update

2024-05-23 15:40:23 +00:00 · 2024-05-23 15:26:54 +00:00
281 changed files with 2838 additions and 14155 deletions
@@ -21,7 +21,7 @@ jobs:
      package: diffusers
      notebook_folder: diffusers_doc
      languages: en ko zh ja pt
-      custom_container: diffusers/diffusers-doc-builder
+
    secrets:
      token: ${{ secrets.HUGGINGFACE_PUSH }}
      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
@@ -20,4 +20,3 @@ jobs:
      install_libgl1: true
      package: diffusers
      languages: en ko zh ja pt
-      custom_container: diffusers/diffusers-doc-builder
@@ -1,85 +0,0 @@
-name: Mirror Community Pipeline
-
-on:
-  # Push changes on the main branch
-  push:
-    branches:
-      - main
-    paths:
-      - 'examples/community/**.py'
-
-    # And on tag creation (e.g. `v0.28.1`)
-    tags:
-      - '*'
-
-  # Manual trigger with ref input
-  workflow_dispatch:
-    inputs:
-      ref:
-        description: "Either 'main' or a tag ref"
-        required: true
-        default: 'main'
-
-jobs:
-  mirror_community_pipeline:
-    runs-on: ubuntu-latest
-    steps:
-      # Checkout to correct ref
-      #   If workflow dispatch
-      #     If ref is 'main', set:
-      #       CHECKOUT_REF=refs/heads/main
-      #       PATH_IN_REPO=main
-      #     Else it must be a tag. Set:
-      #       CHECKOUT_REF=refs/tags/{tag}
-      #       PATH_IN_REPO={tag}
-      #   If not workflow dispatch
-      #     If ref is 'refs/heads/main' => set 'main'
-      #     Else it must be a tag => set {tag}
-      - name: Set checkout_ref and path_in_repo
-        run: | 
-          if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
-            if [ -z "${{ github.event.inputs.ref }}" ]; then
-              echo "Error: Missing ref input"
-              exit 1
-            elif [ "${{ github.event.inputs.ref }}" == "main" ]; then
-              echo "CHECKOUT_REF=refs/heads/main" >> $GITHUB_ENV
-              echo "PATH_IN_REPO=main" >> $GITHUB_ENV
-            else
-              echo "CHECKOUT_REF=refs/tags/${{ github.event.inputs.ref }}" >> $GITHUB_ENV
-              echo "PATH_IN_REPO=${{ github.event.inputs.ref }}" >> $GITHUB_ENV
-            fi
-          elif [ "${{ github.ref }}" == "refs/heads/main" ]; then
-            echo "CHECKOUT_REF=${{ github.ref }}" >> $GITHUB_ENV
-            echo "PATH_IN_REPO=main" >> $GITHUB_ENV
-          else
-            # e.g. refs/tags/v0.28.1 -> v0.28.1
-            echo "CHECKOUT_REF=${{ github.ref }}" >> $GITHUB_ENV
-            echo "PATH_IN_REPO=${${{ github.ref }}#refs/tags/}" >> $GITHUB_ENV
-          fi
-      - uses: actions/checkout@v3
-        with:
-          ref: ${{ env.CHECKOUT_REF }}
-
-      # Setup + install dependencies
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          python -m pip install uv
-          uv pip install --upgrade huggingface_hub
-
-      # Check secret is set
-      - name: whoami
-        run: huggingface-cli whoami
-        env:
-            HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}
-
-      # Push to HF! (under subfolder based on checkout ref)
-      # https://huggingface.co/datasets/diffusers/community-pipelines-mirror
-      - name: Mirror community pipeline to HF
-        run: huggingface-cli upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
-        env:
-            PATH_IN_REPO: ${{ env.PATH_IN_REPO }}
-            HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}
@@ -59,7 +59,7 @@ jobs:
    runs-on: [single-gpu, nvidia-gpu, t4, ci]
    container:
      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0
    steps:
      - name: Checkout diffusers
        uses: actions/checkout@v3
@@ -111,21 +111,3 @@ jobs:
          -s -v \
          --make-reports=tests_${{ matrix.config.report }} \
          tests/lora/
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -s -v \
-          --make-reports=tests_models_lora_${{ matrix.config.report }} \
-          tests/models/ -k "lora"
-        
-    
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_${{ matrix.config.report }}_failures_short.txt
-        cat reports/tests_models_lora_${{ matrix.config.report }}_failures_short.txt
-    
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v2
-      with:
-        name: pr_${{ matrix.config.report }}_test_reports
-        path: reports
@@ -62,7 +62,7 @@ jobs:
    runs-on: [single-gpu, nvidia-gpu, t4, ci]
    container:
      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0
+      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0 --privileged
    steps:
      - name: Checkout diffusers
        uses: actions/checkout@v3
@@ -71,6 +71,12 @@ jobs:
      - name: NVIDIA-SMI
        run: |
          nvidia-smi
+      - name: Tailscale
+        uses: huggingface/tailscale-action@v1
+        with:
+          authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
+          slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
+          slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
      - name: Install dependencies
        run: |
          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
@@ -89,11 +95,18 @@ jobs:
            -s -v -k "not Flax and not Onnx" \
            --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
            tests/pipelines/${{ matrix.module }}
+      - name: Tailscale Wait
+        if: ${{ failure() || runner.debug == '1' }}
+        uses: huggingface/tailscale-action@v1
+        with:
+           waitForSSH: true
+           authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
      - name: Failure short reports
        if: ${{ failure() }}
        run: |
          cat reports/tests_pipeline_${{ matrix.module }}_cuda_stats.txt
          cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
+
      - name: Test suite reports artifacts
        if: ${{ always() }}
        uses: actions/upload-artifact@v2
@@ -189,17 +202,12 @@ jobs:
          -s -v -k "not Flax and not Onnx and not PEFTLoRALoading" \
          --make-reports=tests_peft_cuda \
          tests/lora/
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "lora and not Flax and not Onnx and not PEFTLoRALoading" \
-          --make-reports=tests_peft_cuda_models_lora \
-          tests/models/

    - name: Failure short reports
      if: ${{ failure() }}
      run: |
        cat reports/tests_peft_cuda_stats.txt
        cat reports/tests_peft_cuda_failures_short.txt
-        cat reports/tests_peft_cuda_models_lora_failures_short.txt

    - name: Test suite reports artifacts
      if: ${{ always() }}
@@ -1,73 +0,0 @@
-name: Check running SLOW tests from a PR (only GPU)
-
-on:
-  workflow_dispatch:
-    inputs:
-      docker_image:
-        default: 'diffusers/diffusers-pytorch-cuda'
-        description: 'Name of the Docker image'
-        required: true
-      branch: 
-        description: 'PR Branch to test on'
-        required: true
-      test:
-        description: 'Tests to run (e.g.: `tests/models`).'
-        required: true
-
-env:
-  DIFFUSERS_IS_CI: yes
-  IS_GITHUB_CI: "1"
-  HF_HOME: /mnt/cache
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  PYTEST_TIMEOUT: 600
-  RUN_SLOW: yes
-
-jobs:
-  run_tests:
-    name: "Run a test on our runner from a PR"
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
-    container:
-      image: ${{ github.event.inputs.docker_image }}
-      options: --gpus 0 --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-
-    steps:
-      - name: Validate test files input
-        id: validate_test_files
-        env: 
-          PY_TEST: ${{ github.event.inputs.test }}
-        run: |
-          if [[ ! "$PY_TEST" =~ ^tests/ ]]; then
-            echo "Error: The input string must start with 'tests/'."
-            exit 1
-          fi
-          
-          if [[ ! "$PY_TEST" =~ ^tests/(models|pipelines) ]]; then
-            echo "Error: The input string must contain either 'models' or 'pipelines' after 'tests/'."
-            exit 1
-          fi
-          
-          if [[ "$PY_TEST" == *";"* ]]; then
-            echo "Error: The input string must not contain ';'."
-            exit 1
-          fi
-          echo "$PY_TEST"
-
-      - name: Checkout PR branch
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.branch }}
-          repository: ${{ github.event.pull_request.head.repo.full_name }}
-
-
-      - name: Install pytest 
-        run: | 
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install peft
-      
-      - name: Run tests
-        env: 
-            PY_TEST: ${{ github.event.inputs.test }}
-        run: |
-          pytest "$PY_TEST"
@@ -25,7 +25,7 @@ jobs:
    runs-on: [single-gpu, nvidia-gpu, "${{ github.event.inputs.runner_type }}", ci]
    container:
      image: ${{ github.event.inputs.docker_image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0 --privileged
+      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/

    steps:
      - name: Checkout diffusers
@@ -38,7 +38,7 @@ jobs:
          nvidia-smi

      - name: Tailscale # In order to be able to SSH when a test fails
-        uses: huggingface/tailscale-action@main
+        uses: huggingface/tailscale-action@v1
        with:
          authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
          slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
@@ -77,7 +77,7 @@ Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggi

 ## Quickstart

-Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 25.000+ checkpoints):
+Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 22000+ checkpoints):

 ```python
 from diffusers import DiffusionPipeline
@@ -219,7 +219,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 - https://github.com/deep-floyd/IF
 - https://github.com/bentoml/BentoML
 - https://github.com/bmaltais/kohya_ss
- +11.000 other amazing GitHub repositories 💪
+- +9000 other amazing GitHub repositories 💪

 Thank you for using us ❤️.

@@ -18,8 +18,6 @@ RUN apt install -y bash \
                   python3.10 \
                   python3-pip \
                   libgl1 \
-                   zip \
-                   wget \
                   python3.10-venv && \
    rm -rf /var/lib/apt/lists

@@ -29,8 +29,10 @@
    title: Load community pipelines and components
  - local: using-diffusers/schedulers
    title: Load schedulers and models
+  - local: using-diffusers/using_safetensors
+    title: Load safetensors
  - local: using-diffusers/other-formats
-    title: Model files and layouts
+    title: Load different Stable Diffusion formats
  - local: using-diffusers/loading_adapters
    title: Load adapters
  - local: using-diffusers/push_to_hub
@@ -57,8 +59,6 @@
    title: Distributed inference with multiple GPUs
  - local: using-diffusers/merge_loras
    title: Merge LoRAs
-  - local: using-diffusers/scheduler_features
-    title: Scheduler features
  - local: using-diffusers/callback
    title: Pipeline callbacks
  - local: using-diffusers/reusing_seeds
@@ -68,10 +68,6 @@
  - local: using-diffusers/weighted_prompts
    title: Prompt techniques
  title: Inference techniques
- sections:
-  - local: advanced_inference/outpaint
-    title: Outpainting
-  title: Advanced inference
 - sections:
  - local: using-diffusers/sdxl
    title: Stable Diffusion XL
@@ -97,8 +93,6 @@
    title: Trajectory Consistency Distillation-LoRA
  - local: using-diffusers/svd
    title: Stable Video Diffusion
-  - local: using-diffusers/marigold_usage
-    title: Marigold Computer Vision
  title: Specific pipeline examples
 - sections:
  - local: training/overview
@@ -237,19 +231,13 @@
    - local: api/models/consistency_decoder_vae
      title: ConsistencyDecoderVAE
    - local: api/models/transformer2d
-      title: Transformer2DModel
-    - local: api/models/pixart_transformer2d
-      title: PixArtTransformer2DModel
-    - local: api/models/dit_transformer2d
-      title: DiTTransformer2DModel
-    - local: api/models/hunyuan_transformer2d
-      title: HunyuanDiT2DModel
+      title: Transformer2D
    - local: api/models/transformer_temporal
-      title: TransformerTemporalModel
+      title: Transformer Temporal
    - local: api/models/prior_transformer
-      title: PriorTransformer
+      title: Prior Transformer
    - local: api/models/controlnet
-      title: ControlNetModel
+      title: ControlNet
    title: Models
    isExpanded: false
  - sections:
@@ -291,8 +279,6 @@
      title: DiffEdit
    - local: api/pipelines/dit
      title: DiT
-    - local: api/pipelines/hunyuandit
-      title: Hunyuan-DiT
    - local: api/pipelines/i2vgenxl
      title: I2VGen-XL
    - local: api/pipelines/pix2pix
@@ -309,8 +295,6 @@
      title: Latent Diffusion
    - local: api/pipelines/ledits_pp
      title: LEDITS++
-    - local: api/pipelines/marigold
-      title: Marigold
    - local: api/pipelines/panorama
      title: MultiDiffusion
    - local: api/pipelines/musicldm
@@ -1,231 +0,0 @@
-<!--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.
-->
-
-# Outpainting
-
-Outpainting extends an image beyond its original boundaries, allowing you to add, replace, or modify visual elements in an image while preserving the original image. Like [inpainting](../using-diffusers/inpaint), you want to fill the white area (in this case, the area outside of the original image) with new visual elements while keeping the original image (represented by a mask of black pixels). There are a couple of ways to outpaint, such as with a [ControlNet](https://hf.co/blog/OzzyGT/outpainting-controlnet) or with [Differential Diffusion](https://hf.co/blog/OzzyGT/outpainting-differential-diffusion).
-
-This guide will show you how to outpaint with an inpainting model, ControlNet, and a ZoeDepth estimator.
-
-Before you begin, make sure you have the [controlnet_aux](https://github.com/huggingface/controlnet_aux) library installed so you can use the ZoeDepth estimator.
-
-```py
-!pip install -q controlnet_aux
-```
-
-## Image preparation
-
-Start by picking an image to outpaint with and remove the background with a Space like [BRIA-RMBG-1.4](https://hf.co/spaces/briaai/BRIA-RMBG-1.4).
-
-<iframe
-	src="https://briaai-bria-rmbg-1-4.hf.space"
-	frameborder="0"
-	width="850"
-	height="450"
-></iframe>
-
-For example, remove the background from this image of a pair of shoes.
-
-<div class="flex flex-row gap-4">
-  <div class="flex-1">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/original-jordan.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
-  </div>
-  <div class="flex-1">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/no-background-jordan.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">background removed</figcaption>
-  </div>
-</div>
-
-[Stable Diffusion XL (SDXL)](../using-diffusers/sdxl) models work best with 1024x1024 images, but you can resize the image to any size as long as your hardware has enough memory to support it. The transparent background in the image should also be replaced with a white background. Create a function (like the one below) that scales and pastes the image onto a white background.
-
-```py
-import random
-
-import requests
-import torch
-from controlnet_aux import ZoeDetector
-from PIL import Image, ImageOps
-
-from diffusers import (
-    AutoencoderKL,
-    ControlNetModel,
-    StableDiffusionXLControlNetPipeline,
-    StableDiffusionXLInpaintPipeline,
-)
-
-def scale_and_paste(original_image):
-    aspect_ratio = original_image.width / original_image.height
-
-    if original_image.width > original_image.height:
-        new_width = 1024
-        new_height = round(new_width / aspect_ratio)
-    else:
-        new_height = 1024
-        new_width = round(new_height * aspect_ratio)
-
-    resized_original = original_image.resize((new_width, new_height), Image.LANCZOS)
-    white_background = Image.new("RGBA", (1024, 1024), "white")
-    x = (1024 - new_width) // 2
-    y = (1024 - new_height) // 2
-    white_background.paste(resized_original, (x, y), resized_original)
-
-    return resized_original, white_background
-
-original_image = Image.open(
-    requests.get(
-        "https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/no-background-jordan.png",
-        stream=True,
-    ).raw
-).convert("RGBA")
-resized_img, white_bg_image = scale_and_paste(original_image)
-```
-
-To avoid adding unwanted extra details, use the ZoeDepth estimator to provide additional guidance during generation and to ensure the shoes remain consistent with the original image.
-
-```py
-zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
-image_zoe = zoe(white_bg_image, detect_resolution=512, image_resolution=1024)
-image_zoe
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/zoedepth-jordan.png"/>
-</div>
-
-## Outpaint
-
-Once your image is ready, you can generate content in the white area around the shoes with [controlnet-inpaint-dreamer-sdxl](https://hf.co/destitech/controlnet-inpaint-dreamer-sdxl), a SDXL ControlNet trained for inpainting.
-
-Load the inpainting ControlNet, ZoeDepth model, VAE and pass them to the [`StableDiffusionXLControlNetPipeline`]. Then you can create an optional `generate_image` function (for convenience) to outpaint an initial image.
-
-```py
-controlnets = [
-    ControlNetModel.from_pretrained(
-        "destitech/controlnet-inpaint-dreamer-sdxl", torch_dtype=torch.float16, variant="fp16"
-    ),
-    ControlNetModel.from_pretrained(
-        "diffusers/controlnet-zoe-depth-sdxl-1.0", torch_dtype=torch.float16
-    ),
-]
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to("cuda")
-pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
-    "SG161222/RealVisXL_V4.0", torch_dtype=torch.float16, variant="fp16", controlnet=controlnets, vae=vae
-).to("cuda")
-
-def generate_image(prompt, negative_prompt, inpaint_image, zoe_image, seed: int = None):
-    if seed is None:
-        seed = random.randint(0, 2**32 - 1)
-
-    generator = torch.Generator(device="cpu").manual_seed(seed)
-
-    image = pipeline(
-        prompt,
-        negative_prompt=negative_prompt,
-        image=[inpaint_image, zoe_image],
-        guidance_scale=6.5,
-        num_inference_steps=25,
-        generator=generator,
-        controlnet_conditioning_scale=[0.5, 0.8],
-        control_guidance_end=[0.9, 0.6],
-    ).images[0]
-
-    return image
-
-prompt = "nike air jordans on a basketball court"
-negative_prompt = ""
-
-temp_image = generate_image(prompt, negative_prompt, white_bg_image, image_zoe, 908097)
-```
-
-Paste the original image over the initial outpainted image. You'll improve the outpainted background in a later step.
-
-```py
-x = (1024 - resized_img.width) // 2
-y = (1024 - resized_img.height) // 2
-temp_image.paste(resized_img, (x, y), resized_img)
-temp_image
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/initial-outpaint.png"/>
-</div>
-
-> [!TIP]
-> Now is a good time to free up some memory if you're running low!
->
-> ```py
-> pipeline=None
-> torch.cuda.empty_cache()
-> ```
-
-Now that you have an initial outpainted image, load the [`StableDiffusionXLInpaintPipeline`] with the [RealVisXL](https://hf.co/SG161222/RealVisXL_V4.0) model to generate the final outpainted image with better quality.
-
-```py
-pipeline = StableDiffusionXLInpaintPipeline.from_pretrained(
-    "OzzyGT/RealVisXL_V4.0_inpainting",
-    torch_dtype=torch.float16,
-    variant="fp16",
-    vae=vae,
-).to("cuda")
-```
-
-Prepare a mask for the final outpainted image. To create a more natural transition between the original image and the outpainted background, blur the mask to help it blend better.
-
-```py
-mask = Image.new("L", temp_image.size)
-mask.paste(resized_img.split()[3], (x, y))
-mask = ImageOps.invert(mask)
-final_mask = mask.point(lambda p: p > 128 and 255)
-mask_blurred = pipeline.mask_processor.blur(final_mask, blur_factor=20)
-mask_blurred
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/blurred-mask.png"/>
-</div>
-
-Create a better prompt and pass it to the `generate_outpaint` function to generate the final outpainted image. Again, paste the original image over the final outpainted background.
-
-```py
-def generate_outpaint(prompt, negative_prompt, image, mask, seed: int = None):
-    if seed is None:
-        seed = random.randint(0, 2**32 - 1)
-
-    generator = torch.Generator(device="cpu").manual_seed(seed)
-
-    image = pipeline(
-        prompt,
-        negative_prompt=negative_prompt,
-        image=image,
-        mask_image=mask,
-        guidance_scale=10.0,
-        strength=0.8,
-        num_inference_steps=30,
-        generator=generator,
-    ).images[0]
-
-    return image
-
-prompt = "high quality photo of nike air jordans on a basketball court, highly detailed"
-negative_prompt = ""
-
-final_image = generate_outpaint(prompt, negative_prompt, temp_image, mask_blurred, 7688778)
-x = (1024 - resized_img.width) // 2
-y = (1024 - resized_img.height) // 2
-final_image.paste(resized_img, (x, y), resized_img)
-final_image
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/final-outpaint.png"/>
-</div>
@@ -10,17 +10,13 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->

-# Single files
+# Loading Pipelines and Models via `from_single_file`

-The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
+The `from_single_file` method allows you to load supported pipelines using a single checkpoint file as opposed to the folder format used by Diffusers. This is useful if you are working with many of the Stable Diffusion Web UI's (such as A1111) that extensively rely on a single file to distribute all the components of a diffusion model.

-* a model stored in a single file, which is useful if you're working with models from the diffusion ecosystem, like Automatic1111, and commonly rely on a single-file layout to store and share models
-* a model stored in their originally distributed layout, which is useful if you're working with models finetuned with other services, and want to load it directly into Diffusers model objects and pipelines
+The `from_single_file` method also supports loading models in their originally distributed format. This means that supported models that have been finetuned with other services can be loaded directly into supported Diffusers model objects and pipelines.

-> [!TIP]
-> Read the [Model files and layouts](../../using-diffusers/other-formats) guide to learn more about the Diffusers-multifolder layout versus the single-file layout, and how to load models stored in these different layouts.
-
-## Supported pipelines
+## Pipelines that currently support `from_single_file` loading

 - [`StableDiffusionPipeline`]
 - [`StableDiffusionImg2ImgPipeline`]
@@ -43,13 +39,206 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 - [`LEditsPPPipelineStableDiffusionXL`]
 - [`PIAPipeline`]

-## Supported models
+## Models that currently support `from_single_file` loading

 - [`UNet2DConditionModel`]
 - [`StableCascadeUNet`]
 - [`AutoencoderKL`]
 - [`ControlNetModel`]

+## Usage Examples
+
+## Loading a Pipeline using `from_single_file`
+
+```python
+from diffusers import StableDiffusionXLPipeline
+
+ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
+pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path)
+```
+
+## Setting components in a Pipeline using `from_single_file`
+
+Swap components of the pipeline by passing them directly to the `from_single_file` method. e.g If you would like use a different scheduler than the pipeline default.
+
+```python
+from diffusers import StableDiffusionXLPipeline, DDIMScheduler
+
+ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
+
+scheduler = DDIMScheduler()
+pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path, scheduler=scheduler)
+
+```
+
+```python
+from diffusers import StableDiffusionPipeline, ControlNetModel
+
+ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
+
+controlnet = ControlNetModel.from_pretrained("https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors")
+pipe = StableDiffusionPipeline.from_single_file(ckpt_path, controlnet=controlnet)
+
+```
+
+## Loading a Model using `from_single_file`
+
+```python
+from diffusers import StableCascadeUNet
+
+ckpt_path = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite.safetensors"
+model = StableCascadeUNet.from_single_file(ckpt_path)
+
+```
+
+## Using a Diffusers model repository to configure single file loading
+
+Under the hood, `from_single_file` will try to determine a model repository to use to configure the components of the pipeline. You can also pass in a repository id to the `config` argument of the `from_single_file` method to explicitly set the repository to use.
+
+```python
+from diffusers import StableDiffusionXLPipeline
+
+ckpt_path = "https://huggingface.co/segmind/SSD-1B/blob/main/SSD-1B.safetensors"
+repo_id = "segmind/SSD-1B"
+
+pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path, config=repo_id)
+
+```
+
+## Override configuration options when using single file loading
+
+Override the default model or pipeline configuration options when using `from_single_file` by passing in the relevant arguments directly to the `from_single_file` method. Any argument that is supported by the model or pipeline class can be configured in this way:
+
+```python
+from diffusers import StableDiffusionXLInstructPix2PixPipeline
+
+ckpt_path = "https://huggingface.co/stabilityai/cosxl/blob/main/cosxl_edit.safetensors"
+pipe = StableDiffusionXLInstructPix2PixPipeline.from_single_file(ckpt_path, config="diffusers/sdxl-instructpix2pix-768", is_cosxl_edit=True)
+
+```
+
+```python
+from diffusers import UNet2DConditionModel
+
+ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
+model = UNet2DConditionModel.from_single_file(ckpt_path, upcast_attention=True)
+
+```
+
+In the example above, since we explicitly passed `repo_id="segmind/SSD-1B"`, it will use this [configuration file](https://huggingface.co/segmind/SSD-1B/blob/main/unet/config.json) from the "unet" subfolder in `"segmind/SSD-1B"` to configure the unet component included in the checkpoint; Similarly, it will use the `config.json` file from `"vae"` subfolder to configure the vae model, `config.json` file from text_encoder folder to configure text_encoder and so on.
+
+Note that most of the time you do not need to explicitly a `config` argument, `from_single_file` will automatically map the checkpoint to a repo id (we will discuss this in more details in next section). However, this can be useful in cases where model components might have been changed from what was originally distributed or in cases where a checkpoint file might not have the necessary metadata to correctly determine the configuration to use for the pipeline.
+
+<Tip>
+
+To learn more about how to load single file weights, see the [Load different Stable Diffusion formats](../../using-diffusers/other-formats) loading guide.
+
+</Tip>
+
+## Working with local files
+
+As of `diffusers>=0.28.0` the `from_single_file` method will attempt to configure a pipeline or model by first inferring the model type from the checkpoint file and then using the model type to determine the appropriate model repo configuration to use from the Hugging Face Hub. For example, any single file checkpoint based on the Stable Diffusion XL base model will use the [`stabilityai/stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) model repo to configure the pipeline.
+
+If you are working in an environment with restricted internet access, it is recommended to download the config files and checkpoints for the model to your preferred directory and pass the local paths to the `pretrained_model_link_or_path` and `config` arguments of the `from_single_file` method.
+
+```python
+from huggingface_hub import hf_hub_download, snapshot_download
+
+my_local_checkpoint_path = hf_hub_download(
+    repo_id="segmind/SSD-1B",
+    filename="SSD-1B.safetensors"
+)
+
+my_local_config_path = snapshot_download(
+    repo_id="segmind/SSD-1B",
+    allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
+)
+
+pipe = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
+
+```
+
+By default this will download the checkpoints and config files to the [Hugging Face Hub cache directory](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache). You can also specify a local directory to download the files to by passing the `local_dir` argument to the `hf_hub_download` and `snapshot_download` functions.
+
+```python
+from huggingface_hub import hf_hub_download, snapshot_download
+
+my_local_checkpoint_path = hf_hub_download(
+    repo_id="segmind/SSD-1B",
+    filename="SSD-1B.safetensors"
+    local_dir="my_local_checkpoints"
+)
+
+my_local_config_path = snapshot_download(
+    repo_id="segmind/SSD-1B",
+    allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
+    local_dir="my_local_config"
+)
+
+pipe = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
+
+```
+
+## Working with local files on file systems that do not support symlinking
+
+By default the `from_single_file` method relies on the `huggingface_hub` caching mechanism to fetch and store checkpoints and config files for models and pipelines. If you are working with a file system that does not support symlinking, it is recommended that you first download the checkpoint file to a local directory and disable symlinking by passing the `local_dir_use_symlink=False` argument to the `hf_hub_download` and `snapshot_download` functions.
+
+```python
+from huggingface_hub import hf_hub_download, snapshot_download
+
+my_local_checkpoint_path = hf_hub_download(
+    repo_id="segmind/SSD-1B",
+    filename="SSD-1B.safetensors"
+    local_dir="my_local_checkpoints",
+    local_dir_use_symlinks=False
+)
+print("My local checkpoint: ", my_local_checkpoint_path)
+
+my_local_config_path = snapshot_download(
+    repo_id="segmind/SSD-1B",
+    allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
+    local_dir_use_symlinks=False,
+)
+print("My local config: ", my_local_config_path)
+
+```
+
+Then pass the local paths to the `pretrained_model_link_or_path` and `config` arguments of the `from_single_file` method.
+
+```python
+pipe = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
+
+```
+
+<Tip>
+Disabling symlinking means that the `huggingface_hub` caching mechanism has no way to determine whether a file has already been downloaded to the local directory. This means that the `hf_hub_download` and `snapshot_download` functions will download files to the local directory each time they are executed. If you are disabling symlinking, it is recommended that you separate the model download and loading steps to avoid downloading the same file multiple times.
+
+</Tip>
+
+## Using the original configuration file of a model
+
+If you would like to configure the parameters of the model components in the pipeline using the orignal YAML configuration file, you can pass a local path or url to the original configuration file to the `original_config` argument of the `from_single_file` method.
+
+```python
+from diffusers import StableDiffusionXLPipeline
+
+ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
+repo_id = "stabilityai/stable-diffusion-xl-base-1.0"
+original_config = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
+
+pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path, original_config=original_config)
+```
+
+In the example above, the `original_config` file is only used to configure the parameters of the individual model components of the pipeline. For example it will be used to configure parameters such as the `in_channels` of the `vae` model and `unet` model. It is not used to determine the type of component objects in the pipeline.
+
+
+<Tip>
+When using `original_config` with local_files_only=True`, Diffusers will attempt to infer the components based on the type signatures of pipeline class, rather than attempting to fetch the pipeline config from the Hugging Face Hub. This is to prevent backwards breaking changes in existing code that might not be able to connect to the internet to fetch the necessary pipeline config files.
+
+This is not as reliable as providing a path to a local config repo and might lead to errors when configuring the pipeline. To avoid this, please run the pipeline with `local_files_only=False` once to download the appropriate pipeline config files to the local cache.
+</Tip>
+
+
 ## FromSingleFileMixin

 [[autodoc]] loaders.single_file.FromSingleFileMixin
@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->

-# ControlNetModel
+# ControlNet

 The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.

@@ -1,19 +0,0 @@
-<!--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.
-->
-
-# DiTTransformer2DModel
-
-A Transformer model for image-like data from [DiT](https://huggingface.co/papers/2212.09748).
-
-## DiTTransformer2DModel
-
-[[autodoc]] DiTTransformer2DModel
@@ -1,20 +0,0 @@
-<!--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.
-->
-
-# HunyuanDiT2DModel
-
-A Diffusion Transformer model for 2D data from [Hunyuan-DiT](https://github.com/Tencent/HunyuanDiT).
-
-## HunyuanDiT2DModel
-
-[[autodoc]] HunyuanDiT2DModel
-
@@ -1,19 +0,0 @@
-<!--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.
-->
-
-# PixArtTransformer2DModel
-
-A Transformer model for image-like data from [PixArt-Alpha](https://huggingface.co/papers/2310.00426) and [PixArt-Sigma](https://huggingface.co/papers/2403.04692). 
-
-## PixArtTransformer2DModel
-
-[[autodoc]] PixArtTransformer2DModel
@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->

-# PriorTransformer
+# Prior Transformer

 The Prior Transformer was originally introduced in [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) by Ramesh et al. It is used to predict CLIP image embeddings from CLIP text embeddings; image embeddings are predicted through a denoising diffusion process.

@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->

-# Transformer2DModel
+# Transformer2D

 A Transformer model for image-like data from [CompVis](https://huggingface.co/CompVis) that is based on the [Vision Transformer](https://huggingface.co/papers/2010.11929) introduced by Dosovitskiy et al. The [`Transformer2DModel`] accepts discrete (classes of vector embeddings) or continuous (actual embeddings) inputs.

@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->

-# TransformerTemporalModel
+# Transformer Temporal

 A Transformer model for video-like data.

@@ -24,4 +24,4 @@ The abstract from the paper is:

 ## VQEncoderOutput

-[[autodoc]] models.autoencoders.vq_model.VQEncoderOutput
+[[autodoc]] models.vq_model.VQEncoderOutput
@@ -16,7 +16,7 @@ aMUSEd was introduced in [aMUSEd: An Open MUSE Reproduction](https://huggingface

 Amused is a lightweight text to image model based off of the [MUSE](https://arxiv.org/abs/2301.00704) architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.

-Amused is a vqvae token based transformer that can generate an image in fewer forward passes than many diffusion models. In contrast with muse, it uses the smaller text encoder CLIP-L/14 instead of t5-xxl. Due to its small parameter count and few forward pass generation process, amused can generate many images quickly. This benefit is seen particularly at larger batch sizes.
+Amused is a vqvae token based transformer that can generate an image in fewer forward passes than many diffusion models. In contrast with muse, it uses the smaller text encoder CLIP-L/14 instead of t5-xxl. Due to its small parameter count and few forward pass generation process, amused can generate many images quickly. This benefit is seen particularly at larger batch sizes. 

 The abstract from the paper is:

@@ -165,7 +165,7 @@ from PIL import Image
 adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
 # load SD 1.5 based finetuned model
 model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
-pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
+pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to("cuda")
 scheduler = DDIMScheduler.from_pretrained(
    model_id,
    subfolder="scheduler",
@@ -1,95 +0,0 @@
-<!--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.
-->
-
-# Hunyuan-DiT
-![chinese elements understanding](https://github.com/gnobitab/diffusers-hunyuan/assets/1157982/39b99036-c3cb-4f16-bb1a-40ec25eda573)
-
-[Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://arxiv.org/abs/2405.08748) from Tencent Hunyuan.
-
-The abstract from the paper is:
-
-*We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully design the transformer structure, text encoder, and positional encoding. We also build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images. Finally, Hunyuan-DiT can perform multi-turn multimodal dialogue with users, generating and refining images according to the context. Through our holistic human evaluation protocol with more than 50 professional human evaluators, Hunyuan-DiT sets a new state-of-the-art in Chinese-to-image generation compared with other open-source models.*
-
-
-You can find the original codebase at [Tencent/HunyuanDiT](https://github.com/Tencent/HunyuanDiT) and all the available checkpoints at [Tencent-Hunyuan](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT).
-
-**Highlights**: HunyuanDiT supports Chinese/English-to-image, multi-resolution generation.
-
-HunyuanDiT has the following components:
-* It uses a diffusion transformer as the backbone
-* It combines two text encoders, a bilingual CLIP and a multilingual T5 encoder
-
-<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>
-
-## Optimization
-
-You can optimize the pipeline's runtime and memory consumption with torch.compile and feed-forward chunking. To learn about other optimization methods, check out the [Speed up inference](../../optimization/fp16) and [Reduce memory usage](../../optimization/memory) guides. 
-
-### Inference
-
-Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
-
-First, load the pipeline:
-
-```python
-from diffusers import HunyuanDiTPipeline
-import torch 
-
-pipeline = HunyuanDiTPipeline.from_pretrained(
-	"Tencent-Hunyuan/HunyuanDiT-Diffusers", torch_dtype=torch.float16
-).to("cuda")
-```
-
-Then change the memory layout of the pipelines `transformer` and `vae` components to `torch.channels-last`:
-
-```python
-pipeline.transformer.to(memory_format=torch.channels_last)
-pipeline.vae.to(memory_format=torch.channels_last)
-```
-
-Finally, compile the components and run inference:
-
-```python
-pipeline.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
-pipeline.vae.decode = torch.compile(pipeline.vae.decode, mode="max-autotune", fullgraph=True)
-
-image = pipeline(prompt="一个宇航员在骑马").images[0]
-```
-
-The [benchmark](https://gist.github.com/sayakpaul/29d3a14905cfcbf611fe71ebd22e9b23) results on a 80GB A100 machine are:
-
-```bash
-With torch.compile(): Average inference time: 12.470 seconds.
-Without torch.compile(): Average inference time: 20.570 seconds.
-```
-
-### Memory optimization
-
-By loading the T5 text encoder in 8 bits, you can run the pipeline in just under 6 GBs of GPU VRAM. Refer to [this script](https://gist.github.com/sayakpaul/3154605f6af05b98a41081aaba5ca43e) for details. 
-
-Furthermore, you can use the [`~HunyuanDiT2DModel.enable_forward_chunking`] method to reduce memory usage. Feed-forward chunking runs the feed-forward layers in a transformer block in a loop instead of all at once. This gives you a trade-off between memory consumption and inference runtime.
-
-```diff
-+ pipeline.transformer.enable_forward_chunking(chunk_size=1, dim=1)
-```
-
-
-## HunyuanDiTPipeline
-
-[[autodoc]] HunyuanDiTPipeline
-	- all
-	- __call__
-	
@@ -47,7 +47,6 @@ Sample output with I2VGenXL:
 * Unlike SVD, it additionally accepts text prompts as inputs.
 * It can generate higher resolution videos.
 * When using the [`DDIMScheduler`] (which is default for this pipeline), less than 50 steps for inference leads to bad results.
-* This implementation is 1-stage variant of I2VGenXL. The main figure in the [I2VGen-XL](https://arxiv.org/abs/2311.04145) paper shows a 2-stage variant, however, 1-stage variant works well. See [this discussion](https://github.com/huggingface/diffusers/discussions/7952) for more details.

 ## I2VGenXLPipeline
 [[autodoc]] I2VGenXLPipeline
@@ -11,12 +11,12 @@ specific language governing permissions and limitations under the License.

 Kandinsky 3 is created by [Vladimir Arkhipkin](https://github.com/oriBetelgeuse),[Anastasia Maltseva](https://github.com/NastyaMittseva),[Igor Pavlov](https://github.com/boomb0om),[Andrei Filatov](https://github.com/anvilarth),[Arseniy Shakhmatov](https://github.com/cene555),[Andrey Kuznetsov](https://github.com/kuznetsoffandrey),[Denis Dimitrov](https://github.com/denndimitrov), [Zein Shaheen](https://github.com/zeinsh)

-The description from it's Github page:
+The description from it's Github page: 

 *Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.*

 Its architecture includes 3 main components:
-1. [FLAN-UL2](https://huggingface.co/google/flan-ul2), which is an encoder decoder model based on the T5 architecture.
+1. [FLAN-UL2](https://huggingface.co/google/flan-ul2), which is an encoder decoder model based on the T5 architecture. 
 2. New U-Net architecture featuring BigGAN-deep blocks doubles depth while maintaining the same number of parameters.
 3. Sber-MoVQGAN is a decoder proven to have superior results in image restoration.

@@ -25,11 +25,11 @@ You can find additional information about LEDITS++ on the [project page](https:/
 </Tip>

 <Tip warning={true}>
-Due to some backward compatability issues with the current diffusers implementation of [`~schedulers.DPMSolverMultistepScheduler`] this implementation of LEdits++ can no longer guarantee perfect inversion.
-This issue is unlikely to have any noticeable effects on applied use-cases. However, we provide an alternative implementation that guarantees perfect inversion in a dedicated [GitHub repo](https://github.com/ml-research/ledits_pp).
+Due to some backward compatability issues with the current diffusers implementation of [`~schedulers.DPMSolverMultistepScheduler`] this implementation of LEdits++ can no longer guarantee perfect inversion. 
+This issue is unlikely to have any noticeable effects on applied use-cases. However, we provide an alternative implementation that guarantees perfect inversion in a dedicated [GitHub repo](https://github.com/ml-research/ledits_pp). 
 </Tip>

-We provide two distinct pipelines based on different pre-trained models.
+We provide two distinct pipelines based on different pre-trained models. 

 ## LEditsPPPipelineStableDiffusion
 [[autodoc]] pipelines.ledits_pp.LEditsPPPipelineStableDiffusion
@@ -1,76 +0,0 @@
-<!--Copyright 2024 Marigold authors and The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
-->
-
-# Marigold Pipelines for Computer Vision Tasks
-
-![marigold](https://marigoldmonodepth.github.io/images/teaser_collage_compressed.jpg)
-
-Marigold was proposed in [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://huggingface.co/papers/2312.02145), a CVPR 2024 Oral paper by [Bingxin Ke](http://www.kebingxin.com/), [Anton Obukhov](https://www.obukhov.ai/), [Shengyu Huang](https://shengyuh.github.io/), [Nando Metzger](https://nandometzger.github.io/), [Rodrigo Caye Daudt](https://rcdaudt.github.io/), and [Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en).
-The idea is to repurpose the rich generative prior of Text-to-Image Latent Diffusion Models (LDMs) for traditional computer vision tasks.
-Initially, this idea was explored to fine-tune Stable Diffusion for Monocular Depth Estimation, as shown in the teaser above.
-Later,
- [Tianfu Wang](https://tianfwang.github.io/) trained the first Latent Consistency Model (LCM) of Marigold, which unlocked fast single-step inference;
- [Kevin Qu](https://www.linkedin.com/in/kevin-qu-b3417621b/?locale=en_US) extended the approach to Surface Normals Estimation;
- [Anton Obukhov](https://www.obukhov.ai/) contributed the pipelines and documentation into diffusers (enabled and supported by [YiYi Xu](https://yiyixuxu.github.io/) and [Sayak Paul](https://sayak.dev/)).
-
-The abstract from the paper is:
-
-*Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the growth in model capacity, from relatively modest CNNs to large Transformer architectures. Still, monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout, since their knowledge of the visual world is restricted by the data seen during training, and challenged by zero-shot generalization to new domains. This motivates us to explore whether the extensive priors captured in recent generative diffusion models can enable better, more generalizable depth estimation. We introduce Marigold, a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic training data. It delivers state-of-the-art performance across a wide range of datasets, including over 20% performance gains in specific cases. Project page: https://marigoldmonodepth.github.io.*
-
-## Available Pipelines
-
-Each pipeline supports one Computer Vision task, which takes an input RGB image as input and produces a *prediction* of the modality of interest, such as a depth map of the input image.
-Currently, the following tasks are implemented:
-
-| Pipeline                                                                                                                                    | Predicted Modalities                                                                                             |                                                                       Demos                                                                        |
-|---------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------:|
-| [MarigoldDepthPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py)     | [Depth](https://en.wikipedia.org/wiki/Depth_map), [Disparity](https://en.wikipedia.org/wiki/Binocular_disparity) | [Fast Demo (LCM)](https://huggingface.co/spaces/prs-eth/marigold-lcm), [Slow Original Demo (DDIM)](https://huggingface.co/spaces/prs-eth/marigold) |
-| [MarigoldNormalsPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_normals.py) | [Surface normals](https://en.wikipedia.org/wiki/Normal_mapping)                                                  |                                   [Fast Demo (LCM)](https://huggingface.co/spaces/prs-eth/marigold-normals-lcm)                                    |
-
-
-## Available Checkpoints
-
-The original checkpoints can be found under the [PRS-ETH](https://huggingface.co/prs-eth/) Hugging Face organization.
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section [here](../../using-diffusers/svd#reduce-memory-usage).
-
-</Tip>
-
-<Tip warning={true}>
-
-Marigold pipelines were designed and tested only with `DDIMScheduler` and `LCMScheduler`.
-Depending on the scheduler, the number of inference steps required to get reliable predictions varies, and there is no universal value that works best across schedulers.
-Because of that, the default value of `num_inference_steps` in the `__call__` method of the pipeline is set to `None` (see the API reference).
-Unless set explicitly, its value will be taken from the checkpoint configuration `model_index.json`.
-This is done to ensure high-quality predictions when calling the pipeline with just the `image` argument.
-
-</Tip>
-
-See also Marigold [usage examples](marigold_usage).
-
-## MarigoldDepthPipeline
-[[autodoc]] MarigoldDepthPipeline
-	- all
-	- __call__
-
-## MarigoldNormalsPipeline
-[[autodoc]] MarigoldNormalsPipeline
-	- all
-	- __call__
-
-## MarigoldDepthOutput
-[[autodoc]] pipelines.marigold.pipeline_marigold_depth.MarigoldDepthOutput
-
-## MarigoldNormalsOutput
-[[autodoc]] pipelines.marigold.pipeline_marigold_normals.MarigoldNormalsOutput
@@ -37,7 +37,7 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.m

 ## Inference with under 8GB GPU VRAM

-Run the [`PixArtAlphaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example.
+Run the [`PixArtAlphaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example. 

 First, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library:

@@ -75,10 +75,10 @@ with torch.no_grad():
    prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt)
 ```

-Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up some GPU VRAM:
+Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up som GPU VRAM:

 ```python
-import gc
+import gc 

 def flush():
    gc.collect()
@@ -99,7 +99,7 @@ pipe = PixArtAlphaPipeline.from_pretrained(
 ).to("cuda")

 latents = pipe(
-    negative_prompt=None,
+    negative_prompt=None, 
    prompt_embeds=prompt_embeds,
    negative_prompt_embeds=negative_embeds,
    prompt_attention_mask=prompt_attention_mask,
@@ -146,3 +146,4 @@ While loading the `text_encoder`, you set `load_in_8bit` to `True`. You could al
 [[autodoc]] PixArtAlphaPipeline
 	- all
 	- __call__
+	
@@ -39,7 +39,7 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)

 ## Inference with under 8GB GPU VRAM

-Run the [`PixArtSigmaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example.
+Run the [`PixArtSigmaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example. 

 First, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library:

@@ -59,6 +59,7 @@ text_encoder = T5EncoderModel.from_pretrained(
    subfolder="text_encoder",
    load_in_8bit=True,
    device_map="auto",
+
 )
 pipe = PixArtSigmaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
@@ -76,10 +77,10 @@ with torch.no_grad():
    prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt)
 ```

-Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up some GPU VRAM:
+Since text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up som GPU VRAM:

 ```python
-import gc
+import gc 

 def flush():
    gc.collect()
@@ -100,7 +101,7 @@ pipe = PixArtSigmaPipeline.from_pretrained(
 ).to("cuda")

 latents = pipe(
-    negative_prompt=None,
+    negative_prompt=None, 
    prompt_embeds=prompt_embeds,
    negative_prompt_embeds=negative_embeds,
    prompt_attention_mask=prompt_attention_mask,
@@ -147,3 +148,4 @@ While loading the `text_encoder`, you set `load_in_8bit` to `True`. You could al
 [[autodoc]] PixArtSigmaPipeline
 	- all
 	- __call__
+	
@@ -177,7 +177,7 @@ inpaint = StableDiffusionInpaintPipeline(**text2img.components)

 The Stable Diffusion pipelines are automatically supported in [Gradio](https://github.com/gradio-app/gradio/), a library that makes creating beautiful and user-friendly machine learning apps on the web a breeze. First, make sure you have Gradio installed:

-```sh
+```
 pip install -U gradio
 ```

@@ -209,4 +209,4 @@ gr.Interface.from_pipeline(pipe).launch()
 ```

 By default, the web demo runs on a local server. If you'd like to share it with others, you can generate a temporary public
-link by setting `share=True` in `launch()`. Or, you can host your demo on [Hugging Face Spaces](https://huggingface.co/spaces)https://huggingface.co/spaces for a permanent link.
+link by setting `share=True` in `launch()`. Or, you can host your demo on [Hugging Face Spaces](https://huggingface.co/spaces)https://huggingface.co/spaces for a permanent link. 
@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.

 # EDMDPMSolverMultistepScheduler

-`EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistepScheduler`, a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
+`EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistep`, a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.

 DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality
 samples, and it can generate quite good samples even in 10 steps.
@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.

 # DPMSolverMultistepScheduler

-`DPMSolverMultistepScheduler` is a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
+`DPMSolverMultistep` is a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.

 DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality
 samples, and it can generate quite good samples even in 10 steps.
@@ -70,7 +70,7 @@ The following design principles are followed:
 - Pipelines should be used **only** for inference.
 - Pipelines should be very readable, self-explanatory, and easy to tweak.
 - Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs.
- Pipelines are **not** intended to be feature-complete user interfaces. For feature-complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner).
+- Pipelines are **not** intended to be feature-complete user interfaces. For future complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner).
 - Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines.
 - Pipelines should be named after the task they are intended to solve.
 - In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file.
@@ -36,7 +36,7 @@ Then load and enable the [`DeepCacheSDHelper`](https://github.com/horseee/DeepCa
  image = pipe("a photo of an astronaut on a moon").images[0]
 ```

-The `set_params` method accepts two arguments: `cache_interval` and `cache_branch_id`. `cache_interval` means the frequency of feature caching, specified as the number of steps between each cache operation. `cache_branch_id` identifies which branch of the network (ordered from the shallowest to the deepest layer) is responsible for executing the caching processes.
+The `set_params` method accepts two arguments: `cache_interval` and `cache_branch_id`. `cache_interval` means the frequency of feature caching, specified as the number of steps between each cache operation. `cache_branch_id` identifies which branch of the network (ordered from the shallowest to the deepest layer) is responsible for executing the caching processes. 
 Opting for a lower `cache_branch_id` or a larger `cache_interval` can lead to faster inference speed at the expense of reduced image quality (ablation experiments of these two hyperparameters can be found in the [paper](https://arxiv.org/abs/2312.00858)). Once those arguments are set, use the `enable` or `disable` methods to activate or deactivate the `DeepCacheSDHelper`.

 <div class="flex justify-center">
@@ -6,7 +6,7 @@ Before you begin, make sure you install T-GATE.

 ```bash
 pip install tgate
-pip install -U torch diffusers transformers accelerate DeepCache
+pip install -U pytorch diffusers transformers accelerate DeepCache
 ```


@@ -46,12 +46,12 @@ pipe = TgatePixArtLoader(

 image = pipe.tgate(
       "An alpaca made of colorful building blocks, cyberpunk.",
-       gate_step=gate_step,
+        gate_step=gate_step,
       num_inference_steps=inference_step,
 ).images[0]
 ```
 </hfoption>
-<hfoption id="Stable Diffusion XL">
+<hfoption id="Stable Diffusion XL"> 

 Accelerate `StableDiffusionXLPipeline` with T-GATE:

@@ -78,9 +78,9 @@ pipe = TgateSDXLLoader(
 ).to("cuda")

 image = pipe.tgate(
-       "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
-       gate_step=gate_step,
-       num_inference_steps=inference_step
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
+        gate_step=gate_step,
+        num_inference_steps=inference_step
 ).images[0]
 ```
 </hfoption>
@@ -111,9 +111,9 @@ pipe = TgateSDXLDeepCacheLoader(
 ).to("cuda")

 image = pipe.tgate(
-       "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
-       gate_step=gate_step,
-       num_inference_steps=inference_step
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
+        gate_step=gate_step,
+        num_inference_steps=inference_step
 ).images[0]
 ```
 </hfoption>
@@ -151,9 +151,9 @@ pipe = TgateSDXLLoader(
 ).to("cuda")

 image = pipe.tgate(
-       "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
-       gate_step=gate_step,
-       num_inference_steps=inference_step
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
+        gate_step=gate_step,
+        num_inference_steps=inference_step
 ).images[0]
 ```
 </hfoption>
@@ -440,198 +440,6 @@ Stable Diffusion XL (SDXL) is a powerful text-to-image model that generates high

 The SDXL training script is discussed in more detail in the [SDXL training](sdxl) guide.

-## DeepFloyd IF
-
-DeepFloyd IF is a cascading pixel diffusion model with three stages. The first stage generates a base image and the second and third stages progressively upscales the base image into a high-resolution 1024x1024 image. Use the [train_dreambooth_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py) or [train_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) scripts to train a DeepFloyd IF model with LoRA or the full model.
-
-DeepFloyd IF uses predicted variance, but the Diffusers training scripts uses predicted error so the trained DeepFloyd IF models are switched to a fixed variance schedule. The training scripts will update the scheduler config of the fully trained model for you. However, when you load the saved LoRA weights you must also update the pipeline's scheduler config.
-
-```py
-from diffusers import DiffusionPipeline
-
-pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", use_safetensors=True)
-
-pipe.load_lora_weights("<lora weights path>")
-
-# Update scheduler config to fixed variance schedule
-pipe.scheduler = pipe.scheduler.__class__.from_config(pipe.scheduler.config, variance_type="fixed_small")
-```
-
-The stage 2 model requires additional validation images to upscale. You can download and use a downsized version of the training images for this.
-
-```py
-from huggingface_hub import snapshot_download
-
-local_dir = "./dog_downsized"
-snapshot_download(
-    "diffusers/dog-example-downsized",
-    local_dir=local_dir,
-    repo_type="dataset",
-    ignore_patterns=".gitattributes",
-)
-```
-
-The code samples below provide a brief overview of how to train a DeepFloyd IF model with a combination of DreamBooth and LoRA. Some important parameters to note are:
-
-* `--resolution=64`, a much smaller resolution is required because DeepFloyd IF is a pixel diffusion model and to work on uncompressed pixels, the input images must be smaller
-* `--pre_compute_text_embeddings`, compute the text embeddings ahead of time to save memory because the [`~transformers.T5Model`] can take up a lot of memory
-* `--tokenizer_max_length=77`, you can use a longer default text length with T5 as the text encoder but the default model encoding procedure uses a shorter text length
-* `--text_encoder_use_attention_mask`, to pass the attention mask to the text encoder
-
-<hfoptions id="IF-DreamBooth">
-<hfoption id="Stage 1 LoRA DreamBooth">
-
-Training stage 1 of DeepFloyd IF with LoRA and DreamBooth requires ~28GB of memory.
-
-```bash
-export MODEL_NAME="DeepFloyd/IF-I-XL-v1.0"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="dreambooth_dog_lora"
-
-accelerate launch train_dreambooth_lora.py \
-  --report_to wandb \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="a sks dog" \
-  --resolution=64 \
-  --train_batch_size=4 \
-  --gradient_accumulation_steps=1 \
-  --learning_rate=5e-6 \
-  --scale_lr \
-  --max_train_steps=1200 \
-  --validation_prompt="a sks dog" \
-  --validation_epochs=25 \
-  --checkpointing_steps=100 \
-  --pre_compute_text_embeddings \
-  --tokenizer_max_length=77 \
-  --text_encoder_use_attention_mask
-```
-
-</hfoption>
-<hfoption id="Stage 2 LoRA DreamBooth">
-
-For stage 2 of DeepFloyd IF with LoRA and DreamBooth, pay attention to these parameters:
-
-* `--validation_images`, the images to upscale during validation
-* `--class_labels_conditioning=timesteps`, to additionally conditional the UNet as needed in stage 2
-* `--learning_rate=1e-6`, a lower learning rate is used compared to stage 1
-* `--resolution=256`, the expected resolution for the upscaler
-
-```bash
-export MODEL_NAME="DeepFloyd/IF-II-L-v1.0"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="dreambooth_dog_upscale"
-export VALIDATION_IMAGES="dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"
-
-python train_dreambooth_lora.py \
-    --report_to wandb \
-    --pretrained_model_name_or_path=$MODEL_NAME \
-    --instance_data_dir=$INSTANCE_DIR \
-    --output_dir=$OUTPUT_DIR \
-    --instance_prompt="a sks dog" \
-    --resolution=256 \
-    --train_batch_size=4 \
-    --gradient_accumulation_steps=1 \
-    --learning_rate=1e-6 \ 
-    --max_train_steps=2000 \
-    --validation_prompt="a sks dog" \
-    --validation_epochs=100 \
-    --checkpointing_steps=500 \
-    --pre_compute_text_embeddings \
-    --tokenizer_max_length=77 \
-    --text_encoder_use_attention_mask \
-    --validation_images $VALIDATION_IMAGES \
-    --class_labels_conditioning=timesteps
-```
-
-</hfoption>
-<hfoption id="Stage 1 DreamBooth">
-
-For stage 1 of DeepFloyd IF with DreamBooth, pay attention to these parameters:
-
-* `--skip_save_text_encoder`, to skip saving the full T5 text encoder with the finetuned model
-* `--use_8bit_adam`, to use 8-bit Adam optimizer to save memory due to the size of the optimizer state when training the full model
-* `--learning_rate=1e-7`, a really low learning rate should be used for full model training otherwise the model quality is degraded (you can use a higher learning rate with a larger batch size)
-
-Training with 8-bit Adam and a batch size of 4, the full model can be trained with ~48GB of memory.
-
-```bash
-export MODEL_NAME="DeepFloyd/IF-I-XL-v1.0"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="dreambooth_if"
-
-accelerate launch train_dreambooth.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=64 \
-  --train_batch_size=4 \
-  --gradient_accumulation_steps=1 \
-  --learning_rate=1e-7 \
-  --max_train_steps=150 \
-  --validation_prompt "a photo of sks dog" \
-  --validation_steps 25 \
-  --text_encoder_use_attention_mask \
-  --tokenizer_max_length 77 \
-  --pre_compute_text_embeddings \
-  --use_8bit_adam \
-  --set_grads_to_none \
-  --skip_save_text_encoder \
-  --push_to_hub
-```
-
-</hfoption>
-<hfoption id="Stage 2 DreamBooth">
-
-For stage 2 of DeepFloyd IF with DreamBooth, pay attention to these parameters:
-
-* `--learning_rate=5e-6`, use a lower learning rate with a smaller effective batch size
-* `--resolution=256`, the expected resolution for the upscaler
-* `--train_batch_size=2` and `--gradient_accumulation_steps=6`, to effectively train on images wiht faces requires larger batch sizes
-
-```bash
-export MODEL_NAME="DeepFloyd/IF-II-L-v1.0"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="dreambooth_dog_upscale"
-export VALIDATION_IMAGES="dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"
-
-accelerate launch train_dreambooth.py \
-  --report_to wandb \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="a sks dog" \
-  --resolution=256 \
-  --train_batch_size=2 \
-  --gradient_accumulation_steps=6 \
-  --learning_rate=5e-6 \
-  --max_train_steps=2000 \
-  --validation_prompt="a sks dog" \
-  --validation_steps=150 \
-  --checkpointing_steps=500 \
-  --pre_compute_text_embeddings \
-  --tokenizer_max_length=77 \
-  --text_encoder_use_attention_mask \
-  --validation_images $VALIDATION_IMAGES \
-  --class_labels_conditioning timesteps \
-  --push_to_hub
-```
-
-</hfoption>
-</hfoptions>
-
-### Training tips
-
-Training the DeepFloyd IF model can be challenging, but here are some tips that we've found helpful:
-
- LoRA is sufficient for training the stage 1 model because the model's low resolution makes representing finer details difficult regardless.
- For common or simple objects, you don't necessarily need to finetune the upscaler. Make sure the prompt passed to the upscaler is adjusted to remove the new token from the instance prompt. For example, if your stage 1 prompt is "a sks dog" then your stage 2 prompt should be "a dog".
- For finer details like faces, fully training the stage 2 upscaler is better than training the stage 2 model with LoRA. It also helps to use lower learning rates with larger batch sizes.
- Lower learning rates should be used to train the stage 2 model.
- The [`DDPMScheduler`] works better than the DPMSolver used in the training scripts.
-
 ## Next steps

 Congratulations on training your DreamBooth model! To learn more about how to use your new model, the following guide may be helpful:
@@ -260,7 +260,7 @@ Then, you'll need a way to evaluate the model. For evaluation, you can use the [
 ...     # The default pipeline output type is `List[PIL.Image]`
 ...     images = pipeline(
 ...         batch_size=config.eval_batch_size,
-...         generator=torch.Generator(device='cpu').manual_seed(config.seed), # Use a separate torch generator to avoid rewinding the random state of the main training loop
+...         generator=torch.manual_seed(config.seed),
 ...     ).images

 ...     # Make a grid out of the images
@@ -188,7 +188,7 @@ def latents_to_rgb(latents):
 ```py
 def decode_tensors(pipe, step, timestep, callback_kwargs):
    latents = callback_kwargs["latents"]
-
+    
    image = latents_to_rgb(latents)
    image.save(f"{step}.png")

@@ -12,10 +12,54 @@ specific language governing permissions and limitations under the License.

 # Controlling image quality

-The components of a diffusion model, like the UNet and scheduler, can be optimized to improve the quality of generated images leading to better details. These techniques are especially useful if you don't have the resources to simply use a larger model for inference. You can enable these techniques during inference without any additional training.
+The components of a diffusion model, like the UNet and scheduler, can be optimized to improve the quality of generated images leading to better image lighting and details. These techniques are especially useful if you don't have the resources to simply use a larger model for inference. You can enable these techniques during inference without any additional training.

 This guide will show you how to turn these techniques on in your pipeline and how to configure them to improve the quality of your generated images.

+## Lighting
+
+The Stable Diffusion models aren't very good at generating images that are very bright or dark because the scheduler doesn't start sampling from the last timestep and it doesn't enforce a zero signal-to-noise ratio (SNR). The [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://hf.co/papers/2305.08891) paper fixes these issues which are now available in some Diffusers schedulers.
+
+> [!TIP]
+> For inference, you need a model that has been trained with *v_prediction*. To train your own model with *v_prediction*, add the following flag to the [train_text_to_image.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) or [train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) scripts.
+>
+> ```bash
+> --prediction_type="v_prediction"
+> ```
+
+For example, load the [ptx0/pseudo-journey-v2](https://hf.co/ptx0/pseudo-journey-v2) checkpoint which was trained with `v_prediction` and the [`DDIMScheduler`]. Now you should configure the following parameters in the [`DDIMScheduler`].
+
+* `rescale_betas_zero_snr=True` to rescale the noise schedule to zero SNR
+* `timestep_spacing="trailing"` to start sampling from the last timestep
+
+Set `guidance_rescale` in the pipeline to prevent over-exposure. A lower value increases brightness but some of the details may appear washed out.
+
+```py
+from diffusers import DiffusionPipeline, DDIMScheduler
+
+pipeline = DiffusionPipeline.from_pretrained("ptx0/pseudo-journey-v2", use_safetensors=True)
+
+pipeline.scheduler = DDIMScheduler.from_config(
+    pipeline.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing="trailing"
+)
+pipeline.to("cuda")
+prompt = "cinematic photo of a snowy mountain at night with the northern lights aurora borealis overhead, 35mm photograph, film, professional, 4k, highly detailed"
+generator = torch.Generator(device="cpu").manual_seed(23)
+image = pipeline(prompt, guidance_rescale=0.7, generator=generator).images[0]
+image
+```
+
+<div class="flex gap-4">
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/no-zero-snr.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">default Stable Diffusion v2-1 image</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/zero-snr.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">image with zero SNR and trailing timestep spacing enabled</figcaption>
+  </div>
+</div>
+
 ## Details

 [FreeU](https://hf.co/papers/2309.11497) improves image details by rebalancing the UNet's backbone and skip connection weights. The skip connections can cause the model to overlook some of the backbone semantics which may lead to unnatural image details in the generated image. This technique does not require any additional training and can be applied on the fly during inference for tasks like image-to-image and text-to-video.
@@ -78,7 +78,7 @@ image = pipe(
    prompt=prompt,
    num_inference_steps=4,
    guidance_scale=0,
-    eta=0.3,
+    eta=0.3, 
    generator=torch.Generator(device=device).manual_seed(0),
 ).images[0]
 ```
@@ -156,14 +156,14 @@ image = pipe(
    prompt=prompt,
    num_inference_steps=8,
    guidance_scale=0,
-    eta=0.3,
+    eta=0.3, 
    generator=torch.Generator(device=device).manual_seed(0),
 ).images[0]
 ```

 ![](https://github.com/jabir-zheng/TCD/raw/main/assets/animagine_xl.png)

-TCD-LoRA also supports other LoRAs trained on different styles. For example, let's load the [TheLastBen/Papercut_SDXL](https://huggingface.co/TheLastBen/Papercut_SDXL) LoRA and fuse it with the TCD-LoRA with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method.
+TCD-LoRA also supports other LoRAs trained on different styles. For example, let's load the [TheLastBen/Papercut_SDXL](https://huggingface.co/TheLastBen/Papercut_SDXL) LoRA and fuse it with the TCD-LoRA with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method. 

 > [!TIP]
 > Check out the [Merge LoRAs](merge_loras) guide to learn more about efficient merging methods.
@@ -171,7 +171,7 @@ TCD-LoRA also supports other LoRAs trained on different styles. For example, let
 ```python
 import torch
 from diffusers import StableDiffusionXLPipeline
-from scheduling_tcd import TCDScheduler
+from scheduling_tcd import TCDScheduler 

