel, CLIPTextModelWithProjection
 
@@ -62,17 +62,16 @@ if is_torch_available():
         _import_structure["single_file"].extend(["FromSingleFileMixin"])
         _import_structure["lora"] = ["LoraLoaderMixin", "StableDiffusionXLLoraLoaderMixin"]
         _import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
-        _import_structure["ip_adapter"] = ["IPAdapterMixin"]
 
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     if is_torch_available():
+        from ..models.lora import text_encoder_lora_state_dict
         from .single_file import FromOriginalControlnetMixin, FromOriginalVAEMixin
         from .unet import UNet2DConditionLoadersMixin
         from .utils import AttnProcsLayers
 
         if is_transformers_available():
-            from .ip_adapter import IPAdapterMixin
             from .lora import LoraLoaderMixin, StableDiffusionXLLoraLoaderMixin
             from .single_file import FromSingleFileMixin
             from .textual_inversion import TextualInversionLoaderMixin

src/diffusers/loaders/ip_adapter.py

@@ -1,157 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import os
-from typing import Dict, Union
-
-import torch
-from safetensors import safe_open
-
-from ..utils import (
-    DIFFUSERS_CACHE,
-    HF_HUB_OFFLINE,
-    _get_model_file,
-    is_transformers_available,
-    logging,
-)
-
-
-if is_transformers_available():
-    from transformers import (
-        CLIPImageProcessor,
-        CLIPVisionModelWithProjection,
-    )
-
-    from ..models.attention_processor import (
-        IPAdapterAttnProcessor,
-        IPAdapterAttnProcessor2_0,
-    )
-
-logger = logging.get_logger(__name__)
-
-
-class IPAdapterMixin:
-    """Mixin for handling IP Adapters."""
-
-    def load_ip_adapter(
-        self,
-        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
-        subfolder: str,
-        weight_name: str,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
-                Can be either:
-
-                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
-                      the Hub.
-                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
-                      with [`ModelMixin.save_pretrained`].
-                    - A [torch state
-                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
-
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
-                is not used.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
-                incompletely downloaded files are deleted.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            local_files_only (`bool`, *optional*, defaults to `False`):
-                Whether to only load local model weights and configuration files or not. If set to `True`, the model
-                won't be downloaded from the Hub.
-            use_auth_token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
-                `diffusers-cli login` (stored in `~/.huggingface`) is used.
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
-                allowed by Git.
-            subfolder (`str`, *optional*, defaults to `""`):
-                The subfolder location of a model file within a larger model repository on the Hub or locally.
-        """
-
-        # Load the main state dict first.
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
-        force_download = kwargs.pop("force_download", False)
-        resume_download = kwargs.pop("resume_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
-        use_auth_token = kwargs.pop("use_auth_token", None)
-        revision = kwargs.pop("revision", None)
-
-        user_agent = {
-            "file_type": "attn_procs_weights",
-            "framework": "pytorch",
-        }
-
-        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
-            model_file = _get_model_file(
-                pretrained_model_name_or_path_or_dict,
-                weights_name=weight_name,
-                cache_dir=cache_dir,
-                force_download=force_download,
-                resume_download=resume_download,
-                proxies=proxies,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-                revision=revision,
-                subfolder=subfolder,
-                user_agent=user_agent,
-            )
-            if weight_name.endswith(".safetensors"):
-                state_dict = {"image_proj": {}, "ip_adapter": {}}
-                with safe_open(model_file, framework="pt", device="cpu") as f:
-                    for key in f.keys():
-                        if key.startswith("image_proj."):
-                            state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
-                        elif key.startswith("ip_adapter."):
-                            state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
-            else:
-                state_dict = torch.load(model_file, map_location="cpu")
-        else:
-            state_dict = pretrained_model_name_or_path_or_dict
-
-        keys = list(state_dict.keys())
-        if keys != ["image_proj", "ip_adapter"]:
-            raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.")
-
-        # load CLIP image encoer here if it has not been registered to the pipeline yet
-        if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is None:
-            if not isinstance(pretrained_model_name_or_path_or_dict, dict):
-                logger.info(f"loading image_encoder from {pretrained_model_name_or_path_or_dict}")
-                image_encoder = CLIPVisionModelWithProjection.from_pretrained(
-                    pretrained_model_name_or_path_or_dict,
-                    subfolder=os.path.join(subfolder, "image_encoder"),
-                ).to(self.device, dtype=self.dtype)
-                self.image_encoder = image_encoder
-            else:
-                raise ValueError("`image_encoder` cannot be None when using IP Adapters.")
-
-        # create feature extractor if it has not been registered to the pipeline yet
-        if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None:
-            self.feature_extractor = CLIPImageProcessor()
-
-        # load ip-adapter into unet
-        self.unet._load_ip_adapter_weights(state_dict)
-
-    def set_ip_adapter_scale(self, scale):
-        for attn_processor in self.unet.attn_processors.values():
-            if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):
-                attn_processor.scale = scale

src/diffusers/loaders/lora.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+import re
 from contextlib import nullcontext
 from typing import Callable, Dict, List, Optional, Union
 
@@ -43,7 +44,6 @@ from ..utils import (
     set_adapter_layers,
     set_weights_and_activate_adapters,
 )
-from .lora_conversion_utils import _convert_kohya_lora_to_diffusers, _maybe_map_sgm_blocks_to_diffusers
 
 
 if is_transformers_available():
@@ -68,8 +68,7 @@ LORA_DEPRECATION_MESSAGE = "You are using an old version of LoRA backend. This w
 
 class LoraLoaderMixin:
     r"""
-    Load LoRA layers into [`UNet2DConditionModel`] and
-    [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
+    Load LoRA layers into [`UNet2DConditionModel`] and [`~transformers.CLIPTextModel`].
     """
 
     text_encoder_name = TEXT_ENCODER_NAME
@@ -95,12 +94,28 @@ class LoraLoaderMixin:
 
         Parameters:
             pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
-                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
+                A string (model id of a pretrained model hosted on the Hub), a path to a directory containing the model
+                weights, or a [torch state
+                dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
             kwargs (`dict`, *optional*):
                 See [`~loaders.LoraLoaderMixin.lora_state_dict`].
             adapter_name (`str`, *optional*):
-                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
-                `default_{i}` where i is the total number of adapters being loaded.
+                Name for referencing the loaded adapter model. If not specified, it will use `default_{i}` where `i` is
+                the total number of adapters being loaded. Must have PEFT installed to use.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
+            "cuda"
+        )
+        pipeline.load_lora_weights(
+            "Yntec/pineappleAnimeMix", weight_name="pineappleAnimeMix_pineapple10.1.safetensors", adapter_name="anime"
+        )
+        ```
         """
         # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
         state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
@@ -138,15 +153,7 @@ class LoraLoaderMixin:
         **kwargs,
     ):
         r"""
-        Return state dict for lora weights and the network alphas.
-
-        <Tip warning={true}>
-
-        We support loading A1111 formatted LoRA checkpoints in a limited capacity.
-
-        This function is experimental and might change in the future.
-
-        </Tip>
+        Return state dict and network alphas of the LoRA weights.
 
         Parameters:
             pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
@@ -154,8 +161,7 @@ class LoraLoaderMixin:
 
                     - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                       the Hub.
-                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
-                      with [`ModelMixin.save_pretrained`].
+                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights.
                     - A [torch state
                       dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
 
@@ -191,7 +197,6 @@ class LoraLoaderMixin:
                 Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
                 guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
                 information.
-
         """
         # Load the main state dict first which has the LoRA layers for either of
         # UNet and text encoder or both.
@@ -288,8 +293,8 @@ class LoraLoaderMixin:
             # Map SDXL blocks correctly.
             if unet_config is not None:
                 # use unet config to remap block numbers
-                state_dict = _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config)
-            state_dict, network_alphas = _convert_kohya_lora_to_diffusers(state_dict)
+                state_dict = cls._maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config)
+            state_dict, network_alphas = cls._convert_kohya_lora_to_diffusers(state_dict)
 
         return state_dict, network_alphas
 
@@ -329,6 +334,109 @@ class LoraLoaderMixin:
         weight_name = targeted_files[0]
         return weight_name
 
+    @classmethod
+    def _maybe_map_sgm_blocks_to_diffusers(cls, state_dict, unet_config, delimiter="_", block_slice_pos=5):
+        # 1. get all state_dict_keys
+        all_keys = list(state_dict.keys())
+        sgm_patterns = ["input_blocks", "middle_block", "output_blocks"]
+
+        # 2. check if needs remapping, if not return original dict
+        is_in_sgm_format = False
+        for key in all_keys:
+            if any(p in key for p in sgm_patterns):
+                is_in_sgm_format = True
+                break
+
+        if not is_in_sgm_format:
+            return state_dict
+
+        # 3. Else remap from SGM patterns
+        new_state_dict = {}
+        inner_block_map = ["resnets", "attentions", "upsamplers"]
+
+        # Retrieves # of down, mid and up blocks
+        input_block_ids, middle_block_ids, output_block_ids = set(), set(), set()
+
+        for layer in all_keys:
+            if "text" in layer:
+                new_state_dict[layer] = state_dict.pop(layer)
+            else:
+                layer_id = int(layer.split(delimiter)[:block_slice_pos][-1])
+                if sgm_patterns[0] in layer:
+                    input_block_ids.add(layer_id)
+                elif sgm_patterns[1] in layer:
+                    middle_block_ids.add(layer_id)
+                elif sgm_patterns[2] in layer:
+                    output_block_ids.add(layer_id)
+                else:
+                    raise ValueError(f"Checkpoint not supported because layer {layer} not supported.")
+
+        input_blocks = {
+            layer_id: [key for key in state_dict if f"input_blocks{delimiter}{layer_id}" in key]
+            for layer_id in input_block_ids
+        }
+        middle_blocks = {
+            layer_id: [key for key in state_dict if f"middle_block{delimiter}{layer_id}" in key]
+            for layer_id in middle_block_ids
+        }
+        output_blocks = {
+            layer_id: [key for key in state_dict if f"output_blocks{delimiter}{layer_id}" in key]
+            for layer_id in output_block_ids
+        }
+
+        # Rename keys accordingly
+        for i in input_block_ids:
+            block_id = (i - 1) // (unet_config.layers_per_block + 1)
+            layer_in_block_id = (i - 1) % (unet_config.layers_per_block + 1)
+
+            for key in input_blocks[i]:
+                inner_block_id = int(key.split(delimiter)[block_slice_pos])
+                inner_block_key = inner_block_map[inner_block_id] if "op" not in key else "downsamplers"
+                inner_layers_in_block = str(layer_in_block_id) if "op" not in key else "0"
+                new_key = delimiter.join(
+                    key.split(delimiter)[: block_slice_pos - 1]
+                    + [str(block_id), inner_block_key, inner_layers_in_block]
+                    + key.split(delimiter)[block_slice_pos + 1 :]
+                )
+                new_state_dict[new_key] = state_dict.pop(key)
+
+        for i in middle_block_ids:
+            key_part = None
+            if i == 0:
+                key_part = [inner_block_map[0], "0"]
+            elif i == 1:
+                key_part = [inner_block_map[1], "0"]
+            elif i == 2:
+                key_part = [inner_block_map[0], "1"]
+            else:
+                raise ValueError(f"Invalid middle block id {i}.")
+
+            for key in middle_blocks[i]:
+                new_key = delimiter.join(
+                    key.split(delimiter)[: block_slice_pos - 1] + key_part + key.split(delimiter)[block_slice_pos:]
+                )
+                new_state_dict[new_key] = state_dict.pop(key)
+
+        for i in output_block_ids:
+            block_id = i // (unet_config.layers_per_block + 1)
+            layer_in_block_id = i % (unet_config.layers_per_block + 1)
+
+            for key in output_blocks[i]:
+                inner_block_id = int(key.split(delimiter)[block_slice_pos])
+                inner_block_key = inner_block_map[inner_block_id]
+                inner_layers_in_block = str(layer_in_block_id) if inner_block_id < 2 else "0"
+                new_key = delimiter.join(
+                    key.split(delimiter)[: block_slice_pos - 1]
+                    + [str(block_id), inner_block_key, inner_layers_in_block]
+                    + key.split(delimiter)[block_slice_pos + 1 :]
+                )
+                new_state_dict[new_key] = state_dict.pop(key)
+
+        if len(state_dict) > 0:
+            raise ValueError("At this point all state dict entries have to be converted.")
+
+        return new_state_dict
+
     @classmethod
     def _optionally_disable_offloading(cls, _pipeline):
         """
@@ -365,25 +473,27 @@ class LoraLoaderMixin:
         cls, state_dict, network_alphas, unet, low_cpu_mem_usage=None, adapter_name=None, _pipeline=None
     ):
         """
-        This will load the LoRA layers specified in `state_dict` into `unet`.
+        Load LoRA layers specified in `state_dict` into `unet`.
 
         Parameters:
             state_dict (`dict`):
-                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
-                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
-                encoder lora layers.
+                A standard state dict containing the LoRA layer parameters. The keys can either be indexed directly
+                into the `unet` or prefixed with an additional `unet`, which can be used to distinguish between text
+                encoder LoRA layers.
             network_alphas (`Dict[str, float]`):
-                See `LoRALinearLayer` for more details.
+                See
+                [`LoRALinearLayer`](https://github.com/huggingface/diffusers/blob/c697f524761abd2314c030221a3ad2f7791eab4e/src/diffusers/models/lora.py#L182)
+                for more details.
             unet (`UNet2DConditionModel`):
                 The UNet model to load the LoRA layers into.
             low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
-                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
-                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
-                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
-                argument to `True` will raise an error.
+                Only load and not initialize the pretrained weights. This can speedup model loading and also tries to
+                not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only
+                supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to
+                `True` will raise an error.
             adapter_name (`str`, *optional*):
-                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
-                `default_{i}` where i is the total number of adapters being loaded.
+                Name for referencing the loaded adapter model. If not specified, it will use `default_{i}` where `i` is
+                the total number of adapters being loaded.
         """
         low_cpu_mem_usage = low_cpu_mem_usage if low_cpu_mem_usage is not None else _LOW_CPU_MEM_USAGE_DEFAULT
         # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
@@ -477,26 +587,27 @@ class LoraLoaderMixin:
         _pipeline=None,
     ):
         """
-        This will load the LoRA layers specified in `state_dict` into `text_encoder`
+        Load LoRA layers specified in `state_dict` into `text_encoder`.
 
         Parameters:
             state_dict (`dict`):
-                A standard state dict containing the lora layer parameters. The key should be prefixed with an
-                additional `text_encoder` to distinguish between unet lora layers.
+                A standard state dict containing the LoRA layer parameters. The key should be prefixed with an
+                additional `text_encoder` to distinguish between UNet LoRA layers.
             network_alphas (`Dict[str, float]`):
-                See `LoRALinearLayer` for more details.
+                See
+                [`LoRALinearLayer`](https://github.com/huggingface/diffusers/blob/c697f524761abd2314c030221a3ad2f7791eab4e/src/diffusers/models/lora.py#L182)
+                for more details.
             text_encoder (`CLIPTextModel`):
                 The text encoder model to load the LoRA layers into.
             prefix (`str`):
                 Expected prefix of the `text_encoder` in the `state_dict`.
             lora_scale (`float`):
-                How much to scale the output of the lora linear layer before it is added with the output of the regular
-                lora layer.
+                Scale of `LoRALinearLayer`'s output before it is added with the output of the regular LoRA layer.
             low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
-                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
-                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
-                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
-                argument to `True` will raise an error.
+                Only load and not initialize the pretrained weights. This can speedup model loading and also tries to
+                not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only
+                supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to
+                `True` will raise an error.
             adapter_name (`str`, *optional*):
                 Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                 `default_{i}` where i is the total number of adapters being loaded.
@@ -781,11 +892,11 @@ class LoraLoaderMixin:
         safe_serialization: bool = True,
     ):
         r"""
-        Save the LoRA parameters corresponding to the UNet and text encoder.
+        Save the UNet and text encoder LoRA parameters.
 
         Arguments:
             save_directory (`str` or `os.PathLike`):
-                Directory to save LoRA parameters to. Will be created if it doesn't exist.
+                Directory to save LoRA parameters to (will be created if it doesn't exist).
             unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                 State dict of the LoRA layers corresponding to the `unet`.
             text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
@@ -796,11 +907,30 @@ class LoraLoaderMixin:
                 need to call this function on all processes. In this case, set `is_main_process=True` only on the main
                 process to avoid race conditions.
             save_function (`Callable`):
-                The function to use to save the state dictionary. Useful during distributed training when you need to
-                replace `torch.save` with another method. Can be configured with the environment variable
+                The function to use to save the state dict. Useful during distributed training when you need to replace
+                `torch.save` with another method. Can be configured with the environment variable
                 `DIFFUSERS_SAVE_MODE`.
             safe_serialization (`bool`, *optional*, defaults to `True`):
-                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
+                Whether to save the model using `safetensors` or with `pickle`.
+
+        Example:
+
+        ```py
+        from diffusers import StableDiffusionXLPipeline
+        from peft.utils import get_peft_model_state_dict
+        import torch
+
+        pipeline = StableDiffusionXLPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.fuse_lora()
+
+        # get and save unet state dict
+        unet_state_dict = get_peft_model_state_dict(pipeline.unet, adapter_name="pixel")
+        pipeline.save_lora_weights("fused-model", unet_lora_layers=unet_state_dict)
+        pipeline.load_lora_weights("fused-model", weight_name="pytorch_lora_weights.safetensors")
+        ```
         """
         # Create a flat dictionary.
         state_dict = {}
@@ -869,16 +999,186 @@ class LoraLoaderMixin:
         save_function(state_dict, os.path.join(save_directory, weight_name))
         logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}")
 
+    @classmethod
+    def _convert_kohya_lora_to_diffusers(cls, state_dict):
+        unet_state_dict = {}
+        te_state_dict = {}
+        te2_state_dict = {}
+        network_alphas = {}
+
+        # every down weight has a corresponding up weight and potentially an alpha weight
+        lora_keys = [k for k in state_dict.keys() if k.endswith("lora_down.weight")]
+        for key in lora_keys:
+            lora_name = key.split(".")[0]
+            lora_name_up = lora_name + ".lora_up.weight"
+            lora_name_alpha = lora_name + ".alpha"
+
+            if lora_name.startswith("lora_unet_"):
+                diffusers_name = key.replace("lora_unet_", "").replace("_", ".")
+
+                if "input.blocks" in diffusers_name:
+                    diffusers_name = diffusers_name.replace("input.blocks", "down_blocks")
+                else:
+                    diffusers_name = diffusers_name.replace("down.blocks", "down_blocks")
+
+                if "middle.block" in diffusers_name:
+                    diffusers_name = diffusers_name.replace("middle.block", "mid_block")
+                else:
+                    diffusers_name = diffusers_name.replace("mid.block", "mid_block")
+                if "output.blocks" in diffusers_name:
+                    diffusers_name = diffusers_name.replace("output.blocks", "up_blocks")
+                else:
+                    diffusers_name = diffusers_name.replace("up.blocks", "up_blocks")
+
+                diffusers_name = diffusers_name.replace("transformer.blocks", "transformer_blocks")
+                diffusers_name = diffusers_name.replace("to.q.lora", "to_q_lora")
+                diffusers_name = diffusers_name.replace("to.k.lora", "to_k_lora")
+                diffusers_name = diffusers_name.replace("to.v.lora", "to_v_lora")
+                diffusers_name = diffusers_name.replace("to.out.0.lora", "to_out_lora")
+                diffusers_name = diffusers_name.replace("proj.in", "proj_in")
+                diffusers_name = diffusers_name.replace("proj.out", "proj_out")
+                diffusers_name = diffusers_name.replace("emb.layers", "time_emb_proj")
+
+                # SDXL specificity.
+                if "emb" in diffusers_name and "time.emb.proj" not in diffusers_name:
+                    pattern = r"\.\d+(?=\D*$)"
+                    diffusers_name = re.sub(pattern, "", diffusers_name, count=1)
+                if ".in." in diffusers_name:
+                    diffusers_name = diffusers_name.replace("in.layers.2", "conv1")
+                if ".out." in diffusers_name:
+                    diffusers_name = diffusers_name.replace("out.layers.3", "conv2")
+                if "downsamplers" in diffusers_name or "upsamplers" in diffusers_name:
+                    diffusers_name = diffusers_name.replace("op", "conv")
+                if "skip" in diffusers_name:
+                    diffusers_name = diffusers_name.replace("skip.connection", "conv_shortcut")
+
+                # LyCORIS specificity.
+                if "time.emb.proj" in diffusers_name:
+                    diffusers_name = diffusers_name.replace("time.emb.proj", "time_emb_proj")
+                if "conv.shortcut" in diffusers_name:
+                    diffusers_name = diffusers_name.replace("conv.shortcut", "conv_shortcut")
+
+                # General coverage.
+                if "transformer_blocks" in diffusers_name:
+                    if "attn1" in diffusers_name or "attn2" in diffusers_name:
+                        diffusers_name = diffusers_name.replace("attn1", "attn1.processor")
+                        diffusers_name = diffusers_name.replace("attn2", "attn2.processor")
+                        unet_state_dict[diffusers_name] = state_dict.pop(key)
+                        unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+                    elif "ff" in diffusers_name:
+                        unet_state_dict[diffusers_name] = state_dict.pop(key)
+                        unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+                elif any(key in diffusers_name for key in ("proj_in", "proj_out")):
+                    unet_state_dict[diffusers_name] = state_dict.pop(key)
+                    unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+                else:
+                    unet_state_dict[diffusers_name] = state_dict.pop(key)
+                    unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+
+            elif lora_name.startswith("lora_te_"):
+                diffusers_name = key.replace("lora_te_", "").replace("_", ".")
+                diffusers_name = diffusers_name.replace("text.model", "text_model")
+                diffusers_name = diffusers_name.replace("self.attn", "self_attn")
+                diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
+                diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
+                diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
+                diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
+                if "self_attn" in diffusers_name:
+                    te_state_dict[diffusers_name] = state_dict.pop(key)
+                    te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+                elif "mlp" in diffusers_name:
+                    # Be aware that this is the new diffusers convention and the rest of the code might
+                    # not utilize it yet.
+                    diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
+                    te_state_dict[diffusers_name] = state_dict.pop(key)
+                    te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+
+            # (sayakpaul): Duplicate code. Needs to be cleaned.
+            elif lora_name.startswith("lora_te1_"):
+                diffusers_name = key.replace("lora_te1_", "").replace("_", ".")
+                diffusers_name = diffusers_name.replace("text.model", "text_model")
+                diffusers_name = diffusers_name.replace("self.attn", "self_attn")
+                diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
+                diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
+                diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
+                diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
+                if "self_attn" in diffusers_name:
+                    te_state_dict[diffusers_name] = state_dict.pop(key)
+                    te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+                elif "mlp" in diffusers_name:
+                    # Be aware that this is the new diffusers convention and the rest of the code might
+                    # not utilize it yet.
+                    diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
+                    te_state_dict[diffusers_name] = state_dict.pop(key)
+                    te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+
+            # (sayakpaul): Duplicate code. Needs to be cleaned.
+            elif lora_name.startswith("lora_te2_"):
+                diffusers_name = key.replace("lora_te2_", "").replace("_", ".")
+                diffusers_name = diffusers_name.replace("text.model", "text_model")
+                diffusers_name = diffusers_name.replace("self.attn", "self_attn")
+                diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
+                diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
+                diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
+                diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
+                if "self_attn" in diffusers_name:
+                    te2_state_dict[diffusers_name] = state_dict.pop(key)
+                    te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+                elif "mlp" in diffusers_name:
+                    # Be aware that this is the new diffusers convention and the rest of the code might
+                    # not utilize it yet.
+                    diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
+                    te2_state_dict[diffusers_name] = state_dict.pop(key)
+                    te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
+
+            # Rename the alphas so that they can be mapped appropriately.
+            if lora_name_alpha in state_dict:
+                alpha = state_dict.pop(lora_name_alpha).item()
+                if lora_name_alpha.startswith("lora_unet_"):
+                    prefix = "unet."
+                elif lora_name_alpha.startswith(("lora_te_", "lora_te1_")):
+                    prefix = "text_encoder."
+                else:
+                    prefix = "text_encoder_2."
+                new_name = prefix + diffusers_name.split(".lora.")[0] + ".alpha"
+                network_alphas.update({new_name: alpha})
+
+        if len(state_dict) > 0:
+            raise ValueError(
+                f"The following keys have not been correctly be renamed: \n\n {', '.join(state_dict.keys())}"
+            )
+
+        logger.info("Kohya-style checkpoint detected.")
+        unet_state_dict = {f"{cls.unet_name}.{module_name}": params for module_name, params in unet_state_dict.items()}
+        te_state_dict = {
+            f"{cls.text_encoder_name}.{module_name}": params for module_name, params in te_state_dict.items()
+        }
+        te2_state_dict = (
+            {f"text_encoder_2.{module_name}": params for module_name, params in te2_state_dict.items()}
+            if len(te2_state_dict) > 0
+            else None
+        )
+        if te2_state_dict is not None:
+            te_state_dict.update(te2_state_dict)
+
+        new_state_dict = {**unet_state_dict, **te_state_dict}
+        return new_state_dict, network_alphas
+
     def unload_lora_weights(self):
         """
-        Unloads the LoRA parameters.
+        Unload the LoRA parameters from a pipeline.
 
         Examples:
 
-        ```python
-        >>> # Assuming `pipeline` is already loaded with the LoRA parameters.
-        >>> pipeline.unload_lora_weights()
-        >>> ...
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.unload_lora_weights()
         ```
         """
         if not USE_PEFT_BACKEND:
@@ -907,7 +1207,7 @@ class LoraLoaderMixin:
         safe_fusing: bool = False,
     ):
         r"""
-        Fuses the LoRA parameters into the original parameters of the corresponding blocks.
+        Fuse the LoRA parameters with the original parameters in their corresponding blocks.
 
