remove print

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

.github/workflows/nightly_tests.yml

@@ -180,6 +180,62 @@ jobs:
         pip install slack_sdk tabulate
         python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
+  run_big_gpu_torch_tests:
+    name: Torch tests on big GPU
+    strategy:
+      fail-fast: false
+      max-parallel: 2
+    runs-on:
+      group: aws-g6e-xlarge-plus
+    container:
+      image: diffusers/diffusers-pytorch-cuda
+      options: --shm-size "16gb" --ipc host --gpus 0
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+      - name: NVIDIA-SMI
+        run: nvidia-smi
+      - name: Install dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install pytest-reportlog
+      - name: Environment
+        run: |
+          python utils/print_env.py
+      - name: Selected Torch CUDA Test on big GPU
+        env:
+          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
+          CUBLAS_WORKSPACE_CONFIG: :16:8
+          BIG_GPU_MEMORY: 40
+        run: |
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -m "big_gpu_with_torch_cuda" \
+            --make-reports=tests_big_gpu_torch_cuda \
+            --report-log=tests_big_gpu_torch_cuda.log \
+            tests/
+      - name: Failure short reports
+        if: ${{ failure() }}
+        run: |
+          cat reports/tests_big_gpu_torch_cuda_stats.txt
+          cat reports/tests_big_gpu_torch_cuda_failures_short.txt
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: torch_cuda_big_gpu_test_reports
+          path: reports
+      - name: Generate Report and Notify Channel
+        if: always()
+        run: |
+          pip install slack_sdk tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
   run_flax_tpu_tests:
     name: Nightly Flax TPU Tests
     runs-on: docker-tpu
@@ -291,6 +347,64 @@ jobs:
         pip install slack_sdk tabulate
         python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
+  run_nightly_quantization_tests:
+    name: Torch quantization nightly tests
+    strategy:
+      fail-fast: false
+      max-parallel: 2
+      matrix: 
+        config:
+          - backend: "bitsandbytes"
+            test_location: "bnb"
+    runs-on:
+      group: aws-g6e-xlarge-plus
+    container:
+      image: diffusers/diffusers-pytorch-cuda
+      options: --shm-size "20gb" --ipc host --gpus 0
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+      - name: NVIDIA-SMI
+        run: nvidia-smi
+      - name: Install dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install -U ${{ matrix.config.backend }}
+          python -m uv pip install pytest-reportlog
+      - name: Environment
+        run: |
+          python utils/print_env.py
+      - name: ${{ matrix.config.backend }} quantization tests on GPU
+        env:
+          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
+          CUBLAS_WORKSPACE_CONFIG: :16:8
+          BIG_GPU_MEMORY: 40
+        run: |
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            --make-reports=tests_${{ matrix.config.backend }}_torch_cuda \
+            --report-log=tests_${{ matrix.config.backend }}_torch_cuda.log \
+            tests/quantization/${{ matrix.config.test_location }}
+      - name: Failure short reports
+        if: ${{ failure() }}
+        run: |
+          cat reports/tests_${{ matrix.config.backend }}_torch_cuda_stats.txt
+          cat reports/tests_${{ matrix.config.backend }}_torch_cuda_failures_short.txt
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: torch_cuda_${{ matrix.config.backend }}_reports
+          path: reports
+      - name: Generate Report and Notify Channel
+        if: always()
+        run: |
+          pip install slack_sdk tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
 # M1 runner currently not well supported
 # TODO: (Dhruv) add these back when we setup better testing for Apple Silicon
 #  run_nightly_tests_apple_m1:

.github/workflows/ssh-runner.yml

@@ -4,12 +4,13 @@ on:
   workflow_dispatch:
     inputs:
       runner_type:
-        description: 'Type of runner to test (aws-g6-4xlarge-plus: a10 or aws-g4dn-2xlarge: t4)'
+        description: 'Type of runner to test (aws-g6-4xlarge-plus: a10, aws-g4dn-2xlarge: t4, aws-g6e-xlarge-plus: L40)'
         type: choice
         required: true
         options:
           - aws-g6-4xlarge-plus
           - aws-g4dn-2xlarge
+          - aws-g6e-xlarge-plus
       docker_image:
         description: 'Name of the Docker image'
         required: true

README.md

@@ -114,7 +114,7 @@ Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to l
 | [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview)                                                            | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model.  |
 | [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview)                                                             | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers.                                         |
 | [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview)                                             | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library.               |
-| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview)                                                        | Guides for how to optimize your diffusion model to run faster and consume less memory.                                                                                                          |
+| [Optimization](https://huggingface.co/docs/diffusers/optimization/fp16)                                                        | Guides for how to optimize your diffusion model to run faster and consume less memory.                                                                                                          |
 | [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques.                                                                                               |
 ## Contribution
 

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -28,7 +28,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-        "torch<2.5.0" \
+        torch \
         torchvision \
         torchaudio \
         "onnxruntime-gpu>=1.13.1" \

docker/diffusers-pytorch-compile-cuda/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-    "torch<2.5.0" \
+    torch \
     torchvision \
     torchaudio \
     invisible_watermark && \

docker/diffusers-pytorch-cpu/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-        "torch<2.5.0" \
+        torch \
         torchvision \
         torchaudio \
         invisible_watermark \

docker/diffusers-pytorch-cuda/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-    "torch<2.5.0" \
+    torch \
     torchvision \
     torchaudio \
     invisible_watermark && \

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m pip install --no-cache-dir \
-        "torch<2.5.0" \
+        torch \
         torchvision \
         torchaudio \
         invisible_watermark && \

docs/source/en/_toctree.yml

@@ -55,6 +55,8 @@
 - sections:
   - local: using-diffusers/overview_techniques
     title: Overview
+  - local: using-diffusers/create_a_server
+    title: Create a server
   - local: training/distributed_inference
     title: Distributed inference
   - local: using-diffusers/merge_loras
@@ -252,6 +254,8 @@
         title: SparseControlNetModel
       title: ControlNets
     - sections:
+      - local: api/models/allegro_transformer3d
+        title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
       - local: api/models/cogvideox_transformer3d
@@ -302,6 +306,8 @@
     - sections:
       - local: api/models/autoencoderkl
         title: AutoencoderKL
+      - local: api/models/autoencoderkl_allegro
+        title: AutoencoderKLAllegro
       - local: api/models/autoencoderkl_cogvideox
         title: AutoencoderKLCogVideoX
       - local: api/models/autoencoderkl_mochi
@@ -322,6 +328,8 @@
     sections:
     - local: api/pipelines/overview
       title: Overview
+    - local: api/pipelines/allegro
+      title: Allegro
     - local: api/pipelines/amused
       title: aMUSEd
     - local: api/pipelines/animatediff

docs/source/en/api/models/allegro_transformer3d.md

@@ -0,0 +1,30 @@
+<!-- Copyright 2024 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. -->
+
+# AllegroTransformer3DModel
+
+A Diffusion Transformer model for 3D data from [Allegro](https://github.com/rhymes-ai/Allegro) was introduced in [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) by RhymesAI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AllegroTransformer3DModel
+
+vae = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+```
+
+## AllegroTransformer3DModel
+
+[[autodoc]] AllegroTransformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/autoencoderkl_allegro.md

@@ -0,0 +1,37 @@
+<!-- Copyright 2024 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. -->
+
+# AutoencoderKLAllegro
+
+The 3D variational autoencoder (VAE) model with KL loss used in [Allegro](https://github.com/rhymes-ai/Allegro) was introduced in [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) by RhymesAI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLAllegro
+
+vae = AutoencoderKLCogVideoX.from_pretrained("rhymes-ai/Allegro", subfolder="vae", torch_dtype=torch.float32).to("cuda")
+```
+
+## AutoencoderKLAllegro
+
+[[autodoc]] AutoencoderKLAllegro
+    - decode
+    - encode
+    - all
+
+## AutoencoderKLOutput
+
+[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/controlnet.md

@@ -39,7 +39,7 @@ pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=contro
 
 ## ControlNetOutput
 
-[[autodoc]] models.controlnet.ControlNetOutput
+[[autodoc]] models.controlnets.controlnet.ControlNetOutput
 
 ## FlaxControlNetModel
 
@@ -47,4 +47,4 @@ pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=contro
 
 ## FlaxControlNetOutput
 
-[[autodoc]] models.controlnet_flax.FlaxControlNetOutput
+[[autodoc]] models.controlnets.controlnet_flax.FlaxControlNetOutput

docs/source/en/api/models/controlnet_sd3.md

@@ -38,5 +38,5 @@ pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-di
 
 ## SD3ControlNetOutput
 
-[[autodoc]] models.controlnet_sd3.SD3ControlNetOutput
+[[autodoc]] models.controlnets.controlnet_sd3.SD3ControlNetOutput
 

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

@@ -0,0 +1,34 @@
+<!-- Copyright 2024 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. -->
+
+# Allegro
+
+[Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) from RhymesAI, by Yuan Zhou, Qiuyue Wang, Yuxuan Cai, Huan Yang.
+
+The abstract from the paper is:
+
+*Significant advancements have been made in the field of video generation, with the open-source community contributing a wealth of research papers and tools for training high-quality models. However, despite these efforts, the available information and resources remain insufficient for achieving commercial-level performance. In this report, we open the black box and introduce Allegro, an advanced video generation model that excels in both quality and temporal consistency. We also highlight the current limitations in the field and present a comprehensive methodology for training high-performance, commercial-level video generation models, addressing key aspects such as data, model architecture, training pipeline, and evaluation. Our user study shows that Allegro surpasses existing open-source models and most commercial models, ranking just behind Hailuo and Kling. Code: https://github.com/rhymes-ai/Allegro , Model: https://huggingface.co/rhymes-ai/Allegro , Gallery: https://rhymes.ai/allegro_gallery .*
+
+<Tip>
+
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+## AllegroPipeline
+
+[[autodoc]] AllegroPipeline
+  - all
+  - __call__
+
+## AllegroPipelineOutput
+
+[[autodoc]] pipelines.allegro.pipeline_output.AllegroPipelineOutput

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

@@ -29,16 +29,32 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.m
 
 This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
 
-There are two models available that can be used with the text-to-video and video-to-video CogVideoX pipelines:
-- [`THUDM/CogVideoX-2b`](https://huggingface.co/THUDM/CogVideoX-2b): The recommended dtype for running this model is `fp16`.
-- [`THUDM/CogVideoX-5b`](https://huggingface.co/THUDM/CogVideoX-5b): The recommended dtype for running this model is `bf16`.
+There are three official CogVideoX checkpoints for text-to-video and video-to-video.
 
-There is one model available that can be used with the image-to-video CogVideoX pipeline:
-- [`THUDM/CogVideoX-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-5b-I2V): The recommended dtype for running this model is `bf16`.
+| checkpoints | recommended inference dtype |
+|:---:|:---:|
+| [`THUDM/CogVideoX-2b`](https://huggingface.co/THUDM/CogVideoX-2b) | torch.float16 |
+| [`THUDM/CogVideoX-5b`](https://huggingface.co/THUDM/CogVideoX-5b) | torch.bfloat16 |
+| [`THUDM/CogVideoX1.5-5b`](https://huggingface.co/THUDM/CogVideoX1.5-5b) | torch.bfloat16 |
 
-There are two models that support pose controllable generation (by the [Alibaba-PAI](https://huggingface.co/alibaba-pai) team):
-- [`alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose): The recommended dtype for running this model is `bf16`.
-- [`alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose): The recommended dtype for running this model is `bf16`.
+There are two official CogVideoX checkpoints available for image-to-video.
+
+| checkpoints | recommended inference dtype |
+|:---:|:---:|
+| [`THUDM/CogVideoX-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-5b-I2V) | torch.bfloat16 |
+| [`THUDM/CogVideoX-1.5-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-1.5-5b-I2V) | torch.bfloat16 |
+
+For the CogVideoX 1.5 series:
+- Text-to-video (T2V) works best at a resolution of 1360x768 because it was trained with that specific resolution.
+- Image-to-video (I2V) works for multiple resolutions. The width can vary from 768 to 1360, but the height must be 768. The height/width must be divisible by 16.
+- Both T2V and I2V models support generation with 81 and 161 frames and work best at this value. Exporting videos at 16 FPS is recommended.
+
+There are two official CogVideoX checkpoints that support pose controllable generation (by the [Alibaba-PAI](https://huggingface.co/alibaba-pai) team).
+
+| checkpoints | recommended inference dtype |
+|:---:|:---:|
+| [`alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose) | torch.bfloat16 |
+| [`alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose) | torch.bfloat16 |
 
 ## Inference
 

docs/source/en/api/pipelines/controlnet_sd3.md

@@ -28,6 +28,7 @@ This controlnet code is mainly implemented by [The InstantX Team](https://huggin
 | ControlNet type | Developer | Link |
 | -------- | ---------- | ---- |
 | Canny | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Canny) |
+| Depth | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Depth) |
 | Pose | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Pose) |
 | Tile | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Tile) |
 | Inpainting | [The AlimamaCreative Team](https://huggingface.co/alimama-creative) | [link](https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting) |

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

@@ -22,12 +22,20 @@ Flux can be quite expensive to run on consumer hardware devices. However, you ca
 
 </Tip>
 
-Flux comes in two variants:
+Flux comes in the following variants:
 
-* Timestep-distilled (`black-forest-labs/FLUX.1-schnell`)
-* Guidance-distilled (`black-forest-labs/FLUX.1-dev`)
+| model type | model id |
+|:----------:|:--------:|
+| Timestep-distilled | [`black-forest-labs/FLUX.1-schnell`](https://huggingface.co/black-forest-labs/FLUX.1-schnell) |
+| Guidance-distilled | [`black-forest-labs/FLUX.1-dev`](https://huggingface.co/black-forest-labs/FLUX.1-dev) |
+| Fill Inpainting/Outpainting (Guidance-distilled) | [`black-forest-labs/FLUX.1-Fill-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev) |
+| Canny Control (Guidance-distilled) | [`black-forest-labs/FLUX.1-Canny-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev) |
+| Depth Control (Guidance-distilled) | [`black-forest-labs/FLUX.1-Depth-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev) |
+| Canny Control (LoRA) | [`black-forest-labs/FLUX.1-Canny-dev-lora`](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev-lora) |
+| Depth Control (LoRA) | [`black-forest-labs/FLUX.1-Depth-dev-lora`](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev-lora) |
+| Redux (Adapter) | [`black-forest-labs/FLUX.1-Redux-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Redux-dev) |
 
-Both checkpoints have slightly difference usage which we detail below.
+All checkpoints have different usage which we detail below.
 
 ### Timestep-distilled
 
@@ -77,7 +85,132 @@ out = pipe(
 out.save("image.png")
 ```
 
+### Fill Inpainting/Outpainting
+
+* Flux Fill pipeline does not require `strength` as an input like regular inpainting pipelines.
+* It supports both inpainting and outpainting.
+
+```python
+import torch
+from diffusers import FluxFillPipeline
+from diffusers.utils import load_image
+
+image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup.png")
+mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/cup_mask.png")
+
+repo_id = "black-forest-labs/FLUX.1-Fill-dev"
+pipe = FluxFillPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16).to("cuda")
+
+image = pipe(
+    prompt="a white paper cup",
+    image=image,
+    mask_image=mask,
+    height=1632,
+    width=1232,
+    max_sequence_length=512,
+    generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+image.save(f"output.png")
+```
+
+### Canny Control
+
+**Note:** `black-forest-labs/Flux.1-Canny-dev` is _not_ a [`ControlNetModel`] model. ControlNet models are a separate component from the UNet/Transformer whose residuals are added to the actual underlying model. Canny Control is an alternate architecture that achieves effectively the same results as a ControlNet model would, by using channel-wise concatenation with input control condition and ensuring the transformer learns structure control by following the condition as closely as possible. 
+
+```python
+# !pip install -U controlnet-aux
+import torch
+from controlnet_aux import CannyDetector
+from diffusers import FluxControlPipeline
+from diffusers.utils import load_image
+
+pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Canny-dev", torch_dtype=torch.bfloat16).to("cuda")
+
+prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
+control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
+
+processor = CannyDetector()
+control_image = processor(control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024)
+
+image = pipe(
+    prompt=prompt,
+    control_image=control_image,
+    height=1024,
+    width=1024,
+    num_inference_steps=50,
+    guidance_scale=30.0,
+).images[0]
+image.save("output.png")
+```
+
+### Depth Control
+
+**Note:** `black-forest-labs/Flux.1-Depth-dev` is _not_ a ControlNet model. [`ControlNetModel`] models are a separate component from the UNet/Transformer whose residuals are added to the actual underlying model. Depth Control is an alternate architecture that achieves effectively the same results as a ControlNet model would, by using channel-wise concatenation with input control condition and ensuring the transformer learns structure control by following the condition as closely as possible.
+
+```python
+# !pip install git+https://github.com/huggingface/image_gen_aux
+import torch
+from diffusers import FluxControlPipeline, FluxTransformer2DModel
+from diffusers.utils import load_image
+from image_gen_aux import DepthPreprocessor
+
+pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Depth-dev", torch_dtype=torch.bfloat16).to("cuda")
+
+prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
+control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
+
+processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
+control_image = processor(control_image)[0].convert("RGB")
+
+image = pipe(
+    prompt=prompt,
+    control_image=control_image,
+    height=1024,
+    width=1024,
+    num_inference_steps=30,
+    guidance_scale=10.0,
+    generator=torch.Generator().manual_seed(42),
+).images[0]
+image.save("output.png")
+```
+
+### Redux
+
+* Flux Redux pipeline is an adapter for FLUX.1 base models. It can be used with both flux-dev and flux-schnell, for image-to-image generation.
+* You can first use the `FluxPriorReduxPipeline` to get the `prompt_embeds` and `pooled_prompt_embeds`, and then feed them into the `FluxPipeline` for image-to-image generation.
+* When use `FluxPriorReduxPipeline` with a base pipeline, you can set `text_encoder=None` and `text_encoder_2=None` in the base pipeline, in order to save VRAM.
+
+```python
+import torch
+from diffusers import FluxPriorReduxPipeline, FluxPipeline
+from diffusers.utils import load_image
+device = "cuda"
+dtype = torch.bfloat16
+
+
+repo_redux = "black-forest-labs/FLUX.1-Redux-dev"
+repo_base = "black-forest-labs/FLUX.1-dev" 
+pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(repo_redux, torch_dtype=dtype).to(device)
+pipe = FluxPipeline.from_pretrained(
+    repo_base, 
+    text_encoder=None,
+    text_encoder_2=None,
+    torch_dtype=torch.bfloat16
+).to(device)
+
+image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img5.png")
+pipe_prior_output = pipe_prior_redux(image)
+images = pipe(
+    guidance_scale=2.5,
+    num_inference_steps=50,
+    generator=torch.Generator("cpu").manual_seed(0),
+    **pipe_prior_output,
+).images
+images[0].save("flux-redux.png")
+```
+
 ## Running FP16 inference
+
 Flux can generate high-quality images with FP16 (i.e. to accelerate inference on Turing/Volta GPUs) but produces different outputs compared to FP32/BF16. The issue is that some activations in the text encoders have to be clipped when running in FP16, which affects the overall image. Forcing text encoders to run with FP32 inference thus removes this output difference. See [here](https://github.com/huggingface/diffusers/pull/9097#issuecomment-2272292516) for details.
 
 FP16 inference code:
@@ -188,3 +321,27 @@ image.save("flux-fp8-dev.png")
 [[autodoc]] FluxControlNetImg2ImgPipeline
 	- all
 	- __call__
+
+## FluxControlPipeline
+
+[[autodoc]] FluxControlPipeline
+	- all
+	- __call__
+
+## FluxControlImg2ImgPipeline
+
+[[autodoc]] FluxControlImg2ImgPipeline
+	- all
+	- __call__
+
+## FluxPriorReduxPipeline
+
+[[autodoc]] FluxPriorReduxPipeline
+	- all
+	- __call__
+
+## FluxFillPipeline
+
+[[autodoc]] FluxFillPipeline
+	- all
+	- __call__

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

@@ -96,6 +96,10 @@ Since RegEx is supported as a way for matching layer identifiers, it is crucial
 	- all
 	- __call__
 
+## StableDiffusion3PAGImg2ImgPipeline
+[[autodoc]] StableDiffusion3PAGImg2ImgPipeline
+	- all
+	- __call__
 
 ## PixArtSigmaPAGPipeline
 [[autodoc]] PixArtSigmaPAGPipeline

docs/source/en/conceptual/evaluation.md

@@ -181,7 +181,7 @@ Then we load the [v1-5 checkpoint](https://huggingface.co/stable-diffusion-v1-5/
 
 ```python
 model_ckpt_1_5 = "stable-diffusion-v1-5/stable-diffusion-v1-5"
-sd_pipeline_1_5 = StableDiffusionPipeline.from_pretrained(model_ckpt_1_5, torch_dtype=weight_dtype).to(device)
+sd_pipeline_1_5 = StableDiffusionPipeline.from_pretrained(model_ckpt_1_5, torch_dtype=torch.float16).to("cuda")
 
 images_1_5 = sd_pipeline_1_5(prompts, num_images_per_prompt=1, generator=generator, output_type="np").images
 ```
@@ -280,7 +280,7 @@ from diffusers import StableDiffusionInstructPix2PixPipeline
 
 instruct_pix2pix_pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
     "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
-).to(device)
+).to("cuda")
 ```
 
 Now, we perform the edits:
@@ -326,9 +326,9 @@ from transformers import (
 
 clip_id = "openai/clip-vit-large-patch14"
 tokenizer = CLIPTokenizer.from_pretrained(clip_id)
-text_encoder = CLIPTextModelWithProjection.from_pretrained(clip_id).to(device)
+text_encoder = CLIPTextModelWithProjection.from_pretrained(clip_id).to("cuda")
 image_processor = CLIPImageProcessor.from_pretrained(clip_id)
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_id).to(device)
+image_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_id).to("cuda")
 ```
 
 Notice that we are using a particular CLIP checkpoint, i.e., `openai/clip-vit-large-patch14`. This is because the Stable Diffusion pre-training was performed with this CLIP variant. For more details, refer to the [documentation](https://huggingface.co/docs/transformers/model_doc/clip).
@@ -350,7 +350,7 @@ class DirectionalSimilarity(nn.Module):
 
     def preprocess_image(self, image):
         image = self.image_processor(image, return_tensors="pt")["pixel_values"]
-        return {"pixel_values": image.to(device)}
+        return {"pixel_values": image.to("cuda")}
 
     def tokenize_text(self, text):
         inputs = self.tokenizer(
@@ -360,7 +360,7 @@ class DirectionalSimilarity(nn.Module):
             truncation=True,
             return_tensors="pt",
         )
-        return {"input_ids": inputs.input_ids.to(device)}
+        return {"input_ids": inputs.input_ids.to("cuda")}
 
     def encode_image(self, image):
         preprocessed_image = self.preprocess_image(image)
@@ -459,6 +459,7 @@ with ZipFile(local_filepath, "r") as zipper:
 ```python
 from PIL import Image
 import os
+import numpy as np
 
 dataset_path = "sample-imagenet-images"
 image_paths = sorted([os.path.join(dataset_path, x) for x in os.listdir(dataset_path)])
@@ -477,6 +478,7 @@ Now that the images are loaded, let's apply some lightweight pre-processing on t
 
 ```python
 from torchvision.transforms import functional as F
+import torch
 
 
 def preprocess_image(image):
@@ -498,6 +500,10 @@ dit_pipeline = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=
 dit_pipeline.scheduler = DPMSolverMultistepScheduler.from_config(dit_pipeline.scheduler.config)
 dit_pipeline = dit_pipeline.to("cuda")
 
+seed = 0
+generator = torch.manual_seed(seed)
+
+
 words = [
     "cassette player",
     "chainsaw",

docs/source/en/training/create_dataset.md

@@ -1,6 +1,6 @@
 # Create a dataset for training
 
-There are many datasets on the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads) to train a model on, but if you can't find one you're interested in or want to use your own, you can create a dataset with the 🤗 [Datasets](hf.co/docs/datasets) library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. Another dataset structure may be a directory of images and a text file containing their corresponding text captions for tasks like text-to-image generation.
+There are many datasets on the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads) to train a model on, but if you can't find one you're interested in or want to use your own, you can create a dataset with the 🤗 [Datasets](https://huggingface.co/docs/datasets) library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. Another dataset structure may be a directory of images and a text file containing their corresponding text captions for tasks like text-to-image generation.
 
 This guide will show you two ways to create a dataset to finetune on:
 
@@ -87,4 +87,4 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
 
 Now that you've created a dataset, you can plug it into the `train_data_dir` (if your dataset is local) or `dataset_name` (if your dataset is on the Hub) arguments of a training script.
 
-For your next steps, feel free to try and use your dataset to train a model for [unconditional generation](unconditional_training) or [text-to-image generation](text2image)!
+For your next steps, feel free to try and use your dataset to train a model for [unconditional generation](unconditional_training) or [text-to-image generation](text2image)!

docs/source/en/training/distributed_inference.md

@@ -183,7 +183,7 @@ Add the transformer model to the pipeline for denoising, but set the other model
 
 ```py
 pipeline = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", ,
+    "black-forest-labs/FLUX.1-dev",
     text_encoder=None,
     text_encoder_2=None,
     tokenizer=None,

docs/source/en/tutorials/basic_training.md

@@ -75,7 +75,7 @@ For convenience, create a `TrainingConfig` class containing the training hyperpa
 
 ...     push_to_hub = True  # whether to upload the saved model to the HF Hub
 ...     hub_model_id = "<your-username>/<my-awesome-model>"  # the name of the repository to create on the HF Hub
-...     hub_private_repo = False
+...     hub_private_repo = None
 ...     overwrite_output_dir = True  # overwrite the old model when re-running the notebook
 ...     seed = 0
 

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

@@ -0,0 +1,61 @@
+
+# Create a server
+
+Diffusers' pipelines can be used as an inference engine for a server. It supports concurrent and multithreaded requests to generate images that may be requested by multiple users at the same time.
+
+This guide will show you how to use the [`StableDiffusion3Pipeline`] in a server, but feel free to use any pipeline you want.
+
+
+Start by navigating to the `examples/server` folder and installing all of the dependencies.
+
+```py
+pip install .
+pip install -f requirements.txt
+```
+
+Launch the server with the following command.
+
+```py
+python server.py
+```
+
+The server is accessed at http://localhost:8000. You can curl this model with the following command.
+```
+curl -X POST -H "Content-Type: application/json" --data '{"model": "something", "prompt": "a kitten in front of a fireplace"}' http://localhost:8000/v1/images/generations
+```
+
+If you need to upgrade some dependencies, you can use either [pip-tools](https://github.com/jazzband/pip-tools) or [uv](https://github.com/astral-sh/uv). For example, upgrade the dependencies with `uv` using the following command.
+
+```
+uv pip compile requirements.in -o requirements.txt
+```
+
+
+The server is built with [FastAPI](https://fastapi.tiangolo.com/async/). The endpoint for `v1/images/generations` is shown below.
+```py
+@app.post("/v1/images/generations")
+async def generate_image(image_input: TextToImageInput):
+    try:
+        loop = asyncio.get_event_loop()
+        scheduler = shared_pipeline.pipeline.scheduler.from_config(shared_pipeline.pipeline.scheduler.config)
+        pipeline = StableDiffusion3Pipeline.from_pipe(shared_pipeline.pipeline, scheduler=scheduler)
+        generator = torch.Generator(device="cuda")
+        generator.manual_seed(random.randint(0, 10000000))
+        output = await loop.run_in_executor(None, lambda: pipeline(image_input.prompt, generator = generator))
+        logger.info(f"output: {output}")
+        image_url = save_image(output.images[0])
+        return {"data": [{"url": image_url}]}
+    except Exception as e:
+        if isinstance(e, HTTPException):
+            raise e
+        elif hasattr(e, 'message'):
+            raise HTTPException(status_code=500, detail=e.message + traceback.format_exc())
+        raise HTTPException(status_code=500, detail=str(e) + traceback.format_exc())
+```
+The `generate_image` function is defined as asynchronous with the [async](https://fastapi.tiangolo.com/async/) keyword so that FastAPI knows that whatever is happening in this function won't necessarily return a result right away. Once it hits some point in the function that it needs to await some other [Task](https://docs.python.org/3/library/asyncio-task.html#asyncio.Task), the main thread goes back to answering other HTTP requests. This is shown in the code below with the [await](https://fastapi.tiangolo.com/async/#async-and-await) keyword.
+```py
+output = await loop.run_in_executor(None, lambda: pipeline(image_input.prompt, generator = generator))
+```
+At this point, the execution of the pipeline function is placed onto a [new thread](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor), and the main thread performs other things until a result is returned from the `pipeline`.
+
+Another important aspect of this implementation is creating a `pipeline` from `shared_pipeline`. The goal behind this is to avoid loading the underlying model more than once onto the GPU while still allowing for each new request that is running on a separate thread to have its own generator and scheduler. The scheduler, in particular, is not thread-safe, and it will cause errors like: `IndexError: index 21 is out of bounds for dimension 0 with size 21` if you try to use the same scheduler across multiple threads.

docs/source/ko/api/pipelines/stable_diffusion/stable_diffusion_xl.md

@@ -121,7 +121,7 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inferen
 
 ### 이미지 결과물을 정제하기
 
-[base 모델 체크포인트](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)에서, StableDiffusion-XL 또한 고주파 품질을 향상시키는 이미지를 생성하기 위해 낮은 노이즈 단계 이미지를 제거하는데 특화된 [refiner 체크포인트](huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)를 포함하고 있습니다. 이 refiner 체크포인트는 이미지 품질을 향상시키기 위해 base 체크포인트를 실행한 후 "두 번째 단계" 파이프라인에 사용될 수 있습니다.
+[base 모델 체크포인트](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)에서, StableDiffusion-XL 또한 고주파 품질을 향상시키는 이미지를 생성하기 위해 낮은 노이즈 단계 이미지를 제거하는데 특화된 [refiner 체크포인트](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)를 포함하고 있습니다. 이 refiner 체크포인트는 이미지 품질을 향상시키기 위해 base 체크포인트를 실행한 후 "두 번째 단계" 파이프라인에 사용될 수 있습니다.
 
 refiner를 사용할 때, 쉽게 사용할 수 있습니다
 - 1.) base 모델과 refiner을 사용하는데, 이는 *Denoisers의 앙상블*을 위한 첫 번째 제안된 [eDiff-I](https://research.nvidia.com/labs/dir/eDiff-I/)를 사용하거나
@@ -215,7 +215,7 @@ image = refiner(
 
 #### 2.) 노이즈가 완전히 제거된 기본 이미지에서 이미지 출력을 정제하기
 
-일반적인 [`StableDiffusionImg2ImgPipeline`] 방식에서, 기본 모델에서 생성된 완전히 노이즈가 제거된 이미지는 [refiner checkpoint](huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)를 사용해 더 향상시킬 수 있습니다.
+일반적인 [`StableDiffusionImg2ImgPipeline`] 방식에서, 기본 모델에서 생성된 완전히 노이즈가 제거된 이미지는 [refiner checkpoint](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)를 사용해 더 향상시킬 수 있습니다.
 
 이를 위해, 보통의 "base" text-to-image 파이프라인을 수행 후에 image-to-image 파이프라인으로써 refiner를 실행시킬 수 있습니다. base 모델의 출력을 잠재 공간에 남겨둘 수 있습니다.
 

docs/source/ko/training/create_dataset.md

@@ -1,7 +1,7 @@
 # 학습을 위한 데이터셋 만들기
 
 [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads) 에는 모델 교육을 위한 많은 데이터셋이 있지만,
-관심이 있거나 사용하고 싶은 데이터셋을 찾을 수 없는 경우 🤗 [Datasets](hf.co/docs/datasets) 라이브러리를 사용하여 데이터셋을 만들 수 있습니다.
+관심이 있거나 사용하고 싶은 데이터셋을 찾을 수 없는 경우 🤗 [Datasets](https://huggingface.co/docs/datasets) 라이브러리를 사용하여 데이터셋을 만들 수 있습니다.
 데이터셋 구조는 모델을 학습하려는 작업에 따라 달라집니다.
 가장 기본적인 데이터셋 구조는 unconditional 이미지 생성과 같은 작업을 위한 이미지 디렉토리입니다.
 또 다른 데이터셋 구조는 이미지 디렉토리와 text-to-image 생성과 같은 작업에 해당하는 텍스트 캡션이 포함된 텍스트 파일일 수 있습니다.

docs/source/ko/training/lora.md

@@ -36,7 +36,7 @@ specific language governing permissions and limitations under the License.
 
 [cloneofsimo](https://github.com/cloneofsimo)는 인기 있는 [lora](https://github.com/cloneofsimo/lora) GitHub 리포지토리에서 Stable Diffusion을 위한 LoRA 학습을 최초로 시도했습니다. 🧨 Diffusers는 [text-to-image 생성](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image#training-with-lora) 및 [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#training-with-low-rank-adaptation-of-large-language-models-lora)을 지원합니다. 이 가이드는 두 가지를 모두 수행하는 방법을 보여줍니다.
 