 device = "cuda"
 base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
@@ -191,7 +191,7 @@ image = pipe(
    prompt=prompt,
    num_inference_steps=4,
    guidance_scale=0,
-    eta=0.3,
+    eta=0.3, 
    generator=torch.Generator(device=device).manual_seed(0),
 ).images[0]
 ```
@@ -215,7 +215,7 @@ from PIL import Image
 from transformers import DPTFeatureExtractor, DPTForDepthEstimation
 from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
 from diffusers.utils import load_image, make_image_grid
-from scheduling_tcd import TCDScheduler
+from scheduling_tcd import TCDScheduler 

 device = "cuda"
 depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
@@ -249,13 +249,13 @@ controlnet = ControlNetModel.from_pretrained(
    controlnet_id,
    torch_dtype=torch.float16,
    variant="fp16",
-)
+).to(device)
 pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    base_model_id,
    controlnet=controlnet,
    torch_dtype=torch.float16,
    variant="fp16",
-)
+).to(device)
 pipe.enable_model_cpu_offload()

 pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
@@ -271,9 +271,9 @@ depth_image = get_depth_map(image)
 controlnet_conditioning_scale = 0.5  # recommended for good generalization

 image = pipe(
-    prompt,
-    image=depth_image,
-    num_inference_steps=4,
+    prompt, 
+    image=depth_image, 
+    num_inference_steps=4, 
    guidance_scale=0,
    eta=0.3,
    controlnet_conditioning_scale=controlnet_conditioning_scale,
@@ -290,7 +290,7 @@ grid_image = make_image_grid([depth_image, image], rows=1, cols=2)
 import torch
 from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
 from diffusers.utils import load_image, make_image_grid
-from scheduling_tcd import TCDScheduler
+from scheduling_tcd import TCDScheduler 

 device = "cuda"
 base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
@@ -301,13 +301,13 @@ controlnet = ControlNetModel.from_pretrained(
    controlnet_id,
    torch_dtype=torch.float16,
    variant="fp16",
-)
+).to(device)
 pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    base_model_id,
    controlnet=controlnet,
    torch_dtype=torch.float16,
    variant="fp16",
-)
+).to(device)
 pipe.enable_model_cpu_offload()

 pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
@@ -322,9 +322,9 @@ canny_image = load_image("https://huggingface.co/datasets/hf-internal-testing/di
 controlnet_conditioning_scale = 0.5  # recommended for good generalization

 image = pipe(
-    prompt,
-    image=canny_image,
-    num_inference_steps=4,
+    prompt, 
+    image=canny_image, 
+    num_inference_steps=4, 
    guidance_scale=0,
    eta=0.3,
    controlnet_conditioning_scale=controlnet_conditioning_scale,
@@ -336,7 +336,7 @@ grid_image = make_image_grid([canny_image, image], rows=1, cols=2)
 ![](https://github.com/jabir-zheng/TCD/raw/main/assets/controlnet_canny_tcd.png)

 <Tip>
-The inference parameters in this example might not work for all examples, so we recommend you to try different values for `num_inference_steps`, `guidance_scale`, `controlnet_conditioning_scale` and `cross_attention_kwargs` parameters and choose the best one.
+The inference parameters in this example might not work for all examples, so we recommend you to try different values for `num_inference_steps`, `guidance_scale`, `controlnet_conditioning_scale` and `cross_attention_kwargs` parameters and choose the best one. 
 </Tip>

 </hfoption>
@@ -350,7 +350,7 @@ from diffusers import StableDiffusionXLPipeline
 from diffusers.utils import load_image, make_image_grid

 from ip_adapter import IPAdapterXL
-from scheduling_tcd import TCDScheduler
+from scheduling_tcd import TCDScheduler 

 device = "cuda"
 base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
@@ -359,8 +359,8 @@ ip_ckpt = "sdxl_models/ip-adapter_sdxl.bin"
 tcd_lora_id = "h1t/TCD-SDXL-LoRA"

 pipe = StableDiffusionXLPipeline.from_pretrained(
-    base_model_path,
-    torch_dtype=torch.float16,
+    base_model_path, 
+    torch_dtype=torch.float16, 
    variant="fp16"
 )
 pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
@@ -375,13 +375,13 @@ ref_image = load_image("https://raw.githubusercontent.com/tencent-ailab/IP-Adapt
 prompt = "best quality, high quality, wearing sunglasses"

 image = ip_model.generate(
-    pil_image=ref_image,
+    pil_image=ref_image, 
    prompt=prompt,
    scale=0.5,
-    num_samples=1,
-    num_inference_steps=4,
+    num_samples=1, 
+    num_inference_steps=4, 
    guidance_scale=0,
-    eta=0.3,
+    eta=0.3, 
    seed=0,
 )[0]

@@ -230,7 +230,7 @@ from diffusers.utils import load_image, make_image_grid

 pipeline = AutoPipelineForInpainting.from_pretrained(
    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
-)
+).to("cuda")
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -255,7 +255,7 @@ from diffusers.utils import load_image, make_image_grid

 pipeline = AutoPipelineForInpainting.from_pretrained(
    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
-)
+).to("cuda")
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -296,7 +296,7 @@ from diffusers.utils import load_image, make_image_grid

 pipeline = AutoPipelineForInpainting.from_pretrained(
    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
-)
+).to("cuda")
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -319,7 +319,7 @@ from diffusers.utils import load_image, make_image_grid

 pipeline = AutoPipelineForInpainting.from_pretrained(
    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
-)
+).to("cuda")
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -1,466 +0,0 @@
-<!--Copyright 2024 Marigold authors and The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
-->
-
-# Marigold Pipelines for Computer Vision Tasks
-
-[Marigold](../api/pipelines/marigold) is a novel diffusion-based dense prediction approach, and a set of pipelines for various computer vision tasks, such as monocular depth estimation.
-
-This guide will show you how to use Marigold to obtain fast and high-quality predictions for images and videos.
-
-Each pipeline supports one Computer Vision task, which takes an input RGB image as input and produces a *prediction* of the modality of interest, such as a depth map of the input image.
-Currently, the following tasks are implemented:
-
-| Pipeline                                                                                                                                    | Predicted Modalities                                                                                             |                                                                       Demos                                                                        |
-|---------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------:|
-| [MarigoldDepthPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py)     | [Depth](https://en.wikipedia.org/wiki/Depth_map), [Disparity](https://en.wikipedia.org/wiki/Binocular_disparity) | [Fast Demo (LCM)](https://huggingface.co/spaces/prs-eth/marigold-lcm), [Slow Original Demo (DDIM)](https://huggingface.co/spaces/prs-eth/marigold) |
-| [MarigoldNormalsPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_normals.py) | [Surface normals](https://en.wikipedia.org/wiki/Normal_mapping)                                                  |                                   [Fast Demo (LCM)](https://huggingface.co/spaces/prs-eth/marigold-normals-lcm)                                    |
-
-The original checkpoints can be found under the [PRS-ETH](https://huggingface.co/prs-eth/) Hugging Face organization.
-These checkpoints are meant to work with diffusers pipelines and the [original codebase](https://github.com/prs-eth/marigold).
-The original code can also be used to train new checkpoints.
-
-| Checkpoint                                                                                    | Modality | Comment                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-|-----------------------------------------------------------------------------------------------|----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [prs-eth/marigold-v1-0](https://huggingface.co/prs-eth/marigold-v1-0)                         | Depth    | The first Marigold Depth checkpoint, which predicts *affine-invariant depth* maps. The performance of this checkpoint in benchmarks was studied in the original [paper](https://huggingface.co/papers/2312.02145). Designed to be used with the `DDIMScheduler` at inference, it requires at least 10 steps to get reliable predictions. Affine-invariant depth prediction has a range of values in each pixel between 0 (near plane) and 1 (far plane); both planes are chosen by the model as part of the inference process. See the `MarigoldImageProcessor` reference for visualization utilities. |
-| [prs-eth/marigold-depth-lcm-v1-0](https://huggingface.co/prs-eth/marigold-depth-lcm-v1-0)     | Depth    | The fast Marigold Depth checkpoint, fine-tuned from `prs-eth/marigold-v1-0`. Designed to be used with the `LCMScheduler` at inference, it requires as little as 1 step to get reliable predictions. The prediction reliability saturates at 4 steps and declines after that.                                                                                                                                                                                                                                                                                                                           |
-| [prs-eth/marigold-normals-v0-1](https://huggingface.co/prs-eth/marigold-normals-v0-1)         | Normals  | A preview checkpoint for the Marigold Normals pipeline. Designed to be used with the `DDIMScheduler` at inference, it requires at least 10 steps to get reliable predictions. The surface normals predictions are unit-length 3D vectors with values in the range from -1 to 1. *This checkpoint will be phased out after the release of `v1-0` version.*                                                                                                                                                                                                                                              |
-| [prs-eth/marigold-normals-lcm-v0-1](https://huggingface.co/prs-eth/marigold-normals-lcm-v0-1) | Normals  | The fast Marigold Normals checkpoint, fine-tuned from `prs-eth/marigold-normals-v0-1`. Designed to be used with the `LCMScheduler` at inference, it requires as little as 1 step to get reliable predictions. The prediction reliability saturates at 4 steps and declines after that. *This checkpoint will be phased out after the release of `v1-0` version.*                                                                                                                                                                                                                                       |
-The examples below are mostly given for depth prediction, but they can be universally applied with other supported modalities.
-We showcase the predictions using the same input image of Albert Einstein generated by Midjourney.
-This makes it easier to compare visualizations of the predictions across various modalities and checkpoints.
-
-<div class="flex gap-4" style="justify-content: center; width: 100%;">
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://marigoldmonodepth.github.io/images/einstein.jpg"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Example input image for all Marigold pipelines
-    </figcaption>
-  </div>
-</div>
-
-### Depth Prediction Quick Start
-
-To get the first depth prediction, load `prs-eth/marigold-depth-lcm-v1-0` checkpoint into `MarigoldDepthPipeline` pipeline, put the image through the pipeline, and save the predictions:
-
-```python
-import diffusers
-import torch
-
-pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
-    "prs-eth/marigold-depth-lcm-v1-0", variant="fp16", torch_dtype=torch.float16
-).to("cuda")
-
-image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
-depth = pipe(image)
-
-vis = pipe.image_processor.visualize_depth(depth.prediction)
-vis[0].save("einstein_depth.png")
-
-depth_16bit = pipe.image_processor.export_depth_to_16bit_png(depth.prediction)
-depth_16bit[0].save("einstein_depth_16bit.png")
-```
-
-The visualization function for depth [`~pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_depth`] applies one of [matplotlib's colormaps](https://matplotlib.org/stable/users/explain/colors/colormaps.html) (`Spectral` by default) to map the predicted pixel values from a single-channel `[0, 1]` depth range into an RGB image.
-With the `Spectral` colormap, pixels with near depth are painted red, and far pixels are assigned blue color.
-The 16-bit PNG file stores the single channel values mapped linearly from the `[0, 1]` range into `[0, 65535]`.
-Below are the raw and the visualized predictions; as can be seen, dark areas (mustache) are easier to distinguish in the visualization:
-
-<div class="flex gap-4">
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_lcm_depth_16bit.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Predicted depth (16-bit PNG)
-    </figcaption>
-  </div>
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_lcm_depth.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Predicted depth visualization (Spectral)
-    </figcaption>
-  </div>
-</div>
-
-### Surface Normals Prediction Quick Start
-
-Load `prs-eth/marigold-normals-lcm-v0-1` checkpoint into `MarigoldNormalsPipeline` pipeline, put the image through the pipeline, and save the predictions:
-
-```python
-import diffusers
-import torch
-
-pipe = diffusers.MarigoldNormalsPipeline.from_pretrained(
-    "prs-eth/marigold-normals-lcm-v0-1", variant="fp16", torch_dtype=torch.float16
-).to("cuda")
-
-image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
-normals = pipe(image)
-
-vis = pipe.image_processor.visualize_normals(normals.prediction)
-vis[0].save("einstein_normals.png")
-```
-
-The visualization function for normals [`~pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_normals`] maps the three-dimensional prediction with pixel values in the range `[-1, 1]` into an RGB image.
-The visualization function supports flipping surface normals axes to make the visualization compatible with other choices of the frame of reference.
-Conceptually, each pixel is painted according to the surface normal vector in the frame of reference, where `X` axis points right, `Y` axis points up, and `Z` axis points at the viewer.
-Below is the visualized prediction:
-
-<div class="flex gap-4" style="justify-content: center; width: 100%;">
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_lcm_normals.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Predicted surface normals visualization
-    </figcaption>
-  </div>
-</div>
-
-In this example, the nose tip almost certainly has a point on the surface, in which the surface normal vector points straight at the viewer, meaning that its coordinates are `[0, 0, 1]`.
-This vector maps to the RGB `[128, 128, 255]`, which corresponds to the violet-blue color.
-Similarly, a surface normal on the cheek in the right part of the image has a large `X` component, which increases the red hue.
-Points on the shoulders pointing up with a large `Y` promote green color.
-
-### Speeding up inference
-
-The above quick start snippets are already optimized for speed: they load the LCM checkpoint, use the `fp16` variant of weights and computation, and perform just one denoising diffusion step.
-The `pipe(image)` call completes in 280ms on RTX 3090 GPU.
-Internally, the input image is encoded with the Stable Diffusion VAE encoder, then the U-Net performs one denoising step, and finally, the prediction latent is decoded with the VAE decoder into pixel space.
-In this case, two out of three module calls are dedicated to converting between pixel and latent space of LDM.
-Because Marigold's latent space is compatible with the base Stable Diffusion, it is possible to speed up the pipeline call by more than 3x (85ms on RTX 3090) by using a [lightweight replacement of the SD VAE](../api/models/autoencoder_tiny):
-
-```diff
-  import diffusers
-  import torch
-
-  pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
-      "prs-eth/marigold-depth-lcm-v1-0", variant="fp16", torch_dtype=torch.float16
-  ).to("cuda")
-
-+ pipe.vae = diffusers.AutoencoderTiny.from_pretrained(
-+     "madebyollin/taesd", torch_dtype=torch.float16
-+ ).cuda()
-
-  image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
-  depth = pipe(image)
-```
-
-As suggested in [Optimizations](../optimization/torch2.0#torch.compile), adding `torch.compile` may squeeze extra performance depending on the target hardware:
-
-```diff
-  import diffusers
-  import torch
-
-  pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
-      "prs-eth/marigold-depth-lcm-v1-0", variant="fp16", torch_dtype=torch.float16
-  ).to("cuda")
-
-+ pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-
-  image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
-  depth = pipe(image)
-```
-
-## Qualitative Comparison with Depth Anything
-
-With the above speed optimizations, Marigold delivers predictions with more details and faster than [Depth Anything](https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything) with the largest checkpoint [LiheYoung/depth-anything-large-hf](https://huggingface.co/LiheYoung/depth-anything-large-hf):
-
-<div class="flex gap-4">
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_lcm_depth.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Marigold LCM fp16 with Tiny AutoEncoder
-    </figcaption>
-  </div>
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/einstein_depthanything_large.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Depth Anything Large
-    </figcaption>
-  </div>
-</div>
-
-## Maximizing Precision and Ensembling
-
-Marigold pipelines have a built-in ensembling mechanism combining multiple predictions from different random latents.
-This is a brute-force way of improving the precision of predictions, capitalizing on the generative nature of diffusion.
-The ensembling path is activated automatically when the `ensemble_size` argument is set greater than `1`.
-When aiming for maximum precision, it makes sense to adjust `num_inference_steps` simultaneously with `ensemble_size`.
-The recommended values vary across checkpoints but primarily depend on the scheduler type.
-The effect of ensembling is particularly well-seen with surface normals:
-
-```python
-import diffusers
-
-model_path = "prs-eth/marigold-normals-v1-0"
-
-model_paper_kwargs = {
-	diffusers.schedulers.DDIMScheduler: {
-		"num_inference_steps": 10,
-		"ensemble_size": 10,
-	},
-	diffusers.schedulers.LCMScheduler: {
-		"num_inference_steps": 4,
-		"ensemble_size": 5,
-	},
-}
-
-image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
-
-pipe = diffusers.MarigoldNormalsPipeline.from_pretrained(model_path).to("cuda")
-pipe_kwargs = model_paper_kwargs[type(pipe.scheduler)]
-
-depth = pipe(image, **pipe_kwargs)
-
-vis = pipe.image_processor.visualize_normals(depth.prediction)
-vis[0].save("einstein_normals.png")
-```
-
-<div class="flex gap-4">
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_lcm_normals.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Surface normals, no ensembling
-    </figcaption>
-  </div>
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_normals.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Surface normals, with ensembling
-    </figcaption>
-  </div>
-</div>
-
-As can be seen, all areas with fine-grained structurers, such as hair, got more conservative and on average more correct predictions.
-Such a result is more suitable for precision-sensitive downstream tasks, such as 3D reconstruction.
-
-## Quantitative Evaluation
-
-To evaluate Marigold quantitatively in standard leaderboards and benchmarks (such as NYU, KITTI, and other datasets), follow the evaluation protocol outlined in the paper: load the full precision fp32 model and use appropriate values for `num_inference_steps` and `ensemble_size`.
-Optionally seed randomness to ensure reproducibility. Maximizing `batch_size` will deliver maximum device utilization.
-
-```python
-import diffusers
-import torch
-
-device = "cuda"
-seed = 2024
-model_path = "prs-eth/marigold-v1-0"
-
-model_paper_kwargs = {
-	diffusers.schedulers.DDIMScheduler: {
-		"num_inference_steps": 50,
-		"ensemble_size": 10,
-	},
-	diffusers.schedulers.LCMScheduler: {
-		"num_inference_steps": 4,
-		"ensemble_size": 10,
-	},
-}
-
-image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
-
-generator = torch.Generator(device=device).manual_seed(seed)
-pipe = diffusers.MarigoldDepthPipeline.from_pretrained(model_path).to(device)
-pipe_kwargs = model_paper_kwargs[type(pipe.scheduler)]
-
-depth = pipe(image, generator=generator, **pipe_kwargs)
-
-# evaluate metrics
-```
-
-## Using Predictive Uncertainty
-
-The ensembling mechanism built into Marigold pipelines combines multiple predictions obtained from different random latents.
-As a side effect, it can be used to quantify epistemic (model) uncertainty; simply specify `ensemble_size` greater than 1 and set `output_uncertainty=True`.
-The resulting uncertainty will be available in the `uncertainty` field of the output.
-It can be visualized as follows:
-
-```python
-import diffusers
-import torch
-
-pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
-    "prs-eth/marigold-depth-lcm-v1-0", variant="fp16", torch_dtype=torch.float16
-).to("cuda")
-
-image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
-depth = pipe(
-	image,
-	ensemble_size=10,  # any number greater than 1; higher values yield higher precision
-	output_uncertainty=True,
-)
-
-uncertainty = pipe.image_processor.visualize_uncertainty(depth.uncertainty)
-uncertainty[0].save("einstein_depth_uncertainty.png")
-```
-
-<div class="flex gap-4">
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_depth_uncertainty.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Depth uncertainty
-    </figcaption>
-  </div>
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_normals_uncertainty.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Surface normals uncertainty
-    </figcaption>
-  </div>
-</div>
-
-The interpretation of uncertainty is easy: higher values (white) correspond to pixels, where the model struggles to make consistent predictions.
-Evidently, the depth model is the least confident around edges with discontinuity, where the object depth changes drastically.
-The surface normals model is the least confident in fine-grained structures, such as hair, and dark areas, such as the collar.
-
-## Frame-by-frame Video Processing with Temporal Consistency
-
-Due to Marigold's generative nature, each prediction is unique and defined by the random noise sampled for the latent initialization.
-This becomes an obvious drawback compared to traditional end-to-end dense regression networks, as exemplified in the following videos:
-
-<div class="flex gap-4">
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_obama.gif"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">Input video</figcaption>
-  </div>
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_obama_depth_independent.gif"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">Marigold Depth applied to input video frames independently</figcaption>
-  </div>
-</div>
-
-To address this issue, it is possible to pass `latents` argument to the pipelines, which defines the starting point of diffusion.
-Empirically, we found that a convex combination of the very same starting point noise latent and the latent corresponding to the previous frame prediction give sufficiently smooth results, as implemented in the snippet below:
-
-```python
-import imageio
-from PIL import Image
-from tqdm import tqdm
-import diffusers
-import torch
-
-device = "cuda"
-path_in = "obama.mp4"
-path_out = "obama_depth.gif"
-
-pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
-    "prs-eth/marigold-depth-lcm-v1-0", variant="fp16", torch_dtype=torch.float16
-).to(device)
-pipe.vae = diffusers.AutoencoderTiny.from_pretrained(
-    "madebyollin/taesd", torch_dtype=torch.float16
-).to(device)
-pipe.set_progress_bar_config(disable=True)
-
-with imageio.get_reader(path_in) as reader:
-    size = reader.get_meta_data()['size']
-    last_frame_latent = None
-    latent_common = torch.randn(
-        (1, 4, 768 * size[1] // (8 * max(size)), 768 * size[0] // (8 * max(size)))
-    ).to(device=device, dtype=torch.float16)
-
-    out = []
-    for frame_id, frame in tqdm(enumerate(reader), desc="Processing Video"):
-        frame = Image.fromarray(frame)
-        latents = latent_common
-        if last_frame_latent is not None:
-            latents = 0.9 * latents + 0.1 * last_frame_latent
-
-        depth = pipe(
-			frame, match_input_resolution=False, latents=latents, output_latent=True
-        )
-        last_frame_latent = depth.latent
-        out.append(pipe.image_processor.visualize_depth(depth.prediction)[0])
-
-    diffusers.utils.export_to_gif(out, path_out, fps=reader.get_meta_data()['fps'])
-```
-
-Here, the diffusion process starts from the given computed latent.
-The pipeline sets `output_latent=True` to access `out.latent` and computes its contribution to the next frame's latent initialization.
-The result is much more stable now:
-
-<div class="flex gap-4">
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_obama_depth_independent.gif"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">Marigold Depth applied to input video frames independently</figcaption>
-  </div>
-  <div style="flex: 1 1 50%; max-width: 50%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_obama_depth_consistent.gif"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">Marigold Depth with forced latents initialization</figcaption>
-  </div>
-</div>
-
-## Marigold for ControlNet
-
-A very common application for depth prediction with diffusion models comes in conjunction with ControlNet.
-Depth crispness plays a crucial role in obtaining high-quality results from ControlNet.
-As seen in comparisons with other methods above, Marigold excels at that task.
-The snippet below demonstrates how to load an image, compute depth, and pass it into ControlNet in a compatible format:
-
-```python
-import torch
-import diffusers
-
-device = "cuda"
-generator = torch.Generator(device=device).manual_seed(2024)
-image = diffusers.utils.load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_depth_source.png"
-)
-
-pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
-    "prs-eth/marigold-depth-lcm-v1-0", torch_dtype=torch.float16, variant="fp16"
-).to(device)
-
-depth_image = pipe(image, generator=generator).prediction
-depth_image = pipe.image_processor.visualize_depth(depth_image, color_map="binary")
-depth_image[0].save("motorcycle_controlnet_depth.png")
-
-controlnet = diffusers.ControlNetModel.from_pretrained(
-    "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
-).to(device)
-pipe = diffusers.StableDiffusionXLControlNetPipeline.from_pretrained(
-    "SG161222/RealVisXL_V4.0", torch_dtype=torch.float16, variant="fp16", controlnet=controlnet
-).to(device)
-pipe.scheduler = diffusers.DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
-
-controlnet_out = pipe(
-    prompt="high quality photo of a sports bike, city",
-    negative_prompt="",
-    guidance_scale=6.5,
-    num_inference_steps=25,
-    image=depth_image,
-    controlnet_conditioning_scale=0.7,
-    control_guidance_end=0.7,
-    generator=generator,
-).images
-controlnet_out[0].save("motorcycle_controlnet_out.png")
-```
-
-<div class="flex gap-4">
-  <div style="flex: 1 1 33%; max-width: 33%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_depth_source.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Input image
-    </figcaption>
-  </div>
-  <div style="flex: 1 1 33%; max-width: 33%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/motorcycle_controlnet_depth.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      Depth in the format compatible with ControlNet
-    </figcaption>
-  </div>
-  <div style="flex: 1 1 33%; max-width: 33%;">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/motorcycle_controlnet_out.png"/>
-    <figcaption class="mt-1 text-center text-sm text-gray-500">
-      ControlNet generation, conditioned on depth and prompt: "high quality photo of a sports bike, city"
-    </figcaption>
-  </div>
-</div>
-
-Hopefully, you will find Marigold useful for solving your downstream tasks, be it a part of a more broad generative workflow, or a perception task, such as 3D reconstruction.
@@ -10,86 +10,156 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->

-# Model files and layouts
+# Load different Stable Diffusion formats

 [[open-in-colab]]

-Diffusion models are saved in various file types and organized in different layouts. Diffusers stores model weights as safetensors files in *Diffusers-multifolder* layout and it also supports loading files (like safetensors and ckpt files) from a *single-file* layout which is commonly used in the diffusion ecosystem.
+Stable Diffusion models are available in different formats depending on the framework they're trained and saved with, and where you download them from. Converting these formats for use in 🤗 Diffusers allows you to use all the features supported by the library, such as [using different schedulers](schedulers) for inference, [building your custom pipeline](write_own_pipeline), and a variety of techniques and methods for [optimizing inference speed](../optimization/opt_overview).

-Each layout has its own benefits and use cases, and this guide will show you how to load the different files and layouts, and how to convert them.
+<Tip>

-## Files
+We highly recommend using the `.safetensors` format because it is more secure than traditional pickled files which are vulnerable and can be exploited to execute any code on your machine (learn more in the [Load safetensors](using_safetensors) guide).

-PyTorch model weights are typically saved with Python's [pickle](https://docs.python.org/3/library/pickle.html) utility as ckpt or bin files. However, pickle is not secure and pickled files may contain malicious code that can be executed. This vulnerability is a serious concern given the popularity of model sharing. To address this security issue, the [Safetensors](https://hf.co/docs/safetensors) library was developed as a secure alternative to pickle, which saves models as safetensors files.
+</Tip>

-### safetensors
+This guide will show you how to convert other Stable Diffusion formats to be compatible with 🤗 Diffusers.

-> [!TIP]
-> Learn more about the design decisions and why safetensor files are preferred for saving and loading model weights in the [Safetensors audited as really safe and becoming the default](https://blog.eleuther.ai/safetensors-security-audit/) blog post.
+## PyTorch .ckpt

-[Safetensors](https://hf.co/docs/safetensors) is a safe and fast file format for securely storing and loading tensors. Safetensors restricts the header size to limit certain types of attacks, supports lazy loading (useful for distributed setups), and has generally faster loading speeds.
+The checkpoint - or `.ckpt` - format is commonly used to store and save models. The `.ckpt` file contains the entire model and is typically several GBs in size. While you can load and use a `.ckpt` file directly with the [`~StableDiffusionPipeline.from_single_file`] method, it is generally better to convert the `.ckpt` file to 🤗 Diffusers so both formats are available.

-Make sure you have the [Safetensors](https://hf.co/docs/safetensors) library installed.
+There are two options for converting a `.ckpt` file: use a Space to convert the checkpoint or convert the `.ckpt` file with a script.

-```py
-!pip install safetensors
+### Convert with a Space
+
+The easiest and most convenient way to convert a `.ckpt` file is to use the [SD to Diffusers](https://huggingface.co/spaces/diffusers/sd-to-diffusers) Space. You can follow the instructions on the Space to convert the `.ckpt` file.
+
+This approach works well for basic models, but it may struggle with more customized models. You'll know the Space failed if it returns an empty pull request or error. In this case, you can try converting the `.ckpt` file with a script.
+
+### Convert with a script
+
+🤗 Diffusers provides a [conversion script](https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py) for converting `.ckpt` files. This approach is more reliable than the Space above.
+
+Before you start, make sure you have a local clone of 🤗 Diffusers to run the script and log in to your Hugging Face account so you can open pull requests and push your converted model to the Hub.
+
+```bash
+huggingface-cli login
 ```