         <Tip warning={true}>
 
@@ -921,9 +1221,23 @@ class LoraLoaderMixin:
                 Whether to fuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
                 LoRA parameters then it won't have any effect.
             lora_scale (`float`, defaults to 1.0):
-                Controls how much to influence the outputs with the LoRA parameters.
+                Controls LoRA influence on the outputs.
             safe_fusing (`bool`, defaults to `False`):
-                Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
+                Whether to check fused weights for `NaN` values before fusing and if values are `NaN`, then don't fuse
+                them.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.fuse_lora(lora_scale=0.7)
+        ```
         """
         if fuse_unet or fuse_text_encoder:
             self.num_fused_loras += 1
@@ -972,8 +1286,7 @@ class LoraLoaderMixin:
 
     def unfuse_lora(self, unfuse_unet: bool = True, unfuse_text_encoder: bool = True):
         r"""
-        Reverses the effect of
-        [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora).
+        Unfuse the LoRA parameters from the original parameters in their corresponding blocks.
 
         <Tip warning={true}>
 
@@ -986,6 +1299,20 @@ class LoraLoaderMixin:
             unfuse_text_encoder (`bool`, defaults to `True`):
                 Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
                 LoRA parameters then it won't have any effect.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.fuse_lora(lora_scale=0.7)
+        pipeline.unfuse_lora()
+        ```
         """
         if unfuse_unet:
             if not USE_PEFT_BACKEND:
@@ -1037,16 +1364,32 @@ class LoraLoaderMixin:
         text_encoder_weights: List[float] = None,
     ):
         """
-        Sets the adapter layers for the text encoder.
+        Set the currently active adapter for use in the text encoder.
 
         Args:
             adapter_names (`List[str]` or `str`):
-                The names of the adapters to use.
+                The adapter to activate.
             text_encoder (`torch.nn.Module`, *optional*):
-                The text encoder module to set the adapter layers for. If `None`, it will try to get the `text_encoder`
-                attribute.
+                The text encoder module to activate the adapter layers for. If `None`, it will try to get the
+                `text_encoder` attribute.
             text_encoder_weights (`List[float]`, *optional*):
                 The weights to use for the text encoder. If `None`, the weights are set to `1.0` for all the adapters.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.load_lora_weights(
+            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
+        )
+        pipeline.set_adapters_for_text_encoder("pixel")
+        ```
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -1074,12 +1417,25 @@ class LoraLoaderMixin:
 
     def disable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
         """
-        Disables the LoRA layers for the text encoder.
+        Disable the text encoder's LoRA layers.
 
         Args:
             text_encoder (`torch.nn.Module`, *optional*):
                 The text encoder module to disable the LoRA layers for. If `None`, it will try to get the
                 `text_encoder` attribute.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.disable_lora_for_text_encoder()
+        ```
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -1091,12 +1447,25 @@ class LoraLoaderMixin:
 
     def enable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
         """
-        Enables the LoRA layers for the text encoder.
+        Enables the text encoder's LoRA layers.
 
         Args:
             text_encoder (`torch.nn.Module`, *optional*):
                 The text encoder module to enable the LoRA layers for. If `None`, it will try to get the `text_encoder`
                 attribute.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.enable_lora_for_text_encoder()
+        ```
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -1147,10 +1516,24 @@ class LoraLoaderMixin:
 
     def delete_adapters(self, adapter_names: Union[List[str], str]):
         """
+        Delete an adapter's LoRA layers from the UNet and text encoder(s).
+
         Args:
-        Deletes the LoRA layers of `adapter_name` for the unet and text-encoder(s).
             adapter_names (`Union[List[str], str]`):
-                The names of the adapter to delete. Can be a single string or a list of strings
+                The names (single string or list of strings) of the adapter to delete.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.delete_adapters("pixel")
+        ```
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -1170,7 +1553,7 @@ class LoraLoaderMixin:
 
     def get_active_adapters(self) -> List[str]:
         """
-        Gets the list of the current active adapters.
+        Get a list of currently active adapters.
 
         Example:
 
@@ -1202,7 +1585,22 @@ class LoraLoaderMixin:
 
     def get_list_adapters(self) -> Dict[str, List[str]]:
         """
-        Gets the current list of all available adapters in the pipeline.
+        Get a list of all currently available adapters for each component in the pipeline.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0",
+        ).to("cuda")
+        pipeline.load_lora_weights(
+            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
+        )
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.get_list_adapters()
+        ```
         """
         if not USE_PEFT_BACKEND:
             raise ValueError(
@@ -1224,14 +1622,27 @@ class LoraLoaderMixin:
 
     def set_lora_device(self, adapter_names: List[str], device: Union[torch.device, str, int]) -> None:
         """
-        Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case
-        you want to load multiple adapters and free some GPU memory.
+        Move a LoRA to a target device. Useful for offloading a LoRA to the CPU in case you want to load multiple
+        adapters and free some GPU memory.
 
         Args:
             adapter_names (`List[str]`):
-                List of adapters to send device to.
+                List of adapters to send to device.
             device (`Union[torch.device, str, int]`):
-                Device to send the adapters to. Can be either a torch device, a str or an integer.
+                Device (can be a `torch.device`, `str` or `int`) to place adapters on.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0",
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.set_lora_device(["pixel"], device="cuda")
+        ```
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -1263,7 +1674,7 @@ class LoraLoaderMixin:
 
 
 class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
-    """This class overrides `LoraLoaderMixin` with LoRA loading/saving code that's specific to SDXL"""
+    """This class overrides [`LoraLoaderMixin`] with LoRA loading/saving code that's specific to SDXL."""
 
     # Overrride to properly handle the loading and unloading of the additional text encoder.
     def load_lora_weights(
@@ -1288,12 +1699,26 @@ class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
 