-모델을 저장하거나 커뮤니티와 공유하려면 Hugging Face 계정에 로그인하세요(아직 계정이 없는 경우 [생성](hf.co/join)하세요):
+모델을 저장하거나 커뮤니티와 공유하려면 Hugging Face 계정에 로그인하세요(아직 계정이 없는 경우 [생성](https://huggingface.co/join)하세요):
 
 ```bash
 huggingface-cli login

docs/source/ko/tutorials/basic_training.md

@@ -76,7 +76,7 @@ huggingface-cli login
 ...     output_dir = "ddpm-butterflies-128"  # 로컬 및 HF Hub에 저장되는 모델명
 
 ...     push_to_hub = True  # 저장된 모델을 HF Hub에 업로드할지 여부
-...     hub_private_repo = False
+...     hub_private_repo = None
 ...     overwrite_output_dir = True  # 노트북을 다시 실행할 때 이전 모델에 덮어씌울지
 ...     seed = 0
 

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -1650,6 +1650,8 @@ def main(args):
                 elif isinstance(model, type(unwrap_model(text_encoder_one))):
                     if args.train_text_encoder:  # when --train_text_encoder_ti we don't save the layers
                         text_encoder_one_lora_layers_to_save = get_peft_model_state_dict(model)
+                elif isinstance(model, type(unwrap_model(text_encoder_two))):
+                    pass  # when --train_text_encoder_ti and --enable_t5_ti we don't save the layers
                 else:
                     raise ValueError(f"unexpected save model: {model.__class__}")
 
@@ -1776,15 +1778,10 @@ def main(args):
         if not args.enable_t5_ti:
             # pure textual inversion - only clip
             if pure_textual_inversion:
-                params_to_optimize = [
-                    text_parameters_one_with_lr,
-                ]
+                params_to_optimize = [text_parameters_one_with_lr]
                 te_idx = 0
             else:  # regular te training or regular pivotal for clip
-                params_to_optimize = [
-                    transformer_parameters_with_lr,
-                    text_parameters_one_with_lr,
-                ]
+                params_to_optimize = [transformer_parameters_with_lr, text_parameters_one_with_lr]
                 te_idx = 1
         elif args.enable_t5_ti:
             # pivotal tuning of clip & t5
@@ -1807,9 +1804,7 @@ def main(args):
                 ]
                 te_idx = 1
     else:
-        params_to_optimize = [
-            transformer_parameters_with_lr,
-        ]
+        params_to_optimize = [transformer_parameters_with_lr]
 
     # Optimizer creation
     if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"):
@@ -1869,7 +1864,6 @@ def main(args):
             params_to_optimize[-1]["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,
@@ -2160,6 +2154,7 @@ def main(args):
 
                 # encode batch prompts when custom prompts are provided for each image -
                 if train_dataset.custom_instance_prompts:
+                    elems_to_repeat = 1
                     if freeze_text_encoder:
                         prompt_embeds, pooled_prompt_embeds, text_ids = compute_text_embeddings(
                             prompts, text_encoders, tokenizers
@@ -2174,17 +2169,21 @@ def main(args):
                             max_sequence_length=args.max_sequence_length,
                             add_special_tokens=add_special_tokens_t5,
                         )
+                else:
+                    elems_to_repeat = len(prompts)
 
                 if not freeze_text_encoder:
                     prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
                         text_encoders=[text_encoder_one, text_encoder_two],
                         tokenizers=[None, None],
-                        text_input_ids_list=[tokens_one, tokens_two],
+                        text_input_ids_list=[
+                            tokens_one.repeat(elems_to_repeat, 1),
+                            tokens_two.repeat(elems_to_repeat, 1),
+                        ],
                         max_sequence_length=args.max_sequence_length,
                         device=accelerator.device,
                         prompt=prompts,
                     )
-
                 # Convert images to latent space
                 if args.cache_latents:
                     model_input = latents_cache[step].sample()
@@ -2377,6 +2376,9 @@ def main(args):
                     epoch=epoch,
                     torch_dtype=weight_dtype,
                 )
+                images = None
+                del pipeline
+
                 if freeze_text_encoder:
                     del text_encoder_one, text_encoder_two
                     free_memory()
@@ -2454,6 +2456,8 @@ def main(args):
                 commit_message="End of training",
                 ignore_patterns=["step_*", "epoch_*"],
             )
+        images = None
+        del pipeline
 
     accelerator.end_training()
 

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -39,7 +39,7 @@ from accelerate.logging import get_logger
 from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
 from huggingface_hub import create_repo, upload_folder
 from packaging import version
-from peft import LoraConfig
+from peft import LoraConfig, set_peft_model_state_dict
 from peft.utils import get_peft_model_state_dict
 from PIL import Image
 from PIL.ImageOps import exif_transpose
@@ -59,14 +59,16 @@ from diffusers import (
 )
 from diffusers.loaders import StableDiffusionLoraLoaderMixin
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import compute_snr
+from diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr
 from diffusers.utils import (
     check_min_version,
     convert_all_state_dict_to_peft,
     convert_state_dict_to_diffusers,
     convert_state_dict_to_kohya,
+    convert_unet_state_dict_to_peft,
     is_wandb_available,
 )
+from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.import_utils import is_xformers_available
 
 
@@ -79,30 +81,27 @@ logger = get_logger(__name__)
 def save_model_card(
     repo_id: str,
     use_dora: bool,
-    images=None,
-    base_model=str,
+    images: list = None,
+    base_model: str = None,
     train_text_encoder=False,
     train_text_encoder_ti=False,
     token_abstraction_dict=None,
-    instance_prompt=str,
-    validation_prompt=str,
+    instance_prompt=None,
+    validation_prompt=None,
     repo_folder=None,
     vae_path=None,
 ):
-    img_str = "widget:\n"
     lora = "lora" if not use_dora else "dora"
-    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"
-        """
-    if not images:
-        img_str += f"""
-        - text: '{instance_prompt}'
-        """
+
+    widget_dict = []
+    if images is not None:
+        for i, image in enumerate(images):
+            image.save(os.path.join(repo_folder, f"image_{i}.png"))
+            widget_dict.append(
+                {"text": validation_prompt if validation_prompt else " ", "output": {"url": f"image_{i}.png"}}
+            )
+    else:
+        widget_dict.append({"text": instance_prompt})
     embeddings_filename = f"{repo_folder}_emb"
     instance_prompt_webui = re.sub(r"<s\d+>", "", re.sub(r"<s\d+>", embeddings_filename, instance_prompt, count=1))
     ti_keys = ", ".join(f'"{match}"' for match in re.findall(r"<s\d+>", instance_prompt))
@@ -137,24 +136,7 @@ pipeline.load_textual_inversion(state_dict["clip_l"], token=[{ti_keys}], text_en
                 trigger_str += f"""
 to trigger concept `{key}` → use `{tokens}` in your prompt \n
 """
-
-    yaml = f"""---
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- diffusers-training
-- text-to-image
-- diffusers
-- {lora}
-- template:sd-lora
-{img_str}
-base_model: {base_model}
-instance_prompt: {instance_prompt}
-license: openrail++
----
-"""
-
-    model_card = f"""
+    model_description = f"""
 # SD1.5 LoRA DreamBooth - {repo_id}
 
 <Gallery />
@@ -202,8 +184,28 @@ Pivotal tuning was enabled: {train_text_encoder_ti}.
 Special VAE used for training: {vae_path}.
 
 """
-    with open(os.path.join(repo_folder, "README.md"), "w") as f:
-        f.write(yaml + model_card)
+    model_card = load_or_create_model_card(
+        repo_id_or_path=repo_id,
+        from_training=True,
+        license="openrail++",
+        base_model=base_model,
+        prompt=instance_prompt,
+        model_description=model_description,
+        inference=True,
+        widget=widget_dict,
+    )
+
+    tags = [
+        "text-to-image",
+        "diffusers",
+        "diffusers-training",
+        lora,
+        "template:sd-lora" "stable-diffusion",
+        "stable-diffusion-diffusers",
+    ]
+    model_card = populate_model_card(model_card, tags=tags)
+
+    model_card.save(os.path.join(repo_folder, "README.md"))
 
 
 def import_model_class_from_model_name_or_path(
@@ -1318,6 +1320,37 @@ def main(args):
             else:
                 raise ValueError(f"unexpected save model: {model.__class__}")
 
+        lora_state_dict, network_alphas = StableDiffusionPipeline.lora_state_dict(input_dir)
+
+        unet_state_dict = {f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")}
+        unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)
+        incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name="default")
+        if incompatible_keys is not None:
+            # check only for unexpected keys
+            unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+            if unexpected_keys:
+                logger.warning(
+                    f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                    f" {unexpected_keys}. "
+                )
+
+        if args.train_text_encoder:
+            # Do we need to call `scale_lora_layers()` here?
+            _set_state_dict_into_text_encoder(lora_state_dict, prefix="text_encoder.", text_encoder=text_encoder_one_)
+
+            _set_state_dict_into_text_encoder(
+                lora_state_dict, prefix="text_encoder_2.", text_encoder=text_encoder_one_
+            )
+
+        # Make sure the trainable params are in float32. This is again needed since the base models
+        # are in `weight_dtype`. More details:
+        # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
+        if args.mixed_precision == "fp16":
+            models = [unet_]
+            if args.train_text_encoder:
+                models.extend([text_encoder_one_])
+                # only upcast trainable parameters (LoRA) into fp32
+                cast_training_params(models)
         lora_state_dict, network_alphas = StableDiffusionLoraLoaderMixin.lora_state_dict(input_dir)
         StableDiffusionLoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)
 
@@ -1358,10 +1391,7 @@ def main(args):
             else args.adam_weight_decay,
             "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,
-        ]
+        params_to_optimize = [unet_lora_parameters_with_lr, text_lora_parameters_one_with_lr]
     else:
         params_to_optimize = [unet_lora_parameters_with_lr]
 
@@ -1423,7 +1453,6 @@ def main(args):
 
         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,

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -1794,7 +1794,6 @@ def main(args):
 
         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,

examples/cogvideo/train_cogvideox_image_to_video_lora.py

@@ -947,7 +947,6 @@ def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False):
 
         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,

examples/cogvideo/train_cogvideox_lora.py

@@ -969,7 +969,6 @@ def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False):
 
         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,

examples/community/README.md

@@ -10,22 +10,23 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 
 | Example                                                                                                                               | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | Code Example                                                                              | Colab                                                                                                                                                                                                              |                                                        Author |
 |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
-|Flux with CFG|[Flux with CFG](https://github.com/ToTheBeginning/PuLID/blob/main/docs/pulid_for_flux.md) provides an implementation of using CFG in [Flux](https://blackforestlabs.ai/announcing-black-forest-labs/).|[Flux with CFG](#flux-with-cfg)|NA|[Linoy Tsaban](https://github.com/linoytsaban), [Apolinário](https://github.com/apolinario), and [Sayak Paul](https://github.com/sayakpaul)|
+|Adaptive Mask Inpainting|Adaptive Mask Inpainting algorithm from [Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models](https://github.com/snuvclab/coma) (ECCV '24, Oral) provides a way to insert human inside the scene image without altering the background, by inpainting with adapting mask.|[Adaptive Mask Inpainting](#adaptive-mask-inpainting)|-|[Hyeonwoo Kim](https://sshowbiz.xyz),[Sookwan Han](https://jellyheadandrew.github.io)|
+|Flux with CFG|[Flux with CFG](https://github.com/ToTheBeginning/PuLID/blob/main/docs/pulid_for_flux.md) provides an implementation of using CFG in [Flux](https://blackforestlabs.ai/announcing-black-forest-labs/).|[Flux with CFG](#flux-with-cfg)|[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/flux_with_cfg.ipynb)|[Linoy Tsaban](https://github.com/linoytsaban), [Apolinário](https://github.com/apolinario), and [Sayak Paul](https://github.com/sayakpaul)|
 |Differential Diffusion|[Differential Diffusion](https://github.com/exx8/differential-diffusion) modifies an image according to a text prompt, and according to a map that specifies the amount of change in each region.|[Differential Diffusion](#differential-diffusion)|[![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/exx8/differential-diffusion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/exx8/differential-diffusion/blob/main/examples/SD2.ipynb)|[Eran Levin](https://github.com/exx8) and [Ohad Fried](https://www.ohadf.com/)|
 | HD-Painter                                                                                                                            | [HD-Painter](https://github.com/Picsart-AI-Research/HD-Painter) enables prompt-faithfull and high resolution (up to 2k) image inpainting upon any diffusion-based image inpainting method.                                                                                                                                                                                                                                                                                                               | [HD-Painter](#hd-painter)                                                                 | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/PAIR/HD-Painter)                                                                              | [Manukyan Hayk](https://github.com/haikmanukyan) and [Sargsyan Andranik](https://github.com/AndranikSargsyan) |
 | Marigold Monocular Depth Estimation                                                                                                   | A universal monocular depth estimator, utilizing Stable Diffusion, delivering sharp predictions in the wild. (See the [project page](https://marigoldmonodepth.github.io) and [full codebase](https://github.com/prs-eth/marigold) for more details.)                                                                                                                                                                                                                                                        | [Marigold Depth Estimation](#marigold-depth-estimation)                                   | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/toshas/marigold) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing) | [Bingxin Ke](https://github.com/markkua) and [Anton Obukhov](https://github.com/toshas) |
 | LLM-grounded Diffusion (LMD+)                                                                                                         | LMD greatly improves the prompt following ability of text-to-image generation models by introducing an LLM as a front-end prompt parser and layout planner. [Project page.](https://llm-grounded-diffusion.github.io/) [See our full codebase (also with diffusers).](https://github.com/TonyLianLong/LLM-groundedDiffusion)                                                                                                                                                                                                                                                                                                                                                                                                                                   | [LLM-grounded Diffusion (LMD+)](#llm-grounded-diffusion)                             | [Huggingface Demo](https://huggingface.co/spaces/longlian/llm-grounded-diffusion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SXzMSeAB-LJYISb2yrUOdypLz4OYWUKj) |                [Long (Tony) Lian](https://tonylian.com/) |
 | CLIP Guided Stable Diffusion                                                                                                          | Doing CLIP guidance for text to image generation with Stable Diffusion                                                                                                                                                                                                                                                                                                                                                                                                                                   | [CLIP Guided Stable Diffusion](#clip-guided-stable-diffusion)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb) |                [Suraj Patil](https://github.com/patil-suraj/) |
 | One Step U-Net (Dummy)                                                                                                                | Example showcasing of how to use Community Pipelines (see <https://github.com/huggingface/diffusers/issues/841>)                                                                                                                                                                                                                                                                                                                                                                                           | [One Step U-Net](#one-step-unet)                                                          | -                                                                                                                                                                                                                  |    [Patrick von Platen](https://github.com/patrickvonplaten/) |
-| Stable Diffusion Interpolation                                                                                                        | Interpolate the latent space of Stable Diffusion between different prompts/seeds                                                                                                                                                                                                                                                                                                                                                                                                                         | [Stable Diffusion Interpolation](#stable-diffusion-interpolation)                         | -                                                                                                                                                                                                                  |                       [Nate Raw](https://github.com/nateraw/) |
-| Stable Diffusion Mega                                                                                                                 | **One** Stable Diffusion Pipeline with all functionalities of [Text2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py), [Image2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and [Inpainting](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | [Stable Diffusion Mega](#stable-diffusion-mega)                                           | -                                                                                                                                                                                                                  |    [Patrick von Platen](https://github.com/patrickvonplaten/) |
-| Long Prompt Weighting Stable Diffusion                                                                                                | **One** Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt.                                                                                                                                                                                                                                                                                                                                                                                                  | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion)         | -                                                                                                                                                                                                                  |                           [SkyTNT](https://github.com/SkyTNT) |
-| Speech to Image                                                                                                                       | Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images                                                                                                                                                                                                                                                                                                                                                                                                            | [Speech to Image](#speech-to-image)                                                       | -                                                                                                                                                                                                                  |             [Mikail Duzenli](https://github.com/MikailINTech)
-| Wild Card Stable Diffusion                                                                                                            | Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values                                                                                                                                                                                                                                                                            | [Wildcard Stable Diffusion](#wildcard-stable-diffusion)                                   | -                                                                                                                                                                                                                  |              [Shyam Sudhakaran](https://github.com/shyamsn97) |
+| Stable Diffusion Interpolation                                                                                                        | Interpolate the latent space of Stable Diffusion between different prompts/seeds                                                                                                                                                                                                                                                                                                                                                                                                                         | [Stable Diffusion Interpolation](#stable-diffusion-interpolation)                         | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_interpolation.ipynb)                                                                                                                                                           |                       [Nate Raw](https://github.com/nateraw/) |
+| Stable Diffusion Mega                                                                                                                 | **One** Stable Diffusion Pipeline with all functionalities of [Text2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py), [Image2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and [Inpainting](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | [Stable Diffusion Mega](#stable-diffusion-mega)                                           | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_mega.ipynb)                                                                                                                                                                             |    [Patrick von Platen](https://github.com/patrickvonplaten/) |
+| Long Prompt Weighting Stable Diffusion                                                                                                | **One** Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt.                                                                                                                                                                                                                                                                                                                                                                                                  | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion)         | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/long_prompt_weighting_stable_diffusion.ipynb)                                                                                        |                           [SkyTNT](https://github.com/SkyTNT) |
+| Speech to Image                                                                                                                       | Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images                                                                                                                                                                                                                                                                                                                                                                                                            | [Speech to Image](#speech-to-image)                                                       |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/speech_to_image.ipynb)                                                                                                                                                                                                   |             [Mikail Duzenli](https://github.com/MikailINTech)
+| Wild Card Stable Diffusion                                                                                                            | Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values                                                                                                                                                                                                                                                                            | [Wildcard Stable Diffusion](#wildcard-stable-diffusion)                                   | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/wildcard_stable_diffusion.ipynb)                                                                                                                                                                                 |              [Shyam Sudhakaran](https://github.com/shyamsn97) |
 | [Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | Stable Diffusion Pipeline that supports prompts that contain "&#124;" in prompts (as an AND condition) and weights (separated by "&#124;" as well) to positively / negatively weight prompts.                                                                                                                                                                                                                                                                                                            | [Composable Stable Diffusion](#composable-stable-diffusion)                               | -                                                                                                                                                                                                                  |                      [Mark Rich](https://github.com/MarkRich) |
 | Seed Resizing Stable Diffusion                                                                                                        | Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation.                                                                                                                                                                                                                                                                                                                                                                                       | [Seed Resizing](#seed-resizing)                                                           | -                                                                                                                                                                                                                  |                      [Mark Rich](https://github.com/MarkRich) |
 | Imagic Stable Diffusion                                                                                                               | Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image                                                                                                                                                                                                                                                                                                                                                                                                                   | [Imagic Stable Diffusion](#imagic-stable-diffusion)                                       | -                                                                                                                                                                                                                  |                      [Mark Rich](https://github.com/MarkRich) |
-| Multilingual Stable Diffusion                                                                                                         | Stable Diffusion Pipeline that supports prompts in 50 different languages.                                                                                                                                                                                                                                                                                                                                                                                                                               | [Multilingual Stable Diffusion](#multilingual-stable-diffusion-pipeline)                  | -                                                                                                                                                                                                                  |          [Juan Carlos Piñeros](https://github.com/juancopi81) |
+| Multilingual Stable Diffusion                                                                                                         | Stable Diffusion Pipeline that supports prompts in 50 different languages.                                                                                                                                                                                                                                                                                                                                                                                                                               | [Multilingual Stable Diffusion](#multilingual-stable-diffusion-pipeline)                  | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/multilingual_stable_diffusion.ipynb)                                                                                                                                                                             |          [Juan Carlos Piñeros](https://github.com/juancopi81) |
 | GlueGen Stable Diffusion                                                                                                         | Stable Diffusion Pipeline that supports prompts in different languages using GlueGen adapter.                                                                                                                                                                                                                                                                                                                                                                                                                               | [GlueGen Stable Diffusion](#gluegen-stable-diffusion-pipeline)                  | -                                                                                                                                                                                                                  |          [Phạm Hồng Vinh](https://github.com/rootonchair) |
 | Image to Image Inpainting Stable Diffusion                                                                                            | Stable Diffusion Pipeline that enables the overlaying of two images and subsequent inpainting                                                                                                                                                                                                                                                                                                                                                                                                            | [Image to Image Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) | -                                                                                                                                                                                                                  |                    [Alex McKinney](https://github.com/vvvm23) |
 | Text Based Inpainting Stable Diffusion                                                                                                | Stable Diffusion Inpainting Pipeline that enables passing a text prompt to generate the mask for inpainting                                                                                                                                                                                                                                                                                                                                                                                              | [Text Based Inpainting Stable Diffusion](#text-based-inpainting-stable-diffusion)     | -                                                                                                                                                                                                                  |                   [Dhruv Karan](https://github.com/unography) |
@@ -40,8 +41,8 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 | DDIM Noise Comparative Analysis Pipeline                                                                                              | Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227))                                                                                                                                                                                                                                                                                                                             | [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline)     | - |              [Aengus (Duc-Anh)](https://github.com/aengusng8) |
 | CLIP Guided Img2Img Stable Diffusion Pipeline                                                                                         | Doing CLIP guidance for image to image generation with Stable Diffusion                                                                                                                                                                                                                                                                                                                                                                                                                                  | [CLIP Guided Img2Img Stable Diffusion](#clip-guided-img2img-stable-diffusion)             | - |               [Nipun Jindal](https://github.com/nipunjindal/) |
 | TensorRT Stable Diffusion Text to Image Pipeline                                                                                                    | Accelerates the Stable Diffusion Text2Image Pipeline using TensorRT                                                                                                                                                                                                                                                                                                                                                                                                                                      | [TensorRT Stable Diffusion Text to Image Pipeline](#tensorrt-text2image-stable-diffusion-pipeline)      | - |              [Asfiya Baig](https://github.com/asfiyab-nvidia) |
-| EDICT Image Editing Pipeline                                                                                                          | Diffusion pipeline for text-guided image editing                                                                                                                                                                                                                                                                                                                                                                                                                                                         | [EDICT Image Editing Pipeline](#edict-image-editing-pipeline)                             | - |                    [Joqsan Azocar](https://github.com/Joqsan) |
-| Stable Diffusion RePaint                                                                                                              | Stable Diffusion pipeline using [RePaint](https://arxiv.org/abs/2201.09865) for inpainting.                                                                                                                                                                                                                                                                                                                                                                                                               | [Stable Diffusion RePaint](#stable-diffusion-repaint )                                    | - |                  [Markus Pobitzer](https://github.com/Markus-Pobitzer) |
+| EDICT Image Editing Pipeline                                                                                                          | Diffusion pipeline for text-guided image editing                                                                                                                                                                                                                                                                                                                                                                                                                                                         | [EDICT Image Editing Pipeline](#edict-image-editing-pipeline)                             | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/edict_image_pipeline.ipynb) |                    [Joqsan Azocar](https://github.com/Joqsan) |
+| Stable Diffusion RePaint                                                                                                              | Stable Diffusion pipeline using [RePaint](https://arxiv.org/abs/2201.09865) for inpainting.                                                                                                                                                                                                                                                                                                                                                                                                               | [Stable Diffusion RePaint](#stable-diffusion-repaint )|[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_repaint.ipynb)|                  [Markus Pobitzer](https://github.com/Markus-Pobitzer) |
 | TensorRT Stable Diffusion Image to Image Pipeline                                                                                                    | Accelerates the Stable Diffusion Image2Image Pipeline using TensorRT                                                                                                                                                                                                                                                                                                                                                                                                                                      | [TensorRT Stable Diffusion Image to Image Pipeline](#tensorrt-image2image-stable-diffusion-pipeline)      | - |              [Asfiya Baig](https://github.com/asfiyab-nvidia) |
 | Stable Diffusion IPEX Pipeline | Accelerate Stable Diffusion inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [Stable Diffusion on IPEX](#stable-diffusion-on-ipex) | - | [Yingjie Han](https://github.com/yingjie-han/) |
 | CLIP Guided Images Mixing Stable Diffusion Pipeline | Сombine images using usual diffusion models. | [CLIP Guided Images Mixing Using Stable Diffusion](#clip-guided-images-mixing-with-stable-diffusion) | - | [Karachev Denis](https://github.com/TheDenk) |
@@ -60,19 +61,20 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 |   Regional Prompting Pipeline                                                                                               | Assign multiple prompts for different regions                                                                                                                                                                                                                                                                                                                                                    |  [Regional Prompting Pipeline](#regional-prompting-pipeline) | - | [hako-mikan](https://github.com/hako-mikan) |
 | 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) |
 | AnimateDiff ControlNet Pipeline                                                                                                    | Combines AnimateDiff with precise motion control using ControlNets                                                                                                                                                                                                                                                                                                                                                                                                                                    | [AnimateDiff ControlNet Pipeline](#animatediff-controlnet-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SKboYeGjEQmQPWoFC0aLYpBlYdHXkvAu?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) and [Edoardo Botta](https://github.com/EdoardoBotta) |
-|   DemoFusion Pipeline                                                                                                    | Implementation of [DemoFusion: Democratising High-Resolution Image Generation With No $$$](https://arxiv.org/abs/2311.16973)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [DemoFusion Pipeline](#demofusion)      | - |              [Ruoyi Du](https://github.com/RuoyiDu) |
-|   Instaflow Pipeline                                                                                                    | Implementation of [InstaFlow! One-Step Stable Diffusion with Rectified Flow](https://arxiv.org/abs/2309.06380)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Instaflow Pipeline](#instaflow-pipeline)      | - |              [Ayush Mangal](https://github.com/ayushtues) |
+|   DemoFusion Pipeline                                                                                                    | Implementation of [DemoFusion: Democratising High-Resolution Image Generation With No $$$](https://arxiv.org/abs/2311.16973)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [DemoFusion Pipeline](#demofusion)      | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/demo_fusion.ipynb) |              [Ruoyi Du](https://github.com/RuoyiDu) |
+|   Instaflow Pipeline                                                                                                    | Implementation of [InstaFlow! One-Step Stable Diffusion with Rectified Flow](https://arxiv.org/abs/2309.06380)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Instaflow Pipeline](#instaflow-pipeline)      | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/insta_flow.ipynb) |              [Ayush Mangal](https://github.com/ayushtues) |
 |   Null-Text Inversion Pipeline  | Implement [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://arxiv.org/abs/2211.09794) as a pipeline.                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Null-Text Inversion](https://github.com/google/prompt-to-prompt/)      | - |              [Junsheng Luan](https://github.com/Junsheng121) |
 |   Rerender A Video Pipeline                                                                                                    | Implementation of [[SIGGRAPH Asia 2023] Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation](https://arxiv.org/abs/2306.07954)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Rerender A Video Pipeline](#rerender-a-video)      | - |              [Yifan Zhou](https://github.com/SingleZombie) |
 | StyleAligned Pipeline                                                                                                    | Implementation of [Style Aligned Image Generation via Shared Attention](https://arxiv.org/abs/2312.02133)                                                                                                                                                                                                                                                                                                                                                                                                                                   | [StyleAligned Pipeline](#stylealigned-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/15X2E0jFPTajUIjS0FzX50OaHsCbP2lQ0/view?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
 | AnimateDiff Image-To-Video Pipeline | Experimental Image-To-Video support for AnimateDiff (open to improvements) | [AnimateDiff Image To Video Pipeline](#animatediff-image-to-video-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1TvzCDPHhfFtdcJZe4RLloAwyoLKuttWK/view?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
-|   IP Adapter FaceID Stable Diffusion                                                                                               | Stable Diffusion Pipeline that supports IP Adapter Face ID                                                                                                                                                                                                                                                                                                                                                  |  [IP Adapter Face ID](#ip-adapter-face-id) | - | [Fabio Rigano](https://github.com/fabiorigano) |
+|   IP Adapter FaceID Stable Diffusion                                                                                               | Stable Diffusion Pipeline that supports IP Adapter Face ID                                                                                                                                                                                                                                                                                                                                                  |  [IP Adapter Face ID](#ip-adapter-face-id) |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/ip_adapter_face_id.ipynb)| [Fabio Rigano](https://github.com/fabiorigano) |
 |   InstantID Pipeline                                                                                               | Stable Diffusion XL Pipeline that supports InstantID                                                                                                                                                                                                                                                                                                                                                 |  [InstantID Pipeline](#instantid-pipeline) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/InstantX/InstantID) | [Haofan Wang](https://github.com/haofanwang) |
 |   UFOGen Scheduler                                                                                               | Scheduler for UFOGen Model (compatible with Stable Diffusion pipelines)                                                                                                                                                                                                                                                                                                                                                 |  [UFOGen Scheduler](#ufogen-scheduler) | - | [dg845](https://github.com/dg845) |
 | Stable Diffusion XL IPEX Pipeline | Accelerate Stable Diffusion XL inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [Stable Diffusion XL on IPEX](#stable-diffusion-xl-on-ipex) | - | [Dan Li](https://github.com/ustcuna/) |
 | Stable Diffusion BoxDiff Pipeline | Training-free controlled generation with bounding boxes using [BoxDiff](https://github.com/showlab/BoxDiff) | [Stable Diffusion BoxDiff Pipeline](#stable-diffusion-boxdiff) | - | [Jingyang Zhang](https://github.com/zjysteven/) |
 |   FRESCO V2V Pipeline                                                                                                    | Implementation of [[CVPR 2024] FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation](https://arxiv.org/abs/2403.12962)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [FRESCO V2V Pipeline](#fresco)      | - |              [Yifan Zhou](https://github.com/SingleZombie) |
 | AnimateDiff IPEX Pipeline | Accelerate AnimateDiff inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [AnimateDiff on IPEX](#animatediff-on-ipex) | - | [Dan Li](https://github.com/ustcuna/) |
+PIXART-α Controlnet pipeline | Implementation of the controlnet model for pixart alpha and its diffusers pipeline | [PIXART-α Controlnet pipeline](#pixart-α-controlnet-pipeline) | - | [Raul Ciotescu](https://github.com/raulc0399/) |
 | HunyuanDiT Differential Diffusion Pipeline | Applies [Differential Diffusion](https://github.com/exx8/differential-diffusion) to [HunyuanDiT](https://github.com/huggingface/diffusers/pull/8240). | [HunyuanDiT with Differential Diffusion](#hunyuandit-with-differential-diffusion) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1v44a5fpzyr4Ffr4v2XBQ7BajzG874N4P?usp=sharing) | [Monjoy Choudhury](https://github.com/MnCSSJ4x) |
 | [🪆Matryoshka Diffusion Models](https://huggingface.co/papers/2310.15111) | A diffusion process that denoises inputs at multiple resolutions jointly and uses a NestedUNet architecture where features and parameters for small scale inputs are nested within those of the large scales. See [original codebase](https://github.com/apple/ml-mdm). | [🪆Matryoshka Diffusion Models](#matryoshka-diffusion-models) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/pcuenq/mdm) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/tolgacangoz/1f54875fc7aeaabcf284ebde64820966/matryoshka_hf.ipynb) | [M. Tolga Cangöz](https://github.com/tolgacangoz) |
 
@@ -84,6 +86,161 @@ pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion
 
 ## Example usages
 
+### Adaptive Mask Inpainting
+
+**Hyeonwoo Kim\*, Sookwan Han\*, Patrick Kwon, Hanbyul Joo**
+
+**Seoul National University, Naver Webtoon**
+
+Adaptive Mask Inpainting, presented in the ECCV'24 oral paper [*Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models*](https://snuvclab.github.io/coma), is an algorithm designed to insert humans into scene images without altering the background. Traditional inpainting methods often fail to preserve object geometry and details within the masked region, leading to false affordances. Adaptive Mask Inpainting addresses this issue by progressively specifying the inpainting region over diffusion timesteps, ensuring that the inserted human integrates seamlessly with the existing scene.
+
+Here is the demonstration of Adaptive Mask Inpainting:
+
+<video controls>
+  <source src="https://snuvclab.github.io/coma/static/videos/adaptive_mask_inpainting_vis.mp4" type="video/mp4">
+  Your browser does not support the video tag.
+</video>
+
+![teaser-img](https://snuvclab.github.io/coma/static/images/example_result_adaptive_mask_inpainting.png)
+
+
+You can find additional information about Adaptive Mask Inpainting in the [paper](https://arxiv.org/pdf/2401.12978) or in the [project website](https://snuvclab.github.io/coma).
+
+#### Usage example
+First, clone the diffusers github repository, and run the following command to set environment.
+```Shell
+git clone https://github.com/huggingface/diffusers.git
+cd diffusers
+
+conda create --name ami python=3.9 -y
+conda activate ami
+
+conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge -y
+python -m pip install detectron2==0.6 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
+pip install easydict
+pip install diffusers==0.20.2 accelerate safetensors transformers
+pip install setuptools==59.5.0
+pip install opencv-python
+pip install numpy==1.24.1
+```
+Then, run the below code under 'diffusers' directory.
+```python
+import numpy as np
+import torch
+from PIL import Image
+
+from diffusers import DDIMScheduler
+from diffusers import DiffusionPipeline
+from diffusers.utils import load_image
+
+from examples.community.adaptive_mask_inpainting import download_file, AdaptiveMaskInpaintPipeline, AMI_INSTALL_MESSAGE
+
+print(AMI_INSTALL_MESSAGE)
+
+from easydict import EasyDict
+
+
+
+if __name__ == "__main__":    
+    """
+    Download Necessary Files
+    """
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/model_final_edd263.pkl?download=true",
+        output_file = "model_final_edd263.pkl",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/pointrend_rcnn_R_50_FPN_3x_coco.yaml?download=true",
+        output_file = "pointrend_rcnn_R_50_FPN_3x_coco.yaml",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/input_img.png?download=true",
+        output_file = "input_img.png",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/input_mask.png?download=true",
+        output_file = "input_mask.png",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/Base-PointRend-RCNN-FPN.yaml?download=true",
+        output_file = "Base-PointRend-RCNN-FPN.yaml",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/Base-RCNN-FPN.yaml?download=true",
+        output_file = "Base-RCNN-FPN.yaml",
+        exist_ok=True,
+    )
+    
+    """ 
+    Prepare Adaptive Mask Inpainting Pipeline
+    """
+    # device
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    num_steps = 50
+    
+    # Scheduler
+    scheduler = DDIMScheduler(
+        beta_start=0.00085, 
+        beta_end=0.012, 
+        beta_schedule="scaled_linear", 
+        clip_sample=False, 
+        set_alpha_to_one=False
+    )
+    scheduler.set_timesteps(num_inference_steps=num_steps)
+
+    ## load models as pipelines
+    pipeline = AdaptiveMaskInpaintPipeline.from_pretrained(
+        "Uminosachi/realisticVisionV51_v51VAE-inpainting", 
+        scheduler=scheduler, 
+        torch_dtype=torch.float16, 
+        requires_safety_checker=False
+    ).to(device)
+
+    ## disable safety checker
+    enable_safety_checker = False
+    if not enable_safety_checker:
+        pipeline.safety_checker = None
+    
+    """ 
+    Run Adaptive Mask Inpainting 
+    """
+    default_mask_image = Image.open("./input_mask.png").convert("L")
+    init_image = Image.open("./input_img.png").convert("RGB")
+    
+    
+    seed = 59
+    generator = torch.Generator(device=device)
+    generator.manual_seed(seed)
+    
+    image = pipeline(
+        prompt="a man sitting on a couch",
+        negative_prompt="worst quality, normal quality, low quality, bad anatomy, artifacts, blurry, cropped, watermark, greyscale, nsfw",
+        image=init_image,
+        default_mask_image=default_mask_image,
+        guidance_scale=11.0,
+        strength=0.98,
+        use_adaptive_mask=True,
+        generator=generator,
+        enforce_full_mask_ratio=0.0,
+        visualization_save_dir="./ECCV2024_adaptive_mask_inpainting_demo", # DON'T CHANGE THIS!!!
+        human_detection_thres=0.015,
+    ).images[0]
+
+    
+    image.save(f'final_img.png')
+```
+#### [Troubleshooting]
+
+If you run into an error `cannot import name 'cached_download' from 'huggingface_hub'` (issue [1851](https://github.com/easydiffusion/easydiffusion/issues/1851)), remove `cached_download` from the import line in the file `diffusers/utils/dynamic_modules_utils.py`. 
+
+For example, change the import line from `.../env/lib/python3.8/site-packages/diffusers/utils/dynamic_modules_utils.py`.
+
+
 ### Flux with CFG
 
 Know more about Flux [here](https://blackforestlabs.ai/announcing-black-forest-labs/). Since Flux doesn't use CFG, this implementation provides one, inspired by the [PuLID Flux adaptation](https://github.com/ToTheBeginning/PuLID/blob/main/docs/pulid_for_flux.md).
@@ -94,24 +251,30 @@ Example usage:
 from diffusers import DiffusionPipeline
 import torch
 
+model_name = "black-forest-labs/FLUX.1-dev"
+prompt = "a watercolor painting of a unicorn"
+negative_prompt = "pink"
+
+# Load the diffusion pipeline
 pipeline = DiffusionPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
+    model_name,
     torch_dtype=torch.bfloat16,
     custom_pipeline="pipeline_flux_with_cfg"
 )
 pipeline.enable_model_cpu_offload()
-prompt = "a watercolor painting of a unicorn"
-negative_prompt = "pink"
 
+# Generate the image
 img = pipeline(
     prompt=prompt,
     negative_prompt=negative_prompt,
     true_cfg=1.5,
     guidance_scale=3.5,
-    num_images_per_prompt=1,
     generator=torch.manual_seed(0)
 ).images[0]
+
+# Save the generated image
 img.save("cfg_flux.png")
+print("Image generated and saved successfully.")
 ```
 
 ### Differential Diffusion
@@ -684,6 +847,8 @@ out = pipe(
     wildcard_files=["object.txt", "animal.txt"],
     num_prompt_samples=1
 )
+out.images[0].save("image.png")
+torch.cuda.empty_cache()
 ```
 
 ### Composable Stable diffusion
@@ -2460,16 +2625,17 @@ for obj in range(bs):
 
 ### Stable Diffusion XL Reference
 
-This pipeline uses the Reference. Refer to the [stable_diffusion_reference](https://github.com/huggingface/diffusers/blob/main/examples/community/README.md#stable-diffusion-reference).
+This pipeline uses the Reference. Refer to the [Stable Diffusion Reference](https://github.com/huggingface/diffusers/blob/main/examples/community/README.md#stable-diffusion-reference) section for more information.
 