-Safetensors stores weights in a safetensors file. Diffusers loads safetensors files by default if they're available and the Safetensors library is installed. There are two ways safetensors files can be organized:
+To use the script:

-1. Diffusers-multifolder layout: there may be several separate safetensors files, one for each pipeline component (text encoder, UNet, VAE), organized in subfolders (check out the [runwayml/stable-diffusion-v1-5](https://hf.co/runwayml/stable-diffusion-v1-5/tree/main) repository as an example)
-2. single-file layout: all the model weights may be saved in a single file (check out the [WarriorMama777/OrangeMixs](https://hf.co/WarriorMama777/OrangeMixs/tree/main/Models/AbyssOrangeMix) repository as an example)
+1. Git clone the repository containing the `.ckpt` file you want to convert. For this example, let's convert this [TemporalNet](https://huggingface.co/CiaraRowles/TemporalNet) `.ckpt` file:

-<hfoptions id="safetensors">
-<hfoption id="multifolder">
+```bash
+git lfs install
+git clone https://huggingface.co/CiaraRowles/TemporalNet
+```

-Use the [`~DiffusionPipeline.from_pretrained`] method to load a model with safetensors files stored in multiple folders.
+2. Open a pull request on the repository where you're converting the checkpoint from:
+
+```bash
+cd TemporalNet && git fetch origin refs/pr/13:pr/13
+git checkout pr/13
+```
+
+3. There are several input arguments to configure in the conversion script, but the most important ones are:
+
+    - `checkpoint_path`: the path to the `.ckpt` file to convert.
+    - `original_config_file`: a YAML file defining the configuration of the original architecture. If you can't find this file, try searching for the YAML file in the GitHub repository where you found the `.ckpt` file.
+    - `dump_path`: the path to the converted model.
+
+        For example, you can take the `cldm_v15.yaml` file from the [ControlNet](https://github.com/lllyasviel/ControlNet/tree/main/models) repository because the TemporalNet model is a Stable Diffusion v1.5 and ControlNet model.
+
+4. Now you can run the script to convert the `.ckpt` file:
+
+```bash
+python ../diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path temporalnetv3.ckpt --original_config_file cldm_v15.yaml --dump_path ./ --controlnet
+```
+
+5. Once the conversion is done, upload your converted model and test out the resulting [pull request](https://huggingface.co/CiaraRowles/TemporalNet/discussions/13)!
+
+```bash
+git push origin pr/13:refs/pr/13
+```
+
+## Keras .pb or .h5
+
+<Tip warning={true}>
+
+🧪 This is an experimental feature. Only Stable Diffusion v1 checkpoints are supported by the Convert KerasCV Space at the moment.
+
+</Tip>
+
+[KerasCV](https://keras.io/keras_cv/) supports training for [Stable Diffusion](https://github.com/keras-team/keras-cv/blob/master/keras_cv/models/stable_diffusion) v1 and v2. However, it offers limited support for experimenting with Stable Diffusion models for inference and deployment whereas 🤗 Diffusers has a more complete set of features for this purpose, such as different [noise schedulers](https://huggingface.co/docs/diffusers/using-diffusers/schedulers), [flash attention](https://huggingface.co/docs/diffusers/optimization/xformers), and [other
+optimization techniques](https://huggingface.co/docs/diffusers/optimization/fp16).
+
+The [Convert KerasCV](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers) Space converts `.pb` or `.h5` files to PyTorch, and then wraps them in a [`StableDiffusionPipeline`] so it is ready for inference. The converted checkpoint is stored in a repository on the Hugging Face Hub.
+
+For this example, let's convert the [`sayakpaul/textual-inversion-kerasio`](https://huggingface.co/sayakpaul/textual-inversion-kerasio/tree/main) checkpoint which was trained with Textual Inversion. It uses the special token `<my-funny-cat>` to personalize images with cats.
+
+The Convert KerasCV Space allows you to input the following:
+
+* Your Hugging Face token.
+* Paths to download the UNet and text encoder weights from. Depending on how the model was trained, you don't necessarily need to provide the paths to both the UNet and text encoder. For example, Textual Inversion only requires the embeddings from the text encoder and a text-to-image model only requires the UNet weights.
+* Placeholder token is only applicable for textual inversion models.
+* The `output_repo_prefix` is the name of the repository where the converted model is stored.
+
+Click the **Submit** button to automatically convert the KerasCV checkpoint! Once the checkpoint is successfully converted, you'll see a link to the new repository containing the converted checkpoint. Follow the link to the new repository, and you'll see the Convert KerasCV Space generated a model card with an inference widget to try out the converted model.
+
+If you prefer to run inference with code, click on the **Use in Diffusers** button in the upper right corner of the model card to copy and paste the code snippet:

 ```py
 from diffusers import DiffusionPipeline

 pipeline = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
-    use_safetensors=True
+    "sayakpaul/textual-inversion-cat-kerascv_sd_diffusers_pipeline", use_safetensors=True
 )
 ```

-</hfoption>
-<hfoption id="single file">
-
-Use the [`~loaders.FromSingleFileMixin.from_single_file`] method to load a model with all the weights stored in a single safetensors file.
+Then, you can generate an image like:

 ```py
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline

-pipeline = StableDiffusionPipeline.from_single_file(
-    "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
+pipeline = DiffusionPipeline.from_pretrained(
+    "sayakpaul/textual-inversion-cat-kerascv_sd_diffusers_pipeline", use_safetensors=True
 )
+pipeline.to("cuda")
+
+placeholder_token = "<my-funny-cat-token>"
+prompt = f"two {placeholder_token} getting married, photorealistic, high quality"
+image = pipeline(prompt, num_inference_steps=50).images[0]
 ```