         Parameters:
             pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
-                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
-            adapter_name (`str`, *optional*):
-                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
-                `default_{i}` where i is the total number of adapters being loaded.
+                A string (model id of a pretrained model hosted on the Hub), a path to a directory containing the model
+                weights, or a [torch state
+                dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
             kwargs (`dict`, *optional*):
                 See [`~loaders.LoraLoaderMixin.lora_state_dict`].
+            adapter_name (`str`, *optional*):
+                Name for referencing the loaded adapter model. If not specified, it will use `default_{i}` where `i` is
+                the total number of adapters being loaded. Must have PEFT installed to use.
+
+        Example:
+
+        ```py
+        from diffusers import StableDiffusionXLPipeline
+        import torch
+
+        pipeline = StableDiffusionXLPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        ```
         """
         # We could have accessed the unet config from `lora_state_dict()` too. We pass
         # it here explicitly to be able to tell that it's coming from an SDXL

src/diffusers/loaders/lora_conversion_utils.py

@@ -1,284 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import re
-
-from ..utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-def _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config, delimiter="_", block_slice_pos=5):
-    # 1. get all state_dict_keys
-    all_keys = list(state_dict.keys())
-    sgm_patterns = ["input_blocks", "middle_block", "output_blocks"]
-
-    # 2. check if needs remapping, if not return original dict
-    is_in_sgm_format = False
-    for key in all_keys:
-        if any(p in key for p in sgm_patterns):
-            is_in_sgm_format = True
-            break
-
-    if not is_in_sgm_format:
-        return state_dict
-
-    # 3. Else remap from SGM patterns
-    new_state_dict = {}
-    inner_block_map = ["resnets", "attentions", "upsamplers"]
-
-    # Retrieves # of down, mid and up blocks
-    input_block_ids, middle_block_ids, output_block_ids = set(), set(), set()
-
-    for layer in all_keys:
-        if "text" in layer:
-            new_state_dict[layer] = state_dict.pop(layer)
-        else:
-            layer_id = int(layer.split(delimiter)[:block_slice_pos][-1])
-            if sgm_patterns[0] in layer:
-                input_block_ids.add(layer_id)
-            elif sgm_patterns[1] in layer:
-                middle_block_ids.add(layer_id)
-            elif sgm_patterns[2] in layer:
-                output_block_ids.add(layer_id)
-            else:
-                raise ValueError(f"Checkpoint not supported because layer {layer} not supported.")
-
-    input_blocks = {
-        layer_id: [key for key in state_dict if f"input_blocks{delimiter}{layer_id}" in key]
-        for layer_id in input_block_ids
-    }
-    middle_blocks = {
-        layer_id: [key for key in state_dict if f"middle_block{delimiter}{layer_id}" in key]
-        for layer_id in middle_block_ids
-    }
-    output_blocks = {
-        layer_id: [key for key in state_dict if f"output_blocks{delimiter}{layer_id}" in key]
-        for layer_id in output_block_ids
-    }
-
-    # Rename keys accordingly
-    for i in input_block_ids:
-        block_id = (i - 1) // (unet_config.layers_per_block + 1)
-        layer_in_block_id = (i - 1) % (unet_config.layers_per_block + 1)
-
-        for key in input_blocks[i]:
-            inner_block_id = int(key.split(delimiter)[block_slice_pos])
-            inner_block_key = inner_block_map[inner_block_id] if "op" not in key else "downsamplers"
-            inner_layers_in_block = str(layer_in_block_id) if "op" not in key else "0"
-            new_key = delimiter.join(
-                key.split(delimiter)[: block_slice_pos - 1]
-                + [str(block_id), inner_block_key, inner_layers_in_block]
-                + key.split(delimiter)[block_slice_pos + 1 :]
-            )
-            new_state_dict[new_key] = state_dict.pop(key)
-
-    for i in middle_block_ids:
-        key_part = None
-        if i == 0:
-            key_part = [inner_block_map[0], "0"]
-        elif i == 1:
-            key_part = [inner_block_map[1], "0"]
-        elif i == 2:
-            key_part = [inner_block_map[0], "1"]
-        else:
-            raise ValueError(f"Invalid middle block id {i}.")
-
-        for key in middle_blocks[i]:
-            new_key = delimiter.join(
-                key.split(delimiter)[: block_slice_pos - 1] + key_part + key.split(delimiter)[block_slice_pos:]
-            )
-            new_state_dict[new_key] = state_dict.pop(key)
-
-    for i in output_block_ids:
-        block_id = i // (unet_config.layers_per_block + 1)
-        layer_in_block_id = i % (unet_config.layers_per_block + 1)
-
-        for key in output_blocks[i]:
-            inner_block_id = int(key.split(delimiter)[block_slice_pos])
-            inner_block_key = inner_block_map[inner_block_id]
-            inner_layers_in_block = str(layer_in_block_id) if inner_block_id < 2 else "0"
-            new_key = delimiter.join(
-                key.split(delimiter)[: block_slice_pos - 1]
-                + [str(block_id), inner_block_key, inner_layers_in_block]
-                + key.split(delimiter)[block_slice_pos + 1 :]
-            )
-            new_state_dict[new_key] = state_dict.pop(key)
-
-    if len(state_dict) > 0:
-        raise ValueError("At this point all state dict entries have to be converted.")
-
-    return new_state_dict
-
-
-def _convert_kohya_lora_to_diffusers(state_dict, unet_name="unet", text_encoder_name="text_encoder"):
-    unet_state_dict = {}
-    te_state_dict = {}
-    te2_state_dict = {}
-    network_alphas = {}
-
-    # every down weight has a corresponding up weight and potentially an alpha weight
-    lora_keys = [k for k in state_dict.keys() if k.endswith("lora_down.weight")]
-    for key in lora_keys:
-        lora_name = key.split(".")[0]
-        lora_name_up = lora_name + ".lora_up.weight"
-        lora_name_alpha = lora_name + ".alpha"
-
-        if lora_name.startswith("lora_unet_"):
-            diffusers_name = key.replace("lora_unet_", "").replace("_", ".")
-
-            if "input.blocks" in diffusers_name:
-                diffusers_name = diffusers_name.replace("input.blocks", "down_blocks")
-            else:
-                diffusers_name = diffusers_name.replace("down.blocks", "down_blocks")
-
-            if "middle.block" in diffusers_name:
-                diffusers_name = diffusers_name.replace("middle.block", "mid_block")
-            else:
-                diffusers_name = diffusers_name.replace("mid.block", "mid_block")
-            if "output.blocks" in diffusers_name:
-                diffusers_name = diffusers_name.replace("output.blocks", "up_blocks")
-            else:
-                diffusers_name = diffusers_name.replace("up.blocks", "up_blocks")
-
-            diffusers_name = diffusers_name.replace("transformer.blocks", "transformer_blocks")
-            diffusers_name = diffusers_name.replace("to.q.lora", "to_q_lora")
-            diffusers_name = diffusers_name.replace("to.k.lora", "to_k_lora")
-            diffusers_name = diffusers_name.replace("to.v.lora", "to_v_lora")
-            diffusers_name = diffusers_name.replace("to.out.0.lora", "to_out_lora")
-            diffusers_name = diffusers_name.replace("proj.in", "proj_in")
-            diffusers_name = diffusers_name.replace("proj.out", "proj_out")
-            diffusers_name = diffusers_name.replace("emb.layers", "time_emb_proj")
-
-            # SDXL specificity.
-            if "emb" in diffusers_name and "time.emb.proj" not in diffusers_name:
-                pattern = r"\.\d+(?=\D*$)"
-                diffusers_name = re.sub(pattern, "", diffusers_name, count=1)
-            if ".in." in diffusers_name:
-                diffusers_name = diffusers_name.replace("in.layers.2", "conv1")
-            if ".out." in diffusers_name:
-                diffusers_name = diffusers_name.replace("out.layers.3", "conv2")
-            if "downsamplers" in diffusers_name or "upsamplers" in diffusers_name:
-                diffusers_name = diffusers_name.replace("op", "conv")
-            if "skip" in diffusers_name:
-                diffusers_name = diffusers_name.replace("skip.connection", "conv_shortcut")
-
-            # LyCORIS specificity.
-            if "time.emb.proj" in diffusers_name:
-                diffusers_name = diffusers_name.replace("time.emb.proj", "time_emb_proj")
-            if "conv.shortcut" in diffusers_name:
-                diffusers_name = diffusers_name.replace("conv.shortcut", "conv_shortcut")
-
-            # General coverage.
-            if "transformer_blocks" in diffusers_name:
-                if "attn1" in diffusers_name or "attn2" in diffusers_name:
-                    diffusers_name = diffusers_name.replace("attn1", "attn1.processor")
-                    diffusers_name = diffusers_name.replace("attn2", "attn2.processor")
-                    unet_state_dict[diffusers_name] = state_dict.pop(key)
-                    unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-                elif "ff" in diffusers_name:
-                    unet_state_dict[diffusers_name] = state_dict.pop(key)
-                    unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-            elif any(key in diffusers_name for key in ("proj_in", "proj_out")):
-                unet_state_dict[diffusers_name] = state_dict.pop(key)
-                unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-            else:
-                unet_state_dict[diffusers_name] = state_dict.pop(key)
-                unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-
-        elif lora_name.startswith("lora_te_"):
-            diffusers_name = key.replace("lora_te_", "").replace("_", ".")
-            diffusers_name = diffusers_name.replace("text.model", "text_model")
-            diffusers_name = diffusers_name.replace("self.attn", "self_attn")
-            diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
-            diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
-            diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
-            diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
-            if "self_attn" in diffusers_name:
-                te_state_dict[diffusers_name] = state_dict.pop(key)
-                te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-            elif "mlp" in diffusers_name:
-                # Be aware that this is the new diffusers convention and the rest of the code might
-                # not utilize it yet.
-                diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
-                te_state_dict[diffusers_name] = state_dict.pop(key)
-                te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-
-        # (sayakpaul): Duplicate code. Needs to be cleaned.
-        elif lora_name.startswith("lora_te1_"):
-            diffusers_name = key.replace("lora_te1_", "").replace("_", ".")
-            diffusers_name = diffusers_name.replace("text.model", "text_model")
-            diffusers_name = diffusers_name.replace("self.attn", "self_attn")
-            diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
-            diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
-            diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
-            diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
-            if "self_attn" in diffusers_name:
-                te_state_dict[diffusers_name] = state_dict.pop(key)
-                te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-            elif "mlp" in diffusers_name:
-                # Be aware that this is the new diffusers convention and the rest of the code might
-                # not utilize it yet.
-                diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
-                te_state_dict[diffusers_name] = state_dict.pop(key)
-                te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-
-        # (sayakpaul): Duplicate code. Needs to be cleaned.
-        elif lora_name.startswith("lora_te2_"):
-            diffusers_name = key.replace("lora_te2_", "").replace("_", ".")
-            diffusers_name = diffusers_name.replace("text.model", "text_model")
-            diffusers_name = diffusers_name.replace("self.attn", "self_attn")
-            diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
-            diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
-            diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
-            diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
-            if "self_attn" in diffusers_name:
-                te2_state_dict[diffusers_name] = state_dict.pop(key)
-                te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-            elif "mlp" in diffusers_name:
-                # Be aware that this is the new diffusers convention and the rest of the code might
-                # not utilize it yet.
-                diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
-                te2_state_dict[diffusers_name] = state_dict.pop(key)
-                te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
-
-        # Rename the alphas so that they can be mapped appropriately.
-        if lora_name_alpha in state_dict:
-            alpha = state_dict.pop(lora_name_alpha).item()
-            if lora_name_alpha.startswith("lora_unet_"):
-                prefix = "unet."
-            elif lora_name_alpha.startswith(("lora_te_", "lora_te1_")):
-                prefix = "text_encoder."
-            else:
-                prefix = "text_encoder_2."
-            new_name = prefix + diffusers_name.split(".lora.")[0] + ".alpha"
-            network_alphas.update({new_name: alpha})
-
-    if len(state_dict) > 0:
-        raise ValueError(f"The following keys have not been correctly be renamed: \n\n {', '.join(state_dict.keys())}")
-
-    logger.info("Kohya-style checkpoint detected.")
-    unet_state_dict = {f"{unet_name}.{module_name}": params for module_name, params in unet_state_dict.items()}
-    te_state_dict = {f"{text_encoder_name}.{module_name}": params for module_name, params in te_state_dict.items()}
-    te2_state_dict = (
-        {f"text_encoder_2.{module_name}": params for module_name, params in te2_state_dict.items()}
-        if len(te2_state_dict) > 0
-        else None
-    )
-    if te2_state_dict is not None:
-        te_state_dict.update(te2_state_dict)
-
-    new_state_dict = {**unet_state_dict, **te_state_dict}
-    return new_state_dict, network_alphas

src/diffusers/loaders/textual_inversion.py

@@ -189,7 +189,7 @@ class TextualInversionLoaderMixin:
                 f" `{self.load_textual_inversion.__name__}`"
             )
 
-        if len(pretrained_model_name_or_paths) > 1 and len(pretrained_model_name_or_paths) != len(tokens):
+        if len(pretrained_model_name_or_paths) != len(tokens):
             raise ValueError(
                 f"You have passed a list of models of length {len(pretrained_model_name_or_paths)}, and list of tokens of length {len(tokens)} "
                 f"Make sure both lists have the same length."
@@ -382,9 +382,7 @@ class TextualInversionLoaderMixin:
             if not isinstance(pretrained_model_name_or_path, list)
             else pretrained_model_name_or_path
         )
-        tokens = [token] if not isinstance(token, list) else token
-        if tokens[0] is None:
-            tokens = tokens * len(pretrained_model_name_or_paths)
+        tokens = len(pretrained_model_name_or_paths) * [token] if (isinstance(token, str) or token is None) else token
 
         # 3. Check inputs
         self._check_text_inv_inputs(tokenizer, text_encoder, pretrained_model_name_or_paths, tokens)
@@ -392,16 +390,6 @@ class TextualInversionLoaderMixin:
         # 4. Load state dicts of textual embeddings
         state_dicts = load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs)
 
-        # 4.1 Handle the special case when state_dict is a tensor that contains n embeddings for n tokens
-        if len(tokens) > 1 and len(state_dicts) == 1:
-            if isinstance(state_dicts[0], torch.Tensor):
-                state_dicts = list(state_dicts[0])
-                if len(tokens) != len(state_dicts):
-                    raise ValueError(
-                        f"You have passed a state_dict contains {len(state_dicts)} embeddings, and list of tokens of length {len(tokens)} "
-                        f"Make sure both have the same length."
-                    )
-
         # 4. Retrieve tokens and embeddings
         tokens, embeddings = self._retrieve_tokens_and_embeddings(tokens, state_dicts, tokenizer)
 

src/diffusers/loaders/unet.py

@@ -18,10 +18,8 @@ from typing import Callable, Dict, List, Optional, Union
 
 import safetensors
 import torch
-import torch.nn.functional as F
 from torch import nn
 
-from ..models.embeddings import ImageProjection
 from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
 from ..utils import (
     DIFFUSERS_CACHE,
@@ -664,72 +662,4 @@ class UNet2DConditionLoadersMixin:
             if hasattr(self, "peft_config"):
                 self.peft_config.pop(adapter_name, None)
 
-    def _load_ip_adapter_weights(self, state_dict):
-        from ..models.attention_processor import (
-            AttnProcessor,
-            AttnProcessor2_0,
-            IPAdapterAttnProcessor,
-            IPAdapterAttnProcessor2_0,
-        )
-
-        # set ip-adapter cross-attention processors & load state_dict
-        attn_procs = {}
-        key_id = 1
-        for name in self.attn_processors.keys():
-            cross_attention_dim = None if name.endswith("attn1.processor") else self.config.cross_attention_dim
-            if name.startswith("mid_block"):
-                hidden_size = self.config.block_out_channels[-1]
-            elif name.startswith("up_blocks"):
-                block_id = int(name[len("up_blocks.")])
-                hidden_size = list(reversed(self.config.block_out_channels))[block_id]
-            elif name.startswith("down_blocks"):
-                block_id = int(name[len("down_blocks.")])
-                hidden_size = self.config.block_out_channels[block_id]
-            if cross_attention_dim is None or "motion_modules" in name:
-                attn_processor_class = (
-                    AttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else AttnProcessor
-                )
-                attn_procs[name] = attn_processor_class()
-            else:
-                attn_processor_class = (
-                    IPAdapterAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else IPAdapterAttnProcessor
-                )
-                attn_procs[name] = attn_processor_class(
-                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0
-                ).to(dtype=self.dtype, device=self.device)
-
-                value_dict = {}
-                for k, w in attn_procs[name].state_dict().items():
-                    value_dict.update({f"{k}": state_dict["ip_adapter"][f"{key_id}.{k}"]})
-
-                attn_procs[name].load_state_dict(value_dict)
-                key_id += 2
-
-        self.set_attn_processor(attn_procs)
-
-        # create image projection layers.
-        clip_embeddings_dim = state_dict["image_proj"]["proj.weight"].shape[-1]
-        cross_attention_dim = state_dict["image_proj"]["proj.weight"].shape[0] // 4
-
-        image_projection = ImageProjection(
-            cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim, num_image_text_embeds=4
-        )
-        image_projection.to(dtype=self.dtype, device=self.device)
-
-        # load image projection layer weights
-        image_proj_state_dict = {}
-        image_proj_state_dict.update(
-            {
-                "image_embeds.weight": state_dict["image_proj"]["proj.weight"],
-                "image_embeds.bias": state_dict["image_proj"]["proj.bias"],
-                "norm.weight": state_dict["image_proj"]["norm.weight"],
-                "norm.bias": state_dict["image_proj"]["norm.bias"],
-            }
-        )
-
-        image_projection.load_state_dict(image_proj_state_dict)
-
-        self.encoder_hid_proj = image_projection.to(device=self.device, dtype=self.dtype)
-        self.config.encoder_hid_dim_type = "ip_image_proj"
-
     delete_adapter_layers

src/diffusers/models/__init__.py

@@ -14,12 +14,7 @@
 
 from typing import TYPE_CHECKING
 
-from ..utils import (
-    DIFFUSERS_SLOW_IMPORT,
-    _LazyModule,
-    is_flax_available,
-    is_torch_available,
-)
+from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, is_flax_available, is_torch_available
 
 
 _import_structure = {}
@@ -28,7 +23,6 @@ if is_torch_available():
     _import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
     _import_structure["autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
     _import_structure["autoencoder_kl"] = ["AutoencoderKL"]
-    _import_structure["autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
     _import_structure["autoencoder_tiny"] = ["AutoencoderTiny"]
     _import_structure["consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
     _import_structure["controlnet"] = ["ControlNetModel"]
@@ -42,9 +36,7 @@ if is_torch_available():
     _import_structure["unet_2d"] = ["UNet2DModel"]
     _import_structure["unet_2d_condition"] = ["UNet2DConditionModel"]
     _import_structure["unet_3d_condition"] = ["UNet3DConditionModel"]
-    _import_structure["unet_kandi3"] = ["Kandinsky3UNet"]
     _import_structure["unet_motion_model"] = ["MotionAdapter", "UNetMotionModel"]
-    _import_structure["unet_spatio_temporal_condition"] = ["UNetSpatioTemporalConditionModel"]
     _import_structure["vq_model"] = ["VQModel"]
 
 if is_flax_available():
@@ -58,7 +50,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .adapter import MultiAdapter, T2IAdapter
         from .autoencoder_asym_kl import AsymmetricAutoencoderKL
         from .autoencoder_kl import AutoencoderKL
-        from .autoencoder_kl_temporal_decoder import AutoencoderKLTemporalDecoder
         from .autoencoder_tiny import AutoencoderTiny
         from .consistency_decoder_vae import ConsistencyDecoderVAE
         from .controlnet import ControlNetModel
@@ -72,9 +63,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .unet_2d import UNet2DModel
         from .unet_2d_condition import UNet2DConditionModel
         from .unet_3d_condition import UNet3DConditionModel
-        from .unet_kandi3 import Kandinsky3UNet
         from .unet_motion_model import MotionAdapter, UNetMotionModel
-        from .unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel
         from .vq_model import VQModel
 
     if is_flax_available():

src/diffusers/models/attention.py

@@ -25,31 +25,6 @@ from .lora import LoRACompatibleLinear
 from .normalization import AdaLayerNorm, AdaLayerNormZero
 
 
-def _chunked_feed_forward(
-    ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int, lora_scale: Optional[float] = None
-):
-    # "feed_forward_chunk_size" can be used to save memory
-    if hidden_states.shape[chunk_dim] % chunk_size != 0:
-        raise ValueError(
-            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
-        )
-
-    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
-    if lora_scale is None:
-        ff_output = torch.cat(
-            [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
-            dim=chunk_dim,
-        )
-    else:
-        # TOOD(Patrick): LoRA scale can be removed once PEFT refactor is complete
-        ff_output = torch.cat(
-            [ff(hid_slice, scale=lora_scale) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
-            dim=chunk_dim,
-        )
-
-    return ff_output
-
-
 @maybe_allow_in_graph
 class GatedSelfAttentionDense(nn.Module):
     r"""
@@ -219,12 +194,7 @@ class BasicTransformerBlock(nn.Module):
         if not self.use_ada_layer_norm_single:
             self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
 
-        self.ff = FeedForward(
-            dim,
-            dropout=dropout,
-            activation_fn=activation_fn,
-            final_dropout=final_dropout,
-        )
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
 
         # 4. Fuser
         if attention_type == "gated" or attention_type == "gated-text-image":
@@ -238,7 +208,7 @@ class BasicTransformerBlock(nn.Module):
         self._chunk_size = None
         self._chunk_dim = 0
 
-    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
         # Sets chunk feed-forward
         self._chunk_size = chunk_size
         self._chunk_dim = dim
@@ -341,8 +311,18 @@ class BasicTransformerBlock(nn.Module):
 
         if self._chunk_size is not None:
             # "feed_forward_chunk_size" can be used to save memory
-            ff_output = _chunked_feed_forward(
-                self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale
+            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+                raise ValueError(
+                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+                )
+
+            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+            ff_output = torch.cat(
+                [
+                    self.ff(hid_slice, scale=lora_scale)
+                    for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)
+                ],
+                dim=self._chunk_dim,
             )
         else:
             ff_output = self.ff(norm_hidden_states, scale=lora_scale)
@@ -359,137 +339,6 @@ class BasicTransformerBlock(nn.Module):
         return hidden_states
 
 
-@maybe_allow_in_graph
-class TemporalBasicTransformerBlock(nn.Module):
-    r"""
-    A basic Transformer block for video like data.
-
-    Parameters:
-        dim (`int`): The number of channels in the input and output.
-        time_mix_inner_dim (`int`): The number of channels for temporal attention.
-        num_attention_heads (`int`): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`): The number of channels in each head.
-        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        time_mix_inner_dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        cross_attention_dim: Optional[int] = None,
-    ):
-        super().__init__()
-        self.is_res = dim == time_mix_inner_dim
-
-        self.norm_in = nn.LayerNorm(dim)
-
-        # Define 3 blocks. Each block has its own normalization layer.
-        # 1. Self-Attn
-        self.norm_in = nn.LayerNorm(dim)
-        self.ff_in = FeedForward(
-            dim,
-            dim_out=time_mix_inner_dim,
-            activation_fn="geglu",
-        )
-
-        self.norm1 = nn.LayerNorm(time_mix_inner_dim)
-        self.attn1 = Attention(
-            query_dim=time_mix_inner_dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            cross_attention_dim=None,
-        )
-
-        # 2. Cross-Attn
-        if cross_attention_dim is not None:
-            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
-            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
-            # the second cross attention block.
-            self.norm2 = nn.LayerNorm(time_mix_inner_dim)
-            self.attn2 = Attention(
-                query_dim=time_mix_inner_dim,
-                cross_attention_dim=cross_attention_dim,
-                heads=num_attention_heads,
-                dim_head=attention_head_dim,
-            )  # is self-attn if encoder_hidden_states is none
-        else:
-            self.norm2 = None
-            self.attn2 = None
-
-        # 3. Feed-forward
-        self.norm3 = nn.LayerNorm(time_mix_inner_dim)
-        self.ff = FeedForward(time_mix_inner_dim, activation_fn="geglu")
-
-        # let chunk size default to None
-        self._chunk_size = None
-        self._chunk_dim = None
-
-    def set_chunk_feed_forward(self, chunk_size: Optional[int], **kwargs):
-        # Sets chunk feed-forward
-        self._chunk_size = chunk_size
-        # chunk dim should be hardcoded to 1 to have better speed vs. memory trade-off
-        self._chunk_dim = 1
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        num_frames: int,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-    ) -> torch.FloatTensor:
-        # Notice that normalization is always applied before the real computation in the following blocks.
-        # 0. Self-Attention
-        batch_size = hidden_states.shape[0]
-
-        batch_frames, seq_length, channels = hidden_states.shape
-        batch_size = batch_frames // num_frames
-
-        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels)
-        hidden_states = hidden_states.permute(0, 2, 1, 3)
-        hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels)
-
-        residual = hidden_states
-        hidden_states = self.norm_in(hidden_states)
-
-        if self._chunk_size is not None:
-            hidden_states = _chunked_feed_forward(self.ff, hidden_states, self._chunk_dim, self._chunk_size)
-        else:
-            hidden_states = self.ff_in(hidden_states)
-
-        if self.is_res:
-            hidden_states = hidden_states + residual
-
-        norm_hidden_states = self.norm1(hidden_states)
-        attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
-        hidden_states = attn_output + hidden_states
-
-        # 3. Cross-Attention
-        if self.attn2 is not None:
-            norm_hidden_states = self.norm2(hidden_states)
-            attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
-            hidden_states = attn_output + hidden_states
-
-        # 4. Feed-forward
-        norm_hidden_states = self.norm3(hidden_states)
-
-        if self._chunk_size is not None:
-            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
-        else:
-            ff_output = self.ff(norm_hidden_states)
-
-        if self.is_res:
-            hidden_states = ff_output + hidden_states
-        else:
-            hidden_states = ff_output
-
-        hidden_states = hidden_states[None, :].reshape(batch_size, seq_length, num_frames, channels)
-        hidden_states = hidden_states.permute(0, 2, 1, 3)
-        hidden_states = hidden_states.reshape(batch_size * num_frames, seq_length, channels)
-
-        return hidden_states
-
-
 class FeedForward(nn.Module):
     r"""
     A feed-forward layer.

src/diffusers/models/attention_processor.py

@@ -16,7 +16,7 @@ from typing import Callable, Optional, Union
 
 import torch
 import torch.nn.functional as F
-from torch import einsum, nn
+from torch import nn
 
 from ..utils import USE_PEFT_BACKEND, deprecate, logging
 from ..utils.import_utils import is_xformers_available
@@ -1975,288 +1975,6 @@ class LoRAAttnAddedKVProcessor(nn.Module):
         return attn.processor(attn, hidden_states, *args, **kwargs)
 
 
-class IPAdapterAttnProcessor(nn.Module):
-    r"""
-    Attention processor for IP-Adapater.
-
-    Args:
-        hidden_size (`int`):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`):