 ```py
 import torch
-from PIL import Image
+# from diffusers import DiffusionPipeline
 from diffusers.utils import load_image
-from diffusers import DiffusionPipeline
 from diffusers.schedulers import UniPCMultistepScheduler
 
-input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+from .stable_diffusion_xl_reference import StableDiffusionXLReferencePipeline
+
+input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_input_cat.jpg")
 
 # pipe = DiffusionPipeline.from_pretrained(
 #     "stabilityai/stable-diffusion-xl-base-1.0",
@@ -2487,7 +2653,7 @@ pipe = StableDiffusionXLReferencePipeline.from_pretrained(
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 
 result_img = pipe(ref_image=input_image,
-      prompt="1girl",
+      prompt="a dog",
       num_inference_steps=20,
       reference_attn=True,
       reference_adain=True).images[0]
@@ -2495,14 +2661,14 @@ result_img = pipe(ref_image=input_image,
 
 Reference Image
 
-![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)
+![reference_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_input_cat.jpg)
 
 Output Image
 
-`prompt: 1 girl`
+`prompt: a dog`
 
-`reference_attn=True, reference_adain=True, num_inference_steps=20`
-![Output_image](https://github.com/zideliu/diffusers/assets/34944964/743848da-a215-48f9-ae39-b5e2ae49fb13)
+`reference_attn=False, reference_adain=True, num_inference_steps=20`
+![Output_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_adain_dog.png)
 
 Reference Image
 ![reference_image](https://github.com/huggingface/diffusers/assets/34944964/449bdab6-e744-4fb2-9620-d4068d9a741b)
@@ -2524,6 +2690,88 @@ Output Image
 `reference_attn=True, reference_adain=True, num_inference_steps=20`
 ![output_image](https://github.com/huggingface/diffusers/assets/34944964/9b2f1aca-886f-49c3-89ec-d2031c8e3670)
 
+### Stable Diffusion XL ControlNet Reference
+
+This pipeline uses the Reference Control and with ControlNet. Refer to the [Stable Diffusion ControlNet Reference](https://github.com/huggingface/diffusers/blob/main/examples/community/README.md#stable-diffusion-controlnet-reference) and [Stable Diffusion XL Reference](https://github.com/huggingface/diffusers/blob/main/examples/community/README.md#stable-diffusion-xl-reference) sections for more information.
+
+```py
+from diffusers import ControlNetModel, AutoencoderKL
+from diffusers.schedulers import UniPCMultistepScheduler
+from diffusers.utils import load_image
+import numpy as np
+import torch
+
+import cv2
+from PIL import Image
+
+from .stable_diffusion_xl_controlnet_reference import StableDiffusionXLControlNetReferencePipeline
+
+# download an image
+canny_image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_input_cat.jpg"
+)
+
+ref_image = load_image(
+    "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
+)
+
+# initialize the models and pipeline
+controlnet_conditioning_scale = 0.5  # recommended for good generalization
+controlnet = ControlNetModel.from_pretrained(
+    "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
+)
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+pipe = StableDiffusionXLControlNetReferencePipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16
+).to("cuda:0")
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# get canny image
+image = np.array(canny_image)
+image = cv2.Canny(image, 100, 200)
+image = image[:, :, None]
+image = np.concatenate([image, image, image], axis=2)
+canny_image = Image.fromarray(image)
+
+# generate image
+image = pipe(
+    prompt="a cat",
+    num_inference_steps=20,
+    controlnet_conditioning_scale=controlnet_conditioning_scale,
+    image=canny_image,
+    ref_image=ref_image,
+    reference_attn=False,
+    reference_adain=True,
+    style_fidelity=1.0,
+    generator=torch.Generator("cuda").manual_seed(42)
+).images[0]
+```
+
+Canny ControlNet Image
+
+![canny_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_input_cat.jpg)
+
+Reference Image
+
+![ref_image](https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png)
+
+Output Image
+
+`prompt: a cat`
+
+`reference_attn=True, reference_adain=True, num_inference_steps=20, style_fidelity=1.0`
+
+![Output_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_attn_adain_canny_cat.png)
+
+`reference_attn=False, reference_adain=True, num_inference_steps=20, style_fidelity=1.0`
+
+![Output_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_adain_canny_cat.png)
+
+`reference_attn=True, reference_adain=False, num_inference_steps=20, style_fidelity=1.0`
+
+![Output_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_attn_canny_cat.png)
+
 ### Stable diffusion fabric pipeline
 
 FABRIC approach applicable to a wide range of popular diffusion models, which exploits
@@ -3219,6 +3467,20 @@ best quality, 3persons in garden, a boy blue shirt BREAK
 best quality, 3persons in garden, an old man red suit
 ```
 
+### Use base prompt
+
+You can use a base prompt to apply the prompt to all areas. You can set a base prompt by adding `ADDBASE` at the end. Base prompts can also be combined with common prompts, but the base prompt must be specified first.
+
+```
+2d animation style ADDBASE
+masterpiece, high quality ADDCOMM
+(blue sky)++ BREAK
+green hair twintail BREAK
+book shelf BREAK
+messy desk BREAK
+orange++ dress and sofa
+```
+
 ### Negative prompt
 
 Negative prompts are equally effective across all regions, but it is possible to set region-specific prompts for negative prompts as well. The number of BREAKs must be the same as the number of prompts. If the number of prompts does not match, the negative prompts will be used without being divided into regions.
@@ -3249,6 +3511,7 @@ pipe(prompt=prompt, rp_args=rp_args)
 ### Optional Parameters
 
 - `save_mask`: In `Prompt` mode, choose whether to output the generated mask along with the image. The default is `False`.
+- `base_ratio`: Used with `ADDBASE`. Sets the ratio of the base prompt; if base ratio is set to 0.2, then resulting images will consist of `20%*BASE_PROMPT + 80%*REGION_PROMPT`
 
 The Pipeline supports `compel` syntax. Input prompts using the `compel` structure will be automatically applied and processed.
 
@@ -3577,6 +3840,7 @@ The original repo can be found at [repo](https://github.com/PRIS-CV/DemoFusion).
 
 ```py
 from diffusers import DiffusionPipeline
+import torch
 
 pipe = DiffusionPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
@@ -3700,9 +3964,10 @@ You can also combine it with LORA out of the box, like <https://huggingface.co/a
 from diffusers import DiffusionPipeline
 import torch
 
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 pipe = DiffusionPipeline.from_pretrained("XCLIU/instaflow_0_9B_from_sd_1_5", torch_dtype=torch.float16, custom_pipeline="instaflow_one_step")
-pipe.to("cuda")  ### if GPU is not available, comment this line
+pipe.to(device)  ### if GPU is not available, comment this line
 pipe.load_lora_weights("artificialguybr/logo-redmond-1-5v-logo-lora-for-liberteredmond-sd-1-5")
 prompt = "logo, A logo for a fitness app, dynamic running figure, energetic colors (red, orange) ),LogoRedAF ,"
 images = pipe(prompt=prompt,
@@ -4445,3 +4710,94 @@ grid_image.save(grid_dir + "sample.png")
 `pag_scale` : guidance scale of PAG (ex: 5.0)
 
 `pag_applied_layers_index` : index of the layer to apply perturbation (ex: ['m0'])
+
+# PIXART-α Controlnet pipeline
+
+[Project](https://pixart-alpha.github.io/) / [GitHub](https://github.com/PixArt-alpha/PixArt-alpha/blob/master/asset/docs/pixart_controlnet.md)
+
+This the implementation of the controlnet model and the pipelne for the Pixart-alpha model, adapted to use the HuggingFace Diffusers.
+
+## Example Usage
+
+This example uses the Pixart HED Controlnet model, converted from the control net model as trained by the authors of the paper.
+
+```py
+import sys
+import os
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from pipeline_pixart_alpha_controlnet import PixArtAlphaControlnetPipeline
+from diffusers.utils import load_image
+
+from diffusers.image_processor import PixArtImageProcessor
+
+from controlnet_aux import HEDdetector
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from pixart.controlnet_pixart_alpha import PixArtControlNetAdapterModel
+
+controlnet_repo_id = "raulc0399/pixart-alpha-hed-controlnet"
+
+weight_dtype = torch.float16
+image_size = 1024
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+torch.manual_seed(0)
+
+# load controlnet
+controlnet = PixArtControlNetAdapterModel.from_pretrained(
+    controlnet_repo_id,
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+).to(device)
+
+pipe = PixArtAlphaControlnetPipeline.from_pretrained(
+    "PixArt-alpha/PixArt-XL-2-1024-MS",
+    controlnet=controlnet,
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+).to(device)
+
+images_path = "images"
+control_image_file = "0_7.jpg"
+
+prompt = "battleship in space, galaxy in background"
+
+control_image_name = control_image_file.split('.')[0]
+
+control_image = load_image(f"{images_path}/{control_image_file}")
+print(control_image.size)
+height, width = control_image.size
+
+hed = HEDdetector.from_pretrained("lllyasviel/Annotators")
+
+condition_transform = T.Compose([
+    T.Lambda(lambda img: img.convert('RGB')),
+    T.CenterCrop([image_size, image_size]),
+])
+
+control_image = condition_transform(control_image)
+hed_edge = hed(control_image, detect_resolution=image_size, image_resolution=image_size)
+
+hed_edge.save(f"{images_path}/{control_image_name}_hed.jpg")
+
+# run pipeline
+with torch.no_grad():
+    out = pipe(
+        prompt=prompt,
+        image=hed_edge,
+        num_inference_steps=14,
+        guidance_scale=4.5,
+        height=image_size,
+        width=image_size,
+    )
+
+    out.images[0].save(f"{images_path}//{control_image_name}_output.jpg")
+    
+```
+
+In the folder examples/pixart there is also a script that can be used to train new models.
+Please check the script `train_controlnet_hf_diffusers.sh` on how to start the training.

examples/community/README_community_scripts.md

@@ -6,9 +6,9 @@ If a community script doesn't work as expected, please open an issue and ping th
 
 | Example                                                                                                                               | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | Code Example                                                                              | Colab                                                                                                                                                                                                              |                                                        Author |
 |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
-| Using IP-Adapter with negative noise                                                                                                  | Using negative noise with IP-adapter to better control the generation (see the [original post](https://github.com/huggingface/diffusers/discussions/7167) on the forum for more details)                                                                                                                                                                                                                                                    | [IP-Adapter Negative Noise](#ip-adapter-negative-noise)                                   | | [Álvaro Somoza](https://github.com/asomoza)|
-| asymmetric tiling                                                                                                  |configure seamless image tiling independently for the X and Y axes                                                                                                                                                                                                      | [Asymmetric Tiling](#asymmetric-tiling )                                   | | [alexisrolland](https://github.com/alexisrolland)|
-| Prompt scheduling callback                                                                                                  |Allows changing prompts during a generation                                                                                                                                                                                                      | [Prompt Scheduling](#prompt-scheduling )                                   | | [hlky](https://github.com/hlky)|
+| Using IP-Adapter with Negative Noise                                                                                                  | Using negative noise with IP-adapter to better control the generation (see the [original post](https://github.com/huggingface/diffusers/discussions/7167) on the forum for more details)                                                                                                                                                                                                                                                    | [IP-Adapter Negative Noise](#ip-adapter-negative-noise)                                   |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/ip_adapter_negative_noise.ipynb) | [Álvaro Somoza](https://github.com/asomoza)|
+| Asymmetric Tiling                                                                                                  |configure seamless image tiling independently for the X and Y axes                                                                                                                                                                                                      | [Asymmetric Tiling](#Asymmetric-Tiling )                                   |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/asymetric_tiling.ipynb) | [alexisrolland](https://github.com/alexisrolland)|
+| Prompt Scheduling Callback                                                                                                  |Allows changing prompts during a generation                                                                                                                                                                                                      | [Prompt Scheduling-Callback](#Prompt-Scheduling-Callback )                                   |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/prompt_scheduling_callback.ipynb) | [hlky](https://github.com/hlky)|
 
 
 ## Example usages
@@ -312,4 +312,6 @@ image = pipeline(
     callback_on_step_end=callback,
     callback_on_step_end_tensor_inputs=["prompt_embeds"],
 ).images[0]
+torch.cuda.empty_cache()
+image.save('image.png')
 ```

examples/community/matryoshka.py

@@ -868,7 +868,7 @@ class CrossAttnDownBlock2D(nn.Module):
         blocks = list(zip(self.resnets, self.attentions))
 
         for i, (resnet, attn) in enumerate(blocks):
-            if self.training and self.gradient_checkpointing:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
 
                 def create_custom_forward(module, return_dict=None):
                     def custom_forward(*inputs):
@@ -1029,7 +1029,7 @@ class UNetMidBlock2DCrossAttn(nn.Module):
 
         hidden_states = self.resnets[0](hidden_states, temb)
         for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if self.training and self.gradient_checkpointing:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
 
                 def create_custom_forward(module, return_dict=None):
                     def custom_forward(*inputs):
@@ -1191,7 +1191,7 @@ class CrossAttnUpBlock2D(nn.Module):
 
             hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
 
-            if self.training and self.gradient_checkpointing:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
 
                 def create_custom_forward(module, return_dict=None):
                     def custom_forward(*inputs):
@@ -1364,7 +1364,7 @@ class MatryoshkaTransformer2DModel(LegacyModelMixin, LegacyConfigMixin):
 
         # Blocks
         for block in self.transformer_blocks:
-            if self.training and self.gradient_checkpointing:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
 
                 def create_custom_forward(module, return_dict=None):
                     def custom_forward(*inputs):

examples/community/regional_prompting_stable_diffusion.py

@@ -3,13 +3,12 @@ from typing import Dict, Optional
 
 import torch
 import torchvision.transforms.functional as FF
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
 
 from diffusers import StableDiffusionPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import USE_PEFT_BACKEND
 
 
 try:
@@ -17,6 +16,7 @@ try:
 except ImportError:
     Compel = None
 
+KBASE = "ADDBASE"
 KCOMM = "ADDCOMM"
 KBRK = "BREAK"
 
@@ -34,6 +34,11 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
 
         Optional
             rp_args["save_mask"]: True/False (save masks in prompt mode)
+            rp_args["power"]: int (power for attention maps in prompt mode)
+            rp_args["base_ratio"]:
+                float (Sets the ratio of the base prompt)
+                ex) 0.2 (20%*BASE_PROMPT + 80%*REGION_PROMPT)
+                [Use base prompt](https://github.com/hako-mikan/sd-webui-regional-prompter?tab=readme-ov-file#use-base-prompt)
 
     Pipeline for text-to-image generation using Stable Diffusion.
 
@@ -70,6 +75,7 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPImageProcessor,
+        image_encoder: CLIPVisionModelWithProjection = None,
         requires_safety_checker: bool = True,
     ):
         super().__init__(
@@ -80,6 +86,7 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
             scheduler,
             safety_checker,
             feature_extractor,
+            image_encoder,
             requires_safety_checker,
         )
         self.register_modules(
@@ -90,6 +97,7 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
             scheduler=scheduler,
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
         )
 
     @torch.no_grad()
@@ -110,17 +118,40 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
         rp_args: Dict[str, str] = None,
     ):
         active = KBRK in prompt[0] if isinstance(prompt, list) else KBRK in prompt
+        use_base = KBASE in prompt[0] if isinstance(prompt, list) else KBASE in prompt
         if negative_prompt is None:
             negative_prompt = "" if isinstance(prompt, str) else [""] * len(prompt)
 
         device = self._execution_device
         regions = 0
 
+        self.base_ratio = float(rp_args["base_ratio"]) if "base_ratio" in rp_args else 0.0
         self.power = int(rp_args["power"]) if "power" in rp_args else 1
 
         prompts = prompt if isinstance(prompt, list) else [prompt]
-        n_prompts = negative_prompt if isinstance(prompt, str) else [negative_prompt]
+        n_prompts = negative_prompt if isinstance(prompt, list) else [negative_prompt]
         self.batch = batch = num_images_per_prompt * len(prompts)
+
+        if use_base:
+            bases = prompts.copy()
+            n_bases = n_prompts.copy()
+
+            for i, prompt in enumerate(prompts):
+                parts = prompt.split(KBASE)
+                if len(parts) == 2:
+                    bases[i], prompts[i] = parts
+                elif len(parts) > 2:
+                    raise ValueError(f"Multiple instances of {KBASE} found in prompt: {prompt}")
+            for i, prompt in enumerate(n_prompts):
+                n_parts = prompt.split(KBASE)
+                if len(n_parts) == 2:
+                    n_bases[i], n_prompts[i] = n_parts
+                elif len(n_parts) > 2:
+                    raise ValueError(f"Multiple instances of {KBASE} found in negative prompt: {prompt}")
+
+            all_bases_cn, _ = promptsmaker(bases, num_images_per_prompt)
+            all_n_bases_cn, _ = promptsmaker(n_bases, num_images_per_prompt)
+
         all_prompts_cn, all_prompts_p = promptsmaker(prompts, num_images_per_prompt)
         all_n_prompts_cn, _ = promptsmaker(n_prompts, num_images_per_prompt)
 
@@ -137,8 +168,16 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
 
             conds = getcompelembs(all_prompts_cn)
             unconds = getcompelembs(all_n_prompts_cn)
-            embs = getcompelembs(prompts)
-            n_embs = getcompelembs(n_prompts)
+            base_embs = getcompelembs(all_bases_cn) if use_base else None
+            base_n_embs = getcompelembs(all_n_bases_cn) if use_base else None
+            # When using base, it seems more reasonable to use base prompts as prompt_embeddings rather than regional prompts
+            embs = getcompelembs(prompts) if not use_base else base_embs
+            n_embs = getcompelembs(n_prompts) if not use_base else base_n_embs
+
+            if use_base and self.base_ratio > 0:
+                conds = self.base_ratio * base_embs + (1 - self.base_ratio) * conds
+                unconds = self.base_ratio * base_n_embs + (1 - self.base_ratio) * unconds
+
             prompt = negative_prompt = None
         else:
             conds = self.encode_prompt(prompts, device, 1, True)[0]
@@ -147,6 +186,18 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
                 if equal
                 else self.encode_prompt(all_n_prompts_cn, device, 1, True)[0]
             )
+
+            if use_base and self.base_ratio > 0:
+                base_embs = self.encode_prompt(bases, device, 1, True)[0]
+                base_n_embs = (
+                    self.encode_prompt(n_bases, device, 1, True)[0]
+                    if equal
+                    else self.encode_prompt(all_n_bases_cn, device, 1, True)[0]
+                )
+
+                conds = self.base_ratio * base_embs + (1 - self.base_ratio) * conds
+                unconds = self.base_ratio * base_n_embs + (1 - self.base_ratio) * unconds
+
             embs = n_embs = None
 
         if not active:
@@ -225,8 +276,6 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
 
                     residual = hidden_states
 
-                    args = () if USE_PEFT_BACKEND else (scale,)
-
                     if attn.spatial_norm is not None:
                         hidden_states = attn.spatial_norm(hidden_states, temb)
 
@@ -247,16 +296,15 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
                     if attn.group_norm is not None:
                         hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
 
-                    args = () if USE_PEFT_BACKEND else (scale,)
-                    query = attn.to_q(hidden_states, *args)
+                    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)
 
-                    key = attn.to_k(encoder_hidden_states, *args)
-                    value = attn.to_v(encoder_hidden_states, *args)
+                    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
@@ -283,7 +331,7 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
                     hidden_states = hidden_states.to(query.dtype)
 
                     # linear proj
-                    hidden_states = attn.to_out[0](hidden_states, *args)
+                    hidden_states = attn.to_out[0](hidden_states)
                     # dropout
                     hidden_states = attn.to_out[1](hidden_states)
 
@@ -410,9 +458,9 @@ def promptsmaker(prompts, batch):
         add = ""
         if KCOMM in prompt:
             add, prompt = prompt.split(KCOMM)
-            add = add + " "
-        prompts = prompt.split(KBRK)
-        out_p.append([add + p for p in prompts])
+            add = add.strip() + " "
+        prompts = [p.strip() for p in prompt.split(KBRK)]
+        out_p.append([add + p for i, p in enumerate(prompts)])
     out = [None] * batch * len(out_p[0]) * len(out_p)
     for p, prs in enumerate(out_p):  # inputs prompts
         for r, pr in enumerate(prs):  # prompts for regions
@@ -449,7 +497,6 @@ def make_cells(ratios):
             add = []
             startend(add, inratios[1:])
             icells.append(add)
-
     return ocells, icells, sum(len(cell) for cell in icells)
 
 

examples/community/stable_diffusion_xl_reference.py

@@ -1,5 +1,6 @@
 # Based on stable_diffusion_reference.py
 
+import inspect
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -7,28 +8,33 @@ import PIL.Image
 import torch
 
 from diffusers import StableDiffusionXLPipeline
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput
 from diffusers.models.attention import BasicTransformerBlock
-from diffusers.models.unets.unet_2d_blocks import (
-    CrossAttnDownBlock2D,
-    CrossAttnUpBlock2D,
-    DownBlock2D,
-    UpBlock2D,
-)
-from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
-from diffusers.utils import PIL_INTERPOLATION, logging
+from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers.utils import PIL_INTERPOLATION, deprecate, is_torch_xla_available, logging, replace_example_docstring
 from diffusers.utils.torch_utils import randn_tensor
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm  # type: ignore
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
         >>> import torch
-        >>> from diffusers import UniPCMultistepScheduler
+        >>> from diffusers.schedulers import UniPCMultistepScheduler
         >>> from diffusers.utils import load_image
 
-        >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+        >>> input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_input_cat.jpg")
 
         >>> pipe = StableDiffusionXLReferencePipeline.from_pretrained(
             "stabilityai/stable-diffusion-xl-base-1.0",
@@ -38,7 +44,7 @@ EXAMPLE_DOC_STRING = """
 
         >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
         >>> result_img = pipe(ref_image=input_image,
-                        prompt="1girl",
+                        prompt="a dog",
                         num_inference_steps=20,
                         reference_attn=True,
                         reference_adain=True).images[0]
@@ -56,8 +62,6 @@ def torch_dfs(model: torch.nn.Module):
 
 
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
-
-
 def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     """
     Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
@@ -72,33 +76,102 @@ 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,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    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 override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` 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 and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    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)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, 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 StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
-    def _default_height_width(self, height, width, image):
-        # NOTE: It is possible that a list of images have different
-        # dimensions for each image, so just checking the first image
-        # is not _exactly_ correct, but it is simple.
-        while isinstance(image, list):
-            image = image[0]
+    def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
+        refimage = refimage.to(device=device)
+        if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
+            self.upcast_vae()
+            refimage = refimage.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+        if refimage.dtype != self.vae.dtype:
+            refimage = refimage.to(dtype=self.vae.dtype)
+        # encode the mask image into latents space so we can concatenate it to the latents
+        if isinstance(generator, list):
+            ref_image_latents = [
+                self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(batch_size)
+            ]
+            ref_image_latents = torch.cat(ref_image_latents, dim=0)
+        else:
+            ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
+        ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
 
-        if height is None:
-            if isinstance(image, PIL.Image.Image):
-                height = image.height
-            elif isinstance(image, torch.Tensor):
-                height = image.shape[2]
+        # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
+        if ref_image_latents.shape[0] < batch_size:
+            if not batch_size % ref_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
 
-            height = (height // 8) * 8  # round down to nearest multiple of 8
+        ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
 
-        if width is None:
-            if isinstance(image, PIL.Image.Image):
-                width = image.width
-            elif isinstance(image, torch.Tensor):
-                width = image.shape[3]
+        # aligning device to prevent device errors when concating it with the latent model input
+        ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
+        return ref_image_latents
 
-            width = (width // 8) * 8
-
-        return height, width
-
-    def prepare_image(
+    def prepare_ref_image(
         self,
         image,
         width,
@@ -151,41 +224,42 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
 
         return image
 
-    def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
-        refimage = refimage.to(device=device)
-        if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
-            self.upcast_vae()
-            refimage = refimage.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
-        if refimage.dtype != self.vae.dtype:
-            refimage = refimage.to(dtype=self.vae.dtype)
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            ref_image_latents = [
-                self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(batch_size)
-            ]
-            ref_image_latents = torch.cat(ref_image_latents, dim=0)
-        else:
-            ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
-        ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
+    def check_ref_inputs(
+        self,
+        ref_image,
+        reference_guidance_start,
+        reference_guidance_end,
+        style_fidelity,
+        reference_attn,
+        reference_adain,
+    ):
+        ref_image_is_pil = isinstance(ref_image, PIL.Image.Image)
+        ref_image_is_tensor = isinstance(ref_image, torch.Tensor)
 
-        # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
-        if ref_image_latents.shape[0] < batch_size:
-            if not batch_size % ref_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
+        if not ref_image_is_pil and not ref_image_is_tensor:
+            raise TypeError(
+                f"ref image must be passed and be one of PIL image or torch tensor, but is {type(ref_image)}"
+            )
 
-        ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
+        if not reference_attn and not reference_adain:
+            raise ValueError("`reference_attn` or `reference_adain` must be True.")
 
-        # aligning device to prevent device errors when concating it with the latent model input
-        ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
-        return ref_image_latents
+        if style_fidelity < 0.0:
+            raise ValueError(f"style fidelity: {style_fidelity} can't be smaller than 0.")
+        if style_fidelity > 1.0:
+            raise ValueError(f"style fidelity: {style_fidelity} can't be larger than 1.0.")
+
+        if reference_guidance_start >= reference_guidance_end:
+            raise ValueError(
+                f"reference guidance start: {reference_guidance_start} cannot be larger or equal to reference guidance end: {reference_guidance_end}."
+            )
+        if reference_guidance_start < 0.0:
+            raise ValueError(f"reference guidance start: {reference_guidance_start} can't be smaller than 0.")
+        if reference_guidance_end > 1.0:
+            raise ValueError(f"reference guidance end: {reference_guidance_end} can't be larger than 1.0.")
 
     @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
         self,
         prompt: Union[str, List[str]] = None,
@@ -194,6 +268,8 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
         height: Optional[int] = None,
         width: Optional[int] = None,
         num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
         denoising_end: Optional[float] = None,
         guidance_scale: float = 5.0,
         negative_prompt: Optional[Union[str, List[str]]] = None,
@@ -206,28 +282,220 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
         negative_prompt_embeds: Optional[torch.Tensor] = None,
         pooled_prompt_embeds: Optional[torch.Tensor] = None,
         negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
-        callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         guidance_rescale: float = 0.0,
         original_size: Optional[Tuple[int, int]] = None,
         crops_coords_top_left: Tuple[int, int] = (0, 0),
         target_size: Optional[Tuple[int, int]] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         attention_auto_machine_weight: float = 1.0,
         gn_auto_machine_weight: float = 1.0,
+        reference_guidance_start: float = 0.0,
+        reference_guidance_end: float = 1.0,
         style_fidelity: float = 0.5,
         reference_attn: bool = True,
         reference_adain: bool = True,
+        **kwargs,
     ):
-        assert reference_attn or reference_adain, "`reference_attn` or `reference_adain` must be True."
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            ref_image (`torch.Tensor`, `PIL.Image.Image`):
+                The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.Tensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
+                also be accepted as an image.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            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.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            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`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.Tensor`, *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 will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *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.Tensor`, *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.
+            pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
+                contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+                Guidance rescale factor should fix overexposure when using zero terminal SNR.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. 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 pipeline class.
+            attention_auto_machine_weight (`float`):
+                Weight of using reference query for self attention's context.
+                If attention_auto_machine_weight=1.0, use reference query for all self attention's context.
+            gn_auto_machine_weight (`float`):
+                Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.
+            reference_guidance_start (`float`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the reference ControlNet starts applying.
+            reference_guidance_end (`float`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the reference ControlNet stops applying.
+            style_fidelity (`float`):
+                style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,
+                elif style_fidelity=0.0, prompt more important, else balanced.
+            reference_attn (`bool`):
+                Whether to use reference query for self attention's context.
+            reference_adain (`bool`):
+                Whether to use reference adain.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        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 use `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 use `callback_on_step_end`",
+            )
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
         # 0. Default height and width to unet
-        # height, width = self._default_height_width(height, width, ref_image)
-
         height = height or self.default_sample_size * self.vae_scale_factor
         width = width or self.default_sample_size * self.vae_scale_factor
+
         original_size = original_size or (height, width)
         target_size = target_size or (height, width)
 
@@ -244,8 +512,27 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             negative_prompt_embeds,
             pooled_prompt_embeds,
             negative_pooled_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+            callback_on_step_end_tensor_inputs,
         )
 
+        self.check_ref_inputs(
+            ref_image,
+            reference_guidance_start,
+            reference_guidance_end,
+            style_fidelity,
+            reference_attn,
+            reference_adain,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+        self._interrupt = False
+
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
             batch_size = 1
@@ -256,15 +543,11 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
 
         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
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
         )
+
         (
             prompt_embeds,
             negative_prompt_embeds,
@@ -275,17 +558,19 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             prompt_2=prompt_2,
             device=device,
             num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
             negative_prompt=negative_prompt,
             negative_prompt_2=negative_prompt_2,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
             pooled_prompt_embeds=pooled_prompt_embeds,
             negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
         )
+
         # 4. Preprocess reference image
-        ref_image = self.prepare_image(
+        ref_image = self.prepare_ref_image(
             image=ref_image,
             width=width,
             height=height,
@@ -296,9 +581,9 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
         )
 
         # 5. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-
-        timesteps = self.scheduler.timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
 
         # 6. Prepare latent variables
         num_channels_latents = self.unet.config.in_channels
@@ -312,6 +597,7 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             generator,
             latents,
         )
+
         # 7. Prepare reference latent variables
         ref_image_latents = self.prepare_ref_latents(
             ref_image,
@@ -319,13 +605,21 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             prompt_embeds.dtype,
             device,
             generator,
-            do_classifier_free_guidance,
+            self.do_classifier_free_guidance,
         )
 
         # 8. 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)
 
-        # 9. Modify self attebtion and group norm
+        # 8.1 Create tensor stating which reference controlnets to keep
+        reference_keeps = []
+        for i in range(len(timesteps)):
+            reference_keep = 1.0 - float(
+                i / len(timesteps) < reference_guidance_start or (i + 1) / len(timesteps) > reference_guidance_end
+            )
+            reference_keeps.append(reference_keep)
+
+        # 8.2 Modify self attention and group norm
         MODE = "write"
         uc_mask = (
             torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)
@@ -333,6 +627,8 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             .bool()
         )
 
+        do_classifier_free_guidance = self.do_classifier_free_guidance
+
         def hacked_basic_transformer_inner_forward(
             self,
             hidden_states: torch.Tensor,
@@ -604,7 +900,7 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             return hidden_states
 
         def hacked_UpBlock2D_forward(
-            self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, **kwargs
+            self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, *args, **kwargs
         ):
             eps = 1e-6
             for i, resnet in enumerate(self.resnets):
@@ -684,7 +980,7 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
                 module.var_bank = []
                 module.gn_weight *= 2
 
-        # 10. Prepare added time ids & embeddings
+        # 9. Prepare added time ids & embeddings
         add_text_embeds = pooled_prompt_embeds
         if self.text_encoder_2 is None:
             text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
@@ -698,62 +994,101 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             dtype=prompt_embeds.dtype,
             text_encoder_projection_dim=text_encoder_projection_dim,
         )
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
 
-        if do_classifier_free_guidance:
+        if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
-            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
 
         prompt_embeds = prompt_embeds.to(device)
         add_text_embeds = add_text_embeds.to(device)
         add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
 
-        # 11. Denoising loop
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 10. Denoising loop
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
 
         # 10.1 Apply denoising_end
-        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
             discrete_timestep_cutoff = int(
                 round(
                     self.scheduler.config.num_train_timesteps
-                    - (denoising_end * self.scheduler.config.num_train_timesteps)
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
                 )
             )
             num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
             timesteps = timesteps[:num_inference_steps]
 
+        # 11. 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)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
                 # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
 
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
+                # predict the noise residual
                 added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
 
                 # ref only part
-                noise = randn_tensor(
-                    ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
-                )
-                ref_xt = self.scheduler.add_noise(
-                    ref_image_latents,
-                    noise,
-                    t.reshape(
-                        1,
-                    ),
-                )
-                ref_xt = self.scheduler.scale_model_input(ref_xt, t)
+                if reference_keeps[i] > 0:
+                    noise = randn_tensor(
+                        ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
+                    )
+                    ref_xt = self.scheduler.add_noise(
+                        ref_image_latents,
+                        noise,
+                        t.reshape(
+                            1,
+                        ),
+                    )
+                    ref_xt = self.scheduler.scale_model_input(ref_xt, t)
 
-                MODE = "write"
-
-                self.unet(
-                    ref_xt,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    added_cond_kwargs=added_cond_kwargs,
-                    return_dict=False,
-                )
+                    MODE = "write"
+                    self.unet(
+                        ref_xt,
+                        t,
+                        encoder_hidden_states=prompt_embeds,
+                        cross_attention_kwargs=cross_attention_kwargs,
+                        added_cond_kwargs=added_cond_kwargs,
+                        return_dict=False,
+                    )
 
                 # predict the noise residual
                 MODE = "read"
@@ -761,22 +1096,44 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
                     latent_model_input,
                     t,
                     encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
                     added_cond_kwargs=added_cond_kwargs,
                     return_dict=False,
                 )[0]
 
                 # perform guidance
-                if do_classifier_free_guidance:
+                if self.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)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
 
-                if do_classifier_free_guidance and guidance_rescale > 0.0:
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
                     # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
 
                 # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                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)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
@@ -785,6 +1142,9 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         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
@@ -792,25 +1152,43 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             if needs_upcasting:
                 self.upcast_vae()
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+            elif latents.dtype != self.vae.dtype:
+                if torch.backends.mps.is_available():
+                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                    self.vae = self.vae.to(latents.dtype)
 
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
 
             # cast back to fp16 if needed
             if needs_upcasting:
                 self.vae.to(dtype=torch.float16)
         else:
             image = latents
-            return StableDiffusionXLPipelineOutput(images=image)
 
-        # apply watermark if available
-        if self.watermark is not None:
-            image = self.watermark.apply_watermark(image)
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
 
-        image = self.image_processor.postprocess(image, output_type=output_type)
+            image = self.image_processor.postprocess(image, output_type=output_type)
 
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
+        # Offload all models
+        self.maybe_free_model_hooks()
 
         if not return_dict:
             return (image,)

examples/controlnet/train_controlnet.py

@@ -571,9 +571,6 @@ def parse_args(input_args=None):
     if args.dataset_name is None and args.train_data_dir is None:
         raise ValueError("Specify either `--dataset_name` or `--train_data_dir`")
 
-    if args.dataset_name is not None and args.train_data_dir is not None:
-        raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")
-
     if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
         raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
 
@@ -615,6 +612,7 @@ def make_train_dataset(args, tokenizer, accelerator):
             args.dataset_name,
             args.dataset_config_name,
             cache_dir=args.cache_dir,
+            data_dir=args.train_data_dir,
         )
     else:
         if args.train_data_dir is not None:

examples/controlnet/train_controlnet_flux.py

@@ -152,6 +152,7 @@ def log_validation(
                     guidance_scale=3.5,
                     generator=generator,
                 ).images[0]
+            image = image.resize((args.resolution, args.resolution))
             images.append(image)
         image_logs.append(
             {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}

examples/controlnet/train_controlnet_sdxl.py

@@ -598,9 +598,6 @@ def parse_args(input_args=None):
     if args.dataset_name is None and args.train_data_dir is None:
         raise ValueError("Specify either `--dataset_name` or `--train_data_dir`")
 
-    if args.dataset_name is not None and args.train_data_dir is not None:
-        raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")
-
     if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
         raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
 
@@ -642,6 +639,7 @@ def get_train_dataset(args, accelerator):
             args.dataset_name,
             args.dataset_config_name,
             cache_dir=args.cache_dir,
+            data_dir=args.train_data_dir,
         )
     else:
         if args.train_data_dir is not None:

examples/dreambooth/README_flux.md

@@ -118,7 +118,7 @@ accelerate launch train_dreambooth_flux.py \
 
 To better track our training experiments, we're using the following flags in the command above:
 
-* `report_to="wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.
+* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use it, be sure to install `wandb` with `pip install wandb`. Don't forget to call `wandb login <your_api_key>` before training if you haven't done it before.
 * `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
 
 > [!NOTE]
@@ -170,6 +170,21 @@ accelerate launch train_dreambooth_lora_flux.py \
   --push_to_hub
 ```
 