-</hfoption>
-</hfoptions>
+## A1111 LoRA files

-#### LoRA files
-
-[LoRA](https://hf.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) is a lightweight adapter that is fast and easy to train, making them especially popular for generating images in a certain way or style. These adapters are commonly stored in a safetensors file, and are widely popular on model sharing platforms like [civitai](https://civitai.com/).
-
-LoRAs are loaded into a base model with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method.
+[Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) (A1111) is a popular web UI for Stable Diffusion that supports model sharing platforms like [Civitai](https://civitai.com/). Models trained with the Low-Rank Adaptation (LoRA) technique are especially popular because they're fast to train and have a much smaller file size than a fully finetuned model. 🤗 Diffusers supports loading A1111 LoRA checkpoints with [`~loaders.LoraLoaderMixin.load_lora_weights`]:

 ```py
 from diffusers import StableDiffusionXLPipeline
 import torch

-# base model
 pipeline = StableDiffusionXLPipeline.from_pretrained(
    "Lykon/dreamshaper-xl-1-0", torch_dtype=torch.float16, variant="fp16"
 ).to("cuda")
+```

-# download LoRA weights
-!wget https://civitai.com/api/download/models/168776 -O blueprintify.safetensors
+Download a LoRA checkpoint from Civitai; this example uses the [Blueprintify SD XL 1.0](https://civitai.com/models/150986/blueprintify-sd-xl-10) checkpoint, but feel free to try out any LoRA checkpoint!

-# load LoRA weights
+```py
+# uncomment to download the safetensor weights
+#!wget https://civitai.com/api/download/models/168776 -O blueprintify.safetensors
+```
+
+Load the LoRA checkpoint into the pipeline with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method:
+
+```py
 pipeline.load_lora_weights(".", weight_name="blueprintify.safetensors")
+```
+
+Now you can use the pipeline to generate images:
+
+```py
 prompt = "bl3uprint, a highly detailed blueprint of the empire state building, explaining how to build all parts, many txt, blueprint grid backdrop"
 negative_prompt = "lowres, cropped, worst quality, low quality, normal quality, artifacts, signature, watermark, username, blurry, more than one bridge, bad architecture"

@@ -104,379 +174,3 @@ image
 <div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/blueprint-lora.png"/>
 </div>
-
-### ckpt
-
-> [!WARNING]
-> Pickled files may be unsafe because they can be exploited to execute malicious code. It is recommended to use safetensors files instead where possible, or convert the weights to safetensors files.
-
-PyTorch's [torch.save](https://pytorch.org/docs/stable/generated/torch.save.html) function uses Python's [pickle](https://docs.python.org/3/library/pickle.html) utility to serialize and save models. These files are saved as a ckpt file and they contain the entire model's weights.
-
-Use the [`~loaders.FromSingleFileMixin.from_single_file`] method to directly load a ckpt file.
-
-```py
-from diffusers import StableDiffusionPipeline
-
-pipeline = StableDiffusionPipeline.from_single_file(
-    "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt"
-)
-```
-
-## Storage layout
-
-There are two ways model files are organized, either in a Diffusers-multifolder layout or in a single-file layout. The Diffusers-multifolder layout is the default, and each component file (text encoder, UNet, VAE) is stored in a separate subfolder. Diffusers also supports loading models from a single-file layout where all the components are bundled together.
-
-### Diffusers-multifolder
-
-The Diffusers-multifolder layout is the default storage layout for Diffusers. Each component's (text encoder, UNet, VAE) weights are stored in a separate subfolder. The weights can be stored as safetensors or ckpt files.
-
-<div class="flex flex-row gap-4">
-  <div class="flex-1">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/multifolder-layout.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">multifolder layout</figcaption>
-  </div>
-  <div class="flex-1">
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/multifolder-unet.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">UNet subfolder</figcaption>
-  </div>
-</div>
-
-To load from Diffusers-multifolder layout, use the [`~DiffusionPipeline.from_pretrained`] method.
-
-```py
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    torch_dtype=torch.float16,
-    variant="fp16",
-    use_safetensors=True,
-).to("cuda")
-```
-
-Benefits of using the Diffusers-multifolder layout include:
-
-1. Faster to load each component file individually or in parallel.
-2. Reduced memory usage because you only load the components you need. For example, models like [SDXL Turbo](https://hf.co/stabilityai/sdxl-turbo), [SDXL Lightning](https://hf.co/ByteDance/SDXL-Lightning), and [Hyper-SD](https://hf.co/ByteDance/Hyper-SD) have the same components except for the UNet. You can reuse their shared components with the [`~DiffusionPipeline.from_pipe`] method without consuming any additional memory (take a look at the [Reuse a pipeline](./loading#reuse-a-pipeline) guide) and only load the UNet. This way, you don't need to download redundant components and unnecessarily use more memory.
-
-    ```py
-    import torch
-    from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
-
-    # download one model
-    sdxl_pipeline = StableDiffusionXLPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0",
-        torch_dtype=torch.float16,
-        variant="fp16",
-        use_safetensors=True,
-    ).to("cuda")
-
-    # switch UNet for another model
-    unet = UNet2DConditionModel.from_pretrained(
-        "stabilityai/sdxl-turbo",
-        subfolder="unet",
-        torch_dtype=torch.float16,
-        variant="fp16",
-        use_safetensors=True
-    )
-    # reuse all the same components in new model except for the UNet
-    turbo_pipeline = StableDiffusionXLPipeline.from_pipe(
-        sdxl_pipeline, unet=unet,
-    ).to("cuda")
-    turbo_pipeline.scheduler = EulerDiscreteScheduler.from_config(
-        turbo_pipeline.scheduler.config,
-        timestep+spacing="trailing"
-    )
-    image = turbo_pipeline(
-        "an astronaut riding a unicorn on mars",
-        num_inference_steps=1,
-        guidance_scale=0.0,
-    ).images[0]
-    image
-    ```
-
-3. Reduced storage requirements because if a component, such as the SDXL [VAE](https://hf.co/madebyollin/sdxl-vae-fp16-fix), is shared across multiple models, you only need to download and store a single copy of it instead of downloading and storing it multiple times. For 10 SDXL models, this can save ~3.5GB of storage. The storage savings is even greater for newer models like PixArt Sigma, where the [text encoder](https://hf.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS/tree/main/text_encoder) alone is ~19GB!
-4. Flexibility to replace a component in the model with a newer or better version.
-
-    ```py
-    from diffusers import DiffusionPipeline, AutoencoderKL
-
-    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
-    pipeline = DiffusionPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0",
-        vae=vae,
-        torch_dtype=torch.float16,
-        variant="fp16",
-        use_safetensors=True,
-    ).to("cuda")
-    ```
-
-5. More visibility and information about a model's components, which are stored in a [config.json](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/unet/config.json) file in each component subfolder.
-
-### Single-file
-
-The single-file layout stores all the model weights in a single file. All the model components (text encoder, UNet, VAE) weights are kept together instead of separately in subfolders. This can be a safetensors or ckpt file.
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/single-file-layout.png"/>
-</div>
-
-To load from a single-file layout, use the [`~loaders.FromSingleFileMixin.from_single_file`] method.
-
-```py
-import torch
-from diffusers import StableDiffusionXLPipeline
-
-pipeline = StableDiffusionXLPipeline.from_single_file(
-    "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
-    torch_dtype=torch.float16,
-    variant="fp16",
-    use_safetensors=True,
-).to("cuda")
-```
-
-Benefits of using a single-file layout include:
-
-1. Easy compatibility with diffusion interfaces such as [ComfyUI](https://github.com/comfyanonymous/ComfyUI) or [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) which commonly use a single-file layout.
-2. Easier to manage (download and share) a single file.
-
-## Convert layout and files
-
-Diffusers provides many scripts and methods to convert storage layouts and file formats to enable broader support across the diffusion ecosystem.
-
-Take a look at the [diffusers/scripts](https://github.com/huggingface/diffusers/tree/main/scripts) collection to find a script that fits your conversion needs.
-
-> [!TIP]
-> Scripts that have "`to_diffusers`" appended at the end mean they convert a model to the Diffusers-multifolder layout. Each script has their own specific set of arguments for configuring the conversion, so make sure you check what arguments are available!
-
-For example, to convert a Stable Diffusion XL model stored in Diffusers-multifolder layout to a single-file layout, run the [convert_diffusers_to_original_sdxl.py](https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_sdxl.py) script. Provide the path to the model to convert, and the path to save the converted model to. You can optionally specify whether you want to save the model as a safetensors file and whether to save the model in half-precision.
-
-```bash
-python convert_diffusers_to_original_sdxl.py --model_path path/to/model/to/convert --checkpoint_path path/to/save/model/to --use_safetensors
-```
-
-You can also save a model to Diffusers-multifolder layout with the [`~DiffusionPipeline.save_pretrained`] method. This creates a directory for you if it doesn't already exist, and it also saves the files as a safetensors file by default.
-
-```py
-from diffusers import StableDiffusionXLPipeline
-
-pipeline = StableDiffusionXLPipeline.from_single_file(
-    "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
-)
-pipeline.save_pretrained()
-```
-
-Lastly, there are also Spaces, such as [SD To Diffusers](https://hf.co/spaces/diffusers/sd-to-diffusers) and [SD-XL To Diffusers](https://hf.co/spaces/diffusers/sdxl-to-diffusers), that provide a more user-friendly interface for converting models to Diffusers-multifolder layout. This is the easiest and most convenient option for converting layouts, and it'll open a PR on your model repository with the converted files. However, this option is not as reliable as running a script, and the Space may fail for more complicated models.
-
-## Single-file layout usage
-
-Now that you're familiar with the differences between the Diffusers-multifolder and single-file layout, this section shows you how to load models and pipeline components, customize configuration options for loading, and load local files with the [`~loaders.FromSingleFileMixin.from_single_file`] method.
-
-### Load a pipeline or model
-
-Pass the file path of the pipeline or model to the [`~loaders.FromSingleFileMixin.from_single_file`] method to load it.
-
-<hfoptions id="pipeline-model">
-<hfoption id="pipeline">
-
-```py
-from diffusers import StableDiffusionXLPipeline
-
-ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
-pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path)
-```
-
-</hfoption>
-<hfoption id="model">
-
-```py
-from diffusers import StableCascadeUNet
-
-ckpt_path = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite.safetensors"
-model = StableCascadeUNet.from_single_file(ckpt_path)
-```
-
-</hfoption>
-</hfoptions>
-
-Customize components in the pipeline by passing them directly to the [`~loaders.FromSingleFileMixin.from_single_file`] method. For example, you can use a different scheduler in a pipeline.
-
-```py
-from diffusers import StableDiffusionXLPipeline, DDIMScheduler
-
-ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
-scheduler = DDIMScheduler()
-pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, scheduler=scheduler)
-```
-
-Or you could use a ControlNet model in the pipeline.
-
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-
-ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
-controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
-pipeline = StableDiffusionControlNetPipeline.from_single_file(ckpt_path, controlnet=controlnet)
-```
-
-### Customize configuration options
-
-Models have a configuration file that define their attributes like the number of inputs in a UNet. Pipelines configuration options are available in the pipeline's class. For example, if you look at the [`StableDiffusionXLInstructPix2PixPipeline`] class, there is an option to scale the image latents with the `is_cosxl_edit` parameter.
-
-These configuration files can be found in the models Hub repository or another location from which the configuration file originated (for example, a GitHub repository or locally on your device).
-
-<hfoptions id="config-file">
-<hfoption id="Hub configuration file">
-
-> [!TIP]
-> The [`~loaders.FromSingleFileMixin.from_single_file`] method automatically maps the checkpoint to the appropriate model repository, but there are cases where it is useful to use the `config` parameter. For example, if the model components in the checkpoint are different from the original checkpoint or if a checkpoint doesn't have the necessary metadata to correctly determine the configuration to use for the pipeline.
-
-The [`~loaders.FromSingleFileMixin.from_single_file`] method automatically determines the configuration to use from the configuration file in the model repository. You could also explicitly specify the configuration to use by providing the repository id to the `config` parameter.
-
-```py
-from diffusers import StableDiffusionXLPipeline
-
-ckpt_path = "https://huggingface.co/segmind/SSD-1B/blob/main/SSD-1B.safetensors"
-repo_id = "segmind/SSD-1B"
-
-pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, config=repo_id)
-```
-
-The model loads the configuration file for the [UNet](https://huggingface.co/segmind/SSD-1B/blob/main/unet/config.json), [VAE](https://huggingface.co/segmind/SSD-1B/blob/main/vae/config.json), and [text encoder](https://huggingface.co/segmind/SSD-1B/blob/main/text_encoder/config.json) from their respective subfolders in the repository.
-
-</hfoption>
-<hfoption id="original configuration file">
-
-The [`~loaders.FromSingleFileMixin.from_single_file`] method can also load the original configuration file of a pipeline that is stored elsewhere. Pass a local path or URL of the original configuration file to the `original_config` parameter.
-
-```py
-from diffusers import StableDiffusionXLPipeline
-
-ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
-original_config = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
-
-pipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, original_config=original_config)
-```
-
-> [!TIP]
-> Diffusers attempts to infer the pipeline components based on the type signatures of the pipeline class when you use `original_config` with `local_files_only=True`, instead of fetching the configuration files from the model repository on the Hub. This prevents backward breaking changes in code that can't connect to the internet to fetch the necessary configuration files.
->
-> This is not as reliable as providing a path to a local model repository with the `config` parameter, and might lead to errors during pipeline configuration. To avoid errors, run the pipeline with `local_files_only=False` once to download the appropriate pipeline configuration files to the local cache.
-
-</hfoption>
-</hfoptions>
-
-While the configuration files specify the pipeline or models default parameters, you can override them by providing the parameters directly to the [`~loaders.FromSingleFileMixin.from_single_file`] method. Any parameter supported by the model or pipeline class can be configured in this way.
-
-<hfoptions id="override">
-<hfoption id="pipeline">
-
-For example, to scale the image latents in [`StableDiffusionXLInstructPix2PixPipeline`] pass the `is_cosxl_edit` parameter.
-
-```python
-from diffusers import StableDiffusionXLInstructPix2PixPipeline
-
-ckpt_path = "https://huggingface.co/stabilityai/cosxl/blob/main/cosxl_edit.safetensors"
-pipeline = StableDiffusionXLInstructPix2PixPipeline.from_single_file(ckpt_path, config="diffusers/sdxl-instructpix2pix-768", is_cosxl_edit=True)
-```
-
-</hfoption>
-<hfoption id="model">
-
-For example, to upcast the attention dimensions in a [`UNet2DConditionModel`] pass the `upcast_attention` parameter.
-
-```python
-from diffusers import UNet2DConditionModel
-
-ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors"
-model = UNet2DConditionModel.from_single_file(ckpt_path, upcast_attention=True)
-```
-
-</hfoption>
-</hfoptions>
-
-### Local files
-
-In Diffusers>=v0.28.0, the [`~loaders.FromSingleFileMixin.from_single_file`] method attempts to configure a pipeline or model by inferring the model type from the keys in the checkpoint file. The inferred model type is used to determine the appropriate model repository on the Hugging Face Hub to configure the model or pipeline.
-
-For example, any single file checkpoint based on the Stable Diffusion XL base model will use the [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) model repository to configure the pipeline.
-
-But if you're working in an environment with restricted internet access, you should download the configuration files with the [`~huggingface_hub.snapshot_download`] function, and the model checkpoint with the [`~huggingface_hub.hf_hub_download`] function. By default, these files are downloaded to the Hugging Face Hub [cache directory](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache), but you can specify a preferred directory to download the files to with the `local_dir` parameter.
-
-Pass the configuration and checkpoint paths to the [`~loaders.FromSingleFileMixin.from_single_file`] method to load locally.
-
-<hfoptions id="local">
-<hfoption id="Hub cache directory">
-
-```python
-from huggingface_hub import hf_hub_download, snapshot_download
-
-my_local_checkpoint_path = hf_hub_download(
-    repo_id="segmind/SSD-1B",
-    filename="SSD-1B.safetensors"
-)
-
-my_local_config_path = snapshot_download(
-    repo_id="segmind/SSD-1B",
-    allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
-)
-
-pipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
-```
-
-</hfoption>
-<hfoption id="specific local directory">
-
-```python
-from huggingface_hub import hf_hub_download, snapshot_download
-
-my_local_checkpoint_path = hf_hub_download(
-    repo_id="segmind/SSD-1B",
-    filename="SSD-1B.safetensors"
-    local_dir="my_local_checkpoints"
-)
-
-my_local_config_path = snapshot_download(
-    repo_id="segmind/SSD-1B",
-    allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
-    local_dir="my_local_config"
-)
-
-pipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
-```
-
-</hfoption>
-</hfoptions>
-
-#### Local files without symlink
-
-> [!TIP]
-> In huggingface_hub>=v0.23.0, the `local_dir_use_symlinks` argument isn't necessary for the [`~huggingface_hub.hf_hub_download`] and [`~huggingface_hub.snapshot_download`] functions.
-
-The [`~loaders.FromSingleFileMixin.from_single_file`] method relies on the [huggingface_hub](https://hf.co/docs/huggingface_hub/index) caching mechanism to fetch and store checkpoints and configuration files for models and pipelines. If you're working with a file system that does not support symlinking, you should download the checkpoint file to a local directory first, and disable symlinking with the `local_dir_use_symlink=False` parameter in the [`~huggingface_hub.hf_hub_download`] function and [`~huggingface_hub.snapshot_download`] functions.
-
-```python
-from huggingface_hub import hf_hub_download, snapshot_download
-
-my_local_checkpoint_path = hf_hub_download(
-    repo_id="segmind/SSD-1B",
-    filename="SSD-1B.safetensors"
-    local_dir="my_local_checkpoints",
-    local_dir_use_symlinks=False
-)
-print("My local checkpoint: ", my_local_checkpoint_path)
-
-my_local_config_path = snapshot_download(
-    repo_id="segmind/SSD-1B",
-    allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
-    local_dir_use_symlinks=False,
-)
-print("My local config: ", my_local_config_path)
-
-```
-
-Then you can pass the local paths to the `pretrained_model_link_or_path` and `config` parameters.
-
-```python
-pipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
-```
@@ -1,235 +0,0 @@
-<!--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.
-->
-
-# Scheduler features
-
-The scheduler is an important component of any diffusion model because it controls the entire denoising (or sampling) process. There are many types of schedulers, some are optimized for speed and some for quality. With Diffusers, you can modify the scheduler configuration to use custom noise schedules, sigmas, and rescale the noise schedule. Changing these parameters can have profound effects on inference quality and speed.
-
-This guide will demonstrate how to use these features to improve inference quality.
-
-> [!TIP]
-> Diffusers currently only supports the `timesteps` and `sigmas` parameters for a select list of schedulers and pipelines. Feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you want to extend these parameters to a scheduler and pipeline that does not currently support it!
-
-## Timestep schedules
-
-The timestep or noise schedule determines the amount of noise at each sampling step. The scheduler uses this to generate an image with the corresponding amount of noise at each step. The timestep schedule is generated from the scheduler's default configuration, but you can customize the scheduler to use new and optimized sampling schedules that aren't in Diffusers yet.
-
-For example, [Align Your Steps (AYS)](https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/) is a method for optimizing a sampling schedule to generate a high-quality image in as little as 10 steps. The optimal [10-step schedule](https://github.com/huggingface/diffusers/blob/a7bf77fc284810483f1e60afe34d1d27ad91ce2e/src/diffusers/schedulers/scheduling_utils.py#L51) for Stable Diffusion XL is:
-
-```py
-from diffusers.schedulers import AysSchedules
-
-sampling_schedule = AysSchedules["StableDiffusionXLTimesteps"]
-print(sampling_schedule)
-"[999, 845, 730, 587, 443, 310, 193, 116, 53, 13]"
-```
-
-You can use the AYS sampling schedule in a pipeline by passing it to the `timesteps` parameter.
-
-```py
-pipeline = StableDiffusionXLPipeline.from_pretrained(
-    "SG161222/RealVisXL_V4.0",
-    torch_dtype=torch.float16,
-    variant="fp16",
-).to("cuda")
-pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, algorithm_type="sde-dpmsolver++")
-
-prompt = "A cinematic shot of a cute little rabbit wearing a jacket and doing a thumbs up"
-generator = torch.Generator(device="cpu").manual_seed(2487854446)
-image = pipeline(
-    prompt=prompt,
-    negative_prompt="",
-    generator=generator,
-    timesteps=sampling_schedule,
-).images[0]
-```
-
-<div class="flex gap-4">
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ays.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">AYS timestep schedule 10 steps</figcaption>
-  </div>
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/10.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">Linearly-spaced timestep schedule 10 steps</figcaption>
-  </div>
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/25.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">Linearly-spaced timestep schedule 25 steps</figcaption>
-  </div>
-</div>
-
-## Timestep spacing
-
-The way sample steps are selected in the schedule can affect the quality of the generated image, especially with respect to [rescaling the noise schedule](#rescale-noise-schedule), which can enable a model to generate much brighter or darker images. Diffusers provides three timestep spacing methods:
-
- `leading` creates evenly spaced steps
- `linspace` includes the first and last steps and evenly selects the remaining intermediate steps
- `trailing` only includes the last step and evenly selects the remaining intermediate steps starting from the end
-
-It is recommended to use the `trailing` spacing method because it generates higher quality images with more details when there are fewer sample steps. But the difference in quality is not as obvious for more standard sample step values.
-
-```py
-import torch
-from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler
-
-pipeline = StableDiffusionXLPipeline.from_pretrained(
-    "SG161222/RealVisXL_V4.0",
-    torch_dtype=torch.float16,
-    variant="fp16",
-).to("cuda")
-pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing")
-
-prompt = "A cinematic shot of a cute little black cat sitting on a pumpkin at night"
-generator = torch.Generator(device="cpu").manual_seed(2487854446)
-image = pipeline(
-    prompt=prompt,
-    negative_prompt="",
-    generator=generator,
-    num_inference_steps=5,
-).images[0]
-image
-```
-
-<div class="flex gap-4">
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/trailing_spacing.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">trailing spacing after 5 steps</figcaption>
-  </div>
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/leading_spacing.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">leading spacing after 5 steps</figcaption>
-  </div>
-</div>
-
-## Sigmas
-
-The `sigmas` parameter is the amount of noise added at each timestep according to the timestep schedule. Like the `timesteps` parameter, you can customize the `sigmas` parameter to control how much noise is added at each step. When you use a custom `sigmas` value, the `timesteps` are calculated from the custom `sigmas` value and the default scheduler configuration is ignored.
-
-For example, you can manually pass the [sigmas](https://github.com/huggingface/diffusers/blob/6529ee67ec02fcf58d2fd9242164ea002b351d75/src/diffusers/schedulers/scheduling_utils.py#L55) for something like the 10-step AYS schedule from before to the pipeline.
-
-```py
-import torch
-
-from diffusers import DiffusionPipeline, EulerDiscreteScheduler
-
-model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-pipeline = DiffusionPipeline.from_pretrained(
-  "stabilityai/stable-diffusion-xl-base-1.0",
-  torch_dtype=torch.float16,
-  variant="fp16",
-).to("cuda")
-pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-
-sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.0]
-prompt = "anthropomorphic capybara wearing a suit and working with a computer"
-generator = torch.Generator(device='cuda').manual_seed(123)
-image = pipeline(
-    prompt=prompt,
-    num_inference_steps=10,
-    sigmas=sigmas,
-    generator=generator
-).images[0]
-```
-
-When you take a look at the scheduler's `timesteps` parameter, you'll see that it is the same as the AYS timestep schedule because the `timestep` schedule is calculated from the `sigmas`.
-
-```py
-print(f" timesteps: {pipe.scheduler.timesteps}")
-"timesteps: tensor([999., 845., 730., 587., 443., 310., 193., 116.,  53.,  13.], device='cuda:0')"
-```
-
-### Karras sigmas
-
-> [!TIP]
-> Refer to the scheduler API [overview](../api/schedulers/overview) for a list of schedulers that support Karras sigmas.
->
-> Karras sigmas should not be used for models that weren't trained with them. For example, the base Stable Diffusion XL model shouldn't use Karras sigmas but the [DreamShaperXL](https://hf.co/Lykon/dreamshaper-xl-1-0) model can since they are trained with Karras sigmas.
-
-Karras scheduler's use the timestep schedule and sigmas from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://hf.co/papers/2206.00364) paper. This scheduler variant applies a smaller amount of noise per step as it approaches the end of the sampling process compared to other schedulers, and can increase the level of details in the generated image.
-
-Enable Karras sigmas by setting `use_karras_sigmas=True` in the scheduler.
-
-```py
-import torch
-from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler
-
-pipeline = StableDiffusionXLPipeline.from_pretrained(
-    "SG161222/RealVisXL_V4.0",
-    torch_dtype=torch.float16,
-    variant="fp16",
-).to("cuda")
-pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, algorithm_type="sde-dpmsolver++", use_karras_sigmas=True)
-
-prompt = "A cinematic shot of a cute little rabbit wearing a jacket and doing a thumbs up"
-generator = torch.Generator(device="cpu").manual_seed(2487854446)
-image = pipeline(
-    prompt=prompt,
-    negative_prompt="",
-    generator=generator,
-).images[0]
-```
-
-<div class="flex gap-4">
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/karras_sigmas_true.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">Karras sigmas enabled</figcaption>
-  </div>
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/karras_sigmas_false.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">Karras sigmas disabled</figcaption>
-  </div>
-</div>
-
-## Rescale noise schedule
-
-In the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://hf.co/papers/2305.08891) paper, the authors discovered that common noise schedules allowed some signal to leak into the last timestep. This signal leakage at inference can cause models to only generate images with medium brightness. By enforcing a zero signal-to-noise ratio (SNR) for the timstep schedule and sampling from the last timestep, the model can be improved to generate very bright or dark images.
-
-> [!TIP]
-> For inference, you need a model that has been trained with *v_prediction*. To train your own model with *v_prediction*, add the following flag to the [train_text_to_image.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) or [train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) scripts.
->
-> ```bash
-> --prediction_type="v_prediction"
-> ```
-
-For example, load the [ptx0/pseudo-journey-v2](https://hf.co/ptx0/pseudo-journey-v2) checkpoint which was trained with `v_prediction` and the [`DDIMScheduler`]. Configure the following parameters in the [`DDIMScheduler`]:
-
-* `rescale_betas_zero_snr=True` to rescale the noise schedule to zero SNR
-* `timestep_spacing="trailing"` to start sampling from the last timestep
-
-Set `guidance_rescale` in the pipeline to prevent over-exposure. A lower value increases brightness but some of the details may appear washed out.
-
-```py
-from diffusers import DiffusionPipeline, DDIMScheduler
-
-pipeline = DiffusionPipeline.from_pretrained("ptx0/pseudo-journey-v2", use_safetensors=True)
-
-pipeline.scheduler = DDIMScheduler.from_config(
-    pipeline.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing="trailing"
-)
-pipeline.to("cuda")
-prompt = "cinematic photo of a snowy mountain at night with the northern lights aurora borealis overhead, 35mm photograph, film, professional, 4k, highly detailed"
-generator = torch.Generator(device="cpu").manual_seed(23)
-image = pipeline(prompt, guidance_rescale=0.7, generator=generator).images[0]
-image
-```
-
-<div class="flex gap-4">
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/no-zero-snr.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">default Stable Diffusion v2-1 image</figcaption>
-  </div>
-  <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/zero-snr.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">image with zero SNR and trailing timestep spacing enabled</figcaption>
-  </div>
-</div>
@@ -212,6 +212,62 @@ images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).
 images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
 ```

+## Custom Timestep Schedules
+
+With all our schedulers, you can choose one of the popular timestep schedules using configurations such as `timestep_spacing`, `interpolation_type`, and `use_karras_sigmas`. Some schedulers also provide the flexibility to use a custom timestep schedule. You can use any list of arbitrary timesteps, we will use the AYS timestep schedule here as example. It is a set of 10-step optimized timestep schedules released by researchers from Nvidia that can achieve significantly better quality compared to the preset timestep schedules. You can read more about their research [here](https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/). 
+
+```python
+from diffusers.schedulers import AysSchedules
+sampling_schedule = AysSchedules["StableDiffusionXLTimesteps"]
+print(sampling_schedule)
+```
+```
+[999, 845, 730, 587, 443, 310, 193, 116, 53, 13]
+```
+
+You can then create a pipeline and pass this custom timestep schedule to it as `timesteps`.
+
+```python
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "SG161222/RealVisXL_V4.0",
+    torch_dtype=torch.float16,
+    variant="fp16",
+).to("cuda")
+
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, algorithm_type="sde-dpmsolver++")
+
+prompt = "A cinematic shot of a cute little rabbit wearing a jacket and doing a thumbs up"
+
+generator = torch.Generator(device="cpu").manual_seed(2487854446)
+
+image = pipe(
+    prompt=prompt,
+    negative_prompt="",
+    generator=generator,
+    timesteps=sampling_schedule,
+).images[0]
+```
+The generated image has better quality than the default linear timestep schedule for the same number of steps, and it is similar to the default timestep scheduler when running for 25 steps.
+
+<div class="flex gap-4">
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ays.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">AYS timestep schedule 10 steps</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/10.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">Linearly-spaced timestep schedule 10 steps</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/25.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">Linearly-spaced timestep schedule 25 steps</figcaption>
+  </div>
+</div>
+
+> [!TIP]
+> 🤗 Diffusers currently only supports `timesteps` and `sigmas` for a selected list of schedulers and pipelines, but feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you want to extend feature to a scheduler and pipeline that does not currently support it!
+
+
 ## Models

 Models are loaded from the [`ModelMixin.from_pretrained`] method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, [`~ModelMixin.from_pretrained`] reuses files in the cache instead of re-downloading them.
@@ -0,0 +1,84 @@
+<!--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.
+-->
+
+# Load safetensors
+
+[[open-in-colab]]
+
+[safetensors](https://github.com/huggingface/safetensors) is a safe and fast file format for storing and loading tensors. Typically, PyTorch model weights are saved or *pickled* into a `.bin` file with Python's [`pickle`](https://docs.python.org/3/library/pickle.html) utility. However, `pickle` is not secure and pickled files may contain malicious code that can be executed. safetensors is a secure alternative to `pickle`, making it ideal for sharing model weights.
+
+This guide will show you how you load `.safetensor` files, and how to convert Stable Diffusion model weights stored in other formats to `.safetensor`. Before you start, make sure you have safetensors installed:
+
+```py
+# uncomment to install the necessary libraries in Colab
+#!pip install safetensors
+```
+
+If you look at the [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main) repository, you'll see weights inside the `text_encoder`, `unet` and `vae` subfolders are stored in the `.safetensors` format. By default, 🤗 Diffusers automatically loads these `.safetensors` files from their subfolders if they're available in the model repository.
+
+For more explicit control, you can optionally set `use_safetensors=True` (if `safetensors` is not installed, you'll get an error message asking you to install it):
+
+```py
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True)
+```
+
+However, model weights are not necessarily stored in separate subfolders like in the example above. Sometimes, all the weights are stored in a single `.safetensors` file. In this case, if the weights are Stable Diffusion weights, you can load the file directly with the [`~diffusers.loaders.FromSingleFileMixin.from_single_file`] method:
+
+```py
+from diffusers import StableDiffusionPipeline
+
+pipeline = StableDiffusionPipeline.from_single_file(
+    "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
+)
+```
+
+## Convert to safetensors
+
+Not all weights on the Hub are available in the `.safetensors` format, and you may encounter weights stored as `.bin`. In this case, use the [Convert Space](https://huggingface.co/spaces/diffusers/convert) to convert the weights to `.safetensors`. The Convert Space downloads the pickled weights, converts them, and opens a Pull Request to upload the newly converted `.safetensors` file on the Hub. This way, if there is any malicious code contained in the pickled files, they're uploaded to the Hub - which has a [security scanner](https://huggingface.co/docs/hub/security-pickle#hubs-security-scanner) to detect unsafe files and suspicious pickle imports - instead of your computer.
+
+You can use the model with the new `.safetensors` weights by specifying the reference to the Pull Request in the `revision` parameter (you can also test it in this [Check PR](https://huggingface.co/spaces/diffusers/check_pr) Space on the Hub), for example `refs/pr/22`:
+
+```py
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-2-1", revision="refs/pr/22", use_safetensors=True
+)
+```
+
+## Why use safetensors?
+
+There are several reasons for using safetensors:
+
+- Safety is the number one reason for using safetensors. As open-source and model distribution grows, it is important to be able to trust the model weights you downloaded don't contain any malicious code. The current size of the header in safetensors prevents parsing extremely large JSON files.
+- Loading speed between switching models is another reason to use safetensors, which performs zero-copy of the tensors. It is especially fast compared to `pickle` if you're loading the weights to CPU (the default case), and just as fast if not faster when directly loading the weights to GPU. You'll only notice the performance difference if the model is already loaded, and not if you're downloading the weights or loading the model for the first time.
+
+	The time it takes to load the entire pipeline:
+
+	```py
+ 	from diffusers import StableDiffusionPipeline
+
+ 	pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", use_safetensors=True)
+ 	"Loaded in safetensors 0:00:02.033658"
+ 	"Loaded in PyTorch 0:00:02.663379"
+	```
+
+	But the actual time it takes to load 500MB of the model weights is only:
+
+	```bash
+	safetensors: 3.4873ms
+	PyTorch: 172.7537ms
+	```
+
+- Lazy loading is also supported in safetensors, which is useful in distributed settings to only load some of the tensors. This format allowed the [BLOOM](https://huggingface.co/bigscience/bloom) model to be loaded in 45 seconds on 8 GPUs instead of 10 minutes with regular PyTorch weights.
@@ -34,7 +34,7 @@ Stable Diffusion XL은 Dustin Podell, Zion English, Kyle Lacey, Andreas Blattman
 SDXL을 사용하기 전에 `transformers`, `accelerate`, `safetensors` 와 `invisible_watermark`를 설치하세요.
 다음과 같이 라이브러리를 설치할 수 있습니다:

-```sh
+```
 pip install transformers
 pip install accelerate
 pip install safetensors
@@ -46,7 +46,7 @@ pip install invisible-watermark>=0.2.0
 Stable Diffusion XL로 이미지를 생성할 때 워터마크가 보이지 않도록 추가하는 것을 권장하는데, 이는 다운스트림(downstream) 어플리케이션에서 기계에 합성되었는지를 식별하는데 도움을 줄 수 있습니다. 그렇게 하려면 [invisible_watermark 라이브러리](https://pypi.org/project/invisible-watermark/)를 통해 설치해주세요:


-```sh
+```
 pip install invisible-watermark>=0.2.0
 ```

@@ -75,11 +75,11 @@ prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
 image = pipe(prompt=prompt).images[0]
 ```

-### Image-to-image
+### Image-to-image 

 *image-to-image*를 위해 다음과 같이 SDXL을 사용할 수 있습니다:

-```py
+```py 
 import torch
 from diffusers import StableDiffusionXLImg2ImgPipeline
 from diffusers.utils import load_image
@@ -99,7 +99,7 @@ image = pipe(prompt, image=init_image).images[0]

 *inpainting*를 위해 다음과 같이 SDXL을 사용할 수 있습니다:

-```py
+```py 
 import torch
 from diffusers import StableDiffusionXLInpaintPipeline
 from diffusers.utils import load_image
@@ -352,7 +352,7 @@ out-of-memory 에러가 난다면, [`StableDiffusionXLPipeline.enable_model_cpu_

 **참고** Stable Diffusion XL을 `torch`가 2.0 버전 미만에서 실행시키고 싶을 때, xformers 어텐션을 사용해주세요:

-```sh
+```
 pip install xformers
 ```

@@ -93,13 +93,13 @@ cd diffusers

 **PyTorch의 경우**

-```sh
+```
 pip install -e ".[torch]"
 ```

 **Flax의 경우**

-```sh
+```
 pip install -e ".[flax]"
 ```

@@ -19,13 +19,13 @@ specific language governing permissions and limitations under the License.

 다음 명령어로 ONNX Runtime를 지원하는 🤗 Optimum를 설치합니다:

-```sh
+```
 pip install optimum["onnxruntime"]
 ```

 ## Stable Diffusion 추론

-아래 코드는 ONNX 런타임을 사용하는 방법을 보여줍니다. `StableDiffusionPipeline` 대신 `OnnxStableDiffusionPipeline`을 사용해야 합니다.
+아래 코드는 ONNX 런타임을 사용하는 방법을 보여줍니다. `StableDiffusionPipeline` 대신 `OnnxStableDiffusionPipeline`을 사용해야 합니다. 
 PyTorch 모델을 불러오고 즉시 ONNX 형식으로 변환하려는 경우 `export=True`로 설정합니다.

 ```python
@@ -38,7 +38,7 @@ images = pipe(prompt).images[0]
 pipe.save_pretrained("./onnx-stable-diffusion-v1-5")
 ```

-파이프라인을 ONNX 형식으로 오프라인으로 내보내고 나중에 추론에 사용하려는 경우,
+파이프라인을 ONNX 형식으로 오프라인으로 내보내고 나중에 추론에 사용하려는 경우, 
 [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) 명령어를 사용할 수 있습니다:

 ```bash
@@ -47,7 +47,7 @@ optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/

 그 다음 추론을 수행합니다:

-```python
+```python 
 from optimum.onnxruntime import ORTStableDiffusionPipeline

 model_id = "sd_v15_onnx"
@@ -19,7 +19,7 @@ specific language governing permissions and limitations under the License.

 다음 명령어로 🤗 Optimum을 설치합니다:

-```sh
+```
 pip install optimum["openvino"]
 ```

@@ -59,7 +59,7 @@ image

 먼저 `compel` 라이브러리를 설치해야합니다:

-```sh
+```
 pip install compel
 ```

@@ -95,13 +95,13 @@ cd diffusers

 **PyTorch**

-```sh
+```
 pip install -e ".[torch]"
 ```

 **Flax**

-```sh
+```
 pip install -e ".[flax]"
 ```

@@ -71,7 +71,7 @@ from diffusers.utils.import_utils import is_xformers_available


 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -758,7 +758,7 @@ class TokenEmbeddingsHandler:

            idx += 1

-    # Copied from train_dreambooth_lora_sdxl_advanced.py
+    # copied from train_dreambooth_lora_sdxl_advanced.py
    def save_embeddings(self, file_path: str):
        assert self.train_ids is not None, "Initialize new tokens before saving embeddings."
        tensors = {}
@@ -78,7 +78,7 @@ from diffusers.utils.torch_utils import is_compiled_module


 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -69,7 +69,6 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 |   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) |

 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.

@@ -240,12 +239,12 @@ pipeline_output = pipe(
    # denoising_steps=10,     # (optional) Number of denoising steps of each inference pass. Default: 10.
    # ensemble_size=10,       # (optional) Number of inference passes in the ensemble. Default: 10.
    # ------------------------------------------------
-
+    
    # ----- recommended setting for LCM version ------
    # denoising_steps=4,
    # ensemble_size=5,
    # -------------------------------------------------
-
+    
    # processing_res=768,     # (optional) Maximum resolution of processing. If set to 0: will not resize at all. Defaults to 768.
    # match_input_res=True,   # (optional) Resize depth prediction to match input resolution.
    # batch_size=0,           # (optional) Inference batch size, no bigger than `num_ensemble`. If set to 0, the script will automatically decide the proper batch size. Defaults to 0.
@@ -1032,7 +1031,7 @@ image = pipe().images[0]

 Make sure you have @crowsonkb's <https://github.com/crowsonkb/k-diffusion> installed:

-```sh
+```
 pip install k-diffusion
 ```

@@ -1854,13 +1853,13 @@ To use this pipeline, you need to:

 You can simply use pip to install IPEX with the latest version.

-```sh
+```python
 python -m pip install intel_extension_for_pytorch
 ```

 **Note:** To install a specific version, run with the following command:

-```sh
+```
 python -m pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
 ```

@@ -1958,13 +1957,13 @@ To use this pipeline, you need to:

 You can simply use pip to install IPEX with the latest version.

-```sh
+```python
 python -m pip install intel_extension_for_pytorch
 ```

 **Note:** To install a specific version, run with the following command:

-```sh
+```
 python -m pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
 ```

@@ -3010,8 +3009,8 @@ This code implements a pipeline for the Stable Diffusion model, enabling the div

 ### Sample Code

-```py
-from examples.community.regional_prompting_stable_diffusion import RegionalPromptingStableDiffusionPipeline
+```
+from from examples.community.regional_prompting_stable_diffusion import RegionalPromptingStableDiffusionPipeline
 pipe = RegionalPromptingStableDiffusionPipeline.from_single_file(model_path, vae=vae)

 rp_args = {
@@ -4036,93 +4035,6 @@ onestep_image = pipe(prompt, num_inference_steps=1).images[0]
 multistep_image = pipe(prompt, num_inference_steps=4).images[0]
 ```

-### FRESCO
-
-This is the Diffusers implementation of zero-shot video-to-video translation pipeline [FRESCO](https://github.com/williamyang1991/FRESCO) (without Ebsynth postprocessing and background smooth). To run the code, please install gmflow. Then modify the path in `gmflow_dir`. After that, you can run the pipeline with:
-
-```py
-from PIL import Image
-import cv2
-import torch
-import numpy as np
-
-from diffusers import ControlNetModel,DDIMScheduler, DiffusionPipeline
-import sys
-gmflow_dir = "/path/to/gmflow"
-sys.path.insert(0, gmflow_dir)
-
-def video_to_frame(video_path: str, interval: int):
-    vidcap = cv2.VideoCapture(video_path)
-    success = True
-
-    count = 0
-    res = []
-    while success:
-        count += 1
-        success, image = vidcap.read()
-        if count % interval != 1:
-            continue
-        if image is not None:
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            res.append(image)
-            if len(res) >= 8:
-                break
-
-    vidcap.release()
-    return res
-
-
-input_video_path = 'https://github.com/williamyang1991/FRESCO/raw/main/data/car-turn.mp4'
-output_video_path = 'car.gif'
-
-# You can use any fintuned SD here
-model_path = 'SG161222/Realistic_Vision_V2.0'
-
-prompt = 'a red car turns in the winter'
-a_prompt = ', RAW photo, subject, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3, '
-n_prompt = '(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation'
-
-input_interval = 5
-frames = video_to_frame(
-    input_video_path, input_interval)
-
-control_frames = []
-# get canny image
-for frame in frames:
-    image = cv2.Canny(frame, 50, 100)
-    np_image = np.array(image)
-    np_image = np_image[:, :, None]
-    np_image = np.concatenate([np_image, np_image, np_image], axis=2)
-    canny_image = Image.fromarray(np_image)
-    control_frames.append(canny_image)
-
-# You can use any ControlNet here
-controlnet = ControlNetModel.from_pretrained(
-    "lllyasviel/sd-controlnet-canny").to('cuda')
-
-pipe = DiffusionPipeline.from_pretrained(
-    model_path, controlnet=controlnet, custom_pipeline='fresco_v2v').to('cuda')
-pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-
-generator = torch.manual_seed(0)
-frames = [Image.fromarray(frame) for frame in frames]
-
-output_frames = pipe(
-    prompt + a_prompt,
-    frames,
-    control_frames,
-    num_inference_steps=20,
-    strength=0.75,
-    controlnet_conditioning_scale=0.7,
-    generator=generator,
-    negative_prompt=n_prompt
-).images
-
-output_frames[0].save(output_video_path, save_all=True,
-                 append_images=output_frames[1:], duration=100, loop=0)
-
-```
-
 # Perturbed-Attention Guidance

 [Project](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) / [arXiv](https://arxiv.org/abs/2403.17377) / [GitHub](https://github.com/KU-CVLAB/Perturbed-Attention-Guidance)
@@ -4131,7 +4043,7 @@ This implementation is based on [Diffusers](https://huggingface.co/docs/diffuser

 ## Example Usage

-```py
+```
 import os
 import torch

@@ -25,7 +25,7 @@ from diffusers.utils.torch_utils import randn_tensor

 EXAMPLE_DOC_STRING = """
    Examples:
-        ```py
+        ```
        from io import BytesIO

        import requests
@@ -1,468 +0,0 @@
-# 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.
-
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
-
-from diffusers.configuration_utils import register_to_config
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.models.autoencoders import AutoencoderKL
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel, UNet2DConditionOutput
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class UNet2DConditionModelHighResFix(UNet2DConditionModel):
-    r"""
-    A conditional 2D UNet model that applies Kohya fix proposed for high resolution image generation.
-
-    This model inherits from [`UNet2DConditionModel`]. Check the superclass documentation for learning about all the parameters.
-
-    Parameters:
-        high_res_fix (`List[Dict]`, *optional*, defaults to `[{'timestep': 600, 'scale_factor': 0.5, 'block_num': 1}]`):
-            Enables Kohya fix for high resolution generation. The activation maps are scaled based on the scale_factor up to the timestep at specified block_num.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(self, high_res_fix: List[Dict] = [{"timestep": 600, "scale_factor": 0.5, "block_num": 1}], **kwargs):
-        super().__init__(**kwargs)
-        if high_res_fix:
-            self.config.high_res_fix = sorted(high_res_fix, key=lambda x: x["timestep"], reverse=True)
-
-    @classmethod
-    def _resize(cls, sample, target=None, scale_factor=1, mode="bicubic"):
-        dtype = sample.dtype
-        if dtype == torch.bfloat16:
-            sample = sample.to(torch.float32)
-
-        if target is not None:
-            if sample.shape[-2:] != target.shape[-2:]:
-                sample = nn.functional.interpolate(sample, size=target.shape[-2:], mode=mode, align_corners=False)
-        elif scale_factor != 1:
-            sample = nn.functional.interpolate(sample, scale_factor=scale_factor, mode=mode, align_corners=False)
-
-        return sample.to(dtype)
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        timestep: Union[torch.Tensor, float, int],
-        encoder_hidden_states: torch.Tensor,
-        class_labels: Optional[torch.Tensor] = None,
-        timestep_cond: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
-        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
-        mid_block_additional_residual: Optional[torch.Tensor] = None,
-        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ) -> Union[UNet2DConditionOutput, Tuple]:
-        r"""
-        The [`UNet2DConditionModel`] forward method.
-
-        Args:
-            sample (`torch.FloatTensor`):
-                The noisy input tensor with the following shape `(batch, channel, height, width)`.
-            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
-            encoder_hidden_states (`torch.FloatTensor`):
-                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
-            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
-                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
-            timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
-                Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
-                through the `self.time_embedding` layer to obtain the timestep embeddings.
-            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
-                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
-                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
-                negative values to the attention scores corresponding to "discard" tokens.
-            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).
-            added_cond_kwargs: (`dict`, *optional*):
-                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
-                are passed along to the UNet blocks.
-            down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
-                A tuple of tensors that if specified are added to the residuals of down unet blocks.
-            mid_block_additional_residual: (`torch.Tensor`, *optional*):
-                A tensor that if specified is added to the residual of the middle unet block.
-            down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
-                additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
-            encoder_attention_mask (`torch.Tensor`):
-                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
-                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
-                which adds large negative values to the attention scores corresponding to "discard" tokens.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
-                If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is the sample tensor.
-        """
-        # By default samples have to be AT least a multiple of the overall upsampling factor.
-        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
-        # However, the upsampling interpolation output size can be forced to fit any upsampling size
-        # on the fly if necessary.
-        default_overall_up_factor = 2**self.num_upsamplers
-
-        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
-        forward_upsample_size = False
-        upsample_size = None
-
-        for dim in sample.shape[-2:]:
-            if dim % default_overall_up_factor != 0:
-                # Forward upsample size to force interpolation output size.
-                forward_upsample_size = True
-                break
-
-        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
-        # 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:
-            # 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(sample.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:
-            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
-            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
-
-        # 0. center input if necessary
-        if self.config.center_input_sample:
-            sample = 2 * sample - 1.0
-
-        # 1. time
-        t_emb = self.get_time_embed(sample=sample, timestep=timestep)
-        emb = self.time_embedding(t_emb, timestep_cond)
-        aug_emb = None
-
-        class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
-        if class_emb is not None:
-            if self.config.class_embeddings_concat:
-                emb = torch.cat([emb, class_emb], dim=-1)
-            else:
-                emb = emb + class_emb
-
-        aug_emb = self.get_aug_embed(
-            emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
-        )
-        if self.config.addition_embed_type == "image_hint":
-            aug_emb, hint = aug_emb
-            sample = torch.cat([sample, hint], dim=1)
-
-        emb = emb + aug_emb if aug_emb is not None else emb
-
-        if self.time_embed_act is not None:
-            emb = self.time_embed_act(emb)
-
-        encoder_hidden_states = self.process_encoder_hidden_states(
-            encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
-        )
-
-        # 2. pre-process
-        sample = self.conv_in(sample)
-
-        # 2.5 GLIGEN position net
-        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
-            cross_attention_kwargs = cross_attention_kwargs.copy()
-            gligen_args = cross_attention_kwargs.pop("gligen")
-            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
-
-        # 3. down
-        # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
-        # to the internal blocks and will raise deprecation warnings. this will be confusing for our users.
-        if cross_attention_kwargs is not None:
-            cross_attention_kwargs = cross_attention_kwargs.copy()
-            lora_scale = cross_attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-
-        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
-        # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
-        is_adapter = down_intrablock_additional_residuals is not None
-        # maintain backward compatibility for legacy usage, where
-        #       T2I-Adapter and ControlNet both use down_block_additional_residuals arg
-        #       but can only use one or the other
-        if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
-            deprecate(
-                "T2I should not use down_block_additional_residuals",
-                "1.3.0",
-                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
-                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
-                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
-                standard_warn=False,
-            )
-            down_intrablock_additional_residuals = down_block_additional_residuals
-            is_adapter = True
-
-        down_block_res_samples = (sample,)
-        for down_i, downsample_block in enumerate(self.down_blocks):
-            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
-                # For t2i-adapter CrossAttnDownBlock2D
-                additional_residuals = {}
-                if is_adapter and len(down_intrablock_additional_residuals) > 0:
-                    additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
-
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                    **additional_residuals,
-                )
-
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
-                if is_adapter and len(down_intrablock_additional_residuals) > 0:
-                    sample += down_intrablock_additional_residuals.pop(0)
-
-            down_block_res_samples += res_samples
-
-            # kohya high res fix
-            if self.config.high_res_fix:
-                for high_res_fix in self.config.high_res_fix:
-                    if timestep > high_res_fix["timestep"] and down_i == high_res_fix["block_num"]:
-                        sample = self.__class__._resize(sample, scale_factor=high_res_fix["scale_factor"])
-                        break
-
-        if is_controlnet:
-            new_down_block_res_samples = ()
-
-            for down_block_res_sample, down_block_additional_residual in zip(
-                down_block_res_samples, down_block_additional_residuals
-            ):
-                down_block_res_sample = down_block_res_sample + down_block_additional_residual
-                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
-
-            down_block_res_samples = new_down_block_res_samples
-
-        # 4. mid
-        if self.mid_block is not None:
-            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
-                sample = self.mid_block(
-                    sample,
-                    emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    attention_mask=attention_mask,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    encoder_attention_mask=encoder_attention_mask,
-                )
-            else:
-                sample = self.mid_block(sample, emb)
-
-            # To support T2I-Adapter-XL
-            if (
-                is_adapter
-                and len(down_intrablock_additional_residuals) > 0
-                and sample.shape == down_intrablock_additional_residuals[0].shape
-            ):
-                sample += down_intrablock_additional_residuals.pop(0)
-
-        if is_controlnet:
-            sample = sample + mid_block_additional_residual
-
-        # 5. up
-        for i, upsample_block in enumerate(self.up_blocks):
-            is_final_block = i == len(self.up_blocks) - 1
-
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            # up scaling of kohya high res fix
-            if self.config.high_res_fix is not None:
-                if res_samples[0].shape[-2:] != sample.shape[-2:]:
-                    sample = self.__class__._resize(sample, target=res_samples[0])
-                    res_samples_up_sampled = (res_samples[0],)
-                    for res_sample in res_samples[1:]:
-                        res_samples_up_sampled += (self.__class__._resize(res_sample, target=res_samples[0]),)
-                    res_samples = res_samples_up_sampled
-
-            # if we have not reached the final block and need to forward the
-            # upsample size, we do it here
-            if not is_final_block and forward_upsample_size:
-                upsample_size = down_block_res_samples[-1].shape[2:]
-
-            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    upsample_size=upsample_size,
-                    attention_mask=attention_mask,
-                    encoder_attention_mask=encoder_attention_mask,
-                )
-            else:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=emb,
-                    res_hidden_states_tuple=res_samples,
-                    upsample_size=upsample_size,
-                )
-
-        # 6. post-process
-        if self.conv_norm_out:
-            sample = self.conv_norm_out(sample)
-            sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (sample,)
-
-        return UNet2DConditionOutput(sample=sample)
-
-    @classmethod
-    def from_unet(cls, unet: UNet2DConditionModel, high_res_fix: list):
-        config = dict((unet.config))
-        config["high_res_fix"] = high_res_fix
-        unet_high_res = cls(**config)
-        unet_high_res.load_state_dict(unet.state_dict())
-        unet_high_res.to(unet.dtype)
-        return unet_high_res
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import DiffusionPipeline
-
-        >>> pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4",
-                                         custom_pipeline="kohya_hires_fix",
-                                         torch_dtype=torch.float16,
-                                         high_res_fix=[{'timestep': 600,
-                                                        'scale_factor': 0.5,
-                                                        'block_num': 1}])
-        >>> pipe = pipe.to("cuda")
-
-        >>> prompt = "a photo of an astronaut riding a horse on mars"
-        >>> image = pipe(prompt, height=1000, width=1600).images[0]
-        ```
-"""
-
-
-class StableDiffusionHighResFixPipeline(StableDiffusionPipeline):
-    r"""
-    Pipeline for text-to-image generation using Stable Diffusion with Kohya fix for high resolution generation.
-
-    This model inherits from [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods.
-
-    The pipeline also inherits the following loading methods:
-        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
-        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
-        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
-        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
-        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-        text_encoder ([`~transformers.CLIPTextModel`]):
-            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
-        tokenizer ([`~transformers.CLIPTokenizer`]):
-            A `CLIPTokenizer` to tokenize text.
-        unet ([`UNet2DConditionModel`]):
-            A `UNet2DConditionModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
-        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
-        high_res_fix (`List[Dict]`, *optional*, defaults to `[{'timestep': 600, 'scale_factor': 0.5, 'block_num': 1}]`):
-            Enables Kohya fix for high resolution generation. The activation maps are scaled based on the scale_factor up to the timestep at specified block_num.
-    """
-
-    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
-    _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
-    _exclude_from_cpu_offload = ["safety_checker"]
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        image_encoder: CLIPVisionModelWithProjection = None,
-        requires_safety_checker: bool = True,
-        high_res_fix: List[Dict] = [{"timestep": 600, "scale_factor": 0.5, "block_num": 1}],
-    ):
-        super().__init__(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
-            requires_safety_checker=requires_safety_checker,
-        )
-
-        unet = UNet2DConditionModelHighResFix.from_unet(unet=unet, high_res_fix=high_res_fix)
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
@@ -565,7 +565,7 @@ class LCMSchedulerWithTimestamp(SchedulerMixin, ConfigMixin):
            # Glide cosine schedule
            self.betas = betas_for_alpha_bar(num_train_timesteps)
        else:
-            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")

        # Rescale for zero SNR
        if rescale_betas_zero_snr:
@@ -477,7 +477,7 @@ class LCMScheduler(SchedulerMixin, ConfigMixin):
            # Glide cosine schedule
            self.betas = betas_for_alpha_bar(num_train_timesteps)
        else:
-            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")

        # Rescale for zero SNR
        if rescale_betas_zero_snr:
@@ -1524,35 +1524,35 @@ class LLMGroundedDiffusionPipeline(
        assert emb.shape == (w.shape[0], embedding_dim)
        return emb