-            The number of channels in the `encoder_hidden_states`.
-        num_tokens (`int`, defaults to 4):
-            The context length of the image features.
-        scale (`float`, defaults to 1.0):
-            the weight scale of image prompt.
-    """
-
-    def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=4, scale=1.0):
-        super().__init__()
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.num_tokens = num_tokens
-        self.scale = scale
-
-        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-        scale=1.0,
-    ):
-        if scale != 1.0:
-            logger.warning("`scale` of IPAttnProcessor should be set with `set_ip_adapter_scale`.")
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        # split hidden states
-        end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-        encoder_hidden_states, ip_hidden_states = (
-            encoder_hidden_states[:, :end_pos, :],
-            encoder_hidden_states[:, end_pos:, :],
-        )
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        query = attn.head_to_batch_dim(query)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # for ip-adapter
-        ip_key = self.to_k_ip(ip_hidden_states)
-        ip_value = self.to_v_ip(ip_hidden_states)
-
-        ip_key = attn.head_to_batch_dim(ip_key)
-        ip_value = attn.head_to_batch_dim(ip_value)
-
-        ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
-        ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
-        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
-
-        hidden_states = hidden_states + self.scale * ip_hidden_states
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class IPAdapterAttnProcessor2_0(torch.nn.Module):
-    r"""
-    Attention processor for IP-Adapater for PyTorch 2.0.
-
-    Args:
-        hidden_size (`int`):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`):
-            The number of channels in the `encoder_hidden_states`.
-        num_tokens (`int`, defaults to 4):
-            The context length of the image features.
-        scale (`float`, defaults to 1.0):
-            the weight scale of image prompt.
-    """
-
-    def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=4, scale=1.0):
-        super().__init__()
-
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError(
-                f"{self.__class__.__name__} requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
-            )
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.num_tokens = num_tokens
-        self.scale = scale
-
-        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-        scale=1.0,
-    ):
-        if scale != 1.0:
-            logger.warning("`scale` of IPAttnProcessor should be set by `set_ip_adapter_scale`.")
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        # split hidden states
-        end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-        encoder_hidden_states, ip_hidden_states = (
-            encoder_hidden_states[:, :end_pos, :],
-            encoder_hidden_states[:, end_pos:, :],
-        )
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # for ip-adapter
-        ip_key = self.to_k_ip(ip_hidden_states)
-        ip_value = self.to_v_ip(ip_hidden_states)
-
-        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        ip_hidden_states = F.scaled_dot_product_attention(
-            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
-        )
-
-        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        ip_hidden_states = ip_hidden_states.to(query.dtype)
-
-        hidden_states = hidden_states + self.scale * ip_hidden_states
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-# TODO(Yiyi): This class should not exist, we can replace it with a normal attention processor I believe
-# this way torch.compile and co. will work as well
-class Kandi3AttnProcessor:
-    r"""
-    Default kandinsky3 proccesor for performing attention-related computations.
-    """
-
-    @staticmethod
-    def _reshape(hid_states, h):
-        b, n, f = hid_states.shape
-        d = f // h
-        return hid_states.unsqueeze(-1).reshape(b, n, h, d).permute(0, 2, 1, 3)
-
-    def __call__(
-        self,
-        attn,
-        x,
-        context,
-        context_mask=None,
-    ):
-        query = self._reshape(attn.to_q(x), h=attn.num_heads)
-        key = self._reshape(attn.to_k(context), h=attn.num_heads)
-        value = self._reshape(attn.to_v(context), h=attn.num_heads)
-
-        attention_matrix = einsum("b h i d, b h j d -> b h i j", query, key)
-
-        if context_mask is not None:
-            max_neg_value = -torch.finfo(attention_matrix.dtype).max
-            context_mask = context_mask.unsqueeze(1).unsqueeze(1)
-            attention_matrix = attention_matrix.masked_fill(~(context_mask != 0), max_neg_value)
-        attention_matrix = (attention_matrix * attn.scale).softmax(dim=-1)
-
-        out = einsum("b h i j, b h j d -> b h i d", attention_matrix, value)
-        out = out.permute(0, 2, 1, 3).reshape(out.shape[0], out.shape[2], -1)
-        out = attn.to_out[0](out)
-        return out
-
-
 LORA_ATTENTION_PROCESSORS = (
     LoRAAttnProcessor,
     LoRAAttnProcessor2_0,
@@ -2280,9 +1998,6 @@ CROSS_ATTENTION_PROCESSORS = (
     LoRAAttnProcessor,
     LoRAAttnProcessor2_0,
     LoRAXFormersAttnProcessor,
-    IPAdapterAttnProcessor,
-    IPAdapterAttnProcessor2_0,
-    Kandi3AttnProcessor,
 )
 
 AttentionProcessor = Union[

src/diffusers/models/autoencoder_asym_kl.py

@@ -18,7 +18,7 @@ import torch.nn as nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils.accelerate_utils import apply_forward_hook
-from .modeling_outputs import AutoencoderKLOutput
+from .autoencoder_kl import AutoencoderKLOutput
 from .modeling_utils import ModelMixin
 from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
 
@@ -108,9 +108,6 @@ class AsymmetricAutoencoderKL(ModelMixin, ConfigMixin):
         self.use_slicing = False
         self.use_tiling = False
 
-        self.register_to_config(block_out_channels=up_block_out_channels)
-        self.register_to_config(force_upcast=False)
-
     @apply_forward_hook
     def encode(
         self, x: torch.FloatTensor, return_dict: bool = True

src/diffusers/models/autoencoder_kl.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from dataclasses import dataclass
 from typing import Dict, Optional, Tuple, Union
 
 import torch
@@ -18,6 +19,7 @@ import torch.nn as nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..loaders import FromOriginalVAEMixin
+from ..utils import BaseOutput
 from ..utils.accelerate_utils import apply_forward_hook
 from .attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
@@ -26,11 +28,24 @@ from .attention_processor import (
     AttnAddedKVProcessor,
     AttnProcessor,
 )
-from .modeling_outputs import AutoencoderKLOutput
 from .modeling_utils import ModelMixin
 from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
+@dataclass
+class AutoencoderKLOutput(BaseOutput):
+    """
+    Output of AutoencoderKL encoding method.
+
+    Args:
+        latent_dist (`DiagonalGaussianDistribution`):
+            Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
+            `DiagonalGaussianDistribution` allows for sampling latents from the distribution.
+    """
+
+    latent_dist: "DiagonalGaussianDistribution"
+
+
 class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images.

src/diffusers/models/autoencoder_kl_temporal_decoder.py

@@ -1,402 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import Dict, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..loaders import FromOriginalVAEMixin
-from ..utils import is_torch_version
-from ..utils.accelerate_utils import apply_forward_hook
-from .attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor
-from .modeling_outputs import AutoencoderKLOutput
-from .modeling_utils import ModelMixin
-from .unet_3d_blocks import MidBlockTemporalDecoder, UpBlockTemporalDecoder
-from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
-
-
-class TemporalDecoder(nn.Module):
-    def __init__(
-        self,
-        in_channels: int = 4,
-        out_channels: int = 3,
-        block_out_channels: Tuple[int] = (128, 256, 512, 512),
-        layers_per_block: int = 2,
-    ):
-        super().__init__()
-        self.layers_per_block = layers_per_block
-
-        self.conv_in = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1)
-        self.mid_block = MidBlockTemporalDecoder(
-            num_layers=self.layers_per_block,
-            in_channels=block_out_channels[-1],
-            out_channels=block_out_channels[-1],
-            attention_head_dim=block_out_channels[-1],
-        )
-
-        # up
-        self.up_blocks = nn.ModuleList([])
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        output_channel = reversed_block_out_channels[0]
-        for i in range(len(block_out_channels)):
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-
-            is_final_block = i == len(block_out_channels) - 1
-            up_block = UpBlockTemporalDecoder(
-                num_layers=self.layers_per_block + 1,
-                in_channels=prev_output_channel,
-                out_channels=output_channel,
-                add_upsample=not is_final_block,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-6)
-
-        self.conv_act = nn.SiLU()
-        self.conv_out = torch.nn.Conv2d(
-            in_channels=block_out_channels[0],
-            out_channels=out_channels,
-            kernel_size=3,
-            padding=1,
-        )
-
-        conv_out_kernel_size = (3, 1, 1)
-        padding = [int(k // 2) for k in conv_out_kernel_size]
-        self.time_conv_out = torch.nn.Conv3d(
-            in_channels=out_channels,
-            out_channels=out_channels,
-            kernel_size=conv_out_kernel_size,
-            padding=padding,
-        )
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        image_only_indicator: torch.FloatTensor,
-        num_frames: int = 1,
-    ) -> torch.FloatTensor:
-        r"""The forward method of the `Decoder` class."""
-
-        sample = self.conv_in(sample)
-
-        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
-        if self.training and self.gradient_checkpointing:
-
-            def create_custom_forward(module):
-                def custom_forward(*inputs):
-                    return module(*inputs)
-
-                return custom_forward
-
-            if is_torch_version(">=", "1.11.0"):
-                # middle
-                sample = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(self.mid_block),
-                    sample,
-                    image_only_indicator,
-                    use_reentrant=False,
-                )
-                sample = sample.to(upscale_dtype)
-
-                # up
-                for up_block in self.up_blocks:
-                    sample = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(up_block),
-                        sample,
-                        image_only_indicator,
-                        use_reentrant=False,
-                    )
-            else:
-                # middle
-                sample = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(self.mid_block),
-                    sample,
-                    image_only_indicator,
-                )
-                sample = sample.to(upscale_dtype)
-
-                # up
-                for up_block in self.up_blocks:
-                    sample = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(up_block),
-                        sample,
-                        image_only_indicator,
-                    )
-        else:
-            # middle
-            sample = self.mid_block(sample, image_only_indicator=image_only_indicator)
-            sample = sample.to(upscale_dtype)
-
-            # up
-            for up_block in self.up_blocks:
-                sample = up_block(sample, image_only_indicator=image_only_indicator)
-
-        # post-process
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        batch_frames, channels, height, width = sample.shape
-        batch_size = batch_frames // num_frames
-        sample = sample[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
-        sample = self.time_conv_out(sample)
-
-        sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)
-
-        return sample
-
-
-class AutoencoderKLTemporalDecoder(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
-    r"""
-    A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
-    for all models (such as downloading or saving).
-
-    Parameters:
-        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
-        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
-            Tuple of downsample block types.
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
-            Tuple of block output channels.
-        layers_per_block: (`int`, *optional*, defaults to 1): Number of layers per block.
-        latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
-        sample_size (`int`, *optional*, defaults to `32`): Sample input size.
-        scaling_factor (`float`, *optional*, defaults to 0.18215):
-            The component-wise standard deviation of the trained latent space computed using the first batch of the
-            training set. This is used to scale the latent space to have unit variance when training the diffusion
-            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
-            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
-            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
-            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
-        force_upcast (`bool`, *optional*, default to `True`):
-            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
-            can be fine-tuned / trained to a lower range without loosing too much precision in which case
-            `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 3,
-        out_channels: int = 3,
-        down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
-        block_out_channels: Tuple[int] = (64,),
-        layers_per_block: int = 1,
-        latent_channels: int = 4,
-        sample_size: int = 32,
-        scaling_factor: float = 0.18215,
-        force_upcast: float = True,
-    ):
-        super().__init__()
-
-        # pass init params to Encoder
-        self.encoder = Encoder(
-            in_channels=in_channels,
-            out_channels=latent_channels,
-            down_block_types=down_block_types,
-            block_out_channels=block_out_channels,
-            layers_per_block=layers_per_block,
-            double_z=True,
-        )
-
-        # pass init params to Decoder
-        self.decoder = TemporalDecoder(
-            in_channels=latent_channels,
-            out_channels=out_channels,
-            block_out_channels=block_out_channels,
-            layers_per_block=layers_per_block,
-        )
-
-        self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
-
-        sample_size = (
-            self.config.sample_size[0]
-            if isinstance(self.config.sample_size, (list, tuple))
-            else self.config.sample_size
-        )
-        self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
-        self.tile_overlap_factor = 0.25
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (Encoder, TemporalDecoder)):
-            module.gradient_checkpointing = value
-
-    @property
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "get_processor"):
-                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(
-        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
-    ):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor, _remove_lora=_remove_lora)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    def set_default_attn_processor(self):
-        """
-        Disables custom attention processors and sets the default attention implementation.
-        """
-        if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
-            processor = AttnProcessor()
-        else:
-            raise ValueError(
-                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
-            )
-
-        self.set_attn_processor(processor, _remove_lora=True)
-
-    @apply_forward_hook
-    def encode(
-        self, x: torch.FloatTensor, return_dict: bool = True
-    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
-        """
-        Encode a batch of images into latents.
-
-        Args:
-            x (`torch.FloatTensor`): Input batch of images.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
-
-        Returns:
-                The latent representations of the encoded images. If `return_dict` is True, a
-                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
-        """
-        h = self.encoder(x)
-        moments = self.quant_conv(h)
-        posterior = DiagonalGaussianDistribution(moments)
-
-        if not return_dict:
-            return (posterior,)
-
-        return AutoencoderKLOutput(latent_dist=posterior)
-
-    @apply_forward_hook
-    def decode(
-        self,
-        z: torch.FloatTensor,
-        num_frames: int,
-        return_dict: bool = True,
-    ) -> Union[DecoderOutput, torch.FloatTensor]:
-        """
-        Decode a batch of images.
-
-        Args:
-            z (`torch.FloatTensor`): Input batch of latent vectors.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.vae.DecoderOutput`] or `tuple`:
-                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
-                returned.
-
-        """
-        batch_size = z.shape[0] // num_frames
-        image_only_indicator = torch.zeros(batch_size, num_frames, dtype=z.dtype, device=z.device)
-        decoded = self.decoder(z, num_frames=num_frames, image_only_indicator=image_only_indicator)
-
-        if not return_dict:
-            return (decoded,)
-
-        return DecoderOutput(sample=decoded)
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        sample_posterior: bool = False,
-        return_dict: bool = True,
-        generator: Optional[torch.Generator] = None,
-        num_frames: int = 1,
-    ) -> Union[DecoderOutput, torch.FloatTensor]:
-        r"""
-        Args:
-            sample (`torch.FloatTensor`): Input sample.
-            sample_posterior (`bool`, *optional*, defaults to `False`):
-                Whether to sample from the posterior.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
-        """
-        x = sample
-        posterior = self.encode(x).latent_dist
-        if sample_posterior:
-            z = posterior.sample(generator=generator)
-        else:
-            z = posterior.mode()
-
-        dec = self.decode(z, num_frames=num_frames).sample
-
-        if not return_dict:
-            return (dec,)
-
-        return DecoderOutput(sample=dec)

src/diffusers/models/autoencoder_tiny.py

@@ -148,9 +148,6 @@ class AutoencoderTiny(ModelMixin, ConfigMixin):
         self.tile_sample_min_size = 512
         self.tile_latent_min_size = self.tile_sample_min_size // self.spatial_scale_factor
 
-        self.register_to_config(block_out_channels=decoder_block_out_channels)
-        self.register_to_config(force_upcast=False)
-
     def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
         if isinstance(module, (EncoderTiny, DecoderTiny)):
             module.gradient_checkpointing = value

src/diffusers/models/consistency_decoder_vae.py

@@ -138,7 +138,6 @@ class ConsistencyDecoderVAE(ModelMixin, ConfigMixin):
         )
         self.decoder_scheduler = ConsistencyDecoderScheduler()
         self.register_to_config(block_out_channels=encoder_block_out_channels)
-        self.register_to_config(force_upcast=False)
         self.register_buffer(
             "means",
             torch.tensor([0.38862467, 0.02253063, 0.07381133, -0.0171294])[None, :, None, None],

src/diffusers/models/controlnet.py

@@ -30,7 +30,12 @@ from .attention_processor import (
 )
 from .embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
 from .modeling_utils import ModelMixin
-from .unet_2d_blocks import CrossAttnDownBlock2D, DownBlock2D, UNetMidBlock2D, UNetMidBlock2DCrossAttn, get_down_block
+from .unet_2d_blocks import (
+    CrossAttnDownBlock2D,
+    DownBlock2D,
+    UNetMidBlock2DCrossAttn,
+    get_down_block,
+)
 from .unet_2d_condition import UNet2DConditionModel
 
 
@@ -186,7 +191,6 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
             "CrossAttnDownBlock2D",
             "DownBlock2D",
         ),
-        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
         only_cross_attention: Union[bool, Tuple[bool]] = False,
         block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
         layers_per_block: int = 2,
@@ -405,35 +409,20 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
         controlnet_block = zero_module(controlnet_block)
         self.controlnet_mid_block = controlnet_block
 
-        if mid_block_type == "UNetMidBlock2DCrossAttn":
-            self.mid_block = UNetMidBlock2DCrossAttn(
-                transformer_layers_per_block=transformer_layers_per_block[-1],
-                in_channels=mid_block_channel,
-                temb_channels=time_embed_dim,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                cross_attention_dim=cross_attention_dim,
-                num_attention_heads=num_attention_heads[-1],
-                resnet_groups=norm_num_groups,
-                use_linear_projection=use_linear_projection,
-                upcast_attention=upcast_attention,
-            )
-        elif mid_block_type == "UNetMidBlock2D":
-            self.mid_block = UNetMidBlock2D(
-                in_channels=block_out_channels[-1],
-                temb_channels=time_embed_dim,
-                num_layers=0,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                output_scale_factor=mid_block_scale_factor,
-                resnet_groups=norm_num_groups,
-                resnet_time_scale_shift=resnet_time_scale_shift,
-                add_attention=False,
-            )
-        else:
-            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+        self.mid_block = UNetMidBlock2DCrossAttn(
+            transformer_layers_per_block=transformer_layers_per_block[-1],
+            in_channels=mid_block_channel,
+            temb_channels=time_embed_dim,
+            resnet_eps=norm_eps,
+            resnet_act_fn=act_fn,
+            output_scale_factor=mid_block_scale_factor,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads[-1],
+            resnet_groups=norm_num_groups,
+            use_linear_projection=use_linear_projection,
+            upcast_attention=upcast_attention,
+        )
 
     @classmethod
     def from_unet(
@@ -442,7 +431,6 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
         controlnet_conditioning_channel_order: str = "rgb",
         conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
         load_weights_from_unet: bool = True,
-        conditioning_channels: int = 3,
     ):
         r"""
         Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].
@@ -489,10 +477,8 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
             upcast_attention=unet.config.upcast_attention,
             resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
             projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
-            mid_block_type=unet.config.mid_block_type,
             controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
             conditioning_embedding_out_channels=conditioning_embedding_out_channels,
-            conditioning_channels=conditioning_channels,
         )
 
         if load_weights_from_unet:
@@ -811,16 +797,13 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
 
         # 4. mid
         if self.mid_block is not None:
-            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
-                sample = self.mid_block(
-                    sample,
-                    emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                )
-            else:
-                sample = self.mid_block(sample, emb)
+            sample = self.mid_block(
+                sample,
+                emb,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=attention_mask,
+                cross_attention_kwargs=cross_attention_kwargs,
+            )
 
         # 5. Control net blocks
 

src/diffusers/models/lora.py

@@ -12,15 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
-# IMPORTANT:                                                      #
-###################################################################
-# ----------------------------------------------------------------#
-# This file is deprecated and will be removed soon                #
-# (as soon as PEFT will become a required dependency for LoRA)    #
-# ----------------------------------------------------------------#
-###################################################################
-
 from typing import Optional, Tuple, Union
 
 import torch
@@ -66,6 +57,25 @@ def text_encoder_mlp_modules(text_encoder):
     return mlp_modules
 
 
+def text_encoder_lora_state_dict(text_encoder):
+    state_dict = {}
+
+    for name, module in text_encoder_attn_modules(text_encoder):
+        for k, v in module.q_proj.lora_linear_layer.state_dict().items():
+            state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v
+
+        for k, v in module.k_proj.lora_linear_layer.state_dict().items():
+            state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v
+
+        for k, v in module.v_proj.lora_linear_layer.state_dict().items():
+            state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v
+
+        for k, v in module.out_proj.lora_linear_layer.state_dict().items():
+            state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v
+
+    return state_dict
+
+
 def adjust_lora_scale_text_encoder(text_encoder, lora_scale: float = 1.0):
     for _, attn_module in text_encoder_attn_modules(text_encoder):
         if isinstance(attn_module.q_proj, PatchedLoraProjection):

src/diffusers/models/modeling_outputs.py

@@ -1,17 +0,0 @@
-from dataclasses import dataclass
-
-from ..utils import BaseOutput
-
-
-@dataclass
-class AutoencoderKLOutput(BaseOutput):
-    """
-    Output of AutoencoderKL encoding method.
-
-    Args:
-        latent_dist (`DiagonalGaussianDistribution`):
-            Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
-            `DiagonalGaussianDistribution` allows for sampling latents from the distribution.
-    """
-
-    latent_dist: "DiagonalGaussianDistribution"  # noqa: F821

src/diffusers/models/resnet.py

@@ -165,10 +165,7 @@ class Upsample2D(nn.Module):
             self.Conv2d_0 = conv
 
     def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        output_size: Optional[int] = None,
-        scale: float = 1.0,
+        self, hidden_states: torch.FloatTensor, output_size: Optional[int] = None, scale: float = 1.0
     ) -> torch.FloatTensor:
         assert hidden_states.shape[1] == self.channels
 
@@ -382,11 +379,7 @@ class FirUpsample2D(nn.Module):
             weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))
 
             inverse_conv = F.conv_transpose2d(
-                hidden_states,
-                weight,
-                stride=stride,
-                output_padding=output_padding,
-                padding=0,
+                hidden_states, weight, stride=stride, output_padding=output_padding, padding=0
             )
 
             output = upfirdn2d_native(
@@ -537,14 +530,7 @@ class KDownsample2D(nn.Module):
 
     def forward(self, inputs: torch.Tensor) -> torch.Tensor:
         inputs = F.pad(inputs, (self.pad,) * 4, self.pad_mode)
-        weight = inputs.new_zeros(
-            [
-                inputs.shape[1],
-                inputs.shape[1],
-                self.kernel.shape[0],
-                self.kernel.shape[1],
-            ]
-        )
+        weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
         indices = torch.arange(inputs.shape[1], device=inputs.device)
         kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
         weight[indices, indices] = kernel
@@ -567,14 +553,7 @@ class KUpsample2D(nn.Module):
 
     def forward(self, inputs: torch.Tensor) -> torch.Tensor:
         inputs = F.pad(inputs, ((self.pad + 1) // 2,) * 4, self.pad_mode)
-        weight = inputs.new_zeros(
-            [
-                inputs.shape[1],
-                inputs.shape[1],
-                self.kernel.shape[0],
-                self.kernel.shape[1],
-            ]
-        )
+        weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
         indices = torch.arange(inputs.shape[1], device=inputs.device)
         kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
         weight[indices, indices] = kernel
@@ -711,19 +690,11 @@ class ResnetBlock2D(nn.Module):
         self.conv_shortcut = None
         if self.use_in_shortcut:
             self.conv_shortcut = conv_cls(
-                in_channels,
-                conv_2d_out_channels,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                bias=conv_shortcut_bias,
+                in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias
             )
 
     def forward(
-        self,
-        input_tensor: torch.FloatTensor,
-        temb: torch.FloatTensor,
-        scale: float = 1.0,
+        self, input_tensor: torch.FloatTensor, temb: torch.FloatTensor, scale: float = 1.0
     ) -> torch.FloatTensor:
         hidden_states = input_tensor
 
@@ -895,10 +866,7 @@ class ResidualTemporalBlock1D(nn.Module):
 
 
 def upsample_2d(
-    hidden_states: torch.FloatTensor,
-    kernel: Optional[torch.FloatTensor] = None,
-    factor: int = 2,
-    gain: float = 1,
+    hidden_states: torch.FloatTensor, kernel: Optional[torch.FloatTensor] = None, factor: int = 2, gain: float = 1
 ) -> torch.FloatTensor:
     r"""Upsample2D a batch of 2D images with the given filter.
     Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
@@ -942,10 +910,7 @@ def upsample_2d(
 
 
 def downsample_2d(
-    hidden_states: torch.FloatTensor,
-    kernel: Optional[torch.FloatTensor] = None,
-    factor: int = 2,
-    gain: float = 1,
+    hidden_states: torch.FloatTensor, kernel: Optional[torch.FloatTensor] = None, factor: int = 2, gain: float = 1
 ) -> torch.FloatTensor:
     r"""Downsample2D a batch of 2D images with the given filter.
     Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
@@ -981,20 +946,13 @@ def downsample_2d(
     kernel = kernel * gain
     pad_value = kernel.shape[0] - factor
     output = upfirdn2d_native(
-        hidden_states,
-        kernel.to(device=hidden_states.device),
-        down=factor,
-        pad=((pad_value + 1) // 2, pad_value // 2),
+        hidden_states, kernel.to(device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2)
     )
     return output
 
 
 def upfirdn2d_native(
-    tensor: torch.Tensor,
-    kernel: torch.Tensor,
-    up: int = 1,
-    down: int = 1,
-    pad: Tuple[int, int] = (0, 0),
+    tensor: torch.Tensor, kernel: torch.Tensor, up: int = 1, down: int = 1, pad: Tuple[int, int] = (0, 0)
 ) -> torch.Tensor:
     up_x = up_y = up