+### Target Modules
+When LoRA was first adapted from language models to diffusion models, it was applied to the cross-attention layers in the Unet that relate the image representations with the prompts that describe them. 
+More recently, SOTA text-to-image diffusion models replaced the Unet with a diffusion Transformer(DiT). With this change, we may also want to explore 
+applying LoRA training onto different types of layers and blocks. To allow more flexibility and control over the targeted modules we added `--lora_layers`- in which you can specify in a comma seperated string
+the exact modules for LoRA training. Here are some examples of target modules you can provide: 
+- for attention only layers: `--lora_layers="attn.to_k,attn.to_q,attn.to_v,attn.to_out.0"`
+- to train the same modules as in the fal trainer: `--lora_layers="attn.to_k,attn.to_q,attn.to_v,attn.to_out.0,attn.add_k_proj,attn.add_q_proj,attn.add_v_proj,attn.to_add_out,ff.net.0.proj,ff.net.2,ff_context.net.0.proj,ff_context.net.2"`
+- to train the same modules as in ostris ai-toolkit / replicate trainer: `--lora_blocks="attn.to_k,attn.to_q,attn.to_v,attn.to_out.0,attn.add_k_proj,attn.add_q_proj,attn.add_v_proj,attn.to_add_out,ff.net.0.proj,ff.net.2,ff_context.net.0.proj,ff_context.net.2,norm1_context.linear, norm1.linear,norm.linear,proj_mlp,proj_out"`
+> [!NOTE]
+> `--lora_layers` can also be used to specify which **blocks** to apply LoRA training to. To do so, simply add a block prefix to each layer in the comma seperated string:
+> **single DiT blocks**: to target the ith single transformer block, add the prefix `single_transformer_blocks.i`, e.g. - `single_transformer_blocks.i.attn.to_k`
+> **MMDiT blocks**: to target the ith MMDiT block, add the prefix `transformer_blocks.i`, e.g. - `transformer_blocks.i.attn.to_k` 
+> [!NOTE]
+> keep in mind that while training more layers can improve quality and expressiveness, it also increases the size of the output LoRA weights.
+
 ### Text Encoder Training
 
 Alongside the transformer, fine-tuning of the CLIP text encoder is also supported.

examples/dreambooth/README_sd3.md

@@ -105,7 +105,7 @@ accelerate launch train_dreambooth_sd3.py \
 
 To better track our training experiments, we're using the following flags in the command above:
 
-* `report_to="wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.
+* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use it, be sure to install `wandb` with `pip install wandb`. Don't forget to call `wandb login <your_api_key>` before training if you haven't done it before.
 * `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
 