-    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale
+    @property
    def guidance_scale(self):
        return self._guidance_scale

-    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_rescale
+    @property
    def guidance_rescale(self):
        return self._guidance_rescale

-    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.clip_skip
+    @property
    def clip_skip(self):
        return self._clip_skip

    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
    # corresponds to doing no classifier free guidance.
-    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.do_classifier_free_guidance
+    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None

-    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.cross_attention_kwargs
+    @property
    def cross_attention_kwargs(self):
        return self._cross_attention_kwargs

-    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.num_timesteps
+    @property
    def num_timesteps(self):
        return self._num_timesteps
@@ -43,7 +43,7 @@ from diffusers.utils import BaseOutput, check_min_version


 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.25.0")


 class MarigoldDepthOutput(BaseOutput):
@@ -218,7 +218,7 @@ class UFOGenScheduler(SchedulerMixin, ConfigMixin):
            betas = torch.linspace(-6, 6, num_train_timesteps)
            self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
        else:
-            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")

        # Rescale for zero SNR
        if rescale_betas_zero_snr:
@@ -113,9 +113,9 @@ accelerate launch train_lcm_distill_lora_sdxl_wds.py \
    --push_to_hub \
 ```

-We provide another version for LCM LoRA SDXL that follows best practices of `peft` and leverages the `datasets` library for quick experimentation. The script doesn't load two UNets unlike `train_lcm_distill_lora_sdxl_wds.py` which reduces the memory requirements quite a bit.
+We provide another version for LCM LoRA SDXL that follows best practices of `peft` and leverages the `datasets` library for quick experimentation. The script doesn't load two UNets unlike `train_lcm_distill_lora_sdxl_wds.py` which reduces the memory requirements quite a bit. 

-Below is an example training command that trains an LCM LoRA on the [Narutos dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions):
+Below is an example training command that trains an LCM LoRA on the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions):

 ```bash
 export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
@@ -125,7 +125,7 @@ export VAE_PATH="madebyollin/sdxl-vae-fp16-fix"
 accelerate launch train_lcm_distill_lora_sdxl.py \
  --pretrained_teacher_model=${MODEL_NAME}  \
  --pretrained_vae_model_name_or_path=${VAE_PATH} \
-  --output_dir="narutos-lora-lcm-sdxl" \
+  --output_dir="pokemons-lora-lcm-sdxl" \
  --mixed_precision="fp16" \
  --dataset_name=$DATASET_NAME \
  --resolution=1024 \
@@ -73,7 +73,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -66,7 +66,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -79,7 +79,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -72,7 +72,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -78,7 +78,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -101,7 +101,7 @@ accelerate launch train_controlnet.py \
 `accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)
 for running distributed training with `accelerate`. Here is an example command:

-```bash
+```bash 
 export MODEL_DIR="runwayml/stable-diffusion-v1-5"
 export OUTPUT_DIR="path to save model"

@@ -123,21 +123,21 @@ accelerate launch --mixed_precision="fp16" --multi_gpu train_controlnet.py \

 #### After 300 steps with batch size 8

-| |  |
+| |  | 
 |-------------------|:-------------------------:|
-| | red circle with blue background  |
+| | red circle with blue background  | 
 ![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![red circle with blue background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_300_steps.png) |
-| | cyan circle with brown floral background |
+| | cyan circle with brown floral background | 
 ![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![cyan circle with brown floral background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_300_steps.png) |


 #### After 6000 steps with batch size 8:

-| |  |
+| |  | 
 |-------------------|:-------------------------:|
-| | red circle with blue background  |
+| | red circle with blue background  | 
 ![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![red circle with blue background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_6000_steps.png) |
-| | cyan circle with brown floral background |
+| | cyan circle with brown floral background | 
 ![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![cyan circle with brown floral background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_6000_steps.png) |

 ## Training on a 16 GB GPU
@@ -194,7 +194,7 @@ accelerate launch train_controlnet.py \
 --set_grads_to_none
 ```

-When using `enable_xformers_memory_efficient_attention`, please make sure to install `xformers` by `pip install xformers`.
+When using `enable_xformers_memory_efficient_attention`, please make sure to install `xformers` by `pip install xformers`. 

 ## Training on an 8 GB GPU

@@ -209,7 +209,7 @@ Optimizations:
 - DeepSpeed stage 2 with parameter and optimizer offloading
 - fp16 mixed precision

-[DeepSpeed](https://www.deepspeed.ai/) can offload tensors from VRAM to either
+[DeepSpeed](https://www.deepspeed.ai/) can offload tensors from VRAM to either 
 CPU or NVME. This requires significantly more RAM (about 25 GB).

 Use `accelerate config` to enable DeepSpeed stage 2.
@@ -256,7 +256,7 @@ accelerate launch train_controlnet.py \
 ## Performing inference with the trained ControlNet

 The trained model can be run the same as the original ControlNet pipeline with the newly trained ControlNet.
-Set `base_model_path` and `controlnet_path` to the values `--pretrained_model_name_or_path` and
+Set `base_model_path` and `controlnet_path` to the values `--pretrained_model_name_or_path` and 
 `--output_dir` were respectively set to in the training script.

 ```py
@@ -315,13 +315,13 @@ gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \

 When connected install JAX `0.4.5`:

-```sh
+```
 pip install "jax[tpu]==0.4.5" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
 ```

 To verify that JAX was correctly installed, you can run the following command:

-```py
+```
 import jax
 jax.device_count()
 ```
@@ -351,14 +351,14 @@ pip install wandb

 Now let's downloading two conditioning images that we will use to run validation during the training in order to track our progress

-```sh
+```
 wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png
 wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
 ```

 We encourage you to store or share your model with the community. To use huggingface hub, please login to your Hugging Face account, or ([create one](https://huggingface.co/docs/diffusers/main/en/training/hf.co/join) if you don’t have one already):

-```sh
+```
 huggingface-cli login
 ```

@@ -429,12 +429,12 @@ When work with a larger dataset, you may need to run training process for a long
 ```bash
 --checkpointing_steps=500
 ```
-This will save the trained model in subfolders of your output_dir. Subfolder names is the number of steps performed so far; for example: a checkpoint saved after 500 training steps would be saved in a subfolder named 500
+This will save the trained model in subfolders of your output_dir. Subfolder names is the number of steps performed so far; for example: a checkpoint saved after 500 training steps would be saved in a subfolder named 500 

-You can then start your training from this saved checkpoint with
+You can then start your training from this saved checkpoint with 

 ```bash
- --controlnet_model_name_or_path="./control_out/500"
+ --controlnet_model_name_or_path="./control_out/500" 
 ```

 We support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence by rebalancing the loss. To use it, one needs to set the `--snr_gamma` argument. The recommended value when using it is `5.0`.
@@ -60,7 +60,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -60,7 +60,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = logging.getLogger(__name__)

@@ -61,7 +61,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)
 if is_torch_npu_available():
@@ -63,7 +63,7 @@ from diffusers.utils.import_utils import is_xformers_available


 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -43,7 +43,7 @@ from accelerate.utils import write_basic_config
 write_basic_config()
 ```

-When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
+When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups. 
 Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.

 ### Dog toy example
@@ -231,7 +231,7 @@ accelerate launch --mixed_precision="fp16" train_dreambooth.py \

 ### Fine-tune text encoder with the UNet.

-The script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces.
+The script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces. 
 Pass the `--train_text_encoder` argument to the script to enable training `text_encoder`.

 ___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___
@@ -303,7 +303,7 @@ In a nutshell, LoRA allows to adapt pretrained models by adding pairs of rank-de
 - Rank-decomposition matrices have significantly fewer parameters than the original model, which means that trained LoRA weights are easily portable.
 - LoRA attention layers allow to control to which extent the model is adapted towards new training images via a `scale` parameter.

-[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in
+[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in 
 the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.

 ### Training
@@ -326,7 +326,7 @@ export INSTANCE_DIR="dog"
 export OUTPUT_DIR="path-to-save-model"
 ```

-For this example we want to directly store the trained LoRA embeddings on the Hub, so
+For this example we want to directly store the trained LoRA embeddings on the Hub, so 
 we need to be logged in and add the `--push_to_hub` flag.

 ```bash
@@ -356,7 +356,7 @@ accelerate launch train_dreambooth_lora.py \
  --push_to_hub
 ```

-**___Note: When using LoRA we can use a much higher learning rate compared to vanilla dreambooth. Here we
+**___Note: When using LoRA we can use a much higher learning rate compared to vanilla dreambooth. Here we 
 use *1e-4* instead of the usual *2e-6*.___**

 The final LoRA embedding weights have been uploaded to [patrickvonplaten/lora_dreambooth_dog_example](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example). **___Note: [The final weights](https://huggingface.co/patrickvonplaten/lora/blob/main/pytorch_attn_procs.bin) are only 3 MB in size which is orders of magnitudes smaller than the original model.**
@@ -365,14 +365,14 @@ The training results are summarized [here](https://api.wandb.ai/report/patrickvo
 You can use the `Step` slider to see how the model learned the features of our subject while the model trained.

 Optionally, we can also train additional LoRA layers for the text encoder. Specify the `--train_text_encoder` argument above for that. If you're interested to know more about how we
-enable this support, check out this [PR](https://github.com/huggingface/diffusers/pull/2918).
+enable this support, check out this [PR](https://github.com/huggingface/diffusers/pull/2918). 

 With the default hyperparameters from the above, the training seems to go in a positive direction. Check out [this panel](https://wandb.ai/sayakpaul/dreambooth-lora/reports/test-23-04-17-17-00-13---Vmlldzo0MDkwNjMy). The trained LoRA layers are available [here](https://huggingface.co/sayakpaul/dreambooth).


 ### Inference

-After training, LoRA weights can be loaded very easily into the original pipeline. First, you need to
+After training, LoRA weights can be loaded very easily into the original pipeline. First, you need to 
 load the original pipeline:

 ```python
@@ -394,9 +394,9 @@ image = pipe("A picture of a sks dog in a bucket", num_inference_steps=25).image

 If you are loading the LoRA parameters from the Hub and if the Hub repository has
 a `base_model` tag (such as [this](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example/blob/main/README.md?code=true#L4)), then
-you can do:
+you can do: 

-```py
+```py 
 from huggingface_hub.repocard import RepoCard

 lora_model_id = "patrickvonplaten/lora_dreambooth_dog_example"
@@ -413,7 +413,7 @@ weights. For example:
 ```python
 from huggingface_hub.repocard import RepoCard
 from diffusers import StableDiffusionPipeline
-import torch
+import torch 

 lora_model_id = "sayakpaul/dreambooth-text-encoder-test"
 card = RepoCard.load(lora_model_id)
@@ -430,7 +430,7 @@ Note that the use of [`LoraLoaderMixin.load_lora_weights`](https://huggingface.c

 * LoRA parameters that don't have separate identifiers for the UNet and the text encoder (such as [`"patrickvonplaten/lora_dreambooth_dog_example"`](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example)). So, you can just do:

-  ```py
+  ```py 
  pipe.load_lora_weights(lora_model_path)
  ```

@@ -529,11 +529,11 @@ To save even more memory, pass the `--set_grads_to_none` argument to the script.
 More info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html

 ### Experimental results
-You can refer to [this blog post](https://huggingface.co/blog/dreambooth) that discusses some of DreamBooth experiments in detail. Specifically, it recommends a set of DreamBooth-specific tips and tricks that we have found to work well for a variety of subjects.
+You can refer to [this blog post](https://huggingface.co/blog/dreambooth) that discusses some of DreamBooth experiments in detail. Specifically, it recommends a set of DreamBooth-specific tips and tricks that we have found to work well for a variety of subjects. 

 ## IF

-You can use the lora and full dreambooth scripts to train the text to image [IF model](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0) and the stage II upscaler
+You can use the lora and full dreambooth scripts to train the text to image [IF model](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0) and the stage II upscaler 
 [IF model](https://huggingface.co/DeepFloyd/IF-II-L-v1.0).

 Note that IF has a predicted variance, and our finetuning scripts only train the models predicted error, so for finetuned IF models we switch to a fixed
@@ -553,7 +553,7 @@ pipe.scheduler = pipe.scheduler.__class__.from_config(pipe.scheduler.config, var

 Additionally, a few alternative cli flags are needed for IF.

-`--resolution=64`: IF is a pixel space diffusion model. In order to operate on un-compressed pixels, the input images are of a much smaller resolution.
+`--resolution=64`: IF is a pixel space diffusion model. In order to operate on un-compressed pixels, the input images are of a much smaller resolution. 

 `--pre_compute_text_embeddings`: IF uses [T5](https://huggingface.co/docs/transformers/model_doc/t5) for its text encoder. In order to save GPU memory, we pre compute all text embeddings and then de-allocate
 T5.
@@ -568,7 +568,7 @@ We find LoRA to be sufficient for finetuning the stage I model as the low resolu
 For common and/or not-visually complex object concepts, you can get away with not-finetuning the upscaler. Just be sure to adjust the prompt passed to the
 upscaler to remove the new token from the instance prompt. I.e. if your stage I prompt is "a sks dog", use "a dog" for your stage II prompt.

-For finegrained detail like faces that aren't present in the original training set, we find that full finetuning of the stage II upscaler is better than
+For finegrained detail like faces that aren't present in the original training set, we find that full finetuning of the stage II upscaler is better than 
 LoRA finetuning stage II.

 For finegrained detail like faces, we find that lower learning rates along with larger batch sizes work best.
@@ -647,7 +647,7 @@ python train_dreambooth_lora.py \
    --resolution=256 \
    --train_batch_size=4 \
    --gradient_accumulation_steps=1 \
-    --learning_rate=1e-6 \
+    --learning_rate=1e-6 \ 
    --max_train_steps=2000 \
    --validation_prompt="a sks dog" \
    --validation_epochs=100 \
@@ -663,9 +663,9 @@ python train_dreambooth_lora.py \
 `--skip_save_text_encoder`: When training the full model, this will skip saving the entire T5 with the finetuned model. You can still load the pipeline
 with a T5 loaded from the original model.

-`use_8bit_adam`: Due to the size of the optimizer states, we recommend training the full XL IF model with 8bit adam.
+`use_8bit_adam`: Due to the size of the optimizer states, we recommend training the full XL IF model with 8bit adam. 

-`--learning_rate=1e-7`: For full dreambooth, IF requires very low learning rates. With higher learning rates model quality will degrade. Note that it is
+`--learning_rate=1e-7`: For full dreambooth, IF requires very low learning rates. With higher learning rates model quality will degrade. Note that it is 
 likely the learning rate can be increased with larger batch sizes.

 Using 8bit adam and a batch size of 4, the model can be trained in ~48 GB VRAM.
@@ -741,4 +741,4 @@ accelerate launch train_dreambooth.py \

 ## Stable Diffusion XL

-We support fine-tuning of the UNet shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) with DreamBooth and LoRA via the `train_dreambooth_lora_sdxl.py` script. Please refer to the docs [here](./README_sdxl.md).
+We support fine-tuning of the UNet shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) with DreamBooth and LoRA via the `train_dreambooth_lora_sdxl.py` script. Please refer to the docs [here](./README_sdxl.md). 
@@ -63,7 +63,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -35,7 +35,7 @@ from diffusers.utils import check_min_version


 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 # Cache compiled models across invocations of this script.
 cc.initialize_cache(os.path.expanduser("~/.cache/jax/compilation_cache"))
@@ -70,7 +70,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -78,7 +78,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

@@ -57,7 +57,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__, log_level="INFO")

@@ -60,7 +60,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__, log_level="INFO")

@@ -34,7 +34,7 @@ For this example we want to directly store the trained LoRA embeddings on the Hu

 ___

-### Naruto example
+### Pokemon example

 For all our examples, we will directly store the trained weights on the Hub, so we need to be logged in and add the `--push_to_hub` flag. In order to do that, you have to be a registered user on the 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to the [User Access Tokens](https://huggingface.co/docs/hub/security-tokens) guide.

@@ -44,13 +44,13 @@ Run the following command to authenticate your token
 huggingface-cli login
 ```

-We also use [Weights and Biases](https://docs.wandb.ai/quickstart) logging by default, because it is really useful to monitor the training progress by regularly generating sample images during training. To install wandb, run
+We also use [Weights and Biases](https://docs.wandb.ai/quickstart) logging by default, because it is really useful to monitor the training progress by regularly generating sample images during training. To install wandb, run 

 ```bash
 pip install wandb
 ```

-To disable wandb logging, remove the `--report_to=="wandb"` and `--validation_prompts="A robot naruto, 4k photo"` flags from below examples
+To disable wandb logging, remove the `--report_to=="wandb"` and `--validation_prompts="A robot pokemon, 4k photo"` flags from below examples

 #### Fine-tune decoder
 <br>
@@ -70,10 +70,10 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image_decoder.py \
  --max_grad_norm=1 \
  --checkpoints_total_limit=3 \
  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --validation_prompts="A robot naruto, 4k photo" \
+  --validation_prompts="A robot pokemon, 4k photo" \
  --report_to="wandb" \
  --push_to_hub \
-  --output_dir="kandi2-decoder-naruto-model"
+  --output_dir="kandi2-decoder-pokemon-model" 
 ```
 <!-- accelerate_snippet_end -->

@@ -95,14 +95,14 @@ accelerate launch --mixed_precision="fp16" train_text_to_image_decoder.py \
  --max_grad_norm=1 \
  --checkpoints_total_limit=3 \
  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --validation_prompts="A robot naruto, 4k photo" \
+  --validation_prompts="A robot pokemon, 4k photo" \
  --report_to="wandb" \
  --push_to_hub \
-  --output_dir="kandi22-decoder-naruto-model"
+  --output_dir="kandi22-decoder-pokemon-model" 
 ```


-Once the training is finished the model will be saved in the `output_dir` specified in the command. In this example it's `kandi22-decoder-naruto-model`. To load the fine-tuned model for inference just pass that path to `AutoPipelineForText2Image`
+Once the training is finished the model will be saved in the `output_dir` specified in the command. In this example it's `kandi22-decoder-pokemon-model`. To load the fine-tuned model for inference just pass that path to `AutoPipelineForText2Image`

 ```python
 from diffusers import AutoPipelineForText2Image
@@ -111,9 +111,9 @@ import torch
 pipe = AutoPipelineForText2Image.from_pretrained(output_dir, torch_dtype=torch.float16)
 pipe.enable_model_cpu_offload()

-prompt='A robot naruto, 4k photo'
+prompt='A robot pokemon, 4k photo'
 images = pipe(prompt=prompt).images
-images[0].save("robot-naruto.png")
+images[0].save("robot-pokemon.png")
 ```

 Checkpoints only save the unet, so to run inference from a checkpoint, just load the unet
@@ -127,11 +127,11 @@ unet = UNet2DConditionModel.from_pretrained(model_path + "/checkpoint-<N>/unet")
 pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", unet=unet, torch_dtype=torch.float16)
 pipe.enable_model_cpu_offload()

-image = pipe(prompt="A robot naruto, 4k photo").images[0]
-image.save("robot-naruto.png")
+image = pipe(prompt="A robot pokemon, 4k photo").images[0]
+image.save("robot-pokemon.png")
 ```

-#### Fine-tune prior
+#### Fine-tune prior 

 You can fine-tune the Kandinsky prior model with `train_text_to_image_prior.py` script. Note that we currently do not support `--gradient_checkpointing` for prior model fine-tuning.

@@ -151,15 +151,15 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image_prior.py \
  --max_grad_norm=1 \
  --checkpoints_total_limit=3 \
  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --validation_prompts="A robot naruto, 4k photo" \
+  --validation_prompts="A robot pokemon, 4k photo" \
  --report_to="wandb" \
  --push_to_hub \
-  --output_dir="kandi2-prior-naruto-model"
+  --output_dir="kandi2-prior-pokemon-model" 
 ```
 <!-- accelerate_snippet_end -->


-To perform inference with the fine-tuned prior model, you will need to first create a prior pipeline by passing the `output_dir` to `DiffusionPipeline`. Then create a `KandinskyV22CombinedPipeline` from a pretrained or fine-tuned decoder checkpoint along with all the modules of the prior pipeline you just created.
+To perform inference with the fine-tuned prior model, you will need to first create a prior pipeline by passing the `output_dir` to `DiffusionPipeline`. Then create a `KandinskyV22CombinedPipeline` from a pretrained or fine-tuned decoder checkpoint along with all the modules of the prior pipeline you just created. 

 ```python
 from diffusers import AutoPipelineForText2Image, DiffusionPipeline
@@ -170,12 +170,12 @@ prior_components = {"prior_" + k: v for k,v in pipe_prior.components.items()}
 pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", **prior_components, torch_dtype=torch.float16)

 pipe.enable_model_cpu_offload()
-prompt='A robot naruto, 4k photo'
+prompt='A robot pokemon, 4k photo'
 images = pipe(prompt=prompt, negative_prompt=negative_prompt).images
 images[0]
 ```

-If you want to use a fine-tuned decoder checkpoint along with your fine-tuned prior checkpoint, you can simply replace the "kandinsky-community/kandinsky-2-2-decoder" in above code with your custom model repo name. Note that in order to be able to create a `KandinskyV22CombinedPipeline`, your model repository need to have a prior tag. If you have created your model repo using our training script, the prior tag is automatically included.
+If you want to use a fine-tuned decoder checkpoint along with your fine-tuned prior checkpoint, you can simply replace the "kandinsky-community/kandinsky-2-2-decoder" in above code with your custom model repo name. Note that in order to be able to create a `KandinskyV22CombinedPipeline`, your model repository need to have a prior tag. If you have created your model repo using our training script, the prior tag is automatically included. 

 #### Training with multiple GPUs

@@ -196,10 +196,10 @@ accelerate launch --mixed_precision="fp16" --multi_gpu  train_text_to_image_deco
  --max_grad_norm=1 \
  --checkpoints_total_limit=3 \
  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --validation_prompts="A robot naruto, 4k photo" \
+  --validation_prompts="