     down_x = down_y = down
@@ -1050,13 +1008,7 @@ class TemporalConvLayer(nn.Module):
         dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
     """
 
-    def __init__(
-        self,
-        in_dim: int,
-        out_dim: Optional[int] = None,
-        dropout: float = 0.0,
-        norm_num_groups: int = 32,
-    ):
+    def __init__(self, in_dim: int, out_dim: Optional[int] = None, dropout: float = 0.0, norm_num_groups: int = 32):
         super().__init__()
         out_dim = out_dim or in_dim
         self.in_dim = in_dim
@@ -1064,9 +1016,7 @@ class TemporalConvLayer(nn.Module):
 
         # conv layers
         self.conv1 = nn.Sequential(
-            nn.GroupNorm(norm_num_groups, in_dim),
-            nn.SiLU(),
-            nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0)),
+            nn.GroupNorm(norm_num_groups, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0))
         )
         self.conv2 = nn.Sequential(
             nn.GroupNorm(norm_num_groups, out_dim),
@@ -1108,261 +1058,3 @@ class TemporalConvLayer(nn.Module):
             (hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:]
         )
         return hidden_states
-
-
-class TemporalResnetBlock(nn.Module):
-    r"""
-    A Resnet block.
-
-    Parameters:
-        in_channels (`int`): The number of channels in the input.
-        out_channels (`int`, *optional*, default to be `None`):
-            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
-        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
-        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: Optional[int] = None,
-        temb_channels: int = 512,
-        eps: float = 1e-6,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-
-        kernel_size = (3, 1, 1)
-        padding = [k // 2 for k in kernel_size]
-
-        self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=eps, affine=True)
-        self.conv1 = nn.Conv3d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=1,
-            padding=padding,
-        )
-
-        if temb_channels is not None:
-            self.time_emb_proj = nn.Linear(temb_channels, out_channels)
-        else:
-            self.time_emb_proj = None
-
-        self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=eps, affine=True)
-
-        self.dropout = torch.nn.Dropout(0.0)
-        self.conv2 = nn.Conv3d(
-            out_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=1,
-            padding=padding,
-        )
-
-        self.nonlinearity = get_activation("silu")
-
-        self.use_in_shortcut = self.in_channels != out_channels
-
-        self.conv_shortcut = None
-        if self.use_in_shortcut:
-            self.conv_shortcut = nn.Conv3d(
-                in_channels,
-                out_channels,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-            )
-
-    def forward(self, input_tensor: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:
-        hidden_states = input_tensor
-
-        hidden_states = self.norm1(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-        hidden_states = self.conv1(hidden_states)
-
-        if self.time_emb_proj is not None:
-            temb = self.nonlinearity(temb)
-            temb = self.time_emb_proj(temb)[:, :, :, None, None]
-            temb = temb.permute(0, 2, 1, 3, 4)
-            hidden_states = hidden_states + temb
-
-        hidden_states = self.norm2(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.conv2(hidden_states)
-
-        if self.conv_shortcut is not None:
-            input_tensor = self.conv_shortcut(input_tensor)
-
-        output_tensor = input_tensor + hidden_states
-
-        return output_tensor
-
-
-# VideoResBlock
-class SpatioTemporalResBlock(nn.Module):
-    r"""
-    A SpatioTemporal Resnet block.
-
-    Parameters:
-        in_channels (`int`): The number of channels in the input.
-        out_channels (`int`, *optional*, default to be `None`):
-            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
-        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
-        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the spatial resenet.
-        temporal_eps (`float`, *optional*, defaults to `eps`): The epsilon to use for the temporal resnet.
-        merge_factor (`float`, *optional*, defaults to `0.5`): The merge factor to use for the temporal mixing.
-        merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
-            The merge strategy to use for the temporal mixing.
-        switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
-            If `True`, switch the spatial and temporal mixing.
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: Optional[int] = None,
-        temb_channels: int = 512,
-        eps: float = 1e-6,
-        temporal_eps: Optional[float] = None,
-        merge_factor: float = 0.5,
-        merge_strategy="learned_with_images",
-        switch_spatial_to_temporal_mix: bool = False,
-    ):
-        super().__init__()
-
-        self.spatial_res_block = ResnetBlock2D(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            eps=eps,
-        )
-
-        self.temporal_res_block = TemporalResnetBlock(
-            in_channels=out_channels if out_channels is not None else in_channels,
-            out_channels=out_channels if out_channels is not None else in_channels,
-            temb_channels=temb_channels,
-            eps=temporal_eps if temporal_eps is not None else eps,
-        )
-
-        self.time_mixer = AlphaBlender(
-            alpha=merge_factor,
-            merge_strategy=merge_strategy,
-            switch_spatial_to_temporal_mix=switch_spatial_to_temporal_mix,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        image_only_indicator: Optional[torch.Tensor] = None,
-    ):
-        num_frames = image_only_indicator.shape[-1]
-        hidden_states = self.spatial_res_block(hidden_states, temb)
-
-        batch_frames, channels, height, width = hidden_states.shape
-        batch_size = batch_frames // num_frames
-
-        hidden_states_mix = (
-            hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
-        )
-        hidden_states = (
-            hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
-        )
-
-        if temb is not None:
-            temb = temb.reshape(batch_size, num_frames, -1)
-
-        hidden_states = self.temporal_res_block(hidden_states, temb)
-        hidden_states = self.time_mixer(
-            x_spatial=hidden_states_mix,
-            x_temporal=hidden_states,
-            image_only_indicator=image_only_indicator,
-        )
-
-        hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)
-        return hidden_states
-
-
-class AlphaBlender(nn.Module):
-    r"""
-    A module to blend spatial and temporal features.
-
-    Parameters:
-        alpha (`float`): The initial value of the blending factor.
-        merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
-            The merge strategy to use for the temporal mixing.
-        switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
-            If `True`, switch the spatial and temporal mixing.
-    """
-
-    strategies = ["learned", "fixed", "learned_with_images"]
-
-    def __init__(
-        self,
-        alpha: float,
-        merge_strategy: str = "learned_with_images",
-        switch_spatial_to_temporal_mix: bool = False,
-    ):
-        super().__init__()
-        self.merge_strategy = merge_strategy
-        self.switch_spatial_to_temporal_mix = switch_spatial_to_temporal_mix  # For TemporalVAE
-
-        if merge_strategy not in self.strategies:
-            raise ValueError(f"merge_strategy needs to be in {self.strategies}")
-
-        if self.merge_strategy == "fixed":
-            self.register_buffer("mix_factor", torch.Tensor([alpha]))
-        elif self.merge_strategy == "learned" or self.merge_strategy == "learned_with_images":
-            self.register_parameter("mix_factor", torch.nn.Parameter(torch.Tensor([alpha])))
-        else:
-            raise ValueError(f"Unknown merge strategy {self.merge_strategy}")
-
-    def get_alpha(self, image_only_indicator: torch.Tensor, ndims: int) -> torch.Tensor:
-        if self.merge_strategy == "fixed":
-            alpha = self.mix_factor
-
-        elif self.merge_strategy == "learned":
-            alpha = torch.sigmoid(self.mix_factor)
-
-        elif self.merge_strategy == "learned_with_images":
-            if image_only_indicator is None:
-                raise ValueError("Please provide image_only_indicator to use learned_with_images merge strategy")
-
-            alpha = torch.where(
-                image_only_indicator.bool(),
-                torch.ones(1, 1, device=image_only_indicator.device),
-                torch.sigmoid(self.mix_factor)[..., None],
-            )
-
-            # (batch, channel, frames, height, width)
-            if ndims == 5:
-                alpha = alpha[:, None, :, None, None]
-            # (batch*frames, height*width, channels)
-            elif ndims == 3:
-                alpha = alpha.reshape(-1)[:, None, None]
-            else:
-                raise ValueError(f"Unexpected ndims {ndims}. Dimensions should be 3 or 5")
-
-        else:
-            raise NotImplementedError
-
-        return alpha
-
-    def forward(
-        self,
-        x_spatial: torch.Tensor,
-        x_temporal: torch.Tensor,
-        image_only_indicator: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        alpha = self.get_alpha(image_only_indicator, x_spatial.ndim)
-        alpha = alpha.to(x_spatial.dtype)
-
-        if self.switch_spatial_to_temporal_mix:
-            alpha = 1.0 - alpha
-
-        x = alpha * x_spatial + (1.0 - alpha) * x_temporal
-        return x

src/diffusers/models/transformer_2d.py

@@ -20,7 +20,7 @@ from torch import nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..models.embeddings import ImagePositionalEmbeddings
-from ..utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_torch_version
+from ..utils import USE_PEFT_BACKEND, BaseOutput, deprecate
 from .attention import BasicTransformerBlock
 from .embeddings import CaptionProjection, PatchEmbed
 from .lora import LoRACompatibleConv, LoRACompatibleLinear
@@ -70,8 +70,6 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
             Configure if the `TransformerBlocks` attention should contain a bias parameter.
     """
 
-    _supports_gradient_checkpointing = True
-
     @register_to_config
     def __init__(
         self,
@@ -239,10 +237,6 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
 
         self.gradient_checkpointing = False
 
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -366,19 +360,8 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
 
         for block in self.transformer_blocks:
             if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                 hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
+                    block,
                     hidden_states,
                     attention_mask,
                     encoder_hidden_states,
@@ -386,7 +369,7 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
                     timestep,
                     cross_attention_kwargs,
                     class_labels,
-                    **ckpt_kwargs,
+                    use_reentrant=False,
                 )
             else:
                 hidden_states = block(

src/diffusers/models/transformer_temporal.py

@@ -19,10 +19,8 @@ from torch import nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils import BaseOutput
-from .attention import BasicTransformerBlock, TemporalBasicTransformerBlock
-from .embeddings import TimestepEmbedding, Timesteps
+from .attention import BasicTransformerBlock
 from .modeling_utils import ModelMixin
-from .resnet import AlphaBlender
 
 
 @dataclass
@@ -197,183 +195,3 @@ class TransformerTemporalModel(ModelMixin, ConfigMixin):
             return (output,)
 
         return TransformerTemporalModelOutput(sample=output)
-
-
-class TransformerSpatioTemporalModel(nn.Module):
-    """
-    A Transformer model for video-like data.
-
-    Parameters:
-        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
-        in_channels (`int`, *optional*):
-            The number of channels in the input and output (specify if the input is **continuous**).
-        out_channels (`int`, *optional*):
-            The number of channels in the output (specify if the input is **continuous**).
-        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
-        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
-    """
-
-    def __init__(
-        self,
-        num_attention_heads: int = 16,
-        attention_head_dim: int = 88,
-        in_channels: int = 320,
-        out_channels: Optional[int] = None,
-        num_layers: int = 1,
-        cross_attention_dim: Optional[int] = None,
-    ):
-        super().__init__()
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-
-        inner_dim = num_attention_heads * attention_head_dim
-        self.inner_dim = inner_dim
-
-        # 2. Define input layers
-        self.in_channels = in_channels
-        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6)
-        self.proj_in = nn.Linear(in_channels, inner_dim)
-
-        # 3. Define transformers blocks
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    cross_attention_dim=cross_attention_dim,
-                )
-                for d in range(num_layers)
-            ]
-        )
-
-        time_mix_inner_dim = inner_dim
-        self.temporal_transformer_blocks = nn.ModuleList(
-            [
-                TemporalBasicTransformerBlock(
-                    inner_dim,
-                    time_mix_inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    cross_attention_dim=cross_attention_dim,
-                )
-                for _ in range(num_layers)
-            ]
-        )
-
-        time_embed_dim = in_channels * 4
-        self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels)
-        self.time_proj = Timesteps(in_channels, True, 0)
-        self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images")
-
-        # 4. Define output layers
-        self.out_channels = in_channels if out_channels is None else out_channels
-        # TODO: should use out_channels for continuous projections
-        self.proj_out = nn.Linear(inner_dim, in_channels)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        image_only_indicator: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ):
-        """
-        Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
-                Input hidden_states.
-            num_frames (`int`):
-                The number of frames to be processed per batch. This is used to reshape the hidden states.
-            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
-                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
-                self-attention.
-            image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*):
-                A tensor indicating whether the input contains only images. 1 indicates that the input contains only
-                images, 0 indicates that the input contains video frames.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
-                If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
-                returned, otherwise a `tuple` where the first element is the sample tensor.
-        """
-        # 1. Input
-        batch_frames, _, height, width = hidden_states.shape
-        num_frames = image_only_indicator.shape[-1]
-        batch_size = batch_frames // num_frames
-
-        time_context = encoder_hidden_states
-        time_context_first_timestep = time_context[None, :].reshape(
-            batch_size, num_frames, -1, time_context.shape[-1]
-        )[:, 0]
-        time_context = time_context_first_timestep[None, :].broadcast_to(
-            height * width, batch_size, 1, time_context.shape[-1]
-        )
-        time_context = time_context.reshape(height * width * batch_size, 1, time_context.shape[-1])
-
-        residual = hidden_states
-
-        hidden_states = self.norm(hidden_states)
-        inner_dim = hidden_states.shape[1]
-        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim)
-        hidden_states = self.proj_in(hidden_states)
-
-        num_frames_emb = torch.arange(num_frames, device=hidden_states.device)
-        num_frames_emb = num_frames_emb.repeat(batch_size, 1)
-        num_frames_emb = num_frames_emb.reshape(-1)
-        t_emb = self.time_proj(num_frames_emb)
-
-        # `Timesteps` does not contain any weights and will always return f32 tensors
-        # but time_embedding might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        t_emb = t_emb.to(dtype=hidden_states.dtype)
-
-        emb = self.time_pos_embed(t_emb)
-        emb = emb[:, None, :]
-
-        # 2. Blocks
-        for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):
-            if self.training and self.gradient_checkpointing:
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    block,
-                    hidden_states,
-                    None,
-                    encoder_hidden_states,
-                    None,
-                    use_reentrant=False,
-                )
-            else:
-                hidden_states = block(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                )
-
-            hidden_states_mix = hidden_states
-            hidden_states_mix = hidden_states_mix + emb
-
-            hidden_states_mix = temporal_block(
-                hidden_states_mix,
-                num_frames=num_frames,
-                encoder_hidden_states=time_context,
-            )
-            hidden_states = self.time_mixer(
-                x_spatial=hidden_states,
-                x_temporal=hidden_states_mix,
-                image_only_indicator=image_only_indicator,
-            )
-
-        # 3. Output
-        hidden_states = self.proj_out(hidden_states)
-        hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
-
-        output = hidden_states + residual
-
-        if not return_dict:
-            return (output,)
-
-        return TransformerTemporalModelOutput(sample=output)

src/diffusers/models/unet_2d_condition.py

@@ -1022,15 +1022,6 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
                 )
             image_embeds = added_cond_kwargs.get("image_embeds")
             encoder_hidden_states = self.encoder_hid_proj(image_embeds)
-        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype)
-            encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1)
-
         # 2. pre-process
         sample = self.conv_in(sample)
 

src/diffusers/models/unet_3d_blocks.py

@@ -19,20 +19,10 @@ from torch import nn
 
 from ..utils import is_torch_version
 from ..utils.torch_utils import apply_freeu
-from .attention import Attention
 from .dual_transformer_2d import DualTransformer2DModel
-from .resnet import (
-    Downsample2D,
-    ResnetBlock2D,
-    SpatioTemporalResBlock,
-    TemporalConvLayer,
-    Upsample2D,
-)
+from .resnet import Downsample2D, ResnetBlock2D, TemporalConvLayer, Upsample2D
 from .transformer_2d import Transformer2DModel
-from .transformer_temporal import (
-    TransformerSpatioTemporalModel,
-    TransformerTemporalModel,
-)
+from .transformer_temporal import TransformerTemporalModel
 
 
 def get_down_block(
@@ -55,15 +45,7 @@ def get_down_block(
     resnet_time_scale_shift: str = "default",
     temporal_num_attention_heads: int = 8,
     temporal_max_seq_length: int = 32,
-    transformer_layers_per_block: int = 1,
-) -> Union[
-    "DownBlock3D",
-    "CrossAttnDownBlock3D",
-    "DownBlockMotion",
-    "CrossAttnDownBlockMotion",
-    "DownBlockSpatioTemporal",
-    "CrossAttnDownBlockSpatioTemporal",
-]:
+) -> Union["DownBlock3D", "CrossAttnDownBlock3D", "DownBlockMotion", "CrossAttnDownBlockMotion"]:
     if down_block_type == "DownBlock3D":
         return DownBlock3D(
             num_layers=num_layers,
@@ -136,29 +118,6 @@ def get_down_block(
             temporal_num_attention_heads=temporal_num_attention_heads,
             temporal_max_seq_length=temporal_max_seq_length,
         )
-    elif down_block_type == "DownBlockSpatioTemporal":
-        # added for SDV
-        return DownBlockSpatioTemporal(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-        )
-    elif down_block_type == "CrossAttnDownBlockSpatioTemporal":
-        # added for SDV
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockSpatioTemporal")
-        return CrossAttnDownBlockSpatioTemporal(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            num_layers=num_layers,
-            transformer_layers_per_block=transformer_layers_per_block,
-            add_downsample=add_downsample,
-            cross_attention_dim=cross_attention_dim,
-            num_attention_heads=num_attention_heads,
-        )
 
     raise ValueError(f"{down_block_type} does not exist.")
 
@@ -185,16 +144,7 @@ def get_up_block(
     temporal_num_attention_heads: int = 8,
     temporal_cross_attention_dim: Optional[int] = None,
     temporal_max_seq_length: int = 32,
-    transformer_layers_per_block: int = 1,
-    dropout: float = 0.0,
-) -> Union[
-    "UpBlock3D",
-    "CrossAttnUpBlock3D",
-    "UpBlockMotion",
-    "CrossAttnUpBlockMotion",
-    "UpBlockSpatioTemporal",
-    "CrossAttnUpBlockSpatioTemporal",
-]:
+) -> Union["UpBlock3D", "CrossAttnUpBlock3D", "UpBlockMotion", "CrossAttnUpBlockMotion"]:
     if up_block_type == "UpBlock3D":
         return UpBlock3D(
             num_layers=num_layers,
@@ -271,34 +221,6 @@ def get_up_block(
             temporal_num_attention_heads=temporal_num_attention_heads,
             temporal_max_seq_length=temporal_max_seq_length,
         )
-    elif up_block_type == "UpBlockSpatioTemporal":
-        # added for SDV
-        return UpBlockSpatioTemporal(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            resolution_idx=resolution_idx,
-            add_upsample=add_upsample,
-        )
-    elif up_block_type == "CrossAttnUpBlockSpatioTemporal":
-        # added for SDV
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockSpatioTemporal")
-        return CrossAttnUpBlockSpatioTemporal(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            num_layers=num_layers,
-            transformer_layers_per_block=transformer_layers_per_block,
-            add_upsample=add_upsample,
-            cross_attention_dim=cross_attention_dim,
-            num_attention_heads=num_attention_heads,
-            resolution_idx=resolution_idx,
-        )
-
     raise ValueError(f"{up_block_type} does not exist.")
 
 
@@ -425,10 +347,7 @@ class UNetMidBlock3DCrossAttn(nn.Module):
                 return_dict=False,
             )[0]
             hidden_states = temp_attn(
-                hidden_states,
-                num_frames=num_frames,
-                cross_attention_kwargs=cross_attention_kwargs,
-                return_dict=False,
+                hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, return_dict=False
             )[0]
             hidden_states = resnet(hidden_states, temb)
             hidden_states = temp_conv(hidden_states, num_frames=num_frames)
@@ -524,11 +443,7 @@ class CrossAttnDownBlock3D(nn.Module):
             self.downsamplers = nn.ModuleList(
                 [
                     Downsample2D(
-                        out_channels,
-                        use_conv=True,
-                        out_channels=out_channels,
-                        padding=downsample_padding,
-                        name="op",
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
                     )
                 ]
             )
@@ -561,10 +476,7 @@ class CrossAttnDownBlock3D(nn.Module):
                 return_dict=False,
             )[0]
             hidden_states = temp_attn(
-                hidden_states,
-                num_frames=num_frames,
-                cross_attention_kwargs=cross_attention_kwargs,
-                return_dict=False,
+                hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, return_dict=False
             )[0]
 
             output_states += (hidden_states,)
@@ -631,11 +543,7 @@ class DownBlock3D(nn.Module):
             self.downsamplers = nn.ModuleList(
                 [
                     Downsample2D(
-                        out_channels,
-                        use_conv=True,
-                        out_channels=out_channels,
-                        padding=downsample_padding,
-                        name="op",
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
                     )
                 ]
             )
@@ -645,10 +553,7 @@ class DownBlock3D(nn.Module):
         self.gradient_checkpointing = False
 
     def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        num_frames: int = 1,
+        self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, num_frames: int = 1
     ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
         output_states = ()
 
@@ -811,10 +716,7 @@ class CrossAttnUpBlock3D(nn.Module):
                 return_dict=False,
             )[0]
             hidden_states = temp_attn(
-                hidden_states,
-                num_frames=num_frames,
-                cross_attention_kwargs=cross_attention_kwargs,
-                return_dict=False,
+                hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, return_dict=False
             )[0]
 
         if self.upsamplers is not None:
@@ -988,11 +890,7 @@ class DownBlockMotion(nn.Module):
             self.downsamplers = nn.ModuleList(
                 [
                     Downsample2D(
-                        out_channels,
-                        use_conv=True,
-                        out_channels=out_channels,
-                        padding=downsample_padding,
-                        name="op",
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
                     )
                 ]
             )
@@ -1022,20 +920,14 @@ class DownBlockMotion(nn.Module):
 
                 if is_torch_version(">=", "1.11.0"):
                     hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet),
-                        hidden_states,
-                        temb,
-                        use_reentrant=False,
+                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
                     )
                 else:
                     hidden_states = torch.utils.checkpoint.checkpoint(
                         create_custom_forward(resnet), hidden_states, temb, scale
                     )
                 hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(motion_module),
-                    hidden_states.requires_grad_(),
-                    temb,
-                    num_frames,
+                    create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, num_frames
                 )
 
             else:
@@ -1155,11 +1047,7 @@ class CrossAttnDownBlockMotion(nn.Module):
             self.downsamplers = nn.ModuleList(
                 [
                     Downsample2D(
-                        out_channels,
-                        use_conv=True,
-                        out_channels=out_channels,
-                        padding=downsample_padding,
-                        name="op",
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
                     )
                 ]
             )
@@ -1554,10 +1442,7 @@ class UpBlockMotion(nn.Module):
 
                 if is_torch_version(">=", "1.11.0"):
                     hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet),
-                        hidden_states,
-                        temb,
-                        use_reentrant=False,
+                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
                     )
                 else:
                     hidden_states = torch.utils.checkpoint.checkpoint(
@@ -1751,645 +1636,3 @@ class UNetMidBlockCrossAttnMotion(nn.Module):
                 hidden_states = resnet(hidden_states, temb, scale=lora_scale)
 
         return hidden_states
-
-
-class MidBlockTemporalDecoder(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        attention_head_dim: int = 512,
-        num_layers: int = 1,
-        upcast_attention: bool = False,
-    ):
-        super().__init__()
-
-        resnets = []
-        attentions = []
-        for i in range(num_layers):
-            input_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                SpatioTemporalResBlock(
-                    in_channels=input_channels,
-                    out_channels=out_channels,
-                    temb_channels=None,
-                    eps=1e-6,
-                    temporal_eps=1e-5,
-                    merge_factor=0.0,
-                    merge_strategy="learned",
-                    switch_spatial_to_temporal_mix=True,
-                )
-            )
-
-        attentions.append(
-            Attention(
-                query_dim=in_channels,
-                heads=in_channels // attention_head_dim,
-                dim_head=attention_head_dim,
-                eps=1e-6,
-                upcast_attention=upcast_attention,
-                norm_num_groups=32,
-                bias=True,
-                residual_connection=True,
-            )
-        )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        image_only_indicator: torch.FloatTensor,
-    ):
-        hidden_states = self.resnets[0](
-            hidden_states,
-            image_only_indicator=image_only_indicator,
-        )
-        for resnet, attn in zip(self.resnets[1:], self.attentions):
-            hidden_states = attn(hidden_states)
-            hidden_states = resnet(
-                hidden_states,
-                image_only_indicator=image_only_indicator,
-            )
-
-        return hidden_states
-
-
-class UpBlockTemporalDecoder(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        num_layers: int = 1,
-        add_upsample: bool = True,
-    ):
-        super().__init__()
-        resnets = []
-        for i in range(num_layers):
-            input_channels = in_channels if i == 0 else out_channels
-
-            resnets.append(
-                SpatioTemporalResBlock(
-                    in_channels=input_channels,
-                    out_channels=out_channels,
-                    temb_channels=None,
-                    eps=1e-6,
-                    temporal_eps=1e-5,
-                    merge_factor=0.0,
-                    merge_strategy="learned",
-                    switch_spatial_to_temporal_mix=True,
-                )
-            )
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        image_only_indicator: torch.FloatTensor,
-    ) -> torch.FloatTensor:
-        for resnet in self.resnets:
-            hidden_states = resnet(
-                hidden_states,
-                image_only_indicator=image_only_indicator,
-            )
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class UNetMidBlockSpatioTemporal(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: int,
-        num_layers: int = 1,
-        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
-        num_attention_heads: int = 1,
-        cross_attention_dim: int = 1280,