 > [!NOTE]
@@ -147,6 +147,40 @@ accelerate launch train_dreambooth_lora_sd3.py \
   --push_to_hub
 ```
 
+### Targeting Specific Blocks & Layers
+As image generation models get bigger & more powerful, more fine-tuners come to find that training only part of the 
+transformer blocks (sometimes as little as two) can be enough to get great results. 
+In some cases, it can be even better to maintain some of the blocks/layers frozen.
+
+For **SD3.5-Large** specifically, you may find this information useful (taken from: [Stable Diffusion 3.5 Large Fine-tuning Tutorial](https://stabilityai.notion.site/Stable-Diffusion-3-5-Large-Fine-tuning-Tutorial-11a61cdcd1968027a15bdbd7c40be8c6#12461cdcd19680788a23c650dab26b93):
+> [!NOTE]
+> A commonly believed heuristic that we verified once again during the construction of the SD3.5 family of models is that later/higher layers (i.e. `30 - 37`)* impact tertiary details more heavily. Conversely, earlier layers (i.e. `12 - 24` )* influence the overall composition/primary form more. 
+> So, freezing other layers/targeting specific layers is a viable approach.
+> `*`These suggested layers are speculative and not 100% guaranteed. The tips here are more or less a general idea for next steps.
+> **Photorealism**
+> In preliminary testing, we observed that freezing the last few layers of the architecture significantly improved model training when using a photorealistic dataset, preventing detail degradation introduced by small dataset from happening.
+> **Anatomy preservation**
+> To dampen any possible degradation of anatomy, training only the attention layers and **not** the adaptive linear layers could help. For reference, below is one of the transformer blocks.
+
+
+We've added `--lora_layers` and `--lora_blocks` to make LoRA training modules configurable. 
+- with `--lora_blocks` you can specify the block numbers for training. E.g. passing - 
+```diff
+--lora_blocks "12,13,14,15,16,17,18,19,20,21,22,23,24,30,31,32,33,34,35,36,37"
+```
+will trigger LoRA training of transformer blocks 12-24 and 30-37. By default, all blocks are trained. 
+- with `--lora_layers` you can specify the types of layers you wish to train. 
+By default, the trained layers are -  
+`attn.add_k_proj,attn.add_q_proj,attn.add_v_proj,attn.to_add_out,attn.to_k,attn.to_out.0,attn.to_q,attn.to_v`
+If you wish to have a leaner LoRA / train more blocks over layers you could pass - 
+```diff
++ --lora_layers attn.to_k,attn.to_q,attn.to_v,attn.to_out.0
+```
+This will reduce LoRA size by roughly 50% for the same rank compared to the default. 
+However, if you're after compact LoRAs, it's our impression that maintaining the default setting for `--lora_layers` and
+freezing some of the early & blocks is usually better. 
+
+
 ### Text Encoder Training
 Alongside the transformer, LoRA fine-tuning of the CLIP text encoders is now also supported.
 To do so, just specify `--train_text_encoder` while launching training. Please keep the following points in mind:

examples/dreambooth/README_sdxl.md

@@ -99,7 +99,7 @@ accelerate launch train_dreambooth_lora_sdxl.py \
 
 To better track our training experiments, we're using the following flags in the command above:
 
-* `report_to="wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.
+* `report_to="wandb` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use it, be sure to install `wandb` with `pip install wandb`. Don't forget to call `wandb login <your_api_key>` before training if you haven't done it before.
 * `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
 
 Our experiments were conducted on a single 40GB A100 GPU.

examples/dreambooth/test_dreambooth_lora_flux.py

@@ -37,6 +37,7 @@ class DreamBoothLoRAFlux(ExamplesTestsAccelerate):
     instance_prompt = "photo"
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-pipe"
     script_path = "examples/dreambooth/train_dreambooth_lora_flux.py"
+    transformer_layer_type = "single_transformer_blocks.0.attn.to_k"
 
     def test_dreambooth_lora_flux(self):
         with tempfile.TemporaryDirectory() as tmpdir:
@@ -136,6 +137,43 @@ class DreamBoothLoRAFlux(ExamplesTestsAccelerate):
             starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
             self.assertTrue(starts_with_transformer)
 
+    def test_dreambooth_lora_layers(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                {self.script_path}
+                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
+                --instance_data_dir {self.instance_data_dir}
+                --instance_prompt {self.instance_prompt}
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --cache_latents
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lora_layers {self.transformer_layer_type}
+                --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 `"transformer"` in their names. In this test, we only params of
+            # transformer.single_transformer_blocks.0.attn.to_k should be in the state dict
+            starts_with_transformer = all(
+                key.startswith("transformer.single_transformer_blocks.0.attn.to_k") for key in lora_state_dict.keys()
+            )
+            self.assertTrue(starts_with_transformer)
+
     def test_dreambooth_lora_flux_checkpointing_checkpoints_total_limit(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             test_args = f"""

examples/dreambooth/test_dreambooth_lora_sd3.py

@@ -38,6 +38,9 @@ class DreamBoothLoRASD3(ExamplesTestsAccelerate):
     pretrained_model_name_or_path = "hf-internal-testing/tiny-sd3-pipe"
     script_path = "examples/dreambooth/train_dreambooth_lora_sd3.py"
 
+    transformer_block_idx = 0
+    layer_type = "attn.to_k"
+
     def test_dreambooth_lora_sd3(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             test_args = f"""
@@ -136,6 +139,74 @@ class DreamBoothLoRASD3(ExamplesTestsAccelerate):
             starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
             self.assertTrue(starts_with_transformer)
 
+    def test_dreambooth_lora_block(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                {self.script_path}
+                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
+                --instance_data_dir {self.instance_data_dir}
+                --instance_prompt {self.instance_prompt}
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --lora_blocks {self.transformer_block_idx}
+                --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 `"transformer"` in their names.
+            # In this test, only params of transformer block 0 should be in the state dict
+            starts_with_transformer = all(
+                key.startswith("transformer.transformer_blocks.0") for key in lora_state_dict.keys()
+            )
+            self.assertTrue(starts_with_transformer)
+
+    def test_dreambooth_lora_layer(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                {self.script_path}
+                --pretrained_model_name_or_path {self.pretrained_model_name_or_path}
+                --instance_data_dir {self.instance_data_dir}
+                --instance_prompt {self.instance_prompt}
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --lora_layers {self.layer_type}
+                --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)
+
+            # In this test, only transformer params of attention layers `attn.to_k` should be in the state dict
+            starts_with_transformer = all("attn.to_k" in key for key in lora_state_dict.keys())
+            self.assertTrue(starts_with_transformer)
+
     def test_dreambooth_lora_sd3_checkpointing_checkpoints_total_limit(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             test_args = f"""

examples/dreambooth/train_dreambooth_flux.py

@@ -57,6 +57,7 @@ from diffusers.utils import (
     is_wandb_available,
 )
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
+from diffusers.utils.import_utils import is_torch_npu_available
 from diffusers.utils.torch_utils import is_compiled_module
 
 
@@ -68,6 +69,12 @@ check_min_version("0.32.0.dev0")
 
 logger = get_logger(__name__)
 
+if is_torch_npu_available():
+    import torch_npu
+
+    torch.npu.config.allow_internal_format = False
+    torch.npu.set_compile_mode(jit_compile=False)
+
 
 def save_model_card(
     repo_id: str,
@@ -161,7 +168,7 @@ def log_validation(
         f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
         f" {args.validation_prompt}."
     )
-    pipeline = pipeline.to(accelerator.device, dtype=torch_dtype)
+    pipeline = pipeline.to(accelerator.device)
     pipeline.set_progress_bar_config(disable=True)
 
     # run inference
@@ -189,6 +196,8 @@ def log_validation(
     del pipeline
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
+    elif is_torch_npu_available():
+        torch_npu.npu.empty_cache()
 
     return images
 
@@ -1035,7 +1044,9 @@ def main(args):
         cur_class_images = len(list(class_images_dir.iterdir()))
 
         if cur_class_images < args.num_class_images:
-            has_supported_fp16_accelerator = torch.cuda.is_available() or torch.backends.mps.is_available()
+            has_supported_fp16_accelerator = (
+                torch.cuda.is_available() or torch.backends.mps.is_available() or is_torch_npu_available()
+            )
             torch_dtype = torch.float16 if has_supported_fp16_accelerator else torch.float32
             if args.prior_generation_precision == "fp32":
                 torch_dtype = torch.float32
@@ -1073,6 +1084,8 @@ def main(args):
             del pipeline
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
+            elif is_torch_npu_available():
+                torch_npu.npu.empty_cache()
 
     # Handle the repository creation
     if accelerator.is_main_process:
@@ -1226,10 +1239,7 @@ def main(args):
             "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 = [
-            transformer_parameters_with_lr,
-            text_parameters_one_with_lr,
-        ]
+        params_to_optimize = [transformer_parameters_with_lr, text_parameters_one_with_lr]
     else:
         params_to_optimize = [transformer_parameters_with_lr]
 
@@ -1288,11 +1298,9 @@ def main(args):
             # 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,
@@ -1359,6 +1367,8 @@ def main(args):
         gc.collect()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
+        elif is_torch_npu_available():
+            torch_npu.npu.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
@@ -1580,7 +1590,7 @@ def main(args):
                 )
 
                 # handle guidance
-                if transformer.config.guidance_embeds:
+                if accelerator.unwrap_model(transformer).config.guidance_embeds:
                     guidance = torch.tensor([args.guidance_scale], device=accelerator.device)
                     guidance = guidance.expand(model_input.shape[0])
                 else:
@@ -1694,6 +1704,8 @@ def main(args):
                 # create pipeline
                 if not args.train_text_encoder:
                     text_encoder_one, text_encoder_two = load_text_encoders(text_encoder_cls_one, text_encoder_cls_two)
+                    text_encoder_one.to(weight_dtype)
+                    text_encoder_two.to(weight_dtype)
                 else:  # even when training the text encoder we're only training text encoder one
                     text_encoder_two = text_encoder_cls_two.from_pretrained(
                         args.pretrained_model_name_or_path,
@@ -1722,9 +1734,15 @@ def main(args):
                 )
                 if not args.train_text_encoder:
                     del text_encoder_one, text_encoder_two
-                    torch.cuda.empty_cache()
+                    if torch.cuda.is_available():
+                        torch.cuda.empty_cache()
+                    elif is_torch_npu_available():
+                        torch_npu.npu.empty_cache()
                     gc.collect()
 
+                images = None
+                del pipeline
+
     # Save the lora layers
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
@@ -1783,6 +1801,9 @@ def main(args):
                 ignore_patterns=["step_*", "epoch_*"],
             )
 
+        images = None
+        del pipeline
+
     accelerator.end_training()
 
 

examples/dreambooth/train_dreambooth_lora_flux.py

@@ -177,7 +177,7 @@ def log_validation(
         f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
         f" {args.validation_prompt}."
     )
-    pipeline = pipeline.to(accelerator.device, dtype=torch_dtype)
+    pipeline = pipeline.to(accelerator.device)
     pipeline.set_progress_bar_config(disable=True)
 
     # run inference
@@ -554,6 +554,15 @@ def parse_args(input_args=None):
         "--adam_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder"
     )
 
+    parser.add_argument(
+        "--lora_layers",
+        type=str,
+        default=None,
+        help=(
+            'The transformer modules to apply LoRA training on. Please specify the layers in a comma seperated. E.g. - "to_k,to_q,to_v,to_out.0" will result in lora training of attention layers only'
+        ),
+    )
+
     parser.add_argument(
         "--adam_epsilon",
         type=float,
@@ -1186,12 +1195,30 @@ def main(args):
         if args.train_text_encoder:
             text_encoder_one.gradient_checkpointing_enable()
 
-    # now we will add new LoRA weights to the attention layers
+    if args.lora_layers is not None:
+        target_modules = [layer.strip() for layer in args.lora_layers.split(",")]
+    else:
+        target_modules = [
+            "attn.to_k",
+            "attn.to_q",
+            "attn.to_v",
+            "attn.to_out.0",
+            "attn.add_k_proj",
+            "attn.add_q_proj",
+            "attn.add_v_proj",
+            "attn.to_add_out",
+            "ff.net.0.proj",
+            "ff.net.2",
+            "ff_context.net.0.proj",
+            "ff_context.net.2",
+        ]
+
+    # now we will add new LoRA weights the transformer layers
     transformer_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
         init_lora_weights="gaussian",
-        target_modules=["to_k", "to_q", "to_v", "to_out.0"],
+        target_modules=target_modules,
     )
     transformer.add_adapter(transformer_lora_config)
     if args.train_text_encoder:
@@ -1308,10 +1335,7 @@ def main(args):
             "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 = [
-            transformer_parameters_with_lr,
-            text_parameters_one_with_lr,
-        ]
+        params_to_optimize = [transformer_parameters_with_lr, text_parameters_one_with_lr]
     else:
         params_to_optimize = [transformer_parameters_with_lr]
 
@@ -1367,14 +1391,12 @@ def main(args):
                 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
+            # changes the learning rate of text_encoder_parameters_one 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,
@@ -1626,11 +1648,15 @@ def main(args):
                             prompt=prompts,
                         )
                 else:
+                    elems_to_repeat = len(prompts)
                     if args.train_text_encoder:
                         prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
                             text_encoders=[text_encoder_one, text_encoder_two],
                             tokenizers=[None, None],
-                            text_input_ids_list=[tokens_one, tokens_two],
+                            text_input_ids_list=[
+                                tokens_one.repeat(elems_to_repeat, 1),
+                                tokens_two.repeat(elems_to_repeat, 1),
+                            ],
                             max_sequence_length=args.max_sequence_length,
                             device=accelerator.device,
                             prompt=args.instance_prompt,
@@ -1684,7 +1710,7 @@ def main(args):
                 )
 
                 # handle guidance
-                if transformer.config.guidance_embeds:
+                if accelerator.unwrap_model(transformer).config.guidance_embeds:
                     guidance = torch.tensor([args.guidance_scale], device=accelerator.device)
                     guidance = guidance.expand(model_input.shape[0])
                 else:
@@ -1797,6 +1823,8 @@ def main(args):
                 # create pipeline
                 if not args.train_text_encoder:
                     text_encoder_one, text_encoder_two = load_text_encoders(text_encoder_cls_one, text_encoder_cls_two)
+                    text_encoder_one.to(weight_dtype)
+                    text_encoder_two.to(weight_dtype)
                 pipeline = FluxPipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
                     vae=vae,
@@ -1820,6 +1848,9 @@ def main(args):
                     del text_encoder_one, text_encoder_two
                     free_memory()
 
+                images = None
+                del pipeline
+
     # Save the lora layers
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
@@ -1884,6 +1915,9 @@ def main(args):
                 ignore_patterns=["step_*", "epoch_*"],
             )
 
+        images = None
+        del pipeline
+
     accelerator.end_training()
 
 

examples/dreambooth/train_dreambooth_lora_sd3.py

@@ -571,6 +571,25 @@ def parse_args(input_args=None):
         "--adam_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder"
     )
 
+    parser.add_argument(
+        "--lora_layers",
+        type=str,
+        default=None,
+        help=(
+            "The transformer block layers to apply LoRA training on. Please specify the layers in a comma seperated string."
+            "For examples refer to https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_SD3.md"
+        ),
+    )
+    parser.add_argument(
+        "--lora_blocks",
+        type=str,
+        default=None,
+        help=(
+            "The transformer blocks to apply LoRA training on. Please specify the block numbers in a comma seperated manner."
+            'E.g. - "--lora_blocks 12,30" will result in lora training of transformer blocks 12 and 30. For more examples refer to https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_SD3.md'
+        ),
+    )
+
     parser.add_argument(
         "--adam_epsilon",
         type=float,
@@ -1222,13 +1241,31 @@ def main(args):
         if args.train_text_encoder:
             text_encoder_one.gradient_checkpointing_enable()
             text_encoder_two.gradient_checkpointing_enable()
+    if args.lora_layers is not None:
+        target_modules = [layer.strip() for layer in args.lora_layers.split(",")]
+    else:
+        target_modules = [
+            "attn.add_k_proj",
+            "attn.add_q_proj",
+            "attn.add_v_proj",
+            "attn.to_add_out",
+            "attn.to_k",
+            "attn.to_out.0",
+            "attn.to_q",
+            "attn.to_v",
+        ]
+    if args.lora_blocks is not None:
+        target_blocks = [int(block.strip()) for block in args.lora_blocks.split(",")]
+        target_modules = [
+            f"transformer_blocks.{block}.{module}" for block in target_blocks for module in target_modules
+        ]
 
     # now we will add new LoRA weights to the attention layers
     transformer_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
         init_lora_weights="gaussian",
-        target_modules=["to_k", "to_q", "to_v", "to_out.0"],
+        target_modules=target_modules,
     )
     transformer.add_adapter(transformer_lora_config)
 
@@ -1257,10 +1294,13 @@ def main(args):
             for model in models:
                 if isinstance(model, type(unwrap_model(transformer))):
                     transformer_lora_layers_to_save = get_peft_model_state_dict(model)
-                elif isinstance(model, type(unwrap_model(text_encoder_one))):
-                    text_encoder_one_lora_layers_to_save = get_peft_model_state_dict(model)
-                elif isinstance(model, type(unwrap_model(text_encoder_two))):
-                    text_encoder_two_lora_layers_to_save = get_peft_model_state_dict(model)
+                elif isinstance(model, type(unwrap_model(text_encoder_one))):  # or text_encoder_two
+                    # both text encoders are of the same class, so we check hidden size to distinguish between the two
+                    hidden_size = unwrap_model(model).config.hidden_size
+                    if hidden_size == 768:
+                        text_encoder_one_lora_layers_to_save = get_peft_model_state_dict(model)
+                    elif hidden_size == 1280:
+                        text_encoder_two_lora_layers_to_save = get_peft_model_state_dict(model)
                 else:
                     raise ValueError(f"unexpected save model: {model.__class__}")
 
@@ -1431,7 +1471,6 @@ def main(args):
 
         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,

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -67,6 +67,7 @@ from diffusers.utils import (
     convert_state_dict_to_diffusers,
     convert_state_dict_to_kohya,
     convert_unet_state_dict_to_peft,
+    is_peft_version,
     is_wandb_available,
 )
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
@@ -1183,26 +1184,33 @@ def main(args):
             text_encoder_one.gradient_checkpointing_enable()
             text_encoder_two.gradient_checkpointing_enable()
 
+    def get_lora_config(rank, use_dora, target_modules):
+        base_config = {
+            "r": rank,
+            "lora_alpha": rank,
+            "init_lora_weights": "gaussian",
+            "target_modules": target_modules,
+        }
+        if use_dora:
+            if is_peft_version("<", "0.9.0"):
+                raise ValueError(
+                    "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+                )
+            else:
+                base_config["use_dora"] = True
+
+        return LoraConfig(**base_config)
+
     # now we will add new LoRA weights to the attention layers
-    unet_lora_config = LoraConfig(
-        r=args.rank,
-        use_dora=args.use_dora,
-        lora_alpha=args.rank,
-        init_lora_weights="gaussian",
-        target_modules=["to_k", "to_q", "to_v", "to_out.0"],
-    )
+    unet_target_modules = ["to_k", "to_q", "to_v", "to_out.0"]
+    unet_lora_config = get_lora_config(rank=args.rank, use_dora=args.use_dora, target_modules=unet_target_modules)
     unet.add_adapter(unet_lora_config)
 
     # The text encoder comes from 🤗 transformers, so we cannot directly modify it.
     # So, instead, we monkey-patch the forward calls of its attention-blocks.
     if args.train_text_encoder:
-        text_lora_config = LoraConfig(
-            r=args.rank,
-            use_dora=args.use_dora,
-            lora_alpha=args.rank,
-            init_lora_weights="gaussian",
-            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
-        )
+        text_target_modules = ["q_proj", "k_proj", "v_proj", "out_proj"]
+        text_lora_config = get_lora_config(rank=args.rank, use_dora=args.use_dora, target_modules=text_target_modules)
         text_encoder_one.add_adapter(text_lora_config)
         text_encoder_two.add_adapter(text_lora_config)
 
@@ -1402,7 +1410,6 @@ def main(args):
 
         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,

examples/dreambooth/train_dreambooth_sd3.py

@@ -902,20 +902,26 @@ def _encode_prompt_with_clip(
     tokenizer,
     prompt: str,
     device=None,
+    text_input_ids=None,
     num_images_per_prompt: int = 1,
 ):
     prompt = [prompt] if isinstance(prompt, str) else prompt
     batch_size = len(prompt)
 
-    text_inputs = tokenizer(
-        prompt,
-        padding="max_length",
-        max_length=77,
-        truncation=True,
-        return_tensors="pt",
-    )
+    if tokenizer is not None:
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=77,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids
+    else:
+        if text_input_ids is None:
+            raise ValueError("text_input_ids must be provided when the tokenizer is not specified")
 
-    text_input_ids = text_inputs.input_ids
     prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
 
     pooled_prompt_embeds = prompt_embeds[0]
@@ -937,6 +943,7 @@ def encode_prompt(
     max_sequence_length,
     device=None,
     num_images_per_prompt: int = 1,
+    text_input_ids_list=None,
 ):
     prompt = [prompt] if isinstance(prompt, str) else prompt
 
@@ -945,13 +952,14 @@ def encode_prompt(
 
     clip_prompt_embeds_list = []
     clip_pooled_prompt_embeds_list = []
-    for tokenizer, text_encoder in zip(clip_tokenizers, clip_text_encoders):
+    for i, (tokenizer, text_encoder) in enumerate(zip(clip_tokenizers, clip_text_encoders)):
         prompt_embeds, pooled_prompt_embeds = _encode_prompt_with_clip(
             text_encoder=text_encoder,
             tokenizer=tokenizer,
             prompt=prompt,
             device=device if device is not None else text_encoder.device,
             num_images_per_prompt=num_images_per_prompt,
+            text_input_ids=text_input_ids_list[i] if text_input_ids_list else None,
         )
         clip_prompt_embeds_list.append(prompt_embeds)
         clip_pooled_prompt_embeds_list.append(pooled_prompt_embeds)
@@ -1328,7 +1336,6 @@ def main(args):
 
         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,

examples/reinforcement_learning/README.md

@@ -1,4 +1,13 @@
-# Overview
+
+## Diffusion-based Policy Learning for RL
+
+`diffusion_policy` implements [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/), a diffusion model that predicts robot action sequences in reinforcement learning tasks.
+
+This example implements a robot control model for pushing a T-shaped block into a target area. The model takes in current state observations as input, and outputs a trajectory of subsequent steps to follow.
+
+To execute the script, run `diffusion_policy.py`
+
+## Diffuser Locomotion
 
 These examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers.
 There are two ways to use the script, `run_diffuser_locomotion.py`.

examples/reinforcement_learning/diffusion_policy.py

@@ -0,0 +1,201 @@
+import numpy as np
+import numpy.core.multiarray as multiarray
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+from torch.serialization import add_safe_globals
+
+from diffusers import DDPMScheduler, UNet1DModel
+
+
+add_safe_globals(
+    [
+        multiarray._reconstruct,
+        np.ndarray,
+        np.dtype,
+        np.dtype(np.float32).type,
+        np.dtype(np.float64).type,
+        np.dtype(np.int32).type,
+        np.dtype(np.int64).type,
+        type(np.dtype(np.float32)),
+        type(np.dtype(np.float64)),
+        type(np.dtype(np.int32)),
+        type(np.dtype(np.int64)),
+    ]
+)
+
+"""
+An example of using HuggingFace's diffusers library for diffusion policy,
+generating smooth movement trajectories.
+
+This implements a robot control model for pushing a T-shaped block into a target area.
+The model takes in the robot arm position, block position, and block angle,
+then outputs a sequence of 16 (x,y) positions for the robot arm to follow.
+"""
+
+
+class ObservationEncoder(nn.Module):
+    """
+    Converts raw robot observations (positions/angles) into a more compact representation
+
+    state_dim (int): Dimension of the input state vector (default: 5)
+        [robot_x, robot_y, block_x, block_y, block_angle]
+
+    - Input shape: (batch_size, state_dim)
+    - Output shape: (batch_size, 256)
+    """
+
+    def __init__(self, state_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, 512), nn.ReLU(), nn.Linear(512, 256))
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class ObservationProjection(nn.Module):
+    """
+    Takes the encoded observation and transforms it into 32 values that represent the current robot/block situation.
+    These values are used as additional contextual information during the diffusion model's trajectory generation.
+
+    - Input: 256-dim vector (padded to 512)
+            Shape: (batch_size, 256)
+    - Output: 32 contextual information values for the diffusion model
+            Shape: (batch_size, 32)
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.weight = nn.Parameter(torch.randn(32, 512))
+        self.bias = nn.Parameter(torch.zeros(32))
+
+    def forward(self, x):  # pad 256-dim input to 512-dim with zeros
+        if x.size(-1) == 256:
+            x = torch.cat([x, torch.zeros(*x.shape[:-1], 256, device=x.device)], dim=-1)
+        return nn.functional.linear(x, self.weight, self.bias)
+
+
+class DiffusionPolicy:
+    """
+    Implements diffusion policy for generating robot arm trajectories.
+    Uses diffusion to generate sequences of positions for a robot arm, conditioned on
+    the current state of the robot and the block it needs to push.
+
+    The model expects observations in pixel coordinates (0-512 range) and block angle in radians.
+    It generates trajectories as sequences of (x,y) coordinates also in the 0-512 range.
+    """
+
+    def __init__(self, state_dim=5, device="cpu"):
+        self.device = device
+
+        # define valid ranges for inputs/outputs
+        self.stats = {
+            "obs": {"min": torch.zeros(5), "max": torch.tensor([512, 512, 512, 512, 2 * np.pi])},
+            "action": {"min": torch.zeros(2), "max": torch.full((2,), 512)},
+        }
+
+        self.obs_encoder = ObservationEncoder(state_dim).to(device)
+        self.obs_projection = ObservationProjection().to(device)
+
+        # UNet model that performs the denoising process
+        # takes in concatenated action (2 channels) and context (32 channels) = 34 channels
+        # outputs predicted action (2 channels for x,y coordinates)
+        self.model = UNet1DModel(
+            sample_size=16,  # length of trajectory sequence
+            in_channels=34,
+            out_channels=2,
+            layers_per_block=2,  # number of layers per each UNet block
+            block_out_channels=(128,),  # number of output neurons per layer in each block
+            down_block_types=("DownBlock1D",),  # reduce the resolution of data
+            up_block_types=("UpBlock1D",),  # increase the resolution of data
+        ).to(device)
+
+        # noise scheduler that controls the denoising process
+        self.noise_scheduler = DDPMScheduler(
+            num_train_timesteps=100,  # number of denoising steps
+            beta_schedule="squaredcos_cap_v2",  # type of noise schedule
+        )
+
+        # load pre-trained weights from HuggingFace
+        checkpoint = torch.load(
+            hf_hub_download("dorsar/diffusion_policy", "push_tblock.pt"), weights_only=True, map_location=device
+        )
+        self.model.load_state_dict(checkpoint["model_state_dict"])
+        self.obs_encoder.load_state_dict(checkpoint["encoder_state_dict"])
+        self.obs_projection.load_state_dict(checkpoint["projection_state_dict"])
+
+    # scales data to [-1, 1] range for neural network processing
+    def normalize_data(self, data, stats):
+        return ((data - stats["min"]) / (stats["max"] - stats["min"])) * 2 - 1
+
+    # converts normalized data back to original range
+    def unnormalize_data(self, ndata, stats):
+        return ((ndata + 1) / 2) * (stats["max"] - stats["min"]) + stats["min"]
+
+    @torch.no_grad()
+    def predict(self, observation):
+        """
+        Generates a trajectory of robot arm positions given the current state.
+
+        Args:
+            observation (torch.Tensor): Current state [robot_x, robot_y, block_x, block_y, block_angle]
+                                    Shape: (batch_size, 5)
+
+        Returns:
+            torch.Tensor: Sequence of (x,y) positions for the robot arm to follow
+                        Shape: (batch_size, 16, 2) where:
+                        - 16 is the number of steps in the trajectory
+                        - 2 is the (x,y) coordinates in pixel space (0-512)
+
+        The function first encodes the observation, then uses it to condition a diffusion
+        process that gradually denoises random trajectories into smooth, purposeful movements.
+        """
+        observation = observation.to(self.device)
+        normalized_obs = self.normalize_data(observation, self.stats["obs"])
+
+        # encode the observation into context values for the diffusion model
+        cond = self.obs_projection(self.obs_encoder(normalized_obs))
+        # keeps first & second dimension sizes unchanged, and multiplies last dimension by 16
+        cond = cond.view(normalized_obs.shape[0], -1, 1).expand(-1, -1, 16)
+
+        # initialize action with noise - random noise that will be refined into a trajectory
+        action = torch.randn((observation.shape[0], 2, 16), device=self.device)
+
+        # denoise
+        # at each step `t`, the current noisy trajectory (`action`) & conditioning info (context) are
+        # fed into the model to predict a denoised trajectory, then uses self.noise_scheduler.step to
+        # apply this prediction & slightly reduce the noise in `action` more
+
+        self.noise_scheduler.set_timesteps(100)
+        for t in self.noise_scheduler.timesteps:
+            model_output = self.model(torch.cat([action, cond], dim=1), t)
+            action = self.noise_scheduler.step(model_output.sample, t, action).prev_sample
+
+        action = action.transpose(1, 2)  # reshape to [batch, 16, 2]
+        action = self.unnormalize_data(action, self.stats["action"])  # scale back to coordinates
+        return action
+
+
+if __name__ == "__main__":
+    policy = DiffusionPolicy()
+
+    # sample of a single observation
+    # robot arm starts in center, block is slightly left and up, rotated 90 degrees
+    obs = torch.tensor(
+        [
+            [
+                256.0,  # robot arm x position (middle of screen)
+                256.0,  # robot arm y position (middle of screen)
+                200.0,  # block x position
+                300.0,  # block y position
+                np.pi / 2,  # block angle (90 degrees)
+            ]
+        ]
+    )
+
+    action = policy.predict(obs)
+
+    print("Action shape:", action.shape)  # should be [1, 16, 2] - one trajectory of 16 x,y positions
+    print("\nPredicted trajectory:")
+    for i, (x, y) in enumerate(action[0]):
+        print(f"Step {i:2d}: x={x:6.1f}, y={y:6.1f}")

examples/research_projects/flux_lora_quantization/README.md

@@ -5,7 +5,8 @@
 
 This example shows how to fine-tune [Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) with LoRA and quantization. We show this by using the [`Norod78/Yarn-art-style`](https://huggingface.co/datasets/Norod78/Yarn-art-style) dataset. Steps below summarize the workflow:
 
-* We precompute the text embeddings in `compute_embeddings.py` and serialize them into a parquet file. 
+* We precompute the text embeddings in `compute_embeddings.py` and serialize them into a parquet file.
+  * Even though optional, we load the T5-xxl in NF4 to further reduce the memory foot-print. 
 * `train_dreambooth_lora_flux_miniature.py` takes care of training:
   * Since we already precomputed the text embeddings, we don't load the text encoders.
   * We load the VAE and use it to precompute the image latents and we then delete it. 
@@ -163,4 +164,4 @@ image.save("yarn_merged.png")
 |-------|-------|
 | ![Image A](https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/quantized_flux_training/merged.png) | ![Image B](https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/quantized_flux_training/unmerged.png) |
 
-As we can notice the first column result follows the style more closely.
+As we can notice the first column result follows the style more closely.

examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py

@@ -349,7 +349,7 @@ def parse_args(input_args=None):
         "--optimizer",
         type=str,
         default="AdamW",
-        help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'),
+        choices=["AdamW", "Prodigy", "AdEMAMix"],
     )
 
     parser.add_argument(
@@ -357,6 +357,11 @@ def parse_args(input_args=None):
         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(
+        "--use_8bit_ademamix",
+        action="store_true",
+        help="Whether or not to use 8-bit AdEMAMix from bitsandbytes.",
+    )
 
     parser.add_argument(
         "--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers."
@@ -820,19 +825,18 @@ def main(args):
     params_to_optimize = [transformer_parameters_with_lr]
 
     # Optimizer creation
-    if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"):
-        logger.warning(
-            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.warning(
             f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
             f"set to {args.optimizer.lower()}"
         )
 
+    if args.use_8bit_ademamix and not args.optimizer.lower() == "ademamix":
+        logger.warning(
+            f"use_8bit_ademamix is ignored when optimizer is not set to 'AdEMAMix'. Optimizer was "
+            f"set to {args.optimizer.lower()}"
+        )
+
     if args.optimizer.lower() == "adamw":
         if args.use_8bit_adam:
             try:
@@ -853,6 +857,20 @@ def main(args):
             eps=args.adam_epsilon,
         )
 
+    elif args.optimizer.lower() == "ademamix":
+        try:
+            import bitsandbytes as bnb
+        except ImportError:
+            raise ImportError(
+                "To use AdEMAMix (or its 8bit variant), please install the bitsandbytes library: `pip install -U bitsandbytes`."
+            )
+        if args.use_8bit_ademamix:
+            optimizer_class = bnb.optim.AdEMAMix8bit
+        else:
+            optimizer_class = bnb.optim.AdEMAMix
+
+        optimizer = optimizer_class(params_to_optimize)
+
     if args.optimizer.lower() == "prodigy":
         try:
             import prodigyopt
@@ -868,7 +886,6 @@ def main(args):
 
         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,
@@ -1020,12 +1037,12 @@ def main(args):
                 model_input = (model_input - vae_config_shift_factor) * vae_config_scaling_factor
                 model_input = model_input.to(dtype=weight_dtype)
 
-                vae_scale_factor = 2 ** (len(vae_config_block_out_channels))
+                vae_scale_factor = 2 ** (len(vae_config_block_out_channels) - 1)
 
                 latent_image_ids = FluxPipeline._prepare_latent_image_ids(
                     model_input.shape[0],
-                    model_input.shape[2],
-                    model_input.shape[3],
+                    model_input.shape[2] // 2,
+                    model_input.shape[3] // 2,
                     accelerator.device,
                     weight_dtype,
                 )
@@ -1059,7 +1076,7 @@ def main(args):
                 )
 
                 # handle guidance
-                if transformer.config.guidance_embeds:
+                if unwrap_model(transformer).config.guidance_embeds:
                     guidance = torch.tensor([args.guidance_scale], device=accelerator.device)
                     guidance = guidance.expand(model_input.shape[0])
                 else:
@@ -1082,8 +1099,8 @@ def main(args):
                 )[0]
                 model_pred = FluxPipeline._unpack_latents(
                     model_pred,
-                    height=int(model_input.shape[2] * vae_scale_factor / 2),
-                    width=int(model_input.shape[3] * vae_scale_factor / 2),
+                    height=model_input.shape[2] * vae_scale_factor,
+                    width=model_input.shape[3] * vae_scale_factor,
                     vae_scale_factor=vae_scale_factor,
                 )
 

examples/research_projects/ip_adapter/README.md

@@ -0,0 +1,226 @@
+# IP Adapter Training Example 
+
+[IP Adapter](https://arxiv.org/abs/2308.06721) is a novel approach designed to enhance text-to-image models such as Stable Diffusion by enabling them to generate images based on image prompts rather than text prompts alone. Unlike traditional methods that rely solely on complex text prompts, IP Adapter introduces the concept of using image prompts, leveraging the idea that "an image is worth a thousand words." By decoupling cross-attention layers for text and image features, IP Adapter effectively integrates image prompts into the generation process without the need for extensive fine-tuning or large computing resources.
+
+## Training locally with PyTorch
+
+### Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+**Important**
+
+To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install -e .
+```
+
+Then cd in the example folder and run
+
+```bash
+pip install -r requirements.txt
+```
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+Or for a default accelerate configuration without answering questions about your environment
+
+```bash
+accelerate config default
+```
+
+Or if your environment doesn't support an interactive shell e.g. a notebook
+
+```python
+from accelerate.utils import write_basic_config
+write_basic_config()
+```
+
+Certainly! Below is the documentation in pure Markdown format:
+
+### Accelerate Launch Command Documentation
+
+#### Description:
+The Accelerate launch command is used to train a model using multiple GPUs and mixed precision training. It launches the training script `tutorial_train_ip-adapter.py` with specified parameters and configurations.
+
+#### Usage Example:
+
+```
+accelerate launch --mixed_precision "fp16" \
+tutorial_train_ip-adapter.py \
+--pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \
+--image_encoder_path="{image_encoder_path}" \
+--data_json_file="{data.json}" \
+--data_root_path="{image_path}" \
+--mixed_precision="fp16" \
+--resolution=512 \
+--train_batch_size=8 \
+--dataloader_num_workers=4 \
+--learning_rate=1e-04 \
+--weight_decay=0.01 \
+--output_dir="{output_dir}" \
+--save_steps=10000
+```
+
+### Multi-GPU Script:
+```
+accelerate launch --num_processes 8 --multi_gpu --mixed_precision "fp16" \
+  tutorial_train_ip-adapter.py \
+  --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \
+  --image_encoder_path="{image_encoder_path}" \
+  --data_json_file="{data.json}" \
+  --data_root_path="{image_path}" \
+  --mixed_precision="fp16" \
+  --resolution=512 \
+  --train_batch_size=8 \
+  --dataloader_num_workers=4 \
+  --learning_rate=1e-04 \
+  --weight_decay=0.01 \
+  --output_dir="{output_dir}" \
+  --save_steps=10000
+```
+
+#### Parameters:
+- `--num_processes`: Number of processes to launch for distributed training (in this example, 8 processes).
+- `--multi_gpu`: Flag indicating the usage of multiple GPUs for training.
+- `--mixed_precision "fp16"`: Enables mixed precision training with 16-bit floating-point precision.
+- `tutorial_train_ip-adapter.py`: Name of the training script to be executed.
+- `--pretrained_model_name_or_path`: Path or identifier for a pretrained model.
+- `--image_encoder_path`: Path to the CLIP image encoder.
+- `--data_json_file`: Path to the training data in JSON format.
+- `--data_root_path`: Root path where training images are located.
+- `--resolution`: Resolution of input images (512x512 in this example).
+- `--train_batch_size`: Batch size for training data (8 in this example).
+- `--dataloader_num_workers`: Number of subprocesses for data loading (4 in this example).
+- `--learning_rate`: Learning rate for training (1e-04 in this example).
+- `--weight_decay`: Weight decay for regularization (0.01 in this example).
+- `--output_dir`: Directory to save model checkpoints and predictions.
+- `--save_steps`: Frequency of saving checkpoints during training (10000 in this example).
+
+### Inference
+
+#### Description:
+The provided inference code is used to load a trained model checkpoint and extract the components related to image projection and IP (Image Processing) adapter. These components are then saved into a binary file for later use in inference.
+
+#### Usage Example:
+```python
+from safetensors.torch import load_file, save_file
+
+# Load the trained model checkpoint in safetensors format
+ckpt = "checkpoint-50000/pytorch_model.safetensors"
+sd = load_file(ckpt)  # Using safetensors load function
+
+# Extract image projection and IP adapter components
+image_proj_sd = {}
+ip_sd = {}
+
+for k in sd:
+    if k.startswith("unet"):
+        pass  # Skip unet-related keys
+    elif k.startswith("image_proj_model"):
+        image_proj_sd[k.replace("image_proj_model.", "")] = sd[k]
+    elif k.startswith("adapter_modules"):
+        ip_sd[k.replace("adapter_modules.", "")] = sd[k]
+
+# Save the components into separate safetensors files
+save_file(image_proj_sd, "image_proj.safetensors")
+save_file(ip_sd, "ip_adapter.safetensors")
+```
+
+### Sample Inference Script using the CLIP Model
+
+```python
+
+import torch
+from safetensors.torch import load_file
+from transformers import CLIPProcessor, CLIPModel  # Using the Hugging Face CLIP model 
+
+# Load model components from safetensors
+image_proj_ckpt = "image_proj.safetensors"
+ip_adapter_ckpt = "ip_adapter.safetensors"
+
+# Load the saved weights
+image_proj_sd = load_file(image_proj_ckpt)
+ip_adapter_sd = load_file(ip_adapter_ckpt)
+
+# Define the model Parameters
+class ImageProjectionModel(torch.nn.Module):
+    def __init__(self, input_dim=768, output_dim=512):  # CLIP's default embedding size is 768
+        super().__init__()
+        self.model = torch.nn.Linear(input_dim, output_dim)
+
+    def forward(self, x):
+        return self.model(x)
+
+class IPAdapterModel(torch.nn.Module):
+    def __init__(self, input_dim=512, output_dim=10):  # Example for 10 classes
+        super().__init__()
+        self.model = torch.nn.Linear(input_dim, output_dim)
+
+    def forward(self, x):
+        return self.model(x)
+
+# Initialize models
+image_proj_model = ImageProjectionModel()
+ip_adapter_model = IPAdapterModel()
+
+# Load weights into models
+image_proj_model.load_state_dict(image_proj_sd)
+ip_adapter_model.load_state_dict(ip_adapter_sd)
+
+# Set models to evaluation mode
+image_proj_model.eval()
+ip_adapter_model.eval()
+
+#Inference pipeline
+def inference(image_tensor):
+    """
+    Run inference using the loaded models.
+
+    Args:
+        image_tensor: Preprocessed image tensor from CLIPProcessor
+
+    Returns:
+        Final inference results
+    """
+    with torch.no_grad():
+        # Step 1: Project the image features
+        image_proj = image_proj_model(image_tensor)
+
+        # Step 2: Pass the projected features through the IP Adapter
+        result = ip_adapter_model(image_proj)
+
+    return result
+
+# Using CLIP for image preprocessing
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+
+#Image file path
+image_path = "path/to/image.jpg"
+
+# Preprocess the image
+inputs = processor(images=image_path, return_tensors="pt")
+image_features = clip_model.get_image_features(inputs["pixel_values"])
+
+# Normalize the image features as per CLIP's recommendations
+image_features = image_features / image_features.norm(dim=-1, keepdim=True)
+
+# Run inference
+output = inference(image_features)
+print("Inference output:", output)
+```
+
+#### Parameters:
+- `ckpt`: Path to the trained model checkpoint file.
+- `map_location="cpu"`: Specifies that the model should be loaded onto the CPU.
+- `image_proj_sd`: Dictionary to store the components related to image projection.
+- `ip_sd`: Dictionary to store the components related to the IP adapter.
+- `"unet"`, `"image_proj_model"`, `"adapter_modules"`: Prefixes indicating components of the model.

examples/research_projects/ip_adapter/requirements.txt

@@ -0,0 +1,4 @@
+accelerate
+torchvision
+transformers>=4.25.1
+ip_adapter

examples/research_projects/ip_adapter/tutorial_train_faceid.py

@@ -0,0 +1,415 @@
+import argparse
+import itertools
+import json
+import os
+import random
+import time
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+from accelerate import Accelerator
+from accelerate.utils import ProjectConfiguration
+from ip_adapter.attention_processor_faceid import LoRAAttnProcessor, LoRAIPAttnProcessor
+from ip_adapter.ip_adapter_faceid import MLPProjModel
+from PIL import Image
+from torchvision import transforms
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
+
+
+# Dataset
+class MyDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, json_file, tokenizer, size=512, t_drop_rate=0.05, i_drop_rate=0.05, ti_drop_rate=0.05, image_root_path=""
+    ):
+        super().__init__()
+
+        self.tokenizer = tokenizer
+        self.size = size
+        self.i_drop_rate = i_drop_rate
+        self.t_drop_rate = t_drop_rate
+        self.ti_drop_rate = ti_drop_rate
+        self.image_root_path = image_root_path