-    ):
-        super().__init__()
-
-        self.has_cross_attention = True
-        self.num_attention_heads = num_attention_heads
-
-        # support for variable transformer layers per block
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
-
-        # there is always at least one resnet
-        resnets = [
-            SpatioTemporalResBlock(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=1e-5,
-            )
-        ]
-        attentions = []
-
-        for i in range(num_layers):
-            attentions.append(
-                TransformerSpatioTemporalModel(
-                    num_attention_heads,
-                    in_channels // num_attention_heads,
-                    in_channels=in_channels,
-                    num_layers=transformer_layers_per_block[i],
-                    cross_attention_dim=cross_attention_dim,
-                )
-            )
-
-            resnets.append(
-                SpatioTemporalResBlock(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=1e-5,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        image_only_indicator: Optional[torch.Tensor] = None,
-    ) -> torch.FloatTensor:
-        hidden_states = self.resnets[0](
-            hidden_states,
-            temb,
-            image_only_indicator=image_only_indicator,
-        )
-
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if self.training and self.gradient_checkpointing:  # TODO
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    image_only_indicator=image_only_indicator,
-                    return_dict=False,
-                )[0]
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    image_only_indicator,
-                    **ckpt_kwargs,
-                )
-            else:
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    image_only_indicator=image_only_indicator,
-                    return_dict=False,
-                )[0]
-                hidden_states = resnet(
-                    hidden_states,
-                    temb,
-                    image_only_indicator=image_only_indicator,
-                )
-
-        return hidden_states
-
-
-class DownBlockSpatioTemporal(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        num_layers: int = 1,
-        add_downsample: bool = True,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                SpatioTemporalResBlock(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=1e-5,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels,
-                        use_conv=True,
-                        out_channels=out_channels,
-                        name="op",
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        image_only_indicator: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
-        output_states = ()
-        for resnet in self.resnets:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet),
-                        hidden_states,
-                        temb,
-                        image_only_indicator,
-                        use_reentrant=False,
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet),
-                        hidden_states,
-                        temb,
-                        image_only_indicator,
-                    )
-            else:
-                hidden_states = resnet(
-                    hidden_states,
-                    temb,
-                    image_only_indicator=image_only_indicator,
-                )
-
-            output_states = output_states + (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states = output_states + (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class CrossAttnDownBlockSpatioTemporal(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        temb_channels: int,
-        num_layers: int = 1,
-        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
-        num_attention_heads: int = 1,
-        cross_attention_dim: int = 1280,
-        add_downsample: bool = True,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.num_attention_heads = num_attention_heads
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                SpatioTemporalResBlock(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=1e-6,
-                )
-            )
-            attentions.append(
-                TransformerSpatioTemporalModel(
-                    num_attention_heads,
-                    out_channels // num_attention_heads,
-                    in_channels=out_channels,
-                    num_layers=transformer_layers_per_block[i],
-                    cross_attention_dim=cross_attention_dim,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_downsample:
-            self.downsamplers = nn.ModuleList(
-                [
-                    Downsample2D(
-                        out_channels,
-                        use_conv=True,
-                        out_channels=out_channels,
-                        padding=1,
-                        name="op",
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        image_only_indicator: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
-        output_states = ()
-
-        blocks = list(zip(self.resnets, self.attentions))
-        for resnet, attn in blocks:
-            if self.training and self.gradient_checkpointing:  # TODO
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    image_only_indicator,
-                    **ckpt_kwargs,
-                )
-
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    image_only_indicator=image_only_indicator,
-                    return_dict=False,
-                )[0]
-            else:
-                hidden_states = resnet(
-                    hidden_states,
-                    temb,
-                    image_only_indicator=image_only_indicator,
-                )
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    image_only_indicator=image_only_indicator,
-                    return_dict=False,
-                )[0]
-
-            output_states = output_states + (hidden_states,)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states)
-
-            output_states = output_states + (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class UpBlockSpatioTemporal(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        prev_output_channel: int,
-        out_channels: int,
-        temb_channels: int,
-        resolution_idx: Optional[int] = None,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        add_upsample: bool = True,
-    ):
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                SpatioTemporalResBlock(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        image_only_indicator: Optional[torch.Tensor] = None,
-    ) -> torch.FloatTensor:
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                if is_torch_version(">=", "1.11.0"):
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet),
-                        hidden_states,
-                        temb,
-                        image_only_indicator,
-                        use_reentrant=False,
-                    )
-                else:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(resnet),
-                        hidden_states,
-                        temb,
-                        image_only_indicator,
-                    )
-            else:
-                hidden_states = resnet(
-                    hidden_states,
-                    temb,
-                    image_only_indicator=image_only_indicator,
-                )
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states
-
-
-class CrossAttnUpBlockSpatioTemporal(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        prev_output_channel: int,
-        temb_channels: int,
-        resolution_idx: Optional[int] = None,
-        num_layers: int = 1,
-        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
-        resnet_eps: float = 1e-6,
-        num_attention_heads: int = 1,
-        cross_attention_dim: int = 1280,
-        add_upsample: bool = True,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-
-        self.has_cross_attention = True
-        self.num_attention_heads = num_attention_heads
-
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
-
-        for i in range(num_layers):
-            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
-            resnet_in_channels = prev_output_channel if i == 0 else out_channels
-
-            resnets.append(
-                SpatioTemporalResBlock(
-                    in_channels=resnet_in_channels + res_skip_channels,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                )
-            )
-            attentions.append(
-                TransformerSpatioTemporalModel(
-                    num_attention_heads,
-                    out_channels // num_attention_heads,
-                    in_channels=out_channels,
-                    num_layers=transformer_layers_per_block[i],
-                    cross_attention_dim=cross_attention_dim,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-        self.resolution_idx = resolution_idx
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
-        temb: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        image_only_indicator: Optional[torch.Tensor] = None,
-    ) -> torch.FloatTensor:
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-
-            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
-
-            if self.training and self.gradient_checkpointing:  # TODO
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet),
-                    hidden_states,
-                    temb,
-                    image_only_indicator,
-                    **ckpt_kwargs,
-                )
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    image_only_indicator=image_only_indicator,
-                    return_dict=False,
-                )[0]
-            else:
-                hidden_states = resnet(
-                    hidden_states,
-                    temb,
-                    image_only_indicator=image_only_indicator,
-                )
-                hidden_states = attn(
-                    hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    image_only_indicator=image_only_indicator,
-                    return_dict=False,
-                )[0]
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
-
-        return hidden_states

src/diffusers/models/unet_3d_condition.py

@@ -12,7 +12,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Tuple, Union
 
@@ -23,7 +22,6 @@ import torch.utils.checkpoint
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..loaders import UNet2DConditionLoadersMixin
 from ..utils import BaseOutput, logging
-from .activations import get_activation
 from .attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
     CROSS_ATTENTION_PROCESSORS,
@@ -273,7 +271,7 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
             self.conv_norm_out = nn.GroupNorm(
                 num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
             )
-            self.conv_act = get_activation("silu")
+            self.conv_act = nn.SiLU()
         else:
             self.conv_norm_out = None
             self.conv_act = None

src/diffusers/models/unet_kandi3.py

@@ -1,589 +0,0 @@
-import math
-from dataclasses import dataclass
-from typing import Dict, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch import nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, logging
-from .attention_processor import AttentionProcessor, Kandi3AttnProcessor
-from .embeddings import TimestepEmbedding
-from .modeling_utils import ModelMixin
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@dataclass
-class Kandinsky3UNetOutput(BaseOutput):
-    sample: torch.FloatTensor = None
-
-
-# TODO(Yiyi): This class needs to be removed
-def set_default_item(condition, item_1, item_2=None):
-    if condition:
-        return item_1
-    else:
-        return item_2
-
-
-# TODO(Yiyi): This class needs to be removed
-def set_default_layer(condition, layer_1, args_1=[], kwargs_1={}, layer_2=torch.nn.Identity, args_2=[], kwargs_2={}):
-    if condition:
-        return layer_1(*args_1, **kwargs_1)
-    else:
-        return layer_2(*args_2, **kwargs_2)
-
-
-# TODO(Yiyi): This class should be removed and be replaced by Timesteps
-class SinusoidalPosEmb(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, x, type_tensor=None):
-        half_dim = self.dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, device=x.device) * -emb)
-        emb = x[:, None] * emb[None, :]
-        return torch.cat((emb.sin(), emb.cos()), dim=-1)
-
-
-class Kandinsky3EncoderProj(nn.Module):
-    def __init__(self, encoder_hid_dim, cross_attention_dim):
-        super().__init__()
-        self.projection_linear = nn.Linear(encoder_hid_dim, cross_attention_dim, bias=False)
-        self.projection_norm = nn.LayerNorm(cross_attention_dim)
-
-    def forward(self, x):
-        x = self.projection_linear(x)
-        x = self.projection_norm(x)
-        return x
-
-
-class Kandinsky3UNet(ModelMixin, ConfigMixin):
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 4,
-        time_embedding_dim: int = 1536,
-        groups: int = 32,
-        attention_head_dim: int = 64,
-        layers_per_block: Union[int, Tuple[int]] = 3,
-        block_out_channels: Tuple[int] = (384, 768, 1536, 3072),
-        cross_attention_dim: Union[int, Tuple[int]] = 4096,
-        encoder_hid_dim: int = 4096,
-    ):
-        super().__init__()
-
-        # TOOD(Yiyi): Give better name and put into config for the following 4 parameters
-        expansion_ratio = 4
-        compression_ratio = 2
-        add_cross_attention = (False, True, True, True)
-        add_self_attention = (False, True, True, True)
-
-        out_channels = in_channels
-        init_channels = block_out_channels[0] // 2
-        # TODO(Yiyi): Should be replaced with Timesteps class -> make sure that results are the same
-        # self.time_proj = Timesteps(init_channels, flip_sin_to_cos=False, downscale_freq_shift=1)
-        self.time_proj = SinusoidalPosEmb(init_channels)
-
-        self.time_embedding = TimestepEmbedding(
-            init_channels,
-            time_embedding_dim,
-        )
-
-        self.add_time_condition = Kandinsky3AttentionPooling(
-            time_embedding_dim, cross_attention_dim, attention_head_dim
-        )
-
-        self.conv_in = nn.Conv2d(in_channels, init_channels, kernel_size=3, padding=1)
-
-        self.encoder_hid_proj = Kandinsky3EncoderProj(encoder_hid_dim, cross_attention_dim)
-
-        hidden_dims = [init_channels] + list(block_out_channels)
-        in_out_dims = list(zip(hidden_dims[:-1], hidden_dims[1:]))
-        text_dims = [set_default_item(is_exist, cross_attention_dim) for is_exist in add_cross_attention]
-        num_blocks = len(block_out_channels) * [layers_per_block]
-        layer_params = [num_blocks, text_dims, add_self_attention]
-        rev_layer_params = map(reversed, layer_params)
-
-        cat_dims = []
-        self.num_levels = len(in_out_dims)
-        self.down_blocks = nn.ModuleList([])
-        for level, ((in_dim, out_dim), res_block_num, text_dim, self_attention) in enumerate(
-            zip(in_out_dims, *layer_params)
-        ):
-            down_sample = level != (self.num_levels - 1)
-            cat_dims.append(set_default_item(level != (self.num_levels - 1), out_dim, 0))
-            self.down_blocks.append(
-                Kandinsky3DownSampleBlock(
-                    in_dim,
-                    out_dim,
-                    time_embedding_dim,
-                    text_dim,
-                    res_block_num,
-                    groups,
-                    attention_head_dim,
-                    expansion_ratio,
-                    compression_ratio,
-                    down_sample,
-                    self_attention,
-                )
-            )
-
-        self.up_blocks = nn.ModuleList([])
-        for level, ((out_dim, in_dim), res_block_num, text_dim, self_attention) in enumerate(
-            zip(reversed(in_out_dims), *rev_layer_params)
-        ):
-            up_sample = level != 0
-            self.up_blocks.append(
-                Kandinsky3UpSampleBlock(
-                    in_dim,
-                    cat_dims.pop(),
-                    out_dim,
-                    time_embedding_dim,
-                    text_dim,
-                    res_block_num,
-                    groups,
-                    attention_head_dim,
-                    expansion_ratio,
-                    compression_ratio,
-                    up_sample,
-                    self_attention,
-                )
-            )
-
-        self.conv_norm_out = nn.GroupNorm(groups, init_channels)
-        self.conv_act_out = nn.SiLU()
-        self.conv_out = nn.Conv2d(init_channels, out_channels, kernel_size=3, padding=1)
-
-    @property
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "set_processor"):
-                processors[f"{name}.processor"] = module.processor
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    def set_default_attn_processor(self):
-        """
-        Disables custom attention processors and sets the default attention implementation.
-        """
-        self.set_attn_processor(Kandi3AttnProcessor())
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    def forward(self, sample, timestep, encoder_hidden_states=None, encoder_attention_mask=None, return_dict=True):
-        # TODO(Yiyi): Clean up the following variables - these names should not be used
-        # but instead only the ones that we pass to forward
-        x = sample
-        context_mask = encoder_attention_mask
-        context = encoder_hidden_states
-
-        if not torch.is_tensor(timestep):
-            dtype = torch.float32 if isinstance(timestep, float) else torch.int32
-            timestep = torch.tensor([timestep], dtype=dtype, device=sample.device)
-        elif len(timestep.shape) == 0:
-            timestep = timestep[None].to(sample.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timestep = timestep.expand(sample.shape[0])
-        time_embed_input = self.time_proj(timestep).to(x.dtype)
-        time_embed = self.time_embedding(time_embed_input)
-
-        context = self.encoder_hid_proj(context)
-
-        if context is not None:
-            time_embed = self.add_time_condition(time_embed, context, context_mask)
-
-        hidden_states = []
-        x = self.conv_in(x)
-        for level, down_sample in enumerate(self.down_blocks):
-            x = down_sample(x, time_embed, context, context_mask)
-            if level != self.num_levels - 1:
-                hidden_states.append(x)
-
-        for level, up_sample in enumerate(self.up_blocks):
-            if level != 0:
-                x = torch.cat([x, hidden_states.pop()], dim=1)
-            x = up_sample(x, time_embed, context, context_mask)
-
-        x = self.conv_norm_out(x)
-        x = self.conv_act_out(x)
-        x = self.conv_out(x)
-
-        if not return_dict:
-            return (x,)
-        return Kandinsky3UNetOutput(sample=x)
-
-
-class Kandinsky3UpSampleBlock(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        cat_dim,
-        out_channels,
-        time_embed_dim,
-        context_dim=None,
-        num_blocks=3,
-        groups=32,
-        head_dim=64,
-        expansion_ratio=4,
-        compression_ratio=2,
-        up_sample=True,
-        self_attention=True,
-    ):
-        super().__init__()
-        up_resolutions = [[None, set_default_item(up_sample, True), None, None]] + [[None] * 4] * (num_blocks - 1)
-        hidden_channels = (
-            [(in_channels + cat_dim, in_channels)]
-            + [(in_channels, in_channels)] * (num_blocks - 2)
-            + [(in_channels, out_channels)]
-        )
-        attentions = []
-        resnets_in = []
-        resnets_out = []
-
-        self.self_attention = self_attention
-        self.context_dim = context_dim
-
-        attentions.append(
-            set_default_layer(
-                self_attention,
-                Kandinsky3AttentionBlock,
-                (out_channels, time_embed_dim, None, groups, head_dim, expansion_ratio),
-                layer_2=nn.Identity,
-            )
-        )
-
-        for (in_channel, out_channel), up_resolution in zip(hidden_channels, up_resolutions):
-            resnets_in.append(
-                Kandinsky3ResNetBlock(in_channel, in_channel, time_embed_dim, groups, compression_ratio, up_resolution)
-            )
-            attentions.append(
-                set_default_layer(
-                    context_dim is not None,
-                    Kandinsky3AttentionBlock,
-                    (in_channel, time_embed_dim, context_dim, groups, head_dim, expansion_ratio),
-                    layer_2=nn.Identity,
-                )
-            )
-            resnets_out.append(
-                Kandinsky3ResNetBlock(in_channel, out_channel, time_embed_dim, groups, compression_ratio)
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets_in = nn.ModuleList(resnets_in)
-        self.resnets_out = nn.ModuleList(resnets_out)
-
-    def forward(self, x, time_embed, context=None, context_mask=None, image_mask=None):
-        for attention, resnet_in, resnet_out in zip(self.attentions[1:], self.resnets_in, self.resnets_out):
-            x = resnet_in(x, time_embed)
-            if self.context_dim is not None:
-                x = attention(x, time_embed, context, context_mask, image_mask)
-            x = resnet_out(x, time_embed)
-
-        if self.self_attention:
-            x = self.attentions[0](x, time_embed, image_mask=image_mask)
-        return x
-
-
-class Kandinsky3DownSampleBlock(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        time_embed_dim,
-        context_dim=None,
-        num_blocks=3,
-        groups=32,
-        head_dim=64,
-        expansion_ratio=4,
-        compression_ratio=2,
-        down_sample=True,
-        self_attention=True,
-    ):
-        super().__init__()
-        attentions = []
-        resnets_in = []
-        resnets_out = []
-
-        self.self_attention = self_attention
-        self.context_dim = context_dim
-
-        attentions.append(
-            set_default_layer(
-                self_attention,
-                Kandinsky3AttentionBlock,
-                (in_channels, time_embed_dim, None, groups, head_dim, expansion_ratio),
-                layer_2=nn.Identity,
-            )
-        )
-
-        up_resolutions = [[None] * 4] * (num_blocks - 1) + [[None, None, set_default_item(down_sample, False), None]]
-        hidden_channels = [(in_channels, out_channels)] + [(out_channels, out_channels)] * (num_blocks - 1)
-        for (in_channel, out_channel), up_resolution in zip(hidden_channels, up_resolutions):
-            resnets_in.append(
-                Kandinsky3ResNetBlock(in_channel, out_channel, time_embed_dim, groups, compression_ratio)
-            )
-            attentions.append(
-                set_default_layer(
-                    context_dim is not None,
-                    Kandinsky3AttentionBlock,
-                    (out_channel, time_embed_dim, context_dim, groups, head_dim, expansion_ratio),
-                    layer_2=nn.Identity,
-                )
-            )
-            resnets_out.append(
-                Kandinsky3ResNetBlock(
-                    out_channel, out_channel, time_embed_dim, groups, compression_ratio, up_resolution
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets_in = nn.ModuleList(resnets_in)
-        self.resnets_out = nn.ModuleList(resnets_out)
-
-    def forward(self, x, time_embed, context=None, context_mask=None, image_mask=None):
-        if self.self_attention:
-            x = self.attentions[0](x, time_embed, image_mask=image_mask)
-
-        for attention, resnet_in, resnet_out in zip(self.attentions[1:], self.resnets_in, self.resnets_out):
-            x = resnet_in(x, time_embed)
-            if self.context_dim is not None:
-                x = attention(x, time_embed, context, context_mask, image_mask)
-            x = resnet_out(x, time_embed)
-        return x
-
-
-class Kandinsky3ConditionalGroupNorm(nn.Module):
-    def __init__(self, groups, normalized_shape, context_dim):
-        super().__init__()
-        self.norm = nn.GroupNorm(groups, normalized_shape, affine=False)
-        self.context_mlp = nn.Sequential(nn.SiLU(), nn.Linear(context_dim, 2 * normalized_shape))
-        self.context_mlp[1].weight.data.zero_()
-        self.context_mlp[1].bias.data.zero_()
-
-    def forward(self, x, context):
-        context = self.context_mlp(context)
-
-        for _ in range(len(x.shape[2:])):
-            context = context.unsqueeze(-1)
-
-        scale, shift = context.chunk(2, dim=1)
-        x = self.norm(x) * (scale + 1.0) + shift
-        return x
-
-
-# TODO(Yiyi): This class should ideally not even exist, it slows everything needlessly down. I'm pretty
-# sure we can delete it and instead just pass an attention_mask
-class Attention(nn.Module):
-    def __init__(self, in_channels, out_channels, context_dim, head_dim=64):
-        super().__init__()
-        assert out_channels % head_dim == 0
-        self.num_heads = out_channels // head_dim
-        self.scale = head_dim**-0.5
-
-        # to_q
-        self.to_q = nn.Linear(in_channels, out_channels, bias=False)
-        # to_k
-        self.to_k = nn.Linear(context_dim, out_channels, bias=False)
-        # to_v
-        self.to_v = nn.Linear(context_dim, out_channels, bias=False)
-        processor = Kandi3AttnProcessor()
-        self.set_processor(processor)
-        # to_out
-        self.to_out = nn.ModuleList([])
-        self.to_out.append(nn.Linear(out_channels, out_channels, bias=False))
-
-    def set_processor(self, processor: "AttnProcessor"):  # noqa: F821
-        # if current processor is in `self._modules` and if passed `processor` is not, we need to
-        # pop `processor` from `self._modules`
-        if (
-            hasattr(self, "processor")
-            and isinstance(self.processor, torch.nn.Module)
-            and not isinstance(processor, torch.nn.Module)
-        ):
-            logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
-            self._modules.pop("processor")
-
-        self.processor = processor
-
-    def forward(self, x, context, context_mask=None, image_mask=None):
-        return self.processor(
-            self,
-            x,
-            context=context,
-            context_mask=context_mask,
-        )
-
-
-class Kandinsky3Block(nn.Module):
-    def __init__(self, in_channels, out_channels, time_embed_dim, kernel_size=3, norm_groups=32, up_resolution=None):
-        super().__init__()
-        self.group_norm = Kandinsky3ConditionalGroupNorm(norm_groups, in_channels, time_embed_dim)
-        self.activation = nn.SiLU()
-        self.up_sample = set_default_layer(
-            up_resolution is not None and up_resolution,
-            nn.ConvTranspose2d,
-            (in_channels, in_channels),
-            {"kernel_size": 2, "stride": 2},
-        )
-        padding = int(kernel_size > 1)
-        self.projection = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=padding)
-        self.down_sample = set_default_layer(
-            up_resolution is not None and not up_resolution,
-            nn.Conv2d,
-            (out_channels, out_channels),
-            {"kernel_size": 2, "stride": 2},
-        )
-
-    def forward(self, x, time_embed):
-        x = self.group_norm(x, time_embed)
-        x = self.activation(x)
-        x = self.up_sample(x)
-        x = self.projection(x)
-        x = self.down_sample(x)
-        return x
-
-
-class Kandinsky3ResNetBlock(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, time_embed_dim, norm_groups=32, compression_ratio=2, up_resolutions=4 * [None]
-    ):
-        super().__init__()
-        kernel_sizes = [1, 3, 3, 1]
-        hidden_channel = max(in_channels, out_channels) // compression_ratio
-        hidden_channels = (
-            [(in_channels, hidden_channel)] + [(hidden_channel, hidden_channel)] * 2 + [(hidden_channel, out_channels)]
-        )
-        self.resnet_blocks = nn.ModuleList(
-            [
-                Kandinsky3Block(in_channel, out_channel, time_embed_dim, kernel_size, norm_groups, up_resolution)
-                for (in_channel, out_channel), kernel_size, up_resolution in zip(
-                    hidden_channels, kernel_sizes, up_resolutions
-                )
-            ]
-        )
-        self.shortcut_up_sample = set_default_layer(
-            True in up_resolutions, nn.ConvTranspose2d, (in_channels, in_channels), {"kernel_size": 2, "stride": 2}
-        )
-        self.shortcut_projection = set_default_layer(
-            in_channels != out_channels, nn.Conv2d, (in_channels, out_channels), {"kernel_size": 1}
-        )
-        self.shortcut_down_sample = set_default_layer(
-            False in up_resolutions, nn.Conv2d, (out_channels, out_channels), {"kernel_size": 2, "stride": 2}
-        )
-
-    def forward(self, x, time_embed):
-        out = x
-        for resnet_block in self.resnet_blocks:
-            out = resnet_block(out, time_embed)
-
-        x = self.shortcut_up_sample(x)
-        x = self.shortcut_projection(x)
-        x = self.shortcut_down_sample(x)
-        x = x + out
-        return x
-
-
-class Kandinsky3AttentionPooling(nn.Module):
-    def __init__(self, num_channels, context_dim, head_dim=64):
-        super().__init__()
-        self.attention = Attention(context_dim, num_channels, context_dim, head_dim)
-
-    def forward(self, x, context, context_mask=None):
-        context = self.attention(context.mean(dim=1, keepdim=True), context, context_mask)
-        return x + context.squeeze(1)
-
-
-class Kandinsky3AttentionBlock(nn.Module):
-    def __init__(self, num_channels, time_embed_dim, context_dim=None, norm_groups=32, head_dim=64, expansion_ratio=4):
-        super().__init__()
-        self.in_norm = Kandinsky3ConditionalGroupNorm(norm_groups, num_channels, time_embed_dim)
-        self.attention = Attention(num_channels, num_channels, context_dim or num_channels, head_dim)
-
-        hidden_channels = expansion_ratio * num_channels
-        self.out_norm = Kandinsky3ConditionalGroupNorm(norm_groups, num_channels, time_embed_dim)
-        self.feed_forward = nn.Sequential(
-            nn.Conv2d(num_channels, hidden_channels, kernel_size=1, bias=False),
-            nn.SiLU(),
-            nn.Conv2d(hidden_channels, num_channels, kernel_size=1, bias=False),
-        )
-
-    def forward(self, x, time_embed, context=None, context_mask=None, image_mask=None):
-        height, width = x.shape[-2:]
-        out = self.in_norm(x, time_embed)
-        out = out.reshape(x.shape[0], -1, height * width).permute(0, 2, 1)
-        context = context if context is not None else out
-
-        if image_mask is not None:
-            mask_height, mask_width = image_mask.shape[-2:]
-            kernel_size = (mask_height // height, mask_width // width)
-            image_mask = F.max_pool2d(image_mask, kernel_size, kernel_size)
-            image_mask = image_mask.reshape(image_mask.shape[0], -1)
-
-        out = self.attention(out, context, context_mask, image_mask)
-        out = out.permute(0, 2, 1).unsqueeze(-1).reshape(out.shape[0], -1, height, width)
-        x = x + out
-
-        out = self.out_norm(x, time_embed)
-        out = self.feed_forward(out)
-        x = x + out
-        return x

src/diffusers/models/unet_motion_model.py

@@ -208,8 +208,6 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         motion_max_seq_length: int = 32,
         motion_num_attention_heads: int = 8,
         use_motion_mid_block: int = True,
-        encoder_hid_dim: Optional[int] = None,
-        encoder_hid_dim_type: Optional[str] = None,
     ):
         super().__init__()
 
@@ -250,9 +248,6 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
             act_fn=act_fn,
         )
 
-        if encoder_hid_dim_type is None:
-            self.encoder_hid_proj = None
-
         # class embedding
         self.down_blocks = nn.ModuleList([])
         self.up_blocks = nn.ModuleList([])
@@ -689,7 +684,6 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         timestep_cond: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
         down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
         mid_block_additional_residual: Optional[torch.Tensor] = None,
         return_dict: bool = True,
@@ -773,16 +767,6 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
 
         emb = self.time_embedding(t_emb, timestep_cond)
         emb = emb.repeat_interleave(repeats=num_frames, dim=0)
-
-        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
-            if "image_embeds" not in added_cond_kwargs:
-                raise ValueError(
-                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
-                )
-            image_embeds = added_cond_kwargs.get("image_embeds")
-            image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype)
-            encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1)
-
         encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)
 
         # 2. pre-process

src/diffusers/models/unet_spatio_temporal_condition.py

@@ -1,489 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..loaders import UNet2DConditionLoadersMixin
-from ..utils import BaseOutput, logging
-from .attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor
-from .embeddings import TimestepEmbedding, Timesteps
-from .modeling_utils import ModelMixin
-from .unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@dataclass
-class UNetSpatioTemporalConditionOutput(BaseOutput):
-    """
-    The output of [`UNetSpatioTemporalConditionModel`].
-
-    Args:
-        sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
-            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
-    """
-
-    sample: torch.FloatTensor = None
-
-
-class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
-    r"""
-    A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample
-    shaped output.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
-    for all models (such as downloading or saving).
-
-    Parameters:
-        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
-            Height and width of input/output sample.
-        in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample.
-        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`):
-            The tuple of downsample blocks to use.
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`):
-            The tuple of upsample blocks to use.
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        addition_time_embed_dim: (`int`, defaults to 256):
-            Dimension to to encode the additional time ids.
-        projection_class_embeddings_input_dim (`int`, defaults to 768):
-            The dimension of the projection of encoded `added_time_ids`.
-        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
-        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
-            The dimension of the cross attention features.
-        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
-            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
-            [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`],
-            [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`].
-        num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`):
-            The number of attention heads.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        sample_size: Optional[int] = None,
-        in_channels: int = 8,
-        out_channels: int = 4,
-        down_block_types: Tuple[str] = (
-            "CrossAttnDownBlockSpatioTemporal",
-            "CrossAttnDownBlockSpatioTemporal",
-            "CrossAttnDownBlockSpatioTemporal",
-            "DownBlockSpatioTemporal",
-        ),
-        up_block_types: Tuple[str] = (
-            "UpBlockSpatioTemporal",
-            "CrossAttnUpBlockSpatioTemporal",
-            "CrossAttnUpBlockSpatioTemporal",
-            "CrossAttnUpBlockSpatioTemporal",
-        ),
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
-        addition_time_embed_dim: int = 256,
-        projection_class_embeddings_input_dim: int = 768,
-        layers_per_block: Union[int, Tuple[int]] = 2,
-        cross_attention_dim: Union[int, Tuple[int]] = 1024,
-        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
-        num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20),
-        num_frames: int = 25,
-    ):
-        super().__init__()
-
-        self.sample_size = sample_size
-
-        # Check inputs
-        if len(down_block_types) != len(up_block_types):
-            raise ValueError(
-                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
-            )
-
-        if len(block_out_channels) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
-            )
-
-        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
-            )
-
-        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
-            raise ValueError(
-                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
-            )
-
-        # input
-        self.conv_in = nn.Conv2d(
-            in_channels,
-            block_out_channels[0],
-            kernel_size=3,
-            padding=1,
-        )
-
-        # time
-        time_embed_dim = block_out_channels[0] * 4
-
-        self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0)
-        timestep_input_dim = block_out_channels[0]
-
-        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-
-        self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0)
-        self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
-
-        self.down_blocks = nn.ModuleList([])
-        self.up_blocks = nn.ModuleList([])
-
-        if isinstance(num_attention_heads, int):
-            num_attention_heads = (num_attention_heads,) * len(down_block_types)
-
-        if isinstance(cross_attention_dim, int):
-            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
-
-        if isinstance(layers_per_block, int):
-            layers_per_block = [layers_per_block] * len(down_block_types)
-
-        if isinstance(transformer_layers_per_block, int):
-            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
-
-        blocks_time_embed_dim = time_embed_dim
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=layers_per_block[i],
-                transformer_layers_per_block=transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_downsample=not is_final_block,
-                resnet_eps=1e-5,
-                cross_attention_dim=cross_attention_dim[i],
-                num_attention_heads=num_attention_heads[i],
-                resnet_act_fn="silu",
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        self.mid_block = UNetMidBlockSpatioTemporal(
-            block_out_channels[-1],
-            temb_channels=blocks_time_embed_dim,
-            transformer_layers_per_block=transformer_layers_per_block[-1],
-            cross_attention_dim=cross_attention_dim[-1],
-            num_attention_heads=num_attention_heads[-1],
-        )
-
-        # count how many layers upsample the images
-        self.num_upsamplers = 0
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        reversed_num_attention_heads = list(reversed(num_attention_heads))
-        reversed_layers_per_block = list(reversed(layers_per_block))
-        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
-        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
-
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            is_final_block = i == len(block_out_channels) - 1
-
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            # add upsample block for all BUT final layer
-            if not is_final_block:
-                add_upsample = True
-                self.num_upsamplers += 1
-            else:
-                add_upsample = False
-
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=reversed_layers_per_block[i] + 1,
-                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
-                in_channels=input_channel,
-                out_channels=output_channel,
-                prev_output_channel=prev_output_channel,
-                temb_channels=blocks_time_embed_dim,
-                add_upsample=add_upsample,
-                resnet_eps=1e-5,
-                resolution_idx=i,
-                cross_attention_dim=reversed_cross_attention_dim[i],
-                num_attention_heads=reversed_num_attention_heads[i],
-                resnet_act_fn="silu",
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5)
-        self.conv_act = nn.SiLU()
-
-        self.conv_out = nn.Conv2d(
-            block_out_channels[0],
-            out_channels,
-            kernel_size=3,
-            padding=1,
-        )
-
-    @property
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(
-            name: str,
-            module: torch.nn.Module,
-            processors: Dict[str, AttentionProcessor],
-        ):
-            if hasattr(module, "get_processor"):
-                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    def set_default_attn_processor(self):
-        """
-        Disables custom attention processors and sets the default attention implementation.
-        """
-        if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
-            processor = AttnProcessor()
-        else:
-            raise ValueError(
-                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
-            )
-
-        self.set_attn_processor(processor)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
-    def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
-        """
-        Sets the attention processor to use [feed forward
-        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
-
-        Parameters:
-            chunk_size (`int`, *optional*):
-                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
-                over each tensor of dim=`dim`.
-            dim (`int`, *optional*, defaults to `0`):
-                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
-                or dim=1 (sequence length).
-        """
-        if dim not in [0, 1]:
-            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
-
-        # By default chunk size is 1
-        chunk_size = chunk_size or 1
-
-        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
-            if hasattr(module, "set_chunk_feed_forward"):
-                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
-
-            for child in module.children():
-                fn_recursive_feed_forward(child, chunk_size, dim)
-
-        for module in self.children():
-            fn_recursive_feed_forward(module, chunk_size, dim)
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        timestep: Union[torch.Tensor, float, int],
-        encoder_hidden_states: torch.Tensor,
-        added_time_ids: torch.Tensor,
-        return_dict: bool = True,
-    ) -> Union[UNetSpatioTemporalConditionOutput, Tuple]:
-        r"""
-        The [`UNetSpatioTemporalConditionModel`] forward method.
-
-        Args:
-            sample (`torch.FloatTensor`):
-                The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`.
-            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
-            encoder_hidden_states (`torch.FloatTensor`):
-                The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`.
-            added_time_ids: (`torch.FloatTensor`):
-                The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal
-                embeddings and added to the time embeddings.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead of a plain
-                tuple.
-        Returns:
-            [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`:
-                If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, otherwise
-                a `tuple` is returned where the first element is the sample tensor.
-        """
-        # 1. time
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
-            # This would be a good case for the `match` statement (Python 3.10+)
-            is_mps = sample.device.type == "mps"
-            if isinstance(timestep, float):
-                dtype = torch.float32 if is_mps else torch.float64
-            else:
-                dtype = torch.int32 if is_mps else torch.int64
-            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
-        elif len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(sample.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        batch_size, num_frames = sample.shape[:2]
-        timesteps = timesteps.expand(batch_size)
-
-        t_emb = self.time_proj(timesteps)
-
-        # `Timesteps` does not contain any weights and will always return f32 tensors
-        # but time_embedding might actually be running in fp16. so we need to cast here.
-        # there might be better ways to encapsulate this.
-        t_emb = t_emb.to(dtype=sample.dtype)
-
-        emb = self.time_embedding(t_emb)
-
-        time_embeds = self.add_time_proj(added_time_ids.flatten())
-        time_embeds = time_embeds.reshape((batch_size, -1))
-        time_embeds = time_embeds.to(emb.dtype)
-        aug_emb = self.add_embedding(time_embeds)
-        emb = emb + aug_emb
-
-        # Flatten the batch and frames dimensions
-        # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width]
-        sample = sample.flatten(0, 1)
-        # Repeat the embeddings num_video_frames times
-        # emb: [batch, channels] -> [batch * frames, channels]
-        emb = emb.repeat_interleave(num_frames, dim=0)
-        # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels]
-        encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0)
-
-        # 2. pre-process
-        sample = self.conv_in(sample)
-
-        image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device)
-
-        down_block_res_samples = (sample,)
-        for downsample_block in self.down_blocks:
-            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    image_only_indicator=image_only_indicator,
-                )
-            else:
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    image_only_indicator=image_only_indicator,
-                )
-
-            down_block_res_samples += res_samples
-
-        # 4. mid
-        sample = self.mid_block(
-            hidden_states=sample,
-            temb=emb,
-            encoder_hidden_states=encoder_hidden_states,
-            image_only_indicator=image_only_indicator,
-        )
-
-        # 5. up
-        for i, upsample_block in enumerate(self.up_blocks):
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    encoder_hidden_states=encoder_hidden_states,
-                    image_only_indicator=image_only_indicator,
-                )
-            else:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    image_only_indicator=image_only_indicator,
-                )
-
-        # 6. post-process
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        # 7. Reshape back to original shape
-        sample = sample.reshape(batch_size, num_frames, *sample.shape[1:])
-
-        if not return_dict:
-            return (sample,)
-
-        return UNetSpatioTemporalConditionOutput(sample=sample)

src/diffusers/models/vae.py

@@ -22,12 +22,7 @@ from ..utils import BaseOutput, is_torch_version
 from ..utils.torch_utils import randn_tensor
 from .activations import get_activation
 from .attention_processor import SpatialNorm
-from .unet_2d_blocks import (
-    AutoencoderTinyBlock,
-    UNetMidBlock2D,
-    get_down_block,
-    get_up_block,
-)
+from .unet_2d_blocks import AutoencoderTinyBlock, UNetMidBlock2D, get_down_block, get_up_block
 
 
 @dataclass
@@ -279,9 +274,7 @@ class Decoder(nn.Module):
         self.gradient_checkpointing = False
 
     def forward(
-        self,
-        sample: torch.FloatTensor,
-        latent_embeds: Optional[torch.FloatTensor] = None,
+        self, sample: torch.FloatTensor, latent_embeds: Optional[torch.FloatTensor] = None
     ) -> torch.FloatTensor:
         r"""The forward method of the `Decoder` class."""
 
@@ -299,20 +292,14 @@ class Decoder(nn.Module):
             if is_torch_version(">=", "1.11.0"):
                 # middle
                 sample = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(self.mid_block),
-                    sample,
-                    latent_embeds,
-                    use_reentrant=False,
+                    create_custom_forward(self.mid_block), sample, latent_embeds, use_reentrant=False
                 )
                 sample = sample.to(upscale_dtype)
 
                 # up
                 for up_block in self.up_blocks:
                     sample = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(up_block),
-                        sample,
-                        latent_embeds,
-                        use_reentrant=False,
+                        create_custom_forward(up_block), sample, latent_embeds, use_reentrant=False
                     )
             else:
                 # middle
@@ -553,10 +540,7 @@ class MaskConditionDecoder(nn.Module):
             if is_torch_version(">=", "1.11.0"):
                 # middle
                 sample = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(self.mid_block),
-                    sample,
-                    latent_embeds,
-                    use_reentrant=False,
+                    create_custom_forward(self.mid_block), sample, latent_embeds, use_reentrant=False
                 )
                 sample = sample.to(upscale_dtype)
 
@@ -564,10 +548,7 @@ class MaskConditionDecoder(nn.Module):
                 if image is not None and mask is not None:
                     masked_image = (1 - mask) * image
                     im_x = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(self.condition_encoder),
-                        masked_image,
-                        mask,
-                        use_reentrant=False,
+                        create_custom_forward(self.condition_encoder), masked_image, mask, use_reentrant=False
                     )
 
                 # up
@@ -577,10 +558,7 @@ class MaskConditionDecoder(nn.Module):
                         mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest")
                         sample = sample * mask_ + sample_ * (1 - mask_)
                     sample = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(up_block),
-                        sample,
-                        latent_embeds,
-                        use_reentrant=False,
+                        create_custom_forward(up_block), sample, latent_embeds, use_reentrant=False
                     )
                 if image is not None and mask is not None:
                     sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)
@@ -595,9 +573,7 @@ class MaskConditionDecoder(nn.Module):
                 if image is not None and mask is not None:
                     masked_image = (1 - mask) * image
                     im_x = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(self.condition_encoder),
-                        masked_image,
-                        mask,
+                        create_custom_forward(self.condition_encoder), masked_image, mask
                     )
 
                 # up
@@ -778,10 +754,7 @@ class DiagonalGaussianDistribution(object):
     def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
         # make sure sample is on the same device as the parameters and has same dtype
         sample = randn_tensor(
-            self.mean.shape,
-            generator=generator,
-            device=self.parameters.device,
-            dtype=self.parameters.dtype,
+            self.mean.shape, generator=generator, device=self.parameters.device, dtype=self.parameters.dtype
         )
         x = self.mean + self.std * sample
         return x
@@ -791,10 +764,7 @@ class DiagonalGaussianDistribution(object):
             return torch.Tensor([0.0])
         else:
             if other is None:
-                return 0.5 * torch.sum(
-                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
-                    dim=[1, 2, 3],
-                )
+                return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3])
             else:
                 return 0.5 * torch.sum(
                     torch.pow(self.mean - other.mean, 2) / other.var
@@ -809,10 +779,7 @@ class DiagonalGaussianDistribution(object):
         if self.deterministic:
             return torch.Tensor([0.0])
         logtwopi = np.log(2.0 * np.pi)
-        return 0.5 * torch.sum(
-            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
-            dim=dims,
-        )
+        return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims)
 
     def mode(self) -> torch.Tensor:
         return self.mean
@@ -853,16 +820,7 @@ class EncoderTiny(nn.Module):
             if i == 0:
                 layers.append(nn.Conv2d(in_channels, num_channels, kernel_size=3, padding=1))
             else:
-                layers.append(
-                    nn.Conv2d(
-                        num_channels,
-                        num_channels,
-                        kernel_size=3,
-                        padding=1,
-                        stride=2,
-                        bias=False,
-                    )
-                )
+                layers.append(nn.Conv2d(num_channels, num_channels, kernel_size=3, padding=1, stride=2, bias=False))
 
             for _ in range(num_block):
                 layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn))
@@ -941,15 +899,7 @@ class DecoderTiny(nn.Module):
                 layers.append(nn.Upsample(scale_factor=upsampling_scaling_factor))
 
             conv_out_channel = num_channels if not is_final_block else out_channels
-            layers.append(
-                nn.Conv2d(
-                    num_channels,
-                    conv_out_channel,
-                    kernel_size=3,
-                    padding=1,
-                    bias=is_final_block,
-                )
-            )
+            layers.append(nn.Conv2d(num_channels, conv_out_channel, kernel_size=3, padding=1, bias=is_final_block))
 
         self.layers = nn.Sequential(*layers)
         self.gradient_checkpointing = False

src/diffusers/pipelines/__init__.py

@@ -17,12 +17,7 @@ from ..utils import (
 
 # These modules contain pipelines from multiple libraries/frameworks
 _dummy_objects = {}
-_import_structure = {
-    "controlnet": [],
-    "latent_diffusion": [],
-    "stable_diffusion": [],
-    "stable_diffusion_xl": [],
-}
+_import_structure = {"stable_diffusion": [], "stable_diffusion_xl": [], "latent_diffusion": [], "controlnet": []}
 
 try:
     if not is_torch_available():
@@ -44,11 +39,7 @@ else:
     _import_structure["dit"] = ["DiTPipeline"]
     _import_structure["latent_diffusion"].extend(["LDMSuperResolutionPipeline"])
     _import_structure["latent_diffusion_uncond"] = ["LDMPipeline"]
-    _import_structure["pipeline_utils"] = [
-        "AudioPipelineOutput",
-        "DiffusionPipeline",
-        "ImagePipelineOutput",
-    ]
+    _import_structure["pipeline_utils"] = ["AudioPipelineOutput", "DiffusionPipeline", "ImagePipelineOutput"]
     _import_structure["pndm"] = ["PNDMPipeline"]
     _import_structure["repaint"] = ["RePaintPipeline"]
     _import_structure["score_sde_ve"] = ["ScoreSdeVePipeline"]
@@ -70,10 +61,7 @@ except OptionalDependencyNotAvailable:
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
-    _import_structure["alt_diffusion"] = [
-        "AltDiffusionImg2ImgPipeline",
-        "AltDiffusionPipeline",
-    ]
+    _import_structure["alt_diffusion"] = ["AltDiffusionImg2ImgPipeline", "AltDiffusionPipeline"]
     _import_structure["animatediff"] = ["AnimateDiffPipeline"]
     _import_structure["audioldm"] = ["AudioLDMPipeline"]
     _import_structure["audioldm2"] = [
@@ -122,10 +110,6 @@ else:
         "KandinskyV22PriorEmb2EmbPipeline",
         "KandinskyV22PriorPipeline",
     ]
-    _import_structure["kandinsky3"] = [
-        "Kandinsky3Img2ImgPipeline",
-        "Kandinsky3Pipeline",
-    ]
     _import_structure["latent_consistency_models"] = [
         "LatentConsistencyModelImg2ImgPipeline",
         "LatentConsistencyModelPipeline",
@@ -165,7 +149,6 @@ else:
         ]
     )
     _import_structure["stable_diffusion_safe"] = ["StableDiffusionPipelineSafe"]
-    _import_structure["stable_video_diffusion"] = ["StableVideoDiffusionPipeline"]
     _import_structure["stable_diffusion_xl"].extend(
         [
             "StableDiffusionXLImg2ImgPipeline",
@@ -174,14 +157,10 @@ else:
             "StableDiffusionXLPipeline",
         ]
     )
-    _import_structure["t2i_adapter"] = [
-        "StableDiffusionAdapterPipeline",
-        "StableDiffusionXLAdapterPipeline",
-    ]
+    _import_structure["t2i_adapter"] = ["StableDiffusionAdapterPipeline", "StableDiffusionXLAdapterPipeline"]
     _import_structure["text_to_video_synthesis"] = [
         "TextToVideoSDPipeline",
         "TextToVideoZeroPipeline",
-        "TextToVideoZeroSDXLPipeline",
         "VideoToVideoSDPipeline",
     ]
     _import_structure["unclip"] = ["UnCLIPImageVariationPipeline", "UnCLIPPipeline"]
@@ -235,9 +214,7 @@ try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from ..utils import (
-        dummy_torch_and_transformers_and_k_diffusion_objects,
-    )
+    from ..utils import dummy_torch_and_transformers_and_k_diffusion_objects  # noqa F403
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_and_k_diffusion_objects))
 else:
@@ -280,10 +257,7 @@ except OptionalDependencyNotAvailable:
 
     _dummy_objects.update(get_objects_from_module(dummy_transformers_and_torch_and_note_seq_objects))
 else:
-    _import_structure["spectrogram_diffusion"] = [
-        "MidiProcessor",
-        "SpectrogramDiffusionPipeline",
-    ]
+    _import_structure["spectrogram_diffusion"] = ["MidiProcessor", "SpectrogramDiffusionPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -293,11 +267,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from ..utils.dummy_pt_objects import *  # noqa F403
 
     else:
-        from .auto_pipeline import (
-            AutoPipelineForImage2Image,
-            AutoPipelineForInpainting,
-            AutoPipelineForText2Image,
-        )
+        from .auto_pipeline import AutoPipelineForImage2Image, AutoPipelineForInpainting, AutoPipelineForText2Image
         from .consistency_models import ConsistencyModelPipeline
         from .dance_diffusion import DanceDiffusionPipeline
         from .ddim import DDIMPipeline
@@ -305,11 +275,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .dit import DiTPipeline
         from .latent_diffusion import LDMSuperResolutionPipeline
         from .latent_diffusion_uncond import LDMPipeline
-        from .pipeline_utils import (
-            AudioPipelineOutput,
-            DiffusionPipeline,
-            ImagePipelineOutput,
-        )
+        from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
         from .pndm import PNDMPipeline
         from .repaint import RePaintPipeline
         from .score_sde_ve import ScoreSdeVePipeline
@@ -332,11 +298,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .alt_diffusion import AltDiffusionImg2ImgPipeline, AltDiffusionPipeline
         from .animatediff import AnimateDiffPipeline
         from .audioldm import AudioLDMPipeline
-        from .audioldm2 import (
-            AudioLDM2Pipeline,
-            AudioLDM2ProjectionModel,
-            AudioLDM2UNet2DConditionModel,
-        )
+        from .audioldm2 import AudioLDM2Pipeline, AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel
         from .blip_diffusion import BlipDiffusionPipeline
         from .controlnet import (
             BlipDiffusionControlNetPipeline,
@@ -376,14 +338,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             KandinskyV22PriorEmb2EmbPipeline,
             KandinskyV22PriorPipeline,
         )
-        from .kandinsky3 import (
-            Kandinsky3Img2ImgPipeline,
-            Kandinsky3Pipeline,
-        )
-        from .latent_consistency_models import (
-            LatentConsistencyModelImg2ImgPipeline,
-            LatentConsistencyModelPipeline,
-        )
+        from .latent_consistency_models import LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline
         from .latent_diffusion import LDMTextToImagePipeline
         from .musicldm import MusicLDMPipeline
         from .paint_by_example import PaintByExamplePipeline
@@ -422,15 +377,10 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionXLInstructPix2PixPipeline,
             StableDiffusionXLPipeline,
         )
-        from .stable_video_diffusion import StableVideoDiffusionPipeline
-        from .t2i_adapter import (
-            StableDiffusionAdapterPipeline,
-            StableDiffusionXLAdapterPipeline,
-        )
+        from .t2i_adapter import StableDiffusionAdapterPipeline, StableDiffusionXLAdapterPipeline
         from .text_to_video_synthesis import (
             TextToVideoSDPipeline,
             TextToVideoZeroPipeline,
-            TextToVideoZeroSDXLPipeline,
             VideoToVideoSDPipeline,
         )
         from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
@@ -516,10 +466,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
 
         else:
-            from .spectrogram_diffusion import (
-                MidiProcessor,
-                SpectrogramDiffusionPipeline,
-            )
+            from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline
 
 else:
     import sys

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py

@@ -17,11 +17,11 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 from packaging import version
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, XLMRobertaTokenizer
+from transformers import CLIPImageProcessor, XLMRobertaTokenizer
 
 from ...configuration_utils import FrozenDict
-from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from ...image_processor import VaeImageProcessor
+from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
@@ -73,55 +73,8 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     return noise_cfg
 
 
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    **kwargs,