+
+        self.data = json.load(
+            open(json_file)
+        )  # list of dict: [{"image_file": "1.png", "id_embed_file": "faceid.bin"}]
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(self.size, interpolation=transforms.InterpolationMode.BILINEAR),
+                transforms.CenterCrop(self.size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+    def __getitem__(self, idx):
+        item = self.data[idx]
+        text = item["text"]
+        image_file = item["image_file"]
+
+        # read image
+        raw_image = Image.open(os.path.join(self.image_root_path, image_file))
+        image = self.transform(raw_image.convert("RGB"))
+
+        face_id_embed = torch.load(item["id_embed_file"], map_location="cpu")
+        face_id_embed = torch.from_numpy(face_id_embed)
+
+        # drop
+        drop_image_embed = 0
+        rand_num = random.random()
+        if rand_num < self.i_drop_rate:
+            drop_image_embed = 1
+        elif rand_num < (self.i_drop_rate + self.t_drop_rate):
+            text = ""
+        elif rand_num < (self.i_drop_rate + self.t_drop_rate + self.ti_drop_rate):
+            text = ""
+            drop_image_embed = 1
+        if drop_image_embed:
+            face_id_embed = torch.zeros_like(face_id_embed)
+        # get text and tokenize
+        text_input_ids = self.tokenizer(
+            text,
+            max_length=self.tokenizer.model_max_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+        ).input_ids
+
+        return {
+            "image": image,
+            "text_input_ids": text_input_ids,
+            "face_id_embed": face_id_embed,
+            "drop_image_embed": drop_image_embed,
+        }
+
+    def __len__(self):
+        return len(self.data)
+
+
+def collate_fn(data):
+    images = torch.stack([example["image"] for example in data])
+    text_input_ids = torch.cat([example["text_input_ids"] for example in data], dim=0)
+    face_id_embed = torch.stack([example["face_id_embed"] for example in data])
+    drop_image_embeds = [example["drop_image_embed"] for example in data]
+
+    return {
+        "images": images,
+        "text_input_ids": text_input_ids,
+        "face_id_embed": face_id_embed,
+        "drop_image_embeds": drop_image_embeds,
+    }
+
+
+class IPAdapter(torch.nn.Module):
+    """IP-Adapter"""
+
+    def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None):
+        super().__init__()
+        self.unet = unet
+        self.image_proj_model = image_proj_model
+        self.adapter_modules = adapter_modules
+
+        if ckpt_path is not None:
+            self.load_from_checkpoint(ckpt_path)
+
+    def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds):
+        ip_tokens = self.image_proj_model(image_embeds)
+        encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
+        # Predict the noise residual
+        noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample
+        return noise_pred
+
+    def load_from_checkpoint(self, ckpt_path: str):
+        # Calculate original checksums
+        orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+
+        # Load state dict for image_proj_model and adapter_modules
+        self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=True)
+        self.adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=True)
+
+        # Calculate new checksums
+        new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        # Verify if the weights have changed
+        assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
+        assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
+
+        print(f"Successfully loaded weights from checkpoint {ckpt_path}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--pretrained_ip_adapter_path",
+        type=str,
+        default=None,
+        help="Path to pretrained ip adapter model. If not specified weights are initialized randomly.",
+    )
+    parser.add_argument(
+        "--data_json_file",
+        type=str,
+        default=None,
+        required=True,
+        help="Training data",
+    )
+    parser.add_argument(
+        "--data_root_path",
+        type=str,
+        default="",
+        required=True,
+        help="Training data root path",
+    )
+    parser.add_argument(
+        "--image_encoder_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to CLIP image encoder",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="sd-ip_adapter",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=("The resolution for input images"),
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Learning rate to use.",
+    )
+    parser.add_argument("--weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=2000,
+        help=("Save a checkpoint of the training state every X updates"),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+
+    args = parser.parse_args()
+    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
+
+    return args
+
+
+def main():
+    args = parse_args()
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+    accelerator = Accelerator(
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+    )
+
+    if accelerator.is_main_process:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+    # Load scheduler, tokenizer and models.
+    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+    tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
+    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
+    # image_encoder = CLIPVisionModelWithProjection.from_pretrained(args.image_encoder_path)
+    # freeze parameters of models to save more memory
+    unet.requires_grad_(False)
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    # image_encoder.requires_grad_(False)
+
+    # ip-adapter
+    image_proj_model = MLPProjModel(
+        cross_attention_dim=unet.config.cross_attention_dim,
+        id_embeddings_dim=512,
+        num_tokens=4,
+    )
+    # init adapter modules
+    lora_rank = 128
+    attn_procs = {}
+    unet_sd = unet.state_dict()
+    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]
+        if cross_attention_dim is None:
+            attn_procs[name] = LoRAAttnProcessor(
+                hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=lora_rank
+            )
+        else:
+            layer_name = name.split(".processor")[0]
+            weights = {
+                "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"],
+                "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"],
+            }
+            attn_procs[name] = LoRAIPAttnProcessor(
+                hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=lora_rank
+            )
+            attn_procs[name].load_state_dict(weights, strict=False)
+    unet.set_attn_processor(attn_procs)
+    adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
+
+    ip_adapter = IPAdapter(unet, image_proj_model, adapter_modules, args.pretrained_ip_adapter_path)
+
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+    # unet.to(accelerator.device, dtype=weight_dtype)
+    vae.to(accelerator.device, dtype=weight_dtype)
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+    # image_encoder.to(accelerator.device, dtype=weight_dtype)
+
+    # optimizer
+    params_to_opt = itertools.chain(ip_adapter.image_proj_model.parameters(), ip_adapter.adapter_modules.parameters())
+    optimizer = torch.optim.AdamW(params_to_opt, lr=args.learning_rate, weight_decay=args.weight_decay)
+
+    # dataloader
+    train_dataset = MyDataset(
+        args.data_json_file, tokenizer=tokenizer, size=args.resolution, image_root_path=args.data_root_path
+    )
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        shuffle=True,
+        collate_fn=collate_fn,
+        batch_size=args.train_batch_size,
+        num_workers=args.dataloader_num_workers,
+    )
+
+    # Prepare everything with our `accelerator`.
+    ip_adapter, optimizer, train_dataloader = accelerator.prepare(ip_adapter, optimizer, train_dataloader)
+
+    global_step = 0
+    for epoch in range(0, args.num_train_epochs):
+        begin = time.perf_counter()
+        for step, batch in enumerate(train_dataloader):
+            load_data_time = time.perf_counter() - begin
+            with accelerator.accumulate(ip_adapter):
+                # Convert images to latent space
+                with torch.no_grad():
+                    latents = vae.encode(
+                        batch["images"].to(accelerator.device, dtype=weight_dtype)
+                    ).latent_dist.sample()
+                    latents = latents * vae.config.scaling_factor
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                bsz = latents.shape[0]
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                image_embeds = batch["face_id_embed"].to(accelerator.device, dtype=weight_dtype)
+
+                with torch.no_grad():
+                    encoder_hidden_states = text_encoder(batch["text_input_ids"].to(accelerator.device))[0]
+
+                noise_pred = ip_adapter(noisy_latents, timesteps, encoder_hidden_states, image_embeds)
+
+                loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
+
+                # Gather the losses across all processes for logging (if we use distributed training).
+                avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean().item()
+
+                # Backpropagate
+                accelerator.backward(loss)
+                optimizer.step()
+                optimizer.zero_grad()
+
+                if accelerator.is_main_process:
+                    print(
+                        "Epoch {}, step {}, data_time: {}, time: {}, step_loss: {}".format(
+                            epoch, step, load_data_time, time.perf_counter() - begin, avg_loss
+                        )
+                    )
+
+            global_step += 1
+
+            if global_step % args.save_steps == 0:
+                save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                accelerator.save_state(save_path)
+
+            begin = time.perf_counter()
+
+
+if __name__ == "__main__":
+    main()

examples/research_projects/ip_adapter/tutorial_train_ip-adapter.py

@@ -0,0 +1,422 @@
+import argparse
+import itertools
+import json
+import os
+import random
+import time
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+from accelerate import Accelerator
+from accelerate.utils import ProjectConfiguration
+from ip_adapter.ip_adapter import ImageProjModel
+from ip_adapter.utils import is_torch2_available
+from PIL import Image
+from torchvision import transforms
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
+
+
+if is_torch2_available():
+    from ip_adapter.attention_processor import AttnProcessor2_0 as AttnProcessor
+    from ip_adapter.attention_processor import IPAttnProcessor2_0 as IPAttnProcessor
+else:
+    from ip_adapter.attention_processor import AttnProcessor, IPAttnProcessor
+
+
+# Dataset
+class MyDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, json_file, tokenizer, size=512, t_drop_rate=0.05, i_drop_rate=0.05, ti_drop_rate=0.05, image_root_path=""
+    ):
+        super().__init__()
+
+        self.tokenizer = tokenizer
+        self.size = size
+        self.i_drop_rate = i_drop_rate
+        self.t_drop_rate = t_drop_rate
+        self.ti_drop_rate = ti_drop_rate
+        self.image_root_path = image_root_path
+
+        self.data = json.load(open(json_file))  # list of dict: [{"image_file": "1.png", "text": "A dog"}]
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(self.size, interpolation=transforms.InterpolationMode.BILINEAR),
+                transforms.CenterCrop(self.size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+        self.clip_image_processor = CLIPImageProcessor()
+
+    def __getitem__(self, idx):
+        item = self.data[idx]
+        text = item["text"]
+        image_file = item["image_file"]
+
+        # read image
+        raw_image = Image.open(os.path.join(self.image_root_path, image_file))
+        image = self.transform(raw_image.convert("RGB"))
+        clip_image = self.clip_image_processor(images=raw_image, return_tensors="pt").pixel_values
+
+        # drop
+        drop_image_embed = 0
+        rand_num = random.random()
+        if rand_num < self.i_drop_rate:
+            drop_image_embed = 1
+        elif rand_num < (self.i_drop_rate + self.t_drop_rate):
+            text = ""
+        elif rand_num < (self.i_drop_rate + self.t_drop_rate + self.ti_drop_rate):
+            text = ""
+            drop_image_embed = 1
+        # get text and tokenize
+        text_input_ids = self.tokenizer(
+            text,
+            max_length=self.tokenizer.model_max_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+        ).input_ids
+
+        return {
+            "image": image,
+            "text_input_ids": text_input_ids,
+            "clip_image": clip_image,
+            "drop_image_embed": drop_image_embed,
+        }
+
+    def __len__(self):
+        return len(self.data)
+
+
+def collate_fn(data):
+    images = torch.stack([example["image"] for example in data])
+    text_input_ids = torch.cat([example["text_input_ids"] for example in data], dim=0)
+    clip_images = torch.cat([example["clip_image"] for example in data], dim=0)
+    drop_image_embeds = [example["drop_image_embed"] for example in data]
+
+    return {
+        "images": images,
+        "text_input_ids": text_input_ids,
+        "clip_images": clip_images,
+        "drop_image_embeds": drop_image_embeds,
+    }
+
+
+class IPAdapter(torch.nn.Module):
+    """IP-Adapter"""
+
+    def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None):
+        super().__init__()
+        self.unet = unet
+        self.image_proj_model = image_proj_model
+        self.adapter_modules = adapter_modules
+
+        if ckpt_path is not None:
+            self.load_from_checkpoint(ckpt_path)
+
+    def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds):
+        ip_tokens = self.image_proj_model(image_embeds)
+        encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
+        # Predict the noise residual
+        noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample
+        return noise_pred
+
+    def load_from_checkpoint(self, ckpt_path: str):
+        # Calculate original checksums
+        orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+
+        # Load state dict for image_proj_model and adapter_modules
+        self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=True)
+        self.adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=True)
+
+        # Calculate new checksums
+        new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        # Verify if the weights have changed
+        assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
+        assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
+
+        print(f"Successfully loaded weights from checkpoint {ckpt_path}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--pretrained_ip_adapter_path",
+        type=str,
+        default=None,
+        help="Path to pretrained ip adapter model. If not specified weights are initialized randomly.",
+    )
+    parser.add_argument(
+        "--data_json_file",
+        type=str,
+        default=None,
+        required=True,
+        help="Training data",
+    )
+    parser.add_argument(
+        "--data_root_path",
+        type=str,
+        default="",
+        required=True,
+        help="Training data root path",
+    )
+    parser.add_argument(
+        "--image_encoder_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to CLIP image encoder",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="sd-ip_adapter",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=("The resolution for input images"),
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Learning rate to use.",
+    )
+    parser.add_argument("--weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=2000,
+        help=("Save a checkpoint of the training state every X updates"),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+
+    args = parser.parse_args()
+    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
+
+    return args
+
+
+def main():
+    args = parse_args()
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+    accelerator = Accelerator(
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+    )
+
+    if accelerator.is_main_process:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+    # Load scheduler, tokenizer and models.
+    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+    tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
+    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
+    image_encoder = CLIPVisionModelWithProjection.from_pretrained(args.image_encoder_path)
+    # freeze parameters of models to save more memory
+    unet.requires_grad_(False)
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    image_encoder.requires_grad_(False)
+
+    # ip-adapter
+    image_proj_model = ImageProjModel(
+        cross_attention_dim=unet.config.cross_attention_dim,
+        clip_embeddings_dim=image_encoder.config.projection_dim,
+        clip_extra_context_tokens=4,
+    )
+    # init adapter modules
+    attn_procs = {}
+    unet_sd = unet.state_dict()
+    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]
+        if cross_attention_dim is None:
+            attn_procs[name] = AttnProcessor()
+        else:
+            layer_name = name.split(".processor")[0]
+            weights = {
+                "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"],
+                "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"],
+            }
+            attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
+            attn_procs[name].load_state_dict(weights)
+    unet.set_attn_processor(attn_procs)
+    adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
+
+    ip_adapter = IPAdapter(unet, image_proj_model, adapter_modules, args.pretrained_ip_adapter_path)
+
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+    # unet.to(accelerator.device, dtype=weight_dtype)
+    vae.to(accelerator.device, dtype=weight_dtype)
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+    image_encoder.to(accelerator.device, dtype=weight_dtype)
+
+    # optimizer
+    params_to_opt = itertools.chain(ip_adapter.image_proj_model.parameters(), ip_adapter.adapter_modules.parameters())
+    optimizer = torch.optim.AdamW(params_to_opt, lr=args.learning_rate, weight_decay=args.weight_decay)
+
+    # dataloader
+    train_dataset = MyDataset(
+        args.data_json_file, tokenizer=tokenizer, size=args.resolution, image_root_path=args.data_root_path
+    )
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        shuffle=True,
+        collate_fn=collate_fn,
+        batch_size=args.train_batch_size,
+        num_workers=args.dataloader_num_workers,
+    )
+
+    # Prepare everything with our `accelerator`.
+    ip_adapter, optimizer, train_dataloader = accelerator.prepare(ip_adapter, optimizer, train_dataloader)
+
+    global_step = 0
+    for epoch in range(0, args.num_train_epochs):
+        begin = time.perf_counter()
+        for step, batch in enumerate(train_dataloader):
+            load_data_time = time.perf_counter() - begin
+            with accelerator.accumulate(ip_adapter):
+                # Convert images to latent space
+                with torch.no_grad():
+                    latents = vae.encode(
+                        batch["images"].to(accelerator.device, dtype=weight_dtype)
+                    ).latent_dist.sample()
+                    latents = latents * vae.config.scaling_factor
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                bsz = latents.shape[0]
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                with torch.no_grad():
+                    image_embeds = image_encoder(
+                        batch["clip_images"].to(accelerator.device, dtype=weight_dtype)
+                    ).image_embeds
+                image_embeds_ = []
+                for image_embed, drop_image_embed in zip(image_embeds, batch["drop_image_embeds"]):
+                    if drop_image_embed == 1:
+                        image_embeds_.append(torch.zeros_like(image_embed))
+                    else:
+                        image_embeds_.append(image_embed)
+                image_embeds = torch.stack(image_embeds_)
+
+                with torch.no_grad():
+                    encoder_hidden_states = text_encoder(batch["text_input_ids"].to(accelerator.device))[0]
+
+                noise_pred = ip_adapter(noisy_latents, timesteps, encoder_hidden_states, image_embeds)
+
+                loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
+
+                # Gather the losses across all processes for logging (if we use distributed training).
+                avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean().item()
+
+                # Backpropagate
+                accelerator.backward(loss)
+                optimizer.step()
+                optimizer.zero_grad()
+
+                if accelerator.is_main_process:
+                    print(
+                        "Epoch {}, step {}, data_time: {}, time: {}, step_loss: {}".format(
+                            epoch, step, load_data_time, time.perf_counter() - begin, avg_loss
+                        )
+                    )
+
+            global_step += 1
+
+            if global_step % args.save_steps == 0:
+                save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                accelerator.save_state(save_path)
+
+            begin = time.perf_counter()
+
+
+if __name__ == "__main__":
+    main()

examples/research_projects/ip_adapter/tutorial_train_plus.py

@@ -0,0 +1,445 @@
+import argparse
+import itertools
+import json
+import os
+import random
+import time
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+from accelerate import Accelerator
+from accelerate.utils import ProjectConfiguration
+from ip_adapter.resampler import Resampler
+from ip_adapter.utils import is_torch2_available
+from PIL import Image
+from torchvision import transforms
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
+
+
+if is_torch2_available():
+    from ip_adapter.attention_processor import AttnProcessor2_0 as AttnProcessor
+    from ip_adapter.attention_processor import IPAttnProcessor2_0 as IPAttnProcessor
+else:
+    from ip_adapter.attention_processor import AttnProcessor, IPAttnProcessor
+
+
+# Dataset
+class MyDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, json_file, tokenizer, size=512, t_drop_rate=0.05, i_drop_rate=0.05, ti_drop_rate=0.05, image_root_path=""
+    ):
+        super().__init__()
+
+        self.tokenizer = tokenizer
+        self.size = size
+        self.i_drop_rate = i_drop_rate
+        self.t_drop_rate = t_drop_rate
+        self.ti_drop_rate = ti_drop_rate
+        self.image_root_path = image_root_path
+
+        self.data = json.load(open(json_file))  # list of dict: [{"image_file": "1.png", "text": "A dog"}]
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(self.size, interpolation=transforms.InterpolationMode.BILINEAR),
+                transforms.CenterCrop(self.size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+        self.clip_image_processor = CLIPImageProcessor()
+
+    def __getitem__(self, idx):
+        item = self.data[idx]
+        text = item["text"]
+        image_file = item["image_file"]
+
+        # read image
+        raw_image = Image.open(os.path.join(self.image_root_path, image_file))
+        image = self.transform(raw_image.convert("RGB"))
+        clip_image = self.clip_image_processor(images=raw_image, return_tensors="pt").pixel_values
+
+        # drop
+        drop_image_embed = 0
+        rand_num = random.random()
+        if rand_num < self.i_drop_rate:
+            drop_image_embed = 1
+        elif rand_num < (self.i_drop_rate + self.t_drop_rate):
+            text = ""
+        elif rand_num < (self.i_drop_rate + self.t_drop_rate + self.ti_drop_rate):
+            text = ""
+            drop_image_embed = 1
+        # get text and tokenize
+        text_input_ids = self.tokenizer(
+            text,
+            max_length=self.tokenizer.model_max_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+        ).input_ids
+
+        return {
+            "image": image,
+            "text_input_ids": text_input_ids,
+            "clip_image": clip_image,
+            "drop_image_embed": drop_image_embed,
+        }
+
+    def __len__(self):
+        return len(self.data)
+
+
+def collate_fn(data):
+    images = torch.stack([example["image"] for example in data])
+    text_input_ids = torch.cat([example["text_input_ids"] for example in data], dim=0)
+    clip_images = torch.cat([example["clip_image"] for example in data], dim=0)
+    drop_image_embeds = [example["drop_image_embed"] for example in data]
+
+    return {
+        "images": images,
+        "text_input_ids": text_input_ids,
+        "clip_images": clip_images,
+        "drop_image_embeds": drop_image_embeds,
+    }
+
+
+class IPAdapter(torch.nn.Module):
+    """IP-Adapter"""
+
+    def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None):
+        super().__init__()
+        self.unet = unet
+        self.image_proj_model = image_proj_model
+        self.adapter_modules = adapter_modules
+
+        if ckpt_path is not None:
+            self.load_from_checkpoint(ckpt_path)
+
+    def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds):
+        ip_tokens = self.image_proj_model(image_embeds)
+        encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
+        # Predict the noise residual
+        noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample
+        return noise_pred
+
+    def load_from_checkpoint(self, ckpt_path: str):
+        # Calculate original checksums
+        orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+
+        # Check if 'latents' exists in both the saved state_dict and the current model's state_dict
+        strict_load_image_proj_model = True
+        if "latents" in state_dict["image_proj"] and "latents" in self.image_proj_model.state_dict():
+            # Check if the shapes are mismatched
+            if state_dict["image_proj"]["latents"].shape != self.image_proj_model.state_dict()["latents"].shape:
+                print(f"Shapes of 'image_proj.latents' in checkpoint {ckpt_path} and current model do not match.")
+                print("Removing 'latents' from checkpoint and loading the rest of the weights.")
+                del state_dict["image_proj"]["latents"]
+                strict_load_image_proj_model = False
+
+        # Load state dict for image_proj_model and adapter_modules
+        self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=strict_load_image_proj_model)
+        self.adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=True)
+
+        # Calculate new checksums
+        new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        # Verify if the weights have changed
+        assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
+        assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
+
+        print(f"Successfully loaded weights from checkpoint {ckpt_path}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--pretrained_ip_adapter_path",
+        type=str,
+        default=None,
+        help="Path to pretrained ip adapter model. If not specified weights are initialized randomly.",
+    )
+    parser.add_argument(
+        "--num_tokens",
+        type=int,
+        default=16,
+        help="Number of tokens to query from the CLIP image encoding.",
+    )
+    parser.add_argument(
+        "--data_json_file",
+        type=str,
+        default=None,
+        required=True,
+        help="Training data",
+    )
+    parser.add_argument(
+        "--data_root_path",
+        type=str,
+        default="",
+        required=True,
+        help="Training data root path",
+    )
+    parser.add_argument(
+        "--image_encoder_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to CLIP image encoder",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="sd-ip_adapter",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=("The resolution for input images"),
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Learning rate to use.",
+    )
+    parser.add_argument("--weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=2000,
+        help=("Save a checkpoint of the training state every X updates"),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+
+    args = parser.parse_args()
+    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
+
+    return args
+
+
+def main():
+    args = parse_args()
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+    accelerator = Accelerator(
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+    )
+
+    if accelerator.is_main_process:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+    # Load scheduler, tokenizer and models.
+    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+    tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
+    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
+    image_encoder = CLIPVisionModelWithProjection.from_pretrained(args.image_encoder_path)
+    # freeze parameters of models to save more memory
+    unet.requires_grad_(False)
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    image_encoder.requires_grad_(False)
+
+    # ip-adapter-plus
+    image_proj_model = Resampler(
+        dim=unet.config.cross_attention_dim,
+        depth=4,
+        dim_head=64,
+        heads=12,
+        num_queries=args.num_tokens,
+        embedding_dim=image_encoder.config.hidden_size,
+        output_dim=unet.config.cross_attention_dim,
+        ff_mult=4,
+    )
+    # init adapter modules
+    attn_procs = {}
+    unet_sd = unet.state_dict()
+    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]
+        if cross_attention_dim is None:
+            attn_procs[name] = AttnProcessor()
+        else:
+            layer_name = name.split(".processor")[0]
+            weights = {
+                "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"],
+                "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"],
+            }
+            attn_procs[name] = IPAttnProcessor(
+                hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, num_tokens=args.num_tokens
+            )
+            attn_procs[name].load_state_dict(weights)
+    unet.set_attn_processor(attn_procs)
+    adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
+
+    ip_adapter = IPAdapter(unet, image_proj_model, adapter_modules, args.pretrained_ip_adapter_path)
+
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+    # unet.to(accelerator.device, dtype=weight_dtype)
+    vae.to(accelerator.device, dtype=weight_dtype)
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+    image_encoder.to(accelerator.device, dtype=weight_dtype)
+
+    # optimizer
+    params_to_opt = itertools.chain(ip_adapter.image_proj_model.parameters(), ip_adapter.adapter_modules.parameters())
+    optimizer = torch.optim.AdamW(params_to_opt, lr=args.learning_rate, weight_decay=args.weight_decay)
+
+    # dataloader
+    train_dataset = MyDataset(
+        args.data_json_file, tokenizer=tokenizer, size=args.resolution, image_root_path=args.data_root_path
+    )
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        shuffle=True,
+        collate_fn=collate_fn,
+        batch_size=args.train_batch_size,
+        num_workers=args.dataloader_num_workers,
+    )
+
+    # Prepare everything with our `accelerator`.
+    ip_adapter, optimizer, train_dataloader = accelerator.prepare(ip_adapter, optimizer, train_dataloader)
+
+    global_step = 0
+    for epoch in range(0, args.num_train_epochs):
+        begin = time.perf_counter()
+        for step, batch in enumerate(train_dataloader):
+            load_data_time = time.perf_counter() - begin
+            with accelerator.accumulate(ip_adapter):
+                # Convert images to latent space
+                with torch.no_grad():
+                    latents = vae.encode(
+                        batch["images"].to(accelerator.device, dtype=weight_dtype)
+                    ).latent_dist.sample()
+                    latents = latents * vae.config.scaling_factor
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                bsz = latents.shape[0]
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                clip_images = []
+                for clip_image, drop_image_embed in zip(batch["clip_images"], batch["drop_image_embeds"]):
+                    if drop_image_embed == 1:
+                        clip_images.append(torch.zeros_like(clip_image))
+                    else:
+                        clip_images.append(clip_image)
+                clip_images = torch.stack(clip_images, dim=0)
+                with torch.no_grad():
+                    image_embeds = image_encoder(
+                        clip_images.to(accelerator.device, dtype=weight_dtype), output_hidden_states=True
+                    ).hidden_states[-2]
+
+                with torch.no_grad():
+                    encoder_hidden_states = text_encoder(batch["text_input_ids"].to(accelerator.device))[0]
+
+                noise_pred = ip_adapter(noisy_latents, timesteps, encoder_hidden_states, image_embeds)
+
+                loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
+
+                # Gather the losses across all processes for logging (if we use distributed training).
+                avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean().item()
+
+                # Backpropagate
+                accelerator.backward(loss)
+                optimizer.step()
+                optimizer.zero_grad()
+
+                if accelerator.is_main_process:
+                    print(
+                        "Epoch {}, step {}, data_time: {}, time: {}, step_loss: {}".format(
+                            epoch, step, load_data_time, time.perf_counter() - begin, avg_loss
+                        )
+                    )
+
+            global_step += 1
+
+            if global_step % args.save_steps == 0:
+                save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                accelerator.save_state(save_path)
+
+            begin = time.perf_counter()
+
+
+if __name__ == "__main__":
+    main()

examples/research_projects/ip_adapter/tutorial_train_sdxl.py

@@ -0,0 +1,520 @@
+import argparse
+import itertools
+import json
+import os
+import random
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from accelerate import Accelerator
+from accelerate.utils import ProjectConfiguration
+from ip_adapter.ip_adapter import ImageProjModel
+from ip_adapter.utils import is_torch2_available
+from PIL import Image
+from torchvision import transforms
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
+
+
+if is_torch2_available():
+    from ip_adapter.attention_processor import AttnProcessor2_0 as AttnProcessor
+    from ip_adapter.attention_processor import IPAttnProcessor2_0 as IPAttnProcessor
+else:
+    from ip_adapter.attention_processor import AttnProcessor, IPAttnProcessor
+
+
+# Dataset
+class MyDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        json_file,
+        tokenizer,
+        tokenizer_2,
+        size=1024,
+        center_crop=True,
+        t_drop_rate=0.05,
+        i_drop_rate=0.05,
+        ti_drop_rate=0.05,
+        image_root_path="",
+    ):
+        super().__init__()
+
+        self.tokenizer = tokenizer
+        self.tokenizer_2 = tokenizer_2
+        self.size = size
+        self.center_crop = center_crop
+        self.i_drop_rate = i_drop_rate
+        self.t_drop_rate = t_drop_rate
+        self.ti_drop_rate = ti_drop_rate
+        self.image_root_path = image_root_path
+
+        self.data = json.load(open(json_file))  # list of dict: [{"image_file": "1.png", "text": "A dog"}]
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(self.size, interpolation=transforms.InterpolationMode.BILINEAR),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+        self.clip_image_processor = CLIPImageProcessor()
+
+    def __getitem__(self, idx):
+        item = self.data[idx]
+        text = item["text"]
+        image_file = item["image_file"]
+
+        # read image
+        raw_image = Image.open(os.path.join(self.image_root_path, image_file))
+
+        # original size
+        original_width, original_height = raw_image.size
+        original_size = torch.tensor([original_height, original_width])
+
+        image_tensor = self.transform(raw_image.convert("RGB"))
+        # random crop
+        delta_h = image_tensor.shape[1] - self.size
+        delta_w = image_tensor.shape[2] - self.size
+        assert not all([delta_h, delta_w])
+
+        if self.center_crop:
+            top = delta_h // 2
+            left = delta_w // 2
+        else:
+            top = np.random.randint(0, delta_h + 1)
+            left = np.random.randint(0, delta_w + 1)
+        image = transforms.functional.crop(image_tensor, top=top, left=left, height=self.size, width=self.size)
+        crop_coords_top_left = torch.tensor([top, left])
+
+        clip_image = self.clip_image_processor(images=raw_image, return_tensors="pt").pixel_values
+
+        # drop
+        drop_image_embed = 0
+        rand_num = random.random()
+        if rand_num < self.i_drop_rate:
+            drop_image_embed = 1
+        elif rand_num < (self.i_drop_rate + self.t_drop_rate):
+            text = ""
+        elif rand_num < (self.i_drop_rate + self.t_drop_rate + self.ti_drop_rate):
+            text = ""
+            drop_image_embed = 1
+
+        # get text and tokenize
+        text_input_ids = self.tokenizer(
+            text,
+            max_length=self.tokenizer.model_max_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+        ).input_ids
+
+        text_input_ids_2 = self.tokenizer_2(
+            text,
+            max_length=self.tokenizer_2.model_max_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+        ).input_ids
+
+        return {
+            "image": image,
+            "text_input_ids": text_input_ids,
+            "text_input_ids_2": text_input_ids_2,
+            "clip_image": clip_image,
+            "drop_image_embed": drop_image_embed,
+            "original_size": original_size,
+            "crop_coords_top_left": crop_coords_top_left,
+            "target_size": torch.tensor([self.size, self.size]),
+        }
+
+    def __len__(self):
+        return len(self.data)
+
+
+def collate_fn(data):
+    images = torch.stack([example["image"] for example in data])
+    text_input_ids = torch.cat([example["text_input_ids"] for example in data], dim=0)
+    text_input_ids_2 = torch.cat([example["text_input_ids_2"] for example in data], dim=0)
+    clip_images = torch.cat([example["clip_image"] for example in data], dim=0)
+    drop_image_embeds = [example["drop_image_embed"] for example in data]
+    original_size = torch.stack([example["original_size"] for example in data])
+    crop_coords_top_left = torch.stack([example["crop_coords_top_left"] for example in data])
+    target_size = torch.stack([example["target_size"] for example in data])
+
+    return {
+        "images": images,
+        "text_input_ids": text_input_ids,
+        "text_input_ids_2": text_input_ids_2,
+        "clip_images": clip_images,
+        "drop_image_embeds": drop_image_embeds,
+        "original_size": original_size,
+        "crop_coords_top_left": crop_coords_top_left,
+        "target_size": target_size,
+    }
+
+
+class IPAdapter(torch.nn.Module):
+    """IP-Adapter"""
+
+    def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None):
+        super().__init__()
+        self.unet = unet
+        self.image_proj_model = image_proj_model
+        self.adapter_modules = adapter_modules
+
+        if ckpt_path is not None:
+            self.load_from_checkpoint(ckpt_path)
+
+    def forward(self, noisy_latents, timesteps, encoder_hidden_states, unet_added_cond_kwargs, image_embeds):
+        ip_tokens = self.image_proj_model(image_embeds)
+        encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
+        # Predict the noise residual
+        noise_pred = self.unet(
+            noisy_latents, timesteps, encoder_hidden_states, added_cond_kwargs=unet_added_cond_kwargs
+        ).sample
+        return noise_pred
+
+    def load_from_checkpoint(self, ckpt_path: str):
+        # Calculate original checksums
+        orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+
+        # Load state dict for image_proj_model and adapter_modules
+        self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=True)
+        self.adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=True)
+
+        # Calculate new checksums
+        new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        # Verify if the weights have changed
+        assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
+        assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
+
+        print(f"Successfully loaded weights from checkpoint {ckpt_path}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--pretrained_ip_adapter_path",
+        type=str,
+        default=None,
+        help="Path to pretrained ip adapter model. If not specified weights are initialized randomly.",
+    )
+    parser.add_argument(
+        "--data_json_file",
+        type=str,
+        default=None,
+        required=True,
+        help="Training data",
+    )
+    parser.add_argument(
+        "--data_root_path",
+        type=str,
+        default="",
+        required=True,
+        help="Training data root path",
+    )
+    parser.add_argument(
+        "--image_encoder_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to CLIP image encoder",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="sd-ip_adapter",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=("The resolution for input images"),
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Learning rate to use.",
+    )
+    parser.add_argument("--weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--noise_offset", type=float, default=None, help="noise offset")
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=2000,
+        help=("Save a checkpoint of the training state every X updates"),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+
+    args = parser.parse_args()
+    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
+
+    return args
+
+
+def main():
+    args = parse_args()
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+    accelerator = Accelerator(
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+    )
+
+    if accelerator.is_main_process:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+    # Load scheduler, tokenizer and models.
+    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+    tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
+    tokenizer_2 = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer_2")
+    text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(
+        args.pretrained_model_name_or_path, subfolder="text_encoder_2"
+    )
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
+    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
+    image_encoder = CLIPVisionModelWithProjection.from_pretrained(args.image_encoder_path)
+    # freeze parameters of models to save more memory
+    unet.requires_grad_(False)
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    text_encoder_2.requires_grad_(False)
+    image_encoder.requires_grad_(False)
+
+    # ip-adapter
+    num_tokens = 4
+    image_proj_model = ImageProjModel(
+        cross_attention_dim=unet.config.cross_attention_dim,
+        clip_embeddings_dim=image_encoder.config.projection_dim,
+        clip_extra_context_tokens=num_tokens,
+    )
+    # init adapter modules
+    attn_procs = {}
+    unet_sd = unet.state_dict()
+    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]
+        if cross_attention_dim is None:
+            attn_procs[name] = AttnProcessor()
+        else:
+            layer_name = name.split(".processor")[0]
+            weights = {
+                "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"],
+                "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"],
+            }
+            attn_procs[name] = IPAttnProcessor(
+                hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, num_tokens=num_tokens
+            )
+            attn_procs[name].load_state_dict(weights)
+    unet.set_attn_processor(attn_procs)
+    adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
+
+    ip_adapter = IPAdapter(unet, image_proj_model, adapter_modules, args.pretrained_ip_adapter_path)
+
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+    # unet.to(accelerator.device, dtype=weight_dtype)
+    vae.to(accelerator.device)  # use fp32
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+    text_encoder_2.to(accelerator.device, dtype=weight_dtype)
+    image_encoder.to(accelerator.device, dtype=weight_dtype)
+
+    # optimizer
+    params_to_opt = itertools.chain(ip_adapter.image_proj_model.parameters(), ip_adapter.adapter_modules.parameters())
+    optimizer = torch.optim.AdamW(params_to_opt, lr=args.learning_rate, weight_decay=args.weight_decay)
+
+    # dataloader
+    train_dataset = MyDataset(
+        args.data_json_file,
+        tokenizer=tokenizer,
+        tokenizer_2=tokenizer_2,
+        size=args.resolution,
+        image_root_path=args.data_root_path,
+    )
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        shuffle=True,
+        collate_fn=collate_fn,
+        batch_size=args.train_batch_size,
+        num_workers=args.dataloader_num_workers,
+    )
+
+    # Prepare everything with our `accelerator`.
+    ip_adapter, optimizer, train_dataloader = accelerator.prepare(ip_adapter, optimizer, train_dataloader)
+
+    global_step = 0
+    for epoch in range(0, args.num_train_epochs):
+        begin = time.perf_counter()
+        for step, batch in enumerate(train_dataloader):
+            load_data_time = time.perf_counter() - begin
+            with accelerator.accumulate(ip_adapter):
+                # Convert images to latent space
+                with torch.no_grad():
+                    # vae of sdxl should use fp32
+                    latents = vae.encode(batch["images"].to(accelerator.device, dtype=vae.dtype)).latent_dist.sample()
+                    latents = latents * vae.config.scaling_factor
+                    latents = latents.to(accelerator.device, dtype=weight_dtype)
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                if args.noise_offset:
+                    # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+                    noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1)).to(
+                        accelerator.device, dtype=weight_dtype
+                    )
+
+                bsz = latents.shape[0]
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                with torch.no_grad():
+                    image_embeds = image_encoder(
+                        batch["clip_images"].to(accelerator.device, dtype=weight_dtype)
+                    ).image_embeds
+                image_embeds_ = []
+                for image_embed, drop_image_embed in zip(image_embeds, batch["drop_image_embeds"]):
+                    if drop_image_embed == 1:
+                        image_embeds_.append(torch.zeros_like(image_embed))
+                    else:
+                        image_embeds_.append(image_embed)
+                image_embeds = torch.stack(image_embeds_)
+
+                with torch.no_grad():
+                    encoder_output = text_encoder(
+                        batch["text_input_ids"].to(accelerator.device), output_hidden_states=True
+                    )
+                    text_embeds = encoder_output.hidden_states[-2]
+                    encoder_output_2 = text_encoder_2(
+                        batch["text_input_ids_2"].to(accelerator.device), output_hidden_states=True
+                    )
+                    pooled_text_embeds = encoder_output_2[0]
+                    text_embeds_2 = encoder_output_2.hidden_states[-2]
+                    text_embeds = torch.concat([text_embeds, text_embeds_2], dim=-1)  # concat
+
+                # add cond
+                add_time_ids = [
+                    batch["original_size"].to(accelerator.device),
+                    batch["crop_coords_top_left"].to(accelerator.device),
+                    batch["target_size"].to(accelerator.device),
+                ]
+                add_time_ids = torch.cat(add_time_ids, dim=1).to(accelerator.device, dtype=weight_dtype)
+                unet_added_cond_kwargs = {"text_embeds": pooled_text_embeds, "time_ids": add_time_ids}
+
+                noise_pred = ip_adapter(noisy_latents, timesteps, text_embeds, unet_added_cond_kwargs, image_embeds)
+
+                loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
+
+                # Gather the losses across all processes for logging (if we use distributed training).
+                avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean().item()
+
+                # Backpropagate
+                accelerator.backward(loss)
+                optimizer.step()
+                optimizer.zero_grad()
+
+                if accelerator.is_main_process:
+                    print(
+                        "Epoch {}, step {}, data_time: {}, time: {}, step_loss: {}".format(
+                            epoch, step, load_data_time, time.perf_counter() - begin, avg_loss
+                        )
+                    )
+
+            global_step += 1
+
+            if global_step % args.save_steps == 0:
+                save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                accelerator.save_state(save_path)
+
+            begin = time.perf_counter()
+
+
+if __name__ == "__main__":
+    main()

examples/research_projects/pixart/.gitignore

@@ -0,0 +1,2 @@
+images/
+output/

examples/research_projects/pixart/controlnet_pixart_alpha.py

@@ -0,0 +1,307 @@
+from typing import Any, Dict, Optional
+
+import torch
+from torch import nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models import PixArtTransformer2DModel
+from diffusers.models.attention import BasicTransformerBlock
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils.torch_utils import is_torch_version
+
+
+class PixArtControlNetAdapterBlock(nn.Module):
+    def __init__(
+        self,
+        block_index,
+        # taken from PixArtTransformer2DModel
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 72,
+        dropout: float = 0.0,
+        cross_attention_dim: Optional[int] = 1152,
+        attention_bias: bool = True,
+        activation_fn: str = "gelu-approximate",
+        num_embeds_ada_norm: Optional[int] = 1000,
+        upcast_attention: bool = False,
+        norm_type: str = "ada_norm_single",
+        norm_elementwise_affine: bool = False,
+        norm_eps: float = 1e-6,
+        attention_type: Optional[str] = "default",
+    ):
+        super().__init__()
+
+        self.block_index = block_index
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        # the first block has a zero before layer
+        if self.block_index == 0:
+            self.before_proj = nn.Linear(self.inner_dim, self.inner_dim)
+            nn.init.zeros_(self.before_proj.weight)
+            nn.init.zeros_(self.before_proj.bias)
+
+        self.transformer_block = BasicTransformerBlock(
+            self.inner_dim,
+            num_attention_heads,
+            attention_head_dim,
+            dropout=dropout,
+            cross_attention_dim=cross_attention_dim,
+            activation_fn=activation_fn,
+            num_embeds_ada_norm=num_embeds_ada_norm,
+            attention_bias=attention_bias,
+            upcast_attention=upcast_attention,
+            norm_type=norm_type,
+            norm_elementwise_affine=norm_elementwise_affine,
+            norm_eps=norm_eps,
+            attention_type=attention_type,
+        )
+
+        self.after_proj = nn.Linear(self.inner_dim, self.inner_dim)
+        nn.init.zeros_(self.after_proj.weight)
+        nn.init.zeros_(self.after_proj.bias)
+
+    def train(self, mode: bool = True):
+        self.transformer_block.train(mode)
+
+        if self.block_index == 0:
+            self.before_proj.train(mode)
+
+        self.after_proj.train(mode)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        controlnet_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        added_cond_kwargs: Dict[str, torch.Tensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+    ):
+        if self.block_index == 0:
+            controlnet_states = self.before_proj(controlnet_states)
+            controlnet_states = hidden_states + controlnet_states
+
+        controlnet_states_down = self.transformer_block(
+            hidden_states=controlnet_states,
+            encoder_hidden_states=encoder_hidden_states,
+            timestep=timestep,
+            added_cond_kwargs=added_cond_kwargs,
+            cross_attention_kwargs=cross_attention_kwargs,
+            attention_mask=attention_mask,
+            encoder_attention_mask=encoder_attention_mask,
+            class_labels=None,
+        )
+
+        controlnet_states_left = self.after_proj(controlnet_states_down)
+
+        return controlnet_states_left, controlnet_states_down
+
+
+class PixArtControlNetAdapterModel(ModelMixin, ConfigMixin):
+    # N=13, as specified in the paper https://arxiv.org/html/2401.05252v1/#S4 ControlNet-Transformer
+    @register_to_config
+    def __init__(self, num_layers=13) -> None:
+        super().__init__()
+
+        self.num_layers = num_layers
+
+        self.controlnet_blocks = nn.ModuleList(
+            [PixArtControlNetAdapterBlock(block_index=i) for i in range(num_layers)]
+        )
+
+    @classmethod
+    def from_transformer(cls, transformer: PixArtTransformer2DModel):
+        control_net = PixArtControlNetAdapterModel()
+
+        # copied the specified number of blocks from the transformer
+        for depth in range(control_net.num_layers):
+            control_net.controlnet_blocks[depth].transformer_block.load_state_dict(
+                transformer.transformer_blocks[depth].state_dict()
+            )
+
+        return control_net
+
+    def train(self, mode: bool = True):
+        for block in self.controlnet_blocks:
+            block.train(mode)
+
+
+class PixArtControlNetTransformerModel(ModelMixin, ConfigMixin):
+    def __init__(
+        self,
+        transformer: PixArtTransformer2DModel,
+        controlnet: PixArtControlNetAdapterModel,
+        blocks_num=13,
+        init_from_transformer=False,
+        training=False,
+    ):
+        super().__init__()
+
+        self.blocks_num = blocks_num
+        self.gradient_checkpointing = False
+        self.register_to_config(**transformer.config)
+        self.training = training
+
+        if init_from_transformer:
+            # copies the specified number of blocks from the transformer
+            controlnet.from_transformer(transformer, self.blocks_num)
+
+        self.transformer = transformer
+        self.controlnet = controlnet
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        controlnet_cond: Optional[torch.Tensor] = None,
+        added_cond_kwargs: Dict[str, torch.Tensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ):
+        if self.transformer.use_additional_conditions and added_cond_kwargs is None:
+            raise ValueError("`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`.")
+
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
+        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
+        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None and attention_mask.ndim == 2:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
+            encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 1. Input
+        batch_size = hidden_states.shape[0]
+        height, width = (
+            hidden_states.shape[-2] // self.transformer.config.patch_size,
+            hidden_states.shape[-1] // self.transformer.config.patch_size,
+        )
+        hidden_states = self.transformer.pos_embed(hidden_states)
+
+        timestep, embedded_timestep = self.transformer.adaln_single(
+            timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
+        )
+
+        if self.transformer.caption_projection is not None:
+            encoder_hidden_states = self.transformer.caption_projection(encoder_hidden_states)
+            encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])
+
+        controlnet_states_down = None
+        if controlnet_cond is not None:
+            controlnet_states_down = self.transformer.pos_embed(controlnet_cond)
+
+        # 2. Blocks
+        for block_index, block in enumerate(self.transformer.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                # rc todo: for training and gradient checkpointing
+                print("Gradient checkpointing is not supported for the controlnet transformer model, yet.")
+                exit(1)
+
+                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),
+                    hidden_states,
+                    attention_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    timestep,
+                    cross_attention_kwargs,
+                    None,
+                    **ckpt_kwargs,
+                )
+            else:
+                # the control nets are only used for the blocks 1 to self.blocks_num
+                if block_index > 0 and block_index <= self.blocks_num and controlnet_states_down is not None:
+                    controlnet_states_left, controlnet_states_down = self.controlnet.controlnet_blocks[
+                        block_index - 1
+                    ](
+                        hidden_states=hidden_states,  # used only in the first block
+                        controlnet_states=controlnet_states_down,
+                        encoder_hidden_states=encoder_hidden_states,
+                        timestep=timestep,
+                        added_cond_kwargs=added_cond_kwargs,
+                        cross_attention_kwargs=cross_attention_kwargs,
+                        attention_mask=attention_mask,
+                        encoder_attention_mask=encoder_attention_mask,
+                    )
+
+                    hidden_states = hidden_states + controlnet_states_left
+
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    timestep=timestep,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    class_labels=None,
+                )
+
+        # 3. Output
+        shift, scale = (
+            self.transformer.scale_shift_table[None]
+            + embedded_timestep[:, None].to(self.transformer.scale_shift_table.device)
+        ).chunk(2, dim=1)
+        hidden_states = self.transformer.norm_out(hidden_states)
+        # Modulation
+        hidden_states = hidden_states * (1 + scale.to(hidden_states.device)) + shift.to(hidden_states.device)
+        hidden_states = self.transformer.proj_out(hidden_states)
+        hidden_states = hidden_states.squeeze(1)
+
+        # unpatchify
+        hidden_states = hidden_states.reshape(
+            shape=(
+                -1,
+                height,
+                width,
+                self.transformer.config.patch_size,
+                self.transformer.config.patch_size,
+                self.transformer.out_channels,
+            )
+        )
+        hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
+        output = hidden_states.reshape(
+            shape=(
+                -1,
+                self.transformer.out_channels,
+                height * self.transformer.config.patch_size,
+                width * self.transformer.config.patch_size,
+            )
+        )
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)

examples/research_projects/pixart/requirements.txt

@@ -0,0 +1,6 @@
+transformers
+SentencePiece
+torchvision
+controlnet-aux
+datasets
+# wandb

examples/research_projects/pixart/run_pixart_alpha_controlnet_pipeline.py

@@ -0,0 +1,75 @@
+import torch
+import torchvision.transforms as T
+from controlnet_aux import HEDdetector
+
+from diffusers.utils import load_image
+from examples.research_projects.pixart.controlnet_pixart_alpha import PixArtControlNetAdapterModel
+from examples.research_projects.pixart.pipeline_pixart_alpha_controlnet import PixArtAlphaControlnetPipeline
+
+
+controlnet_repo_id = "raulc0399/pixart-alpha-hed-controlnet"
+
+weight_dtype = torch.float16
+image_size = 1024
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+torch.manual_seed(0)
+
+# load controlnet
+controlnet = PixArtControlNetAdapterModel.from_pretrained(
+    controlnet_repo_id,
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+).to(device)
+
+pipe = PixArtAlphaControlnetPipeline.from_pretrained(
+    "PixArt-alpha/PixArt-XL-2-1024-MS",
+    controlnet=controlnet,
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+).to(device)
+
+images_path = "images"
+control_image_file = "0_7.jpg"
+
+# prompt = "cinematic photo of superman in action . 35mm photograph, film, bokeh, professional, 4k, highly detailed"
+# prompt = "yellow modern car, city in background, beautiful rainy day"
+# prompt = "modern villa, clear sky, suny day . 35mm photograph, film, bokeh, professional, 4k, highly detailed"
+# prompt = "robot dog toy in park . 35mm photograph, film, bokeh, professional, 4k, highly detailed"
+# prompt = "purple car, on highway, beautiful sunny day"
+# prompt = "realistical photo of a loving couple standing in the open kitchen of the living room, cooking ."
+prompt = "battleship in space, galaxy in background"
+
+control_image_name = control_image_file.split(".")[0]
+
+control_image = load_image(f"{images_path}/{control_image_file}")
+print(control_image.size)
+height, width = control_image.size
+
+hed = HEDdetector.from_pretrained("lllyasviel/Annotators")
+
+condition_transform = T.Compose(
+    [
+        T.Lambda(lambda img: img.convert("RGB")),
+        T.CenterCrop([image_size, image_size]),
+    ]
+)
+
+control_image = condition_transform(control_image)
+hed_edge = hed(control_image, detect_resolution=image_size, image_resolution=image_size)
+
+hed_edge.save(f"{images_path}/{control_image_name}_hed.jpg")
+
+# run pipeline
+with torch.no_grad():
+    out = pipe(
+        prompt=prompt,
+        image=hed_edge,
+        num_inference_steps=14,
+        guidance_scale=4.5,
+        height=image_size,
+        width=image_size,
+    )
+
+    out.images[0].save(f"{images_path}//{control_image_name}_output.jpg")

examples/research_projects/pixart/train_controlnet_hf_diffusers.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+
+# run
+# accelerate config
+
+# check with
+# accelerate env
+
+export MODEL_DIR="PixArt-alpha/PixArt-XL-2-512x512"
+export OUTPUT_DIR="output/pixart-controlnet-hf-diffusers-test"
+
+accelerate launch ./train_pixart_controlnet_hf.py --mixed_precision="fp16" \
+ --pretrained_model_name_or_path=$MODEL_DIR \
+ --output_dir=$OUTPUT_DIR \
+ --dataset_name=fusing/fill50k \
+ --resolution=512 \
+ --learning_rate=1e-5 \
+ --train_batch_size=1 \
+ --gradient_accumulation_steps=4 \
+ --report_to="wandb" \
+ --seed=42 \
+ --dataloader_num_workers=8
+#  --lr_scheduler="cosine" --lr_warmup_steps=0 \

examples/research_projects/promptdiffusion/promptdiffusioncontrolnet.py

@@ -229,11 +229,11 @@ class PromptDiffusionControlNetModel(ControlNetModel):
                 In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
                 you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
             return_dict (`bool`, defaults to `True`):
-                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
+                Whether or not to return a [`~models.controlnets.controlnet.ControlNetOutput`] instead of a plain tuple.
 
         Returns:
-            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
-                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
+            [`~models.controlnets.controlnet.ControlNetOutput`] **or** `tuple`:
+                If `return_dict` is `True`, a [`~models.controlnets.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
                 returned where the first element is the sample tensor.
         """
         # check channel order

examples/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth_lora_sdxl.py

@@ -1475,7 +1475,6 @@ def main(args):
 
         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,

examples/server/README.md

@@ -0,0 +1,61 @@
+
+# Create a server
+
+Diffusers' pipelines can be used as an inference engine for a server. It supports concurrent and multithreaded requests to generate images that may be requested by multiple users at the same time.
+
+This guide will show you how to use the [`StableDiffusion3Pipeline`] in a server, but feel free to use any pipeline you want.
+
+
+Start by navigating to the `examples/server` folder and installing all of the dependencies.
+
+```py
+pip install .
+pip install -f requirements.txt
+```
+
+Launch the server with the following command.
+
+```py
+python server.py
+```
+
+The server is accessed at http://localhost:8000. You can curl this model with the following command.
+```
+curl -X POST -H "Content-Type: application/json" --data '{"model": "something", "prompt": "a kitten in front of a fireplace"}' http://localhost:8000/v1/images/generations
+```
+
+If you need to upgrade some dependencies, you can use either [pip-tools](https://github.com/jazzband/pip-tools) or [uv](https://github.com/astral-sh/uv). For example, upgrade the dependencies with `uv` using the following command.
+
+```
+uv pip compile requirements.in -o requirements.txt
+```
+
+
+The server is built with [FastAPI](https://fastapi.tiangolo.com/async/). The endpoint for `v1/images/generations` is shown below.
+```py
+@app.post("/v1/images/generations")
+async def generate_image(image_input: TextToImageInput):
+    try:
+        loop = asyncio.get_event_loop()
+        scheduler = shared_pipeline.pipeline.scheduler.from_config(shared_pipeline.pipeline.scheduler.config)
+        pipeline = StableDiffusion3Pipeline.from_pipe(shared_pipeline.pipeline, scheduler=scheduler)
+        generator = torch.Generator(device="cuda")
+        generator.manual_seed(random.randint(0, 10000000))
+        output = await loop.run_in_executor(None, lambda: pipeline(image_input.prompt, generator = generator))
+        logger.info(f"output: {output}")
+        image_url = save_image(output.images[0])
+        return {"data": [{"url": image_url}]}
+    except Exception as e:
+        if isinstance(e, HTTPException):
+            raise e
+        elif hasattr(e, 'message'):
+            raise HTTPException(status_code=500, detail=e.message + traceback.format_exc())
+        raise HTTPException(status_code=500, detail=str(e) + traceback.format_exc())
+```
+The `generate_image` function is defined as asynchronous with the [async](https://fastapi.tiangolo.com/async/) keyword so that FastAPI knows that whatever is happening in this function won't necessarily return a result right away. Once it hits some point in the function that it needs to await some other [Task](https://docs.python.org/3/library/asyncio-task.html#asyncio.Task), the main thread goes back to answering other HTTP requests. This is shown in the code below with the [await](https://fastapi.tiangolo.com/async/#async-and-await) keyword.
+```py
+output = await loop.run_in_executor(None, lambda: pipeline(image_input.prompt, generator = generator))
+```
+At this point, the execution of the pipeline function is placed onto a [new thread](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor), and the main thread performs other things until a result is returned from the `pipeline`.
+
+Another important aspect of this implementation is creating a `pipeline` from `shared_pipeline`. The goal behind this is to avoid loading the underlying model more than once onto the GPU while still allowing for each new request that is running on a separate thread to have its own generator and scheduler. The scheduler, in particular, is not thread-safe, and it will cause errors like: `IndexError: index 21 is out of bounds for dimension 0 with size 21` if you try to use the same scheduler across multiple threads.

examples/server/requirements.in

@@ -0,0 +1,9 @@
+torch~=2.4.0
+transformers==4.46.1
+sentencepiece
+aiohttp
+py-consul
+prometheus_client >= 0.18.0
+prometheus-fastapi-instrumentator >= 7.0.0
+fastapi
+uvicorn

examples/server/requirements.txt

@@ -0,0 +1,124 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile requirements.in -o requirements.txt
+aiohappyeyeballs==2.4.3
+    # via aiohttp
+aiohttp==3.10.10
+    # via -r requirements.in
+aiosignal==1.3.1
+    # via aiohttp
+annotated-types==0.7.0
+    # via pydantic
+anyio==4.6.2.post1
+    # via starlette
+attrs==24.2.0
+    # via aiohttp
+certifi==2024.8.30
+    # via requests
+charset-normalizer==3.4.0
+    # via requests
+click==8.1.7
+    # via uvicorn
+fastapi==0.115.3
+    # via -r requirements.in
+filelock==3.16.1
+    # via
+    #   huggingface-hub
+    #   torch
+    #   transformers
+frozenlist==1.5.0
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec==2024.10.0
+    # via
+    #   huggingface-hub
+    #   torch
+h11==0.14.0
+    # via uvicorn
+huggingface-hub==0.26.1
+    # via
+    #   tokenizers
+    #   transformers
+idna==3.10
+    # via
+    #   anyio
+    #   requests
+    #   yarl
+jinja2==3.1.4
+    # via torch
+markupsafe==3.0.2
+    # via jinja2
+mpmath==1.3.0
+    # via sympy
+multidict==6.1.0
+    # via
+    #   aiohttp
+    #   yarl
+networkx==3.4.2
+    # via torch
+numpy==2.1.2
+    # via transformers
+packaging==24.1
+    # via
+    #   huggingface-hub
+    #   transformers
+prometheus-client==0.21.0
+    # via
+    #   -r requirements.in
+    #   prometheus-fastapi-instrumentator
+prometheus-fastapi-instrumentator==7.0.0
+    # via -r requirements.in
+propcache==0.2.0
+    # via yarl
+py-consul==1.5.3
+    # via -r requirements.in
+pydantic==2.9.2
+    # via fastapi
+pydantic-core==2.23.4
+    # via pydantic
+pyyaml==6.0.2
+    # via
+    #   huggingface-hub
+    #   transformers
+regex==2024.9.11
+    # via transformers
+requests==2.32.3
+    # via
+    #   huggingface-hub
+    #   py-consul
+    #   transformers
+safetensors==0.4.5
+    # via transformers
+sentencepiece==0.2.0
+    # via -r requirements.in
+sniffio==1.3.1
+    # via anyio
+starlette==0.41.0
+    # via
+    #   fastapi
+    #   prometheus-fastapi-instrumentator
+sympy==1.13.3
+    # via torch
+tokenizers==0.20.1
+    # via transformers
+torch==2.4.1
+    # via -r requirements.in
+tqdm==4.66.5
+    # via
+    #   huggingface-hub
+    #   transformers
+transformers==4.46.1
+    # via -r requirements.in
+typing-extensions==4.12.2
+    # via
+    #   fastapi
+    #   huggingface-hub
+    #   pydantic
+    #   pydantic-core
+    #   torch
+urllib3==2.2.3
+    # via requests
+uvicorn==0.32.0
+    # via -r requirements.in
+yarl==1.16.0
+    # via aiohttp

examples/server/server.py

@@ -0,0 +1,133 @@
+import asyncio
+import logging
+import os
+import random
+import tempfile
+import traceback
+import uuid
+
+import aiohttp
+import torch
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+
+from diffusers.pipelines.stable_diffusion_3 import StableDiffusion3Pipeline
+
+
+logger = logging.getLogger(__name__)
+
+
+class TextToImageInput(BaseModel):
+    model: str
+    prompt: str
+    size: str | None = None
+    n: int | None = None
+
+
+class HttpClient:
+    session: aiohttp.ClientSession = None
+
+    def start(self):
+        self.session = aiohttp.ClientSession()
+
+    async def stop(self):
+        await self.session.close()
+        self.session = None
+
+    def __call__(self) -> aiohttp.ClientSession:
+        assert self.session is not None
+        return self.session
+
+
+class TextToImagePipeline:
+    pipeline: StableDiffusion3Pipeline = None
+    device: str = None
+
+    def start(self):
+        if torch.cuda.is_available():
+            model_path = os.getenv("MODEL_PATH", "stabilityai/stable-diffusion-3.5-large")
+            logger.info("Loading CUDA")
+            self.device = "cuda"
+            self.pipeline = StableDiffusion3Pipeline.from_pretrained(
+                model_path,
+                torch_dtype=torch.bfloat16,
+            ).to(device=self.device)
+        elif torch.backends.mps.is_available():
+            model_path = os.getenv("MODEL_PATH", "stabilityai/stable-diffusion-3.5-medium")
+            logger.info("Loading MPS for Mac M Series")
+            self.device = "mps"
+            self.pipeline = StableDiffusion3Pipeline.from_pretrained(
+                model_path,
+                torch_dtype=torch.bfloat16,
+            ).to(device=self.device)
+        else:
+            raise Exception("No CUDA or MPS device available")
+
+
+app = FastAPI()
+service_url = os.getenv("SERVICE_URL", "http://localhost:8000")
+image_dir = os.path.join(tempfile.gettempdir(), "images")
+if not os.path.exists(image_dir):
+    os.makedirs(image_dir)
+app.mount("/images", StaticFiles(directory=image_dir), name="images")
+http_client = HttpClient()
+shared_pipeline = TextToImagePipeline()
+
+# Configure CORS settings
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allows all origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allows all methods, e.g., GET, POST, OPTIONS, etc.
+    allow_headers=["*"],  # Allows all headers
+)
+
+
+@app.on_event("startup")
+def startup():
+    http_client.start()
+    shared_pipeline.start()
+
+
+def save_image(image):
+    filename = "draw" + str(uuid.uuid4()).split("-")[0] + ".png"
+    image_path = os.path.join(image_dir, filename)
+    # write image to disk at image_path
+    logger.info(f"Saving image to {image_path}")
+    image.save(image_path)
+    return os.path.join(service_url, "images", filename)
+
+
+@app.get("/")
+@app.post("/")
+@app.options("/")
+async def base():
+    return "Welcome to Diffusers! Where you can use diffusion models to generate images"
+
+
+@app.post("/v1/images/generations")
+async def generate_image(image_input: TextToImageInput):
+    try:
+        loop = asyncio.get_event_loop()
+        scheduler = shared_pipeline.pipeline.scheduler.from_config(shared_pipeline.pipeline.scheduler.config)
+        pipeline = StableDiffusion3Pipeline.from_pipe(shared_pipeline.pipeline, scheduler=scheduler)
+        generator = torch.Generator(device=shared_pipeline.device)
+        generator.manual_seed(random.randint(0, 10000000))
+        output = await loop.run_in_executor(None, lambda: pipeline(image_input.prompt, generator=generator))
+        logger.info(f"output: {output}")
+        image_url = save_image(output.images[0])
+        return {"data": [{"url": image_url}]}
+    except Exception as e:
+        if isinstance(e, HTTPException):
+            raise e
+        elif hasattr(e, "message"):
+            raise HTTPException(status_code=500, detail=e.message + traceback.format_exc())
+        raise HTTPException(status_code=500, detail=str(e) + traceback.format_exc())
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)

examples/text_to_image/train_text_to_image_sdxl.py

@@ -483,7 +483,6 @@ def parse_args(input_args=None):
     # Sanity checks
     if args.dataset_name is None and args.train_data_dir is None:
         raise ValueError("Need either a dataset name or a training folder.")
-
     if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
         raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
 
@@ -824,9 +823,7 @@ def main(args):
     if args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         dataset = load_dataset(
-            args.dataset_name,
-            args.dataset_config_name,
-            cache_dir=args.cache_dir,
+            args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir
         )
     else:
         data_files = {}

scripts/convert_cogvideox_to_diffusers.py

@@ -80,6 +80,8 @@ TRANSFORMER_KEYS_RENAME_DICT = {
     "post_attn1_layernorm": "norm2.norm",
     "time_embed.0": "time_embedding.linear_1",
     "time_embed.2": "time_embedding.linear_2",
+    "ofs_embed.0": "ofs_embedding.linear_1",
+    "ofs_embed.2": "ofs_embedding.linear_2",
     "mixins.patch_embed": "patch_embed",
     "mixins.final_layer.norm_final": "norm_out.norm",
     "mixins.final_layer.linear": "proj_out",
@@ -140,6 +142,7 @@ def convert_transformer(
     use_rotary_positional_embeddings: bool,
     i2v: bool,
     dtype: torch.dtype,
+    init_kwargs: Dict[str, Any],
 ):
     PREFIX_KEY = "model.diffusion_model."
 
@@ -149,7 +152,9 @@ def convert_transformer(
         num_layers=num_layers,
         num_attention_heads=num_attention_heads,
         use_rotary_positional_embeddings=use_rotary_positional_embeddings,
-        use_learned_positional_embeddings=i2v,
+        ofs_embed_dim=512 if (i2v and init_kwargs["patch_size_t"] is not None) else None,  # CogVideoX1.5-5B-I2V
+        use_learned_positional_embeddings=i2v and init_kwargs["patch_size_t"] is None,  # CogVideoX-5B-I2V
+        **init_kwargs,
     ).to(dtype=dtype)
 
     for key in list(original_state_dict.keys()):
@@ -163,13 +168,18 @@ def convert_transformer(
             if special_key not in key:
                 continue
             handler_fn_inplace(key, original_state_dict)
+
     transformer.load_state_dict(original_state_dict, strict=True)
     return transformer
 
 
-def convert_vae(ckpt_path: str, scaling_factor: float, dtype: torch.dtype):
+def convert_vae(ckpt_path: str, scaling_factor: float, version: str, dtype: torch.dtype):
+    init_kwargs = {"scaling_factor": scaling_factor}
+    if version == "1.5":
+        init_kwargs.update({"invert_scale_latents": True})
+
     original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True))
-    vae = AutoencoderKLCogVideoX(scaling_factor=scaling_factor).to(dtype=dtype)
+    vae = AutoencoderKLCogVideoX(**init_kwargs).to(dtype=dtype)
 
     for key in list(original_state_dict.keys()):
         new_key = key[:]
@@ -187,6 +197,34 @@ def convert_vae(ckpt_path: str, scaling_factor: float, dtype: torch.dtype):
     return vae
 
 
+def get_transformer_init_kwargs(version: str):
+    if version == "1.0":
+        vae_scale_factor_spatial = 8
+        init_kwargs = {
+            "patch_size": 2,
+            "patch_size_t": None,
+            "patch_bias": True,
+            "sample_height": 480 // vae_scale_factor_spatial,
+            "sample_width": 720 // vae_scale_factor_spatial,
+            "sample_frames": 49,
+        }
+
+    elif version == "1.5":
+        vae_scale_factor_spatial = 8
+        init_kwargs = {
+            "patch_size": 2,
+            "patch_size_t": 2,
+            "patch_bias": False,
+            "sample_height": 300,
+            "sample_width": 300,
+            "sample_frames": 81,
+        }
+    else:
+        raise ValueError("Unsupported version of CogVideoX.")
+
+    return init_kwargs
+
+
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -202,6 +240,12 @@ def get_args():
     parser.add_argument(
         "--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory"
     )
+    parser.add_argument(
+        "--typecast_text_encoder",
+        action="store_true",
+        default=False,
+        help="Whether or not to apply fp16/bf16 precision to text_encoder",
+    )
     # For CogVideoX-2B, num_layers is 30. For 5B, it is 42
     parser.add_argument("--num_layers", type=int, default=30, help="Number of transformer blocks")
     # For CogVideoX-2B, num_attention_heads is 30. For 5B, it is 48
@@ -214,7 +258,18 @@ def get_args():
     parser.add_argument("--scaling_factor", type=float, default=1.15258426, help="Scaling factor in the VAE")
     # For CogVideoX-2B, snr_shift_scale is 3.0. For 5B, it is 1.0
     parser.add_argument("--snr_shift_scale", type=float, default=3.0, help="Scaling factor in the VAE")
-    parser.add_argument("--i2v", action="store_true", default=False, help="Whether to save the model weights in fp16")
+    parser.add_argument(
+        "--i2v",
+        action="store_true",
+        default=False,
+        help="Whether the model to be converted is the Image-to-Video version of CogVideoX.",
+    )
+    parser.add_argument(
+        "--version",
+        choices=["1.0", "1.5"],
+        default="1.0",
+        help="Which version of CogVideoX to use for initializing default modeling parameters.",
+    )
     return parser.parse_args()
 
 
@@ -230,6 +285,7 @@ if __name__ == "__main__":
     dtype = torch.float16 if args.fp16 else torch.bfloat16 if args.bf16 else torch.float32
 
     if args.transformer_ckpt_path is not None:
+        init_kwargs = get_transformer_init_kwargs(args.version)
         transformer = convert_transformer(
             args.transformer_ckpt_path,
             args.num_layers,
@@ -237,14 +293,19 @@ if __name__ == "__main__":
             args.use_rotary_positional_embeddings,
             args.i2v,
             dtype,
+            init_kwargs,
         )
     if args.vae_ckpt_path is not None:
-        vae = convert_vae(args.vae_ckpt_path, args.scaling_factor, dtype)
+        # Keep VAE in float32 for better quality
+        vae = convert_vae(args.vae_ckpt_path, args.scaling_factor, args.version, torch.float32)
 
     text_encoder_id = "google/t5-v1_1-xxl"
     tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH)
     text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir)
 
+    if args.typecast_text_encoder:
+        text_encoder = text_encoder.to(dtype=dtype)
+
     # Apparently, the conversion does not work anymore without this :shrug:
     for param in text_encoder.parameters():
         param.data = param.data.contiguous()
@@ -276,11 +337,6 @@ if __name__ == "__main__":
         scheduler=scheduler,
     )
 
-    if args.fp16:
-        pipe = pipe.to(dtype=torch.float16)
-    if args.bf16:
-        pipe = pipe.to(dtype=torch.bfloat16)
-
     # We don't use variant here because the model must be run in fp16 (2B) or bf16 (5B). It would be weird
     # for users to specify variant when the default is not fp32 and they want to run with the correct default (which
     # is either fp16/bf16 here).

scripts/convert_cogview3_to_diffusers.py

@@ -36,7 +36,7 @@ from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint
 from diffusers.utils.import_utils import is_accelerate_available
 
 
-CTX = init_empty_weights if is_accelerate_available else nullcontext
+CTX = init_empty_weights if is_accelerate_available() else nullcontext
 
 TOKENIZER_MAX_LENGTH = 224
 

scripts/convert_flux_to_diffusers.py

@@ -31,12 +31,14 @@ python scripts/convert_flux_to_diffusers.py  \
 --vae
 """
 
-CTX = init_empty_weights if is_accelerate_available else nullcontext
+CTX = init_empty_weights if is_accelerate_available() else nullcontext
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--original_state_dict_repo_id", default=None, type=str)
 parser.add_argument("--filename", default="flux.safetensors", type=str)
 parser.add_argument("--checkpoint_path", default=None, type=str)
+parser.add_argument("--in_channels", type=int, default=64)
+parser.add_argument("--out_channels", type=int, default=None)
 parser.add_argument("--vae", action="store_true")
 parser.add_argument("--transformer", action="store_true")
 parser.add_argument("--output_path", type=str)
@@ -279,10 +281,13 @@ def main(args):
         num_single_layers = 38
         inner_dim = 3072
         mlp_ratio = 4.0
+
         converted_transformer_state_dict = convert_flux_transformer_checkpoint_to_diffusers(
             original_ckpt, num_layers, num_single_layers, inner_dim, mlp_ratio=mlp_ratio
         )
-        transformer = FluxTransformer2DModel(guidance_embeds=has_guidance)
+        transformer = FluxTransformer2DModel(
+            in_channels=args.in_channels, out_channels=args.out_channels, guidance_embeds=has_guidance
+        )
         transformer.load_state_dict(converted_transformer_state_dict, strict=True)
 
         print(

scripts/convert_mochi_to_diffusers.py

@@ -10,13 +10,14 @@ from diffusers import AutoencoderKLMochi, FlowMatchEulerDiscreteScheduler, Mochi
 from diffusers.utils.import_utils import is_accelerate_available
 
 
-CTX = init_empty_weights if is_accelerate_available else nullcontext
+CTX = init_empty_weights if is_accelerate_available() else nullcontext
 
 TOKENIZER_MAX_LENGTH = 256
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--transformer_checkpoint_path", default=None, type=str)
-parser.add_argument("--vae_checkpoint_path", default=None, type=str)
+parser.add_argument("--vae_encoder_checkpoint_path", default=None, type=str)
+parser.add_argument("--vae_decoder_checkpoint_path", default=None, type=str)
 parser.add_argument("--output_path", required=True, type=str)
 parser.add_argument("--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving")
 parser.add_argument("--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory")
@@ -33,6 +34,12 @@ def swap_scale_shift(weight, dim):
     return new_weight
 
 
+def swap_proj_gate(weight):
+    proj, gate = weight.chunk(2, dim=0)
+    new_weight = torch.cat([gate, proj], dim=0)
+    return new_weight
+
+
 def convert_mochi_transformer_checkpoint_to_diffusers(ckpt_path):
     original_state_dict = load_file(ckpt_path, device="cpu")
     new_state_dict = {}
@@ -119,11 +126,13 @@ def convert_mochi_transformer_checkpoint_to_diffusers(ckpt_path):
             )
 
         # MLP
-        new_state_dict[block_prefix + "ff.net.0.proj.weight"] = original_state_dict.pop(old_prefix + "mlp_x.w1.weight")
+        new_state_dict[block_prefix + "ff.net.0.proj.weight"] = swap_proj_gate(
+            original_state_dict.pop(old_prefix + "mlp_x.w1.weight")
+        )
         new_state_dict[block_prefix + "ff.net.2.weight"] = original_state_dict.pop(old_prefix + "mlp_x.w2.weight")
         if i < num_layers - 1:
-            new_state_dict[block_prefix + "ff_context.net.0.proj.weight"] = original_state_dict.pop(
-                old_prefix + "mlp_y.w1.weight"
+            new_state_dict[block_prefix + "ff_context.net.0.proj.weight"] = swap_proj_gate(
+                original_state_dict.pop(old_prefix + "mlp_y.w1.weight")
             )
             new_state_dict[block_prefix + "ff_context.net.2.weight"] = original_state_dict.pop(
                 old_prefix + "mlp_y.w2.weight"
@@ -144,104 +153,255 @@ def convert_mochi_transformer_checkpoint_to_diffusers(ckpt_path):
     return new_state_dict
 
 
-def convert_mochi_decoder_state_dict_to_diffusers(ckpt_path):
-    original_state_dict = load_file(ckpt_path, device="cpu")
-
+def convert_mochi_vae_state_dict_to_diffusers(encoder_ckpt_path, decoder_ckpt_path):
+    encoder_state_dict = load_file(encoder_ckpt_path, device="cpu")
+    decoder_state_dict = load_file(decoder_ckpt_path, device="cpu")
     new_state_dict = {}
+
+    # ==== Decoder =====
     prefix = "decoder."
 
     # Convert conv_in
-    new_state_dict[f"{prefix}conv_in.weight"] = original_state_dict["blocks.0.0.weight"]
-    new_state_dict[f"{prefix}conv_in.bias"] = original_state_dict["blocks.0.0.bias"]
+    new_state_dict[f"{prefix}conv_in.weight"] = decoder_state_dict.pop("blocks.0.0.weight")
+    new_state_dict[f"{prefix}conv_in.bias"] = decoder_state_dict.pop("blocks.0.0.bias")
 
     # Convert block_in (MochiMidBlock3D)
     for i in range(3):  # layers_per_block[-1] = 3
-        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = original_state_dict[
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop(
             f"blocks.0.{i+1}.stack.0.weight"
-        ]
-        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop(
             f"blocks.0.{i+1}.stack.0.bias"
-        ]
-        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop(
             f"blocks.0.{i+1}.stack.2.weight"
-        ]
-        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop(
             f"blocks.0.{i+1}.stack.2.bias"
-        ]
-        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop(
             f"blocks.0.{i+1}.stack.3.weight"
-        ]
-        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop(
             f"blocks.0.{i+1}.stack.3.bias"
-        ]
-        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop(
             f"blocks.0.{i+1}.stack.5.weight"
-        ]
-        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop(
             f"blocks.0.{i+1}.stack.5.bias"
-        ]
+        )
 
     # Convert up_blocks (MochiUpBlock3D)
-    up_block_layers = [6, 4, 3]  # layers_per_block[-2], layers_per_block[-3], layers_per_block[-4]
+    down_block_layers = [6, 4, 3]  # layers_per_block[-2], layers_per_block[-3], layers_per_block[-4]
     for block in range(3):
-        for i in range(up_block_layers[block]):
-            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"] = original_state_dict[
+        for i in range(down_block_layers[block]):
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop(
                 f"blocks.{block+1}.blocks.{i}.stack.0.weight"
-            ]
-            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = original_state_dict[
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop(
                 f"blocks.{block+1}.blocks.{i}.stack.0.bias"
-            ]
-            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight"] = original_state_dict[
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop(
                 f"blocks.{block+1}.blocks.{i}.stack.2.weight"
-            ]
-            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias"] = original_state_dict[
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop(
                 f"blocks.{block+1}.blocks.{i}.stack.2.bias"
-            ]
-            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"] = original_state_dict[
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop(
                 f"blocks.{block+1}.blocks.{i}.stack.3.weight"
-            ]
-            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = original_state_dict[
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop(
                 f"blocks.{block+1}.blocks.{i}.stack.3.bias"
-            ]
-            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight"] = original_state_dict[
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop(
                 f"blocks.{block+1}.blocks.{i}.stack.5.weight"
-            ]
-            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias"] = original_state_dict[
+            )
+            new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop(
                 f"blocks.{block+1}.blocks.{i}.stack.5.bias"
-            ]
-        new_state_dict[f"{prefix}up_blocks.{block}.proj.weight"] = original_state_dict[f"blocks.{block+1}.proj.weight"]
-        new_state_dict[f"{prefix}up_blocks.{block}.proj.bias"] = original_state_dict[f"blocks.{block+1}.proj.bias"]
+            )
+        new_state_dict[f"{prefix}up_blocks.{block}.proj.weight"] = decoder_state_dict.pop(
+            f"blocks.{block+1}.proj.weight"
+        )
+        new_state_dict[f"{prefix}up_blocks.{block}.proj.bias"] = decoder_state_dict.pop(f"blocks.{block+1}.proj.bias")
 
     # Convert block_out (MochiMidBlock3D)
     for i in range(3):  # layers_per_block[0] = 3
-        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = original_state_dict[
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop(
             f"blocks.4.{i}.stack.0.weight"
-        ]
-        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop(
             f"blocks.4.{i}.stack.0.bias"
-        ]
-        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop(
             f"blocks.4.{i}.stack.2.weight"
-        ]
-        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop(
             f"blocks.4.{i}.stack.2.bias"
-        ]
-        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop(
             f"blocks.4.{i}.stack.3.weight"
-        ]
-        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop(
             f"blocks.4.{i}.stack.3.bias"
-        ]
-        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop(
             f"blocks.4.{i}.stack.5.weight"
-        ]
-        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = original_state_dict[
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop(
             f"blocks.4.{i}.stack.5.bias"
-        ]
+        )
 
-    # Convert conv_out (Conv1x1)
-    new_state_dict[f"{prefix}conv_out.weight"] = original_state_dict["output_proj.weight"]
-    new_state_dict[f"{prefix}conv_out.bias"] = original_state_dict["output_proj.bias"]
+    # Convert proj_out (Conv1x1 ~= nn.Linear)
+    new_state_dict[f"{prefix}proj_out.weight"] = decoder_state_dict.pop("output_proj.weight")
+    new_state_dict[f"{prefix}proj_out.bias"] = decoder_state_dict.pop("output_proj.bias")
+
+    print("Remaining Decoder Keys:", decoder_state_dict.keys())
+
+    # ==== Encoder =====
+    prefix = "encoder."
+
+    new_state_dict[f"{prefix}proj_in.weight"] = encoder_state_dict.pop("layers.0.weight")
+    new_state_dict[f"{prefix}proj_in.bias"] = encoder_state_dict.pop("layers.0.bias")
+
+    # Convert block_in (MochiMidBlock3D)
+    for i in range(3):  # layers_per_block[0] = 3
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.0.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.0.bias"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.2.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.2.bias"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.3.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.3.bias"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.5.weight"
+        )
+        new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{i+1}.stack.5.bias"
+        )
+
+    # Convert down_blocks (MochiDownBlock3D)
+    down_block_layers = [3, 4, 6]  # layers_per_block[1], layers_per_block[2], layers_per_block[3]
+    for block in range(3):
+        new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{block+4}.layers.0.weight"
+        )
+        new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{block+4}.layers.0.bias"
+        )
+
+        for i in range(down_block_layers[block]):
+            # Convert resnets
+            new_state_dict[
+                f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"
+            ] = encoder_state_dict.pop(f"layers.{block+4}.layers.{i+1}.stack.0.weight")
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.0.bias"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.2.weight"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.2.bias"
+            )
+            new_state_dict[
+                f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"
+            ] = encoder_state_dict.pop(f"layers.{block+4}.layers.{i+1}.stack.3.weight")
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.3.bias"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.5.weight"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.stack.5.bias"
+            )
+
+            # Convert attentions
+            qkv_weight = encoder_state_dict.pop(f"layers.{block+4}.layers.{i+1}.attn_block.attn.qkv.weight")
+            q, k, v = qkv_weight.chunk(3, dim=0)
+
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_q.weight"] = q
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_k.weight"] = k
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_v.weight"] = v
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.weight"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.attn_block.attn.out.bias"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.attn_block.norm.weight"
+            )
+            new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop(
+                f"layers.{block+4}.layers.{i+1}.attn_block.norm.bias"
+            )
+
+    # Convert block_out (MochiMidBlock3D)
+    for i in range(3):  # layers_per_block[-1] = 3
+        # Convert resnets
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.0.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.0.bias"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.2.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.2.bias"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.3.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.3.bias"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.5.weight"
+        )
+        new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.stack.5.bias"
+        )
+
+        # Convert attentions
+        qkv_weight = encoder_state_dict.pop(f"layers.{i+7}.attn_block.attn.qkv.weight")
+        q, k, v = qkv_weight.chunk(3, dim=0)
+
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_q.weight"] = q
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_k.weight"] = k
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_v.weight"] = v
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.attn_block.attn.out.weight"
+        )
+        new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.attn_block.attn.out.bias"
+        )
+        new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop(
+            f"layers.{i+7}.attn_block.norm.weight"
+        )
+        new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop(
+            f"layers.{i+7}.attn_block.norm.bias"
+        )
+
+    # Convert output layers
+    new_state_dict[f"{prefix}norm_out.norm_layer.weight"] = encoder_state_dict.pop("output_norm.weight")
+    new_state_dict[f"{prefix}norm_out.norm_layer.bias"] = encoder_state_dict.pop("output_norm.bias")
+    new_state_dict[f"{prefix}proj_out.weight"] = encoder_state_dict.pop("output_proj.weight")
+
+    print("Remaining Encoder Keys:", encoder_state_dict.keys())
 
     return new_state_dict
 
@@ -270,9 +430,11 @@ def main(args):
         if dtype is not None:
             transformer = transformer.to(dtype=dtype)
 
-    if args.vae_checkpoint_path is not None:
+    if args.vae_encoder_checkpoint_path is not None and args.vae_decoder_checkpoint_path is not None:
         vae = AutoencoderKLMochi(latent_channels=12, out_channels=3)
-        converted_vae_state_dict = convert_mochi_decoder_state_dict_to_diffusers(args.vae_checkpoint_path)
+        converted_vae_state_dict = convert_mochi_vae_state_dict_to_diffusers(
+            args.vae_encoder_checkpoint_path, args.vae_decoder_checkpoint_path
+        )
         vae.load_state_dict(converted_vae_state_dict, strict=True)
         if dtype is not None:
             vae = vae.to(dtype=dtype)
@@ -286,7 +448,7 @@ def main(args):
         param.data = param.data.contiguous()
 
     pipe = MochiPipeline(
-        scheduler=FlowMatchEulerDiscreteScheduler(),
+        scheduler=FlowMatchEulerDiscreteScheduler(invert_sigmas=True),
         vae=vae,
         text_encoder=text_encoder,
         tokenizer=tokenizer,

scripts/convert_sd3_controlnet_to_diffusers.py

@@ -0,0 +1,185 @@
+"""
+A script to convert Stable Diffusion 3.5 ControlNet checkpoints to the Diffusers format.
+
+Example:
+    Convert a SD3.5 ControlNet checkpoint to Diffusers format using local file:
+    ```bash
+    python scripts/convert_sd3_controlnet_to_diffusers.py \
+        --checkpoint_path "path/to/local/sd3.5_large_controlnet_canny.safetensors" \
+        --output_path "output/sd35-controlnet-canny" \
+        --dtype "fp16"  # optional, defaults to fp32
+    ```
+
+    Or download and convert from HuggingFace repository:
+    ```bash
+    python scripts/convert_sd3_controlnet_to_diffusers.py \
+        --original_state_dict_repo_id "stabilityai/stable-diffusion-3.5-controlnets" \
+        --filename "sd3.5_large_controlnet_canny.safetensors" \
+        --output_path "/raid/yiyi/sd35-controlnet-canny-diffusers" \
+        --dtype "fp32"  # optional, defaults to fp32
+    ```
+
+Note:
+    The script supports the following ControlNet types from SD3.5:
+    - Canny edge detection
+    - Depth estimation
+    - Blur detection
+
+    The checkpoint files can be downloaded from:
+    https://huggingface.co/stabilityai/stable-diffusion-3.5-controlnets
+"""
+
+import argparse
+
+import safetensors.torch
+import torch
+from huggingface_hub import hf_hub_download
+
+from diffusers import SD3ControlNetModel
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--checkpoint_path", type=str, default=None, help="Path to local checkpoint file")
+parser.add_argument(
+    "--original_state_dict_repo_id", type=str, default=None, help="HuggingFace repo ID containing the checkpoint"
+)
+parser.add_argument("--filename", type=str, default=None, help="Filename of the checkpoint in the HF repo")
+parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model")
+parser.add_argument(
+    "--dtype", type=str, default="fp32", help="Data type for the converted model (fp16, bf16, or fp32)"
+)
+
+args = parser.parse_args()
+
+
+def load_original_checkpoint(args):
+    if args.original_state_dict_repo_id is not None:
+        if args.filename is None:
+            raise ValueError("When using `original_state_dict_repo_id`, `filename` must also be specified")
+        print(f"Downloading checkpoint from {args.original_state_dict_repo_id}/{args.filename}")
+        ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename)
+    elif args.checkpoint_path is not None:
+        print(f"Loading checkpoint from local path: {args.checkpoint_path}")
+        ckpt_path = args.checkpoint_path
+    else:
+        raise ValueError("Please provide either `original_state_dict_repo_id` or a local `checkpoint_path`")
+
+    original_state_dict = safetensors.torch.load_file(ckpt_path)
+    return original_state_dict
+
+
+def convert_sd3_controlnet_checkpoint_to_diffusers(original_state_dict):
+    converted_state_dict = {}
+
+    # Direct mappings for controlnet blocks
+    for i in range(19):  # 19 controlnet blocks
+        converted_state_dict[f"controlnet_blocks.{i}.weight"] = original_state_dict[f"controlnet_blocks.{i}.weight"]
+        converted_state_dict[f"controlnet_blocks.{i}.bias"] = original_state_dict[f"controlnet_blocks.{i}.bias"]
+
+    # Positional embeddings
+    converted_state_dict["pos_embed_input.proj.weight"] = original_state_dict["pos_embed_input.proj.weight"]
+    converted_state_dict["pos_embed_input.proj.bias"] = original_state_dict["pos_embed_input.proj.bias"]
+
+    # Time and text embeddings
+    time_text_mappings = {
+        "time_text_embed.timestep_embedder.linear_1.weight": "time_text_embed.timestep_embedder.linear_1.weight",
+        "time_text_embed.timestep_embedder.linear_1.bias": "time_text_embed.timestep_embedder.linear_1.bias",
+        "time_text_embed.timestep_embedder.linear_2.weight": "time_text_embed.timestep_embedder.linear_2.weight",
+        "time_text_embed.timestep_embedder.linear_2.bias": "time_text_embed.timestep_embedder.linear_2.bias",
+        "time_text_embed.text_embedder.linear_1.weight": "time_text_embed.text_embedder.linear_1.weight",
+        "time_text_embed.text_embedder.linear_1.bias": "time_text_embed.text_embedder.linear_1.bias",
+        "time_text_embed.text_embedder.linear_2.weight": "time_text_embed.text_embedder.linear_2.weight",
+        "time_text_embed.text_embedder.linear_2.bias": "time_text_embed.text_embedder.linear_2.bias",
+    }
+
+    for new_key, old_key in time_text_mappings.items():
+        if old_key in original_state_dict:
+            converted_state_dict[new_key] = original_state_dict[old_key]
+
+    # Transformer blocks
+    for i in range(19):
+        # Split QKV into separate Q, K, V
+        qkv_weight = original_state_dict[f"transformer_blocks.{i}.attn.qkv.weight"]
+        qkv_bias = original_state_dict[f"transformer_blocks.{i}.attn.qkv.bias"]
+        q, k, v = torch.chunk(qkv_weight, 3, dim=0)
+        q_bias, k_bias, v_bias = torch.chunk(qkv_bias, 3, dim=0)
+
+        block_mappings = {
+            f"transformer_blocks.{i}.attn.to_q.weight": q,
+            f"transformer_blocks.{i}.attn.to_q.bias": q_bias,
+            f"transformer_blocks.{i}.attn.to_k.weight": k,
+            f"transformer_blocks.{i}.attn.to_k.bias": k_bias,
+            f"transformer_blocks.{i}.attn.to_v.weight": v,
+            f"transformer_blocks.{i}.attn.to_v.bias": v_bias,
+            # Output projections
+            f"transformer_blocks.{i}.attn.to_out.0.weight": original_state_dict[
+                f"transformer_blocks.{i}.attn.proj.weight"
+            ],
+            f"transformer_blocks.{i}.attn.to_out.0.bias": original_state_dict[
+                f"transformer_blocks.{i}.attn.proj.bias"
+            ],
+            # Feed forward
+            f"transformer_blocks.{i}.ff.net.0.proj.weight": original_state_dict[
+                f"transformer_blocks.{i}.mlp.fc1.weight"
+            ],
+            f"transformer_blocks.{i}.ff.net.0.proj.bias": original_state_dict[f"transformer_blocks.{i}.mlp.fc1.bias"],
+            f"transformer_blocks.{i}.ff.net.2.weight": original_state_dict[f"transformer_blocks.{i}.mlp.fc2.weight"],
+            f"transformer_blocks.{i}.ff.net.2.bias": original_state_dict[f"transformer_blocks.{i}.mlp.fc2.bias"],
+            # Norms
+            f"transformer_blocks.{i}.norm1.linear.weight": original_state_dict[
+                f"transformer_blocks.{i}.adaLN_modulation.1.weight"
+            ],
+            f"transformer_blocks.{i}.norm1.linear.bias": original_state_dict[
+                f"transformer_blocks.{i}.adaLN_modulation.1.bias"
+            ],
+        }
+        converted_state_dict.update(block_mappings)
+
+    return converted_state_dict
+
+
+def main(args):
+    original_ckpt = load_original_checkpoint(args)
+    original_dtype = next(iter(original_ckpt.values())).dtype
+
+    # Initialize dtype with fp32 as default
+    if args.dtype == "fp16":
+        dtype = torch.float16
+    elif args.dtype == "bf16":
+        dtype = torch.bfloat16
+    elif args.dtype == "fp32":
+        dtype = torch.float32
+    else:
+        raise ValueError(f"Unsupported dtype: {args.dtype}. Must be one of: fp16, bf16, fp32")
+
+    if dtype != original_dtype:
+        print(
+            f"Converting checkpoint from {original_dtype} to {dtype}. This can lead to unexpected results, proceed with caution."
+        )
+
+    converted_controlnet_state_dict = convert_sd3_controlnet_checkpoint_to_diffusers(original_ckpt)
+
+    controlnet = SD3ControlNetModel(
+        patch_size=2,
+        in_channels=16,
+        num_layers=19,
+        attention_head_dim=64,
+        num_attention_heads=38,
+        joint_attention_dim=None,
+        caption_projection_dim=2048,
+        pooled_projection_dim=2048,
+        out_channels=16,
+        pos_embed_max_size=None,
+        pos_embed_type=None,
+        use_pos_embed=False,
+        force_zeros_for_pooled_projection=False,
+    )
+
+    controlnet.load_state_dict(converted_controlnet_state_dict, strict=True)
+
+    print(f"Saving SD3 ControlNet in Diffusers format in {args.output_path}.")
+    controlnet.to(dtype).save_pretrained(args.output_path)
+
+
+if __name__ == "__main__":
+    main(args)

scripts/convert_sd3_to_diffusers.py

@@ -11,7 +11,7 @@ from diffusers.models.modeling_utils import load_model_dict_into_meta
 from diffusers.utils.import_utils import is_accelerate_available
 
 
-CTX = init_empty_weights if is_accelerate_available else nullcontext
+CTX = init_empty_weights if is_accelerate_available() else nullcontext
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--checkpoint_path", type=str)

setup.py

@@ -130,7 +130,7 @@ _deps = [
     "regex!=2019.12.17",
     "requests",
     "tensorboard",
-    "torch>=1.4,<2.5.0",
+    "torch>=1.4",
     "torchvision",
     "transformers>=4.41.2",
     "urllib3<=2.0.0",

src/diffusers/__init__.py

@@ -77,9 +77,11 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["models"].extend(
         [
+            "AllegroTransformer3DModel",
             "AsymmetricAutoencoderKL",
             "AuraFlowTransformer2DModel",
             "AutoencoderKL",
+            "AutoencoderKLAllegro",
             "AutoencoderKLCogVideoX",
             "AutoencoderKLMochi",
             "AutoencoderKLTemporalDecoder",
@@ -105,6 +107,7 @@ else:
             "ModelMixin",
             "MotionAdapter",
             "MultiAdapter",
+            "MultiControlNetModel",
             "PixArtTransformer2DModel",
             "PriorTransformer",
             "SD3ControlNetModel",
@@ -239,6 +242,7 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["pipelines"].extend(
         [
+            "AllegroPipeline",
             "AltDiffusionImg2ImgPipeline",
             "AltDiffusionPipeline",
             "AmusedImg2ImgPipeline",
@@ -265,12 +269,16 @@ else:
             "CogVideoXVideoToVideoPipeline",
             "CogView3PlusPipeline",
             "CycleDiffusionPipeline",
+            "FluxControlImg2ImgPipeline",
             "FluxControlNetImg2ImgPipeline",
             "FluxControlNetInpaintPipeline",
             "FluxControlNetPipeline",
+            "FluxControlPipeline",
+            "FluxFillPipeline",
             "FluxImg2ImgPipeline",
             "FluxInpaintPipeline",
             "FluxPipeline",
+            "FluxPriorReduxPipeline",
             "HunyuanDiTControlNetPipeline",
             "HunyuanDiTPAGPipeline",
             "HunyuanDiTPipeline",
@@ -317,6 +325,7 @@ else:
             "PixArtAlphaPipeline",
             "PixArtSigmaPAGPipeline",
             "PixArtSigmaPipeline",
+            "ReduxImageEncoder",
             "SemanticStableDiffusionPipeline",
             "ShapEImg2ImgPipeline",
             "ShapEPipeline",
@@ -329,6 +338,7 @@ else:
             "StableDiffusion3ControlNetPipeline",
             "StableDiffusion3Img2ImgPipeline",
             "StableDiffusion3InpaintPipeline",
+            "StableDiffusion3PAGImg2ImgPipeline",
             "StableDiffusion3PAGPipeline",
             "StableDiffusion3Pipeline",
             "StableDiffusionAdapterPipeline",
@@ -484,7 +494,7 @@ except OptionalDependencyNotAvailable:
 
 
 else:
-    _import_structure["models.controlnet_flax"] = ["FlaxControlNetModel"]
+    _import_structure["models.controlnets.controlnet_flax"] = ["FlaxControlNetModel"]
     _import_structure["models.modeling_flax_utils"] = ["FlaxModelMixin"]
     _import_structure["models.unets.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
     _import_structure["models.vae_flax"] = ["FlaxAutoencoderKL"]
@@ -559,9 +569,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .utils.dummy_pt_objects import *  # noqa F403
     else:
         from .models import (
+            AllegroTransformer3DModel,
             AsymmetricAutoencoderKL,
             AuraFlowTransformer2DModel,
             AutoencoderKL,
+            AutoencoderKLAllegro,
             AutoencoderKLCogVideoX,
             AutoencoderKLMochi,
             AutoencoderKLTemporalDecoder,
@@ -587,6 +599,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ModelMixin,
             MotionAdapter,
             MultiAdapter,
+            MultiControlNetModel,
             PixArtTransformer2DModel,
             PriorTransformer,
             SD3ControlNetModel,
@@ -702,6 +715,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
     else:
         from .pipelines import (
+            AllegroPipeline,
             AltDiffusionImg2ImgPipeline,
             AltDiffusionPipeline,
             AmusedImg2ImgPipeline,
@@ -726,12 +740,16 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             CogVideoXVideoToVideoPipeline,
             CogView3PlusPipeline,
             CycleDiffusionPipeline,
+            FluxControlImg2ImgPipeline,
             FluxControlNetImg2ImgPipeline,
             FluxControlNetInpaintPipeline,
             FluxControlNetPipeline,
+            FluxControlPipeline,
+            FluxFillPipeline,
             FluxImg2ImgPipeline,
             FluxInpaintPipeline,
             FluxPipeline,
+            FluxPriorReduxPipeline,
             HunyuanDiTControlNetPipeline,
             HunyuanDiTPAGPipeline,
             HunyuanDiTPipeline,
@@ -778,6 +796,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             PixArtAlphaPipeline,
             PixArtSigmaPAGPipeline,
             PixArtSigmaPipeline,
+            ReduxImageEncoder,
             SemanticStableDiffusionPipeline,
             ShapEImg2ImgPipeline,
             ShapEPipeline,
@@ -789,6 +808,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusion3ControlNetPipeline,
             StableDiffusion3Img2ImgPipeline,
             StableDiffusion3InpaintPipeline,
+            StableDiffusion3PAGImg2ImgPipeline,
             StableDiffusion3PAGPipeline,
             StableDiffusion3Pipeline,
             StableDiffusionAdapterPipeline,
@@ -908,7 +928,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     except OptionalDependencyNotAvailable:
         from .utils.dummy_flax_objects import *  # noqa F403
     else:
-        from .models.controlnet_flax import FlaxControlNetModel
+        from .models.controlnets.controlnet_flax import FlaxControlNetModel
         from .models.modeling_flax_utils import FlaxModelMixin
         from .models.unets.unet_2d_condition_flax import FlaxUNet2DConditionModel
         from .models.vae_flax import FlaxAutoencoderKL

src/diffusers/configuration_utils.py

@@ -170,7 +170,7 @@ class ConfigMixin:
 
         if push_to_hub:
             commit_message = kwargs.pop("commit_message", None)
-            private = kwargs.pop("private", False)
+            private = kwargs.pop("private", None)
             create_pr = kwargs.pop("create_pr", False)
             token = kwargs.pop("token", None)
             repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
@@ -347,6 +347,7 @@ class ConfigMixin:
         _ = kwargs.pop("mirror", None)
         subfolder = kwargs.pop("subfolder", None)
         user_agent = kwargs.pop("user_agent", {})
+        dduf_reader = kwargs.pop("dduf_reader", None)
 
         user_agent = {**user_agent, "file_type": "config"}
         user_agent = http_user_agent(user_agent)
@@ -358,8 +359,22 @@ class ConfigMixin:
                 "`self.config_name` is not defined. Note that one should not load a config from "
                 "`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
             )
-
-        if os.path.isfile(pretrained_model_name_or_path):
+        # Custom path for now
+        if dduf_reader:
+            if subfolder is not None:
+                if dduf_reader.has_file(os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)):
+                    config_file = os.path.join(subfolder, cls.config_name)
+                else:
+                    raise ValueError(
+                        f"We did not manage to find the file {os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)} in the archive. We only have the following files {dduf_reader.files}"
+                    )
+            elif dduf_reader.has_file(os.path.join(pretrained_model_name_or_path, cls.config_name)):
+                config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
+            else:
+                raise ValueError(
+                    f"We did not manage to find the file {os.path.join(pretrained_model_name_or_path, cls.config_name)} in the archive. We only have the following files {dduf_reader.files}"
+                )
+        elif os.path.isfile(pretrained_model_name_or_path):
             config_file = pretrained_model_name_or_path
         elif os.path.isdir(pretrained_model_name_or_path):
             if subfolder is not None and os.path.isfile(
@@ -426,10 +441,8 @@ class ConfigMixin:
                     f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                     f"containing a {cls.config_name} file"
                 )
-
         try:
-            # Load config dict
-            config_dict = cls._dict_from_json_file(config_file)
+            config_dict = cls._dict_from_json_file(config_file, dduf_reader=dduf_reader)
 
             commit_hash = extract_commit_hash(config_file)
         except (json.JSONDecodeError, UnicodeDecodeError):
@@ -552,9 +565,12 @@ class ConfigMixin:
         return init_dict, unused_kwargs, hidden_config_dict
 
     @classmethod
-    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
-        with open(json_file, "r", encoding="utf-8") as reader:
-            text = reader.read()
+    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike], dduf_reader=None):
+        if dduf_reader:
+            text = dduf_reader.read_file(json_file, encoding="utf-8")
+        else:
+            with open(json_file, "r", encoding="utf-8") as reader:
+                text = reader.read()
         return json.loads(text)
 
     def __repr__(self):

src/diffusers/dependency_versions_table.py

@@ -38,7 +38,7 @@ deps = {
     "regex": "regex!=2019.12.17",
     "requests": "requests",
     "tensorboard": "tensorboard",
-    "torch": "torch>=1.4,<2.5.0",
+    "torch": "torch>=1.4",
     "torchvision": "torchvision",
     "transformers": "transformers>=4.41.2",
     "urllib3": "urllib3<=2.0.0",

src/diffusers/image_processor.py

@@ -795,13 +795,11 @@ class VaeImageProcessor(ConfigMixin):
                 The final image with the overlay applied.
         """
 
-        width, height = image.width, image.height
-
-        init_image = self.resize(init_image, width=width, height=height)
-        mask = self.resize(mask, width=width, height=height)
+        width, height = init_image.width, init_image.height
 
         init_image_masked = PIL.Image.new("RGBa", (width, height))
         init_image_masked.paste(init_image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert("L")))
+
         init_image_masked = init_image_masked.convert("RGBA")
 
         if crop_coords is not None:

src/diffusers/loaders/__init__.py

@@ -68,6 +68,7 @@ if is_torch_available():
             "LoraLoaderMixin",
             "FluxLoraLoaderMixin",
             "CogVideoXLoraLoaderMixin",
+            "Mochi1LoraLoaderMixin",
         ]
         _import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
         _import_structure["ip_adapter"] = ["IPAdapterMixin"]
@@ -88,6 +89,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
                 CogVideoXLoraLoaderMixin,
                 FluxLoraLoaderMixin,
                 LoraLoaderMixin,
+                Mochi1LoraLoaderMixin,
                 SD3LoraLoaderMixin,
                 StableDiffusionLoraLoaderMixin,
                 StableDiffusionXLLoraLoaderMixin,

src/diffusers/loaders/ip_adapter.py

@@ -33,16 +33,14 @@ from .unet_loader_utils import _maybe_expand_lora_scales
 
 
 if is_transformers_available():
-    from transformers import (
-        CLIPImageProcessor,
-        CLIPVisionModelWithProjection,
-    )
+    from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
     from ..models.attention_processor import (
         AttnProcessor,
         AttnProcessor2_0,
         IPAdapterAttnProcessor,
         IPAdapterAttnProcessor2_0,
+        IPAdapterXFormersAttnProcessor,
     )
 
 logger = logging.get_logger(__name__)
@@ -76,7 +74,7 @@ class IPAdapterMixin:
                 list is passed, it should have the same length as `weight_name`.
             weight_name (`str` or `List[str]`):
                 The name of the weight file to load. If a list is passed, it should have the same length as
-                `weight_name`.
+                `subfolder`.
             image_encoder_folder (`str`, *optional*, defaults to `image_encoder`):
                 The subfolder location of the image encoder within a larger model repository on the Hub or locally.
                 Pass `None` to not load the image encoder. If the image encoder is located in a folder inside
@@ -284,7 +282,9 @@ class IPAdapterMixin:
         scale_configs = _maybe_expand_lora_scales(unet, scale, default_scale=0.0)
 
         for attn_name, attn_processor in unet.attn_processors.items():
-            if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):
+            if isinstance(
+                attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor)
+            ):
                 if len(scale_configs) != len(attn_processor.scale):
                     raise ValueError(
                         f"Cannot assign {len(scale_configs)} scale_configs to "
@@ -342,7 +342,9 @@ class IPAdapterMixin:
             )
             attn_procs[name] = (
                 attn_processor_class
-                if isinstance(value, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0))
+                if isinstance(
+                    value, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor)
+                )
                 else value.__class__()
             )
         self.unet.set_attn_processor(attn_procs)

src/diffusers/loaders/lora_base.py

@@ -51,6 +51,9 @@ if is_accelerate_available():
 
 logger = logging.get_logger(__name__)
 
+LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"
+LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"
+
 
 def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False, adapter_names=None):
     """
@@ -181,6 +184,119 @@ def _remove_text_encoder_monkey_patch(text_encoder):
         text_encoder._hf_peft_config_loaded = None
 
 
+def _fetch_state_dict(
+    pretrained_model_name_or_path_or_dict,
+    weight_name,
+    use_safetensors,
+    local_files_only,
+    cache_dir,
+    force_download,
+    proxies,
+    token,
+    revision,
+    subfolder,
+    user_agent,
+    allow_pickle,
+):
+    model_file = None
+    if not isinstance(pretrained_model_name_or_path_or_dict, dict):
+        # Let's first try to load .safetensors weights
+        if (use_safetensors and weight_name is None) or (
+            weight_name is not None and weight_name.endswith(".safetensors")
+        ):
+            try:
+                # Here we're relaxing the loading check to enable more Inference API
+                # friendliness where sometimes, it's not at all possible to automatically
+                # determine `weight_name`.
+                if weight_name is None:
+                    weight_name = _best_guess_weight_name(
+                        pretrained_model_name_or_path_or_dict,
+                        file_extension=".safetensors",
+                        local_files_only=local_files_only,
+                    )
+                model_file = _get_model_file(
+                    pretrained_model_name_or_path_or_dict,
+                    weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    token=token,
+                    revision=revision,
+                    subfolder=subfolder,
+                    user_agent=user_agent,
+                )
+                state_dict = safetensors.torch.load_file(model_file, device="cpu")
+            except (IOError, safetensors.SafetensorError) as e:
+                if not allow_pickle:
+                    raise e
+                # try loading non-safetensors weights
+                model_file = None
+                pass
+
+        if model_file is None:
+            if weight_name is None:
+                weight_name = _best_guess_weight_name(
+                    pretrained_model_name_or_path_or_dict, file_extension=".bin", local_files_only=local_files_only
+                )
+            model_file = _get_model_file(
+                pretrained_model_name_or_path_or_dict,
+                weights_name=weight_name or LORA_WEIGHT_NAME,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+                subfolder=subfolder,
+                user_agent=user_agent,
+            )
+            state_dict = load_state_dict(model_file)
+    else:
+        state_dict = pretrained_model_name_or_path_or_dict
+
+    return state_dict
+
+
+def _best_guess_weight_name(
+    pretrained_model_name_or_path_or_dict, file_extension=".safetensors", local_files_only=False
+):
+    if local_files_only or HF_HUB_OFFLINE:
+        raise ValueError("When using the offline mode, you must specify a `weight_name`.")
+
+    targeted_files = []
+
+    if os.path.isfile(pretrained_model_name_or_path_or_dict):
+        return
+    elif os.path.isdir(pretrained_model_name_or_path_or_dict):
+        targeted_files = [f for f in os.listdir(pretrained_model_name_or_path_or_dict) if f.endswith(file_extension)]
+    else:
+        files_in_repo = model_info(pretrained_model_name_or_path_or_dict).siblings
+        targeted_files = [f.rfilename for f in files_in_repo if f.rfilename.endswith(file_extension)]
+    if len(targeted_files) == 0:
+        return
+
+    # "scheduler" does not correspond to a LoRA checkpoint.
+    # "optimizer" does not correspond to a LoRA checkpoint
+    # only top-level checkpoints are considered and not the other ones, hence "checkpoint".
+    unallowed_substrings = {"scheduler", "optimizer", "checkpoint"}
+    targeted_files = list(
+        filter(lambda x: all(substring not in x for substring in unallowed_substrings), targeted_files)
+    )
+
+    if any(f.endswith(LORA_WEIGHT_NAME) for f in targeted_files):
+        targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME), targeted_files))
+    elif any(f.endswith(LORA_WEIGHT_NAME_SAFE) for f in targeted_files):
+        targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME_SAFE), targeted_files))
+
+    if len(targeted_files) > 1:
+        raise ValueError(
+            f"Provided path contains more than one weights file in the {file_extension} format. Either specify `weight_name` in `load_lora_weights` or make sure there's only one  `.safetensors` or `.bin` file in  {pretrained_model_name_or_path_or_dict}."
+        )
+    weight_name = targeted_files[0]
+    return weight_name
+
+
 class LoraBaseMixin:
     """Utility class for handling LoRAs."""
 
@@ -234,124 +350,16 @@ class LoraBaseMixin:
         return (is_model_cpu_offload, is_sequential_cpu_offload)
 
     @classmethod
-    def _fetch_state_dict(
-        cls,
-        pretrained_model_name_or_path_or_dict,
-        weight_name,
-        use_safetensors,
-        local_files_only,
-        cache_dir,
-        force_download,
-        proxies,
-        token,
-        revision,
-        subfolder,
-        user_agent,
-        allow_pickle,
-    ):
-        from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
-
-        model_file = None
-        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
-            # Let's first try to load .safetensors weights
-            if (use_safetensors and weight_name is None) or (
-                weight_name is not None and weight_name.endswith(".safetensors")
-            ):
-                try:
-                    # Here we're relaxing the loading check to enable more Inference API
-                    # friendliness where sometimes, it's not at all possible to automatically
-                    # determine `weight_name`.
-                    if weight_name is None:
-                        weight_name = cls._best_guess_weight_name(
-                            pretrained_model_name_or_path_or_dict,
-                            file_extension=".safetensors",
-                            local_files_only=local_files_only,
-                        )
-                    model_file = _get_model_file(
-                        pretrained_model_name_or_path_or_dict,
-                        weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
-                        cache_dir=cache_dir,
-                        force_download=force_download,
-                        proxies=proxies,
-                        local_files_only=local_files_only,
-                        token=token,
-                        revision=revision,
-                        subfolder=subfolder,
-                        user_agent=user_agent,
-                    )
-                    state_dict = safetensors.torch.load_file(model_file, device="cpu")
-                except (IOError, safetensors.SafetensorError) as e:
-                    if not allow_pickle:
-                        raise e
-                    # try loading non-safetensors weights
-                    model_file = None
-                    pass
-
-            if model_file is None:
-                if weight_name is None:
-                    weight_name = cls._best_guess_weight_name(
-                        pretrained_model_name_or_path_or_dict, file_extension=".bin", local_files_only=local_files_only
-                    )
-                model_file = _get_model_file(
-                    pretrained_model_name_or_path_or_dict,
-                    weights_name=weight_name or LORA_WEIGHT_NAME,
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    proxies=proxies,
-                    local_files_only=local_files_only,
-                    token=token,
-                    revision=revision,
-                    subfolder=subfolder,
-                    user_agent=user_agent,
-                )
-                state_dict = load_state_dict(model_file)
-        else:
-            state_dict = pretrained_model_name_or_path_or_dict
-
-        return state_dict
+    def _fetch_state_dict(cls, *args, **kwargs):
+        deprecation_message = f"Using the `_fetch_state_dict()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _fetch_state_dict`."
+        deprecate("_fetch_state_dict", "0.35.0", deprecation_message)
+        return _fetch_state_dict(*args, **kwargs)
 
     @classmethod
-    def _best_guess_weight_name(
-        cls, pretrained_model_name_or_path_or_dict, file_extension=".safetensors", local_files_only=False
-    ):
-        from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
-
-        if local_files_only or HF_HUB_OFFLINE:
-            raise ValueError("When using the offline mode, you must specify a `weight_name`.")
-
-        targeted_files = []
-
-        if os.path.isfile(pretrained_model_name_or_path_or_dict):
-            return
-        elif os.path.isdir(pretrained_model_name_or_path_or_dict):
-            targeted_files = [
-                f for f in os.listdir(pretrained_model_name_or_path_or_dict) if f.endswith(file_extension)
-            ]
-        else:
-            files_in_repo = model_info(pretrained_model_name_or_path_or_dict).siblings
-            targeted_files = [f.rfilename for f in files_in_repo if f.rfilename.endswith(file_extension)]
-        if len(targeted_files) == 0:
-            return
-
-        # "scheduler" does not correspond to a LoRA checkpoint.
-        # "optimizer" does not correspond to a LoRA checkpoint
-        # only top-level checkpoints are considered and not the other ones, hence "checkpoint".
-        unallowed_substrings = {"scheduler", "optimizer", "checkpoint"}
-        targeted_files = list(
-            filter(lambda x: all(substring not in x for substring in unallowed_substrings), targeted_files)
-        )
-
-        if any(f.endswith(LORA_WEIGHT_NAME) for f in targeted_files):
-            targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME), targeted_files))
-        elif any(f.endswith(LORA_WEIGHT_NAME_SAFE) for f in targeted_files):
-            targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME_SAFE), targeted_files))
-
-        if len(targeted_files) > 1:
-            raise ValueError(
-                f"Provided path contains more than one weights file in the {file_extension} format. Either specify `weight_name` in `load_lora_weights` or make sure there's only one  `.safetensors` or `.bin` file in  {pretrained_model_name_or_path_or_dict}."
-            )
-        weight_name = targeted_files[0]
-        return weight_name
+    def _best_guess_weight_name(cls, *args, **kwargs):
+        deprecation_message = f"Using the `_best_guess_weight_name()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _best_guess_weight_name`."
+        deprecate("_best_guess_weight_name", "0.35.0", deprecation_message)
+        return _best_guess_weight_name(*args, **kwargs)
 
     def unload_lora_weights(self):
         """
@@ -725,8 +733,6 @@ class LoraBaseMixin:
         save_function: Callable,
         safe_serialization: bool,
     ):
-        from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
-
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return

src/diffusers/loaders/lora_conversion_utils.py

@@ -636,10 +636,15 @@ def _convert_xlabs_flux_lora_to_diffusers(old_state_dict):
             block_num = re.search(r"single_blocks\.(\d+)", old_key).group(1)
             new_key = f"transformer.single_transformer_blocks.{block_num}"
 
-            if "proj_lora1" in old_key or "proj_lora2" in old_key:
+            if "proj_lora" in old_key:
                 new_key += ".proj_out"
-            elif "qkv_lora1" in old_key or "qkv_lora2" in old_key:
-                new_key += ".norm.linear"
+            elif "qkv_lora" in old_key and "up" not in old_key:
+                handle_qkv(
+                    old_state_dict,
+                    new_state_dict,
+                    old_key,
+                    [f"transformer.single_transformer_blocks.{block_num}.norm.linear"],
+                )
 
             if "down" in old_key:
                 new_key += ".lora_A.weight"

src/diffusers/loaders/peft.py

@@ -13,27 +13,46 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import inspect
+import os
 from functools import partial
+from pathlib import Path
 from typing import Dict, List, Optional, Union
 
+import safetensors
+import torch
+import torch.nn as nn
+
 from ..utils import (
     MIN_PEFT_VERSION,
     USE_PEFT_BACKEND,
     check_peft_version,
+    convert_unet_state_dict_to_peft,
     delete_adapter_layers,
+    get_adapter_name,
+    get_peft_kwargs,
+    is_accelerate_available,
     is_peft_available,
+    is_peft_version,
+    logging,
     set_adapter_layers,
     set_weights_and_activate_adapters,
 )
+from .lora_base import _fetch_state_dict
 from .unet_loader_utils import _maybe_expand_lora_scales
 
 
+if is_accelerate_available():
+    from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
+
+logger = logging.get_logger(__name__)
+
 _SET_ADAPTER_SCALE_FN_MAPPING = {
     "UNet2DConditionModel": _maybe_expand_lora_scales,
     "UNetMotionModel": _maybe_expand_lora_scales,
     "SD3Transformer2DModel": lambda model_cls, weights: weights,
     "FluxTransformer2DModel": lambda model_cls, weights: weights,
     "CogVideoXTransformer3DModel": lambda model_cls, weights: weights,
+    "MochiTransformer3DModel": lambda model_cls, weights: weights,
 }
 
 
@@ -53,6 +72,283 @@ class PeftAdapterMixin:
 
     _hf_peft_config_loaded = False
 
+    @classmethod
+    # Copied from diffusers.loaders.lora_base.LoraBaseMixin._optionally_disable_offloading
+    def _optionally_disable_offloading(cls, _pipeline):
+        """
+        Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.
+
+        Args:
+            _pipeline (`DiffusionPipeline`):
+                The pipeline to disable offloading for.
+
+        Returns:
+            tuple:
+                A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
+        """
+        is_model_cpu_offload = False
+        is_sequential_cpu_offload = False
+
+        if _pipeline is not None and _pipeline.hf_device_map is None:
+            for _, component in _pipeline.components.items():
+                if isinstance(component, nn.Module) and hasattr(component, "_hf_hook"):
+                    if not is_model_cpu_offload:
+                        is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)
+                    if not is_sequential_cpu_offload:
+                        is_sequential_cpu_offload = (
+                            isinstance(component._hf_hook, AlignDevicesHook)
+                            or hasattr(component._hf_hook, "hooks")
+                            and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)
+                        )
+
+                    logger.info(
+                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
+                    )
+                    remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
+
+        return (is_model_cpu_offload, is_sequential_cpu_offload)
+
+    def load_lora_adapter(self, pretrained_model_name_or_path_or_dict, prefix="transformer", **kwargs):
+        r"""
+        Loads a LoRA adapter into the underlying model.
+
+        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).
+
+            prefix (`str`, *optional*): Prefix to filter the 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.
+            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.
+            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.
+            network_alphas (`Dict[str, float]`):
+                The value of the network alpha used for stable learning and preventing underflow. This value has the
+                same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this
+                link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).
+            low_cpu_mem_usage (`bool`, *optional*):
+                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
+                weights.
+        """
+        from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
+
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+        subfolder = kwargs.pop("subfolder", None)
+        weight_name = kwargs.pop("weight_name", None)
+        use_safetensors = kwargs.pop("use_safetensors", None)
+        adapter_name = kwargs.pop("adapter_name", None)
+        network_alphas = kwargs.pop("network_alphas", None)
+        _pipeline = kwargs.pop("_pipeline", None)
+        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", False)
+        allow_pickle = False
+
+        if low_cpu_mem_usage and is_peft_version("<=", "0.13.0"):
+            raise ValueError(
+                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+            )
+
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+
+        state_dict = _fetch_state_dict(
+            pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
+            weight_name=weight_name,
+            use_safetensors=use_safetensors,
+            local_files_only=local_files_only,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            proxies=proxies,
+            token=token,
+            revision=revision,
+            subfolder=subfolder,
+            user_agent=user_agent,
+            allow_pickle=allow_pickle,
+        )
+        if network_alphas is not None and prefix is None:
+            raise ValueError("`network_alphas` cannot be None when `prefix` is None.")
+
+        if prefix is not None:
+            keys = list(state_dict.keys())
+            model_keys = [k for k in keys if k.startswith(f"{prefix}.")]
+            if len(model_keys) > 0:
+                state_dict = {k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in model_keys}
+
+        if len(state_dict) > 0:
+            if adapter_name in getattr(self, "peft_config", {}):
+                raise ValueError(
+                    f"Adapter name {adapter_name} already in use in the model - please select a new adapter name."
+                )
+
+            # check with first key if is not in peft format
+            first_key = next(iter(state_dict.keys()))
+            if "lora_A" not in first_key:
+                state_dict = convert_unet_state_dict_to_peft(state_dict)
+
+            rank = {}
+            for key, val in state_dict.items():
+                if "lora_B" in key:
+                    rank[key] = val.shape[1]
+
+            if network_alphas is not None and len(network_alphas) >= 1:
+                alpha_keys = [k for k in network_alphas.keys() if k.startswith(f"{prefix}.")]
+                network_alphas = {k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys}
+
+            lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=network_alphas, peft_state_dict=state_dict)
+            if "use_dora" in lora_config_kwargs:
+                if lora_config_kwargs["use_dora"]:
+                    if is_peft_version("<", "0.9.0"):
+                        raise ValueError(
+                            "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+                        )
+                else:
+                    if is_peft_version("<", "0.9.0"):
+                        lora_config_kwargs.pop("use_dora")
+            lora_config = LoraConfig(**lora_config_kwargs)
+
+            # adapter_name
+            if adapter_name is None:
+                adapter_name = get_adapter_name(self)
+
+            # <Unsafe code
+            # We can be sure that the following works as it just sets attention processors, lora layers and puts all in the same dtype
+            # Now we remove any existing hooks to `_pipeline`.
+
+            # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
+            # otherwise loading LoRA weights will lead to an error
+            is_model_cpu_offload, is_sequential_cpu_offload = self._optionally_disable_offloading(_pipeline)
+
+            peft_kwargs = {}
+            if is_peft_version(">=", "0.13.1"):
+                peft_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
+
+            inject_adapter_in_model(lora_config, self, adapter_name=adapter_name, **peft_kwargs)
+            incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name, **peft_kwargs)
+
+            warn_msg = ""
+            if incompatible_keys is not None:
+                # Check only for unexpected keys.
+                unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+                if unexpected_keys:
+                    lora_unexpected_keys = [k for k in unexpected_keys if "lora_" in k and adapter_name in k]
+                    if lora_unexpected_keys:
+                        warn_msg = (
+                            f"Loading adapter weights from state_dict led to unexpected keys found in the model:"
+                            f" {', '.join(lora_unexpected_keys)}. "
+                        )
+
+                # Filter missing keys specific to the current adapter.
+                missing_keys = getattr(incompatible_keys, "missing_keys", None)
+                if missing_keys:
+                    lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
+                    if lora_missing_keys:
+                        warn_msg += (
+                            f"Loading adapter weights from state_dict led to missing keys in the model:"
+                            f" {', '.join(lora_missing_keys)}."
+                        )
+
+            if warn_msg:
+                logger.warning(warn_msg)
+
+            # Offload back.
+            if is_model_cpu_offload:
+                _pipeline.enable_model_cpu_offload()
+            elif is_sequential_cpu_offload:
+                _pipeline.enable_sequential_cpu_offload()
+            # Unsafe code />
+
+    def save_lora_adapter(
+        self,
+        save_directory,
+        adapter_name: str = "default",
+        upcast_before_saving: bool = False,
+        safe_serialization: bool = True,
+        weight_name: Optional[str] = None,
+    ):
+        """
+        Save the LoRA parameters corresponding to the underlying model.
+
+        Arguments:
+            save_directory (`str` or `os.PathLike`):
+                Directory to save LoRA parameters to. Will be created if it doesn't exist.
+            adapter_name: (`str`, defaults to "default"): The name of the adapter to serialize. Useful when the
+                underlying model has multiple adapters loaded.
+            upcast_before_saving (`bool`, defaults to `False`):
+                Whether to cast the underlying model to `torch.float32` before serialization.
+            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
+                `DIFFUSERS_SAVE_MODE`.
+            safe_serialization (`bool`, *optional*, defaults to `True`):
+                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
+            weight_name: (`str`, *optional*, defaults to `None`): Name of the file to serialize the state dict with.
+        """
+        from peft.utils import get_peft_model_state_dict
+
+        from .lora_base import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
+
+        if adapter_name is None:
+            adapter_name = get_adapter_name(self)
+
+        if adapter_name not in getattr(self, "peft_config", {}):
+            raise ValueError(f"Adapter name {adapter_name} not found in the model.")
+
+        lora_layers_to_save = get_peft_model_state_dict(
+            self.to(dtype=torch.float32 if upcast_before_saving else None), adapter_name=adapter_name
+        )
+        if os.path.isfile(save_directory):
+            raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
+
+        if safe_serialization:
+
+            def save_function(weights, filename):
+                return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"})
+
+        else:
+            save_function = torch.save
+
+        os.makedirs(save_directory, exist_ok=True)
+
+        if weight_name is None:
+            if safe_serialization:
+                weight_name = LORA_WEIGHT_NAME_SAFE
+            else:
+                weight_name = LORA_WEIGHT_NAME
+
+        # TODO: we could consider saving the `peft_config` as well.
+        save_path = Path(save_directory, weight_name).as_posix()
+        save_function(lora_layers_to_save, save_path)
+        logger.info(f"Model weights saved in {save_path}")
+
     def set_adapters(
         self,
         adapter_names: Union[List[str], str],

src/diffusers/loaders/single_file_model.py

@@ -269,6 +269,7 @@ class FromOriginalModelMixin:
                 pretrained_model_name_or_path=default_pretrained_model_config_name,
                 subfolder=subfolder,
                 local_files_only=local_files_only,
+                token=token,
             )
             expected_kwargs, optional_kwargs = cls._get_signature_keys(cls)