-):
-    """
-    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
-    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
-    Args:
-        scheduler (`SchedulerMixin`):
-            The scheduler to get timesteps from.
-        num_inference_steps (`int`):
-            The number of diffusion steps used when generating samples with a pre-trained model. If used,
-            `timesteps` must be `None`.
-        device (`str` or `torch.device`, *optional*):
-            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        timesteps (`List[int]`, *optional*):
-                Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
-                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
-                must be `None`.
-
-    Returns:
-        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
-        second element is the number of inference steps.
-    """
-    if timesteps is not None:
-        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accepts_timesteps:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" timestep schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
-class AltDiffusionPipeline(
-    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
-):
+class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
     r"""
     Pipeline for text-to-image generation using Alt Diffusion.
 
@@ -133,7 +86,6 @@ class AltDiffusionPipeline(
         - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
         - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
         - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
-        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
 
     Args:
         vae ([`AutoencoderKL`]):
@@ -156,7 +108,7 @@ class AltDiffusionPipeline(
     """
 
     model_cpu_offload_seq = "text_encoder->unet->vae"
-    _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
+    _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
 
@@ -169,7 +121,6 @@ class AltDiffusionPipeline(
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPImageProcessor,
-        image_encoder: CLIPVisionModelWithProjection = None,
         requires_safety_checker: bool = True,
     ):
         super().__init__()
@@ -246,9 +197,10 @@ class AltDiffusionPipeline(
             scheduler=scheduler,
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
         )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.vae_scale_factor = 2 ** (
+            len(getattr(self.vae.config, "block_out_channels", self.vae.config.decoder_block_out_channels)) - 1
+        )
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
         self.register_to_config(requires_safety_checker=requires_safety_checker)
 
@@ -494,19 +446,6 @@ class AltDiffusionPipeline(
 
         return prompt_embeds, negative_prompt_embeds
 
-    def encode_image(self, image, device, num_images_per_prompt):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        image_embeds = self.image_encoder(image).image_embeds
-        image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-
-        uncond_image_embeds = torch.zeros_like(image_embeds)
-        return image_embeds, uncond_image_embeds
-
     def run_safety_checker(self, image, device, dtype):
         if self.safety_checker is None:
             has_nsfw_concept = None
@@ -707,7 +646,6 @@ class AltDiffusionPipeline(
         height: Optional[int] = None,
         width: Optional[int] = None,
         num_inference_steps: int = 50,
-        timesteps: List[int] = None,
         guidance_scale: float = 7.5,
         negative_prompt: Optional[Union[str, List[str]]] = None,
         num_images_per_prompt: Optional[int] = 1,
@@ -716,7 +654,6 @@ class AltDiffusionPipeline(
         latents: Optional[torch.FloatTensor] = None,
         prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
@@ -739,10 +676,6 @@ class AltDiffusionPipeline(
             num_inference_steps (`int`, *optional*, defaults to 50):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                 expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
-                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
-                passed will be used. Must be in descending order.
             guidance_scale (`float`, *optional*, defaults to 7.5):
                 A higher guidance scale value encourages the model to generate images closely linked to the text
                 `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
@@ -767,7 +700,6 @@ class AltDiffusionPipeline(
             negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
                 not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
@@ -867,20 +799,15 @@ class AltDiffusionPipeline(
             lora_scale=lora_scale,
             clip_skip=self.clip_skip,
         )
-
         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
-        if ip_adapter_image is not None:
-            image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
-            if self.do_classifier_free_guidance:
-                image_embeds = torch.cat([negative_image_embeds, image_embeds])
-
         # 4. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
 
         # 5. Prepare latent variables
         num_channels_latents = self.unet.config.in_channels
@@ -898,10 +825,7 @@ class AltDiffusionPipeline(
         # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
-        # 6.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
-
-        # 6.2 Optionally get Guidance Scale Embedding
+        # 6.5 Optionally get Guidance Scale Embedding
         timestep_cond = None
         if self.unet.config.time_cond_proj_dim is not None:
             guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
@@ -925,7 +849,6 @@ class AltDiffusionPipeline(
                     encoder_hidden_states=prompt_embeds,
                     timestep_cond=timestep_cond,
                     cross_attention_kwargs=self.cross_attention_kwargs,
-                    added_cond_kwargs=added_cond_kwargs,
                     return_dict=False,
                 )[0]
 

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -19,11 +19,11 @@ import numpy as np
 import PIL.Image
 import torch
 from packaging import version
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, XLMRobertaTokenizer
+from transformers import CLIPImageProcessor, XLMRobertaTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
@@ -76,13 +76,9 @@ EXAMPLE_DOC_STRING = """
 
 
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
-def retrieve_latents(
-    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
-):
-    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
         return encoder_output.latent_dist.sample(generator)
-    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
-        return encoder_output.latent_dist.mode()
     elif hasattr(encoder_output, "latents"):
         return encoder_output.latents
     else:
@@ -113,54 +109,9 @@ def preprocess(image):
     return image
 
 
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    **kwargs,
-):
-    """
-    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
-    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
-    Args:
-        scheduler (`SchedulerMixin`):
-            The scheduler to get timesteps from.
-        num_inference_steps (`int`):
-            The number of diffusion steps used when generating samples with a pre-trained model. If used,
-            `timesteps` must be `None`.
-        device (`str` or `torch.device`, *optional*):
-            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        timesteps (`List[int]`, *optional*):
-                Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
-                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
-                must be `None`.
-
-    Returns:
-        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
-        second element is the number of inference steps.
-    """
-    if timesteps is not None:
-        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accepts_timesteps:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" timestep schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
 class AltDiffusionImg2ImgPipeline(
-    DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FromSingleFileMixin
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
 ):
     r"""
     Pipeline for text-guided image-to-image generation using Alt Diffusion.
@@ -173,7 +124,6 @@ class AltDiffusionImg2ImgPipeline(
         - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
         - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
         - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
-        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
 
     Args:
         vae ([`AutoencoderKL`]):
@@ -196,7 +146,7 @@ class AltDiffusionImg2ImgPipeline(
     """
 
     model_cpu_offload_seq = "text_encoder->unet->vae"
-    _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
+    _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
 
@@ -209,7 +159,6 @@ class AltDiffusionImg2ImgPipeline(
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPImageProcessor,
-        image_encoder: CLIPVisionModelWithProjection = None,
         requires_safety_checker: bool = True,
     ):
         super().__init__()
@@ -286,9 +235,10 @@ class AltDiffusionImg2ImgPipeline(
             scheduler=scheduler,
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
         )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.vae_scale_factor = 2 ** (
+            len(getattr(self.vae.config, "block_out_channels", self.vae.config.decoder_block_out_channels)) - 1
+        )
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
         self.register_to_config(requires_safety_checker=requires_safety_checker)
 
@@ -505,19 +455,6 @@ class AltDiffusionImg2ImgPipeline(
 
         return prompt_embeds, negative_prompt_embeds
 
-    def encode_image(self, image, device, num_images_per_prompt):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        image_embeds = self.image_encoder(image).image_embeds
-        image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-
-        uncond_image_embeds = torch.zeros_like(image_embeds)
-        return image_embeds, uncond_image_embeds
-
     def run_safety_checker(self, image, device, dtype):
         if self.safety_checker is None:
             has_nsfw_concept = None
@@ -763,7 +700,6 @@ class AltDiffusionImg2ImgPipeline(
         image: PipelineImageInput = None,
         strength: float = 0.8,
         num_inference_steps: Optional[int] = 50,
-        timesteps: List[int] = None,
         guidance_scale: Optional[float] = 7.5,
         negative_prompt: Optional[Union[str, List[str]]] = None,
         num_images_per_prompt: Optional[int] = 1,
@@ -771,7 +707,6 @@ class AltDiffusionImg2ImgPipeline(
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
@@ -801,10 +736,6 @@ class AltDiffusionImg2ImgPipeline(
             num_inference_steps (`int`, *optional*, defaults to 50):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                 expense of slower inference. This parameter is modulated by `strength`.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
-                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
-                passed will be used. Must be in descending order.
             guidance_scale (`float`, *optional*, defaults to 7.5):
                 A higher guidance scale value encourages the model to generate images closely linked to the text
                 `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
@@ -825,7 +756,6 @@ class AltDiffusionImg2ImgPipeline(
             negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
                 not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
@@ -918,16 +848,11 @@ class AltDiffusionImg2ImgPipeline(
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
-        if ip_adapter_image is not None:
-            image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
-            if self.do_classifier_free_guidance:
-                image_embeds = torch.cat([negative_image_embeds, image_embeds])
-
         # 4. Preprocess image
         image = self.image_processor.preprocess(image)
 
         # 5. set timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
@@ -945,10 +870,7 @@ class AltDiffusionImg2ImgPipeline(
         # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
-        # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
-
-        # 7.2 Optionally get Guidance Scale Embedding
+        # 7.5 Optionally get Guidance Scale Embedding
         timestep_cond = None
         if self.unet.config.time_cond_proj_dim is not None:
             guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
@@ -972,7 +894,6 @@ class AltDiffusionImg2ImgPipeline(
                     encoder_hidden_states=prompt_embeds,
                     timestep_cond=timestep_cond,
                     cross_attention_kwargs=self.cross_attention_kwargs,
-                    added_cond_kwargs=added_cond_kwargs,
                     return_dict=False,
                 )[0]
 

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -18,10 +18,10 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import numpy as np
 import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+from transformers import CLIPTextModel, CLIPTokenizer
 
-from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from ...image_processor import VaeImageProcessor
+from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel, UNetMotionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...models.unet_motion_model import MotionAdapter
@@ -77,7 +77,7 @@ class AnimateDiffPipelineOutput(BaseOutput):
     frames: Union[torch.Tensor, np.ndarray]
 
 
-class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin):
+class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
     r"""
     Pipeline for text-to-video generation.
 
@@ -101,7 +101,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
     """
 
     model_cpu_offload_seq = "text_encoder->unet->vae"
-    _optional_components = ["feature_extractor", "image_encoder"]
 
     def __init__(
         self,
@@ -118,8 +117,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
             EulerAncestralDiscreteScheduler,
             DPMSolverMultistepScheduler,
         ],
-        feature_extractor: CLIPImageProcessor = None,
-        image_encoder: CLIPVisionModelWithProjection = None,
     ):
         super().__init__()
         unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
@@ -131,10 +128,10 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
             unet=unet,
             motion_adapter=motion_adapter,
             scheduler=scheduler,
-            feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
         )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.vae_scale_factor = 2 ** (
+            len(getattr(self.vae.config, "block_out_channels", self.vae.config.decoder_block_out_channels)) - 1
+        )
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
@@ -319,20 +316,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
 
         return prompt_embeds, negative_prompt_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
-    def encode_image(self, image, device, num_images_per_prompt):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        image_embeds = self.image_encoder(image).image_embeds
-        image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-
-        uncond_image_embeds = torch.zeros_like(image_embeds)
-        return image_embeds, uncond_image_embeds
-
     # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
     def decode_latents(self, latents):
         latents = 1 / self.vae.config.scaling_factor * latents
@@ -531,7 +514,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
         latents: Optional[torch.FloatTensor] = None,
         prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
@@ -578,7 +560,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
             negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
                 not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or
                 `np.array`.
@@ -650,11 +631,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
         if do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
-        if ip_adapter_image is not None:
-            image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_videos_per_prompt)
-            if do_classifier_free_guidance:
-                image_embeds = torch.cat([negative_image_embeds, image_embeds])
-
         # 4. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps = self.scheduler.timesteps
@@ -675,8 +651,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
 
         # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-        # 7 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
 
         # Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
@@ -692,7 +666,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                     t,
                     encoder_hidden_states=prompt_embeds,
                     cross_attention_kwargs=cross_attention_kwargs,
-                    added_cond_kwargs=added_cond_kwargs,
                 ).sample
 
                 # perform guidance

src/diffusers/pipelines/audioldm/pipeline_audioldm.py

@@ -94,7 +94,9 @@ class AudioLDMPipeline(DiffusionPipeline):
             scheduler=scheduler,
             vocoder=vocoder,
         )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.vae_scale_factor = 2 ** (
+            len(getattr(self.vae.config, "block_out_channels", self.vae.config.decoder_block_out_channels)) - 1
+        )
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
     def enable_vae_slicing(self):

src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py

@@ -171,7 +171,9 @@ class AudioLDM2Pipeline(DiffusionPipeline):
             scheduler=scheduler,
             vocoder=vocoder,
         )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.vae_scale_factor = 2 ** (
+            len(getattr(self.vae.config, "block_out_channels", self.vae.config.decoder_block_out_channels)) - 1
+        )
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
     def enable_vae_slicing(self):

src/diffusers/pipelines/auto_pipeline.py

@@ -42,7 +42,6 @@ from .kandinsky2_2 import (
     KandinskyV22InpaintPipeline,
     KandinskyV22Pipeline,
 )
-from .kandinsky3 import Kandinsky3Img2ImgPipeline, Kandinsky3Pipeline
 from .latent_consistency_models import LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline
 from .pixart_alpha import PixArtAlphaPipeline
 from .stable_diffusion import (
@@ -65,7 +64,6 @@ AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
         ("if", IFPipeline),
         ("kandinsky", KandinskyCombinedPipeline),
         ("kandinsky22", KandinskyV22CombinedPipeline),
-        ("kandinsky3", Kandinsky3Pipeline),
         ("stable-diffusion-controlnet", StableDiffusionControlNetPipeline),
         ("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetPipeline),
         ("wuerstchen", WuerstchenCombinedPipeline),
@@ -81,7 +79,6 @@ AUTO_IMAGE2IMAGE_PIPELINES_MAPPING = OrderedDict(
         ("if", IFImg2ImgPipeline),
         ("kandinsky", KandinskyImg2ImgCombinedPipeline),
         ("kandinsky22", KandinskyV22Img2ImgCombinedPipeline),
-        ("kandinsky3", Kandinsky3Img2ImgPipeline),
         ("stable-diffusion-controlnet", StableDiffusionControlNetImg2ImgPipeline),
         ("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetImg2ImgPipeline),
         ("lcm", LatentConsistencyModelImg2ImgPipeline),

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -20,10 +20,10 @@ import numpy as np
 import PIL.Image
 import torch
 import torch.nn.functional as F
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
@@ -91,53 +91,8 @@ EXAMPLE_DOC_STRING = """
 """
 
 
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    **kwargs,
-):
-    """
-    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
-    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
-    Args:
-        scheduler (`SchedulerMixin`):
-            The scheduler to get timesteps from.
-        num_inference_steps (`int`):
-            The number of diffusion steps used when generating samples with a pre-trained model. If used,
-            `timesteps` must be `None`.
-        device (`str` or `torch.device`, *optional*):
-            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        timesteps (`List[int]`, *optional*):
-                Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
-                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
-                must be `None`.
-
-    Returns:
-        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
-        second element is the number of inference steps.
-    """
-    if timesteps is not None:
-        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accepts_timesteps:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" timestep schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-
 class StableDiffusionControlNetPipeline(
-    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
 ):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
@@ -147,7 +102,6 @@ class StableDiffusionControlNetPipeline(
 
     The pipeline also inherits the following loading methods:
         - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
-        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
 
     Args:
         vae ([`AutoencoderKL`]):
@@ -174,9 +128,8 @@ class StableDiffusionControlNetPipeline(
     """
 
     model_cpu_offload_seq = "text_encoder->unet->vae"
-    _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
+    _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
 
     def __init__(
         self,
@@ -188,7 +141,6 @@ class StableDiffusionControlNetPipeline(
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPImageProcessor,
-        image_encoder: CLIPVisionModelWithProjection = None,
         requires_safety_checker: bool = True,
     ):
         super().__init__()
@@ -221,9 +173,10 @@ class StableDiffusionControlNetPipeline(
             scheduler=scheduler,
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
         )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.vae_scale_factor = 2 ** (
+            len(getattr(self.vae.config, "block_out_channels", self.vae.config.decoder_block_out_channels)) - 1
+        )
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
         self.control_image_processor = VaeImageProcessor(
             vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
@@ -478,20 +431,6 @@ class StableDiffusionControlNetPipeline(
 
         return prompt_embeds, negative_prompt_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
-    def encode_image(self, image, device, num_images_per_prompt):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        image_embeds = self.image_encoder(image).image_embeds
-        image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-
-        uncond_image_embeds = torch.zeros_like(image_embeds)
-        return image_embeds, uncond_image_embeds
-
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
     def run_safety_checker(self, image, device, dtype):
         if self.safety_checker is None:
@@ -548,21 +487,15 @@ class StableDiffusionControlNetPipeline(
         controlnet_conditioning_scale=1.0,
         control_guidance_start=0.0,
         control_guidance_end=1.0,
-        callback_on_step_end_tensor_inputs=None,
     ):
-        if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
             raise ValueError(
                 f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                 f" {type(callback_steps)}."
             )
 
-        if callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
-        ):
-            raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
-            )
-
         if prompt is not None and prompt_embeds is not None:
             raise ValueError(
                 f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
@@ -829,10 +762,6 @@ class StableDiffusionControlNetPipeline(
     def guidance_scale(self):
         return self._guidance_scale
 
-    @property
-    def clip_skip(self):
-        return self._clip_skip
-
     # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
     # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
     # corresponds to doing no classifier free guidance.
@@ -840,14 +769,6 @@ class StableDiffusionControlNetPipeline(
     def do_classifier_free_guidance(self):
         return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
 
-    @property
-    def cross_attention_kwargs(self):
-        return self._cross_attention_kwargs
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -857,7 +778,6 @@ class StableDiffusionControlNetPipeline(
         height: Optional[int] = None,
         width: Optional[int] = None,
         num_inference_steps: int = 50,
-        timesteps: List[int] = None,
         guidance_scale: float = 7.5,
         negative_prompt: Optional[Union[str, List[str]]] = None,
         num_images_per_prompt: Optional[int] = 1,
@@ -866,18 +786,16 @@ class StableDiffusionControlNetPipeline(
         latents: Optional[torch.FloatTensor] = None,
         prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
         guess_mode: bool = False,
         control_guidance_start: Union[float, List[float]] = 0.0,
         control_guidance_end: Union[float, List[float]] = 1.0,
         clip_skip: Optional[int] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        **kwargs,
     ):
         r"""
         The call function to the pipeline for generation.
@@ -900,10 +818,6 @@ class StableDiffusionControlNetPipeline(
             num_inference_steps (`int`, *optional*, defaults to 50):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                 expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
-                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
-                passed will be used. Must be in descending order.
             guidance_scale (`float`, *optional*, defaults to 7.5):
                 A higher guidance scale value encourages the model to generate images closely linked to the text
                 `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
@@ -928,7 +842,6 @@ class StableDiffusionControlNetPipeline(
             negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
                 not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
@@ -957,15 +870,6 @@ class StableDiffusionControlNetPipeline(
             clip_skip (`int`, *optional*):
                 Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                 the output of the pre-final layer will be used for computing the prompt embeddings.
-            callback_on_step_end (`Callable`, *optional*):
-                A function that calls at the end of each denoising steps during the inference. The function is called
-                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
-                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
-                `callback_on_step_end_tensor_inputs`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeine class.
 
         Examples:
 
@@ -976,23 +880,6 @@ class StableDiffusionControlNetPipeline(
                 second element is a list of `bool`s indicating whether the corresponding generated image contains
                 "not-safe-for-work" (nsfw) content.
         """
-
-        callback = kwargs.pop("callback", None)
-        callback_steps = kwargs.pop("callback_steps", None)
-
-        if callback is not None:
-            deprecate(
-                "callback",
-                "1.0.0",
-                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
-            )
-        if callback_steps is not None:
-            deprecate(
-                "callback_steps",
-                "1.0.0",
-                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
-            )
-
         controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
 
         # align format for control guidance
@@ -1018,12 +905,9 @@ class StableDiffusionControlNetPipeline(
             controlnet_conditioning_scale,
             control_guidance_start,
             control_guidance_end,
-            callback_on_step_end_tensor_inputs,
         )
 
         self._guidance_scale = guidance_scale
-        self._clip_skip = clip_skip
-        self._cross_attention_kwargs = cross_attention_kwargs
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
@@ -1047,7 +931,7 @@ class StableDiffusionControlNetPipeline(
 
         # 3. Encode input prompt
         text_encoder_lora_scale = (
-            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
         )
         prompt_embeds, negative_prompt_embeds = self.encode_prompt(
             prompt,
@@ -1058,7 +942,7 @@ class StableDiffusionControlNetPipeline(
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
             lora_scale=text_encoder_lora_scale,
-            clip_skip=self.clip_skip,
+            clip_skip=clip_skip,
         )
         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
@@ -1066,11 +950,6 @@ class StableDiffusionControlNetPipeline(
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
 
-        if ip_adapter_image is not None:
-            image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
-            if self.do_classifier_free_guidance:
-                image_embeds = torch.cat([negative_image_embeds, image_embeds])
-
         # 4. Prepare image
         if isinstance(controlnet, ControlNetModel):
             image = self.prepare_image(
@@ -1109,8 +988,8 @@ class StableDiffusionControlNetPipeline(
             assert False
 
         # 5. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
-        self._num_timesteps = len(timesteps)
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
 
         # 6. Prepare latent variables
         num_channels_latents = self.unet.config.in_channels
@@ -1136,10 +1015,7 @@ class StableDiffusionControlNetPipeline(
         # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
-        # 7.1 Add image embeds for IP-Adapter
-        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
-
-        # 7.2 Create tensor stating which controlnets to keep
+        # 7.1 Create tensor stating which controlnets to keep
         controlnet_keep = []
         for i in range(len(timesteps)):
             keeps = [
@@ -1204,31 +1080,20 @@ class StableDiffusionControlNetPipeline(
                     t,
                     encoder_hidden_states=prompt_embeds,
                     timestep_cond=timestep_cond,
-                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    cross_attention_kwargs=cross_attention_kwargs,
                     down_block_additional_residuals=down_block_res_samples,
                     mid_block_additional_residual=mid_block_res_sample,
-                    added_cond_kwargs=added_cond_kwargs,
                     return_dict=False,
                 )[0]
 
                 # perform guidance
                 if self.do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
 
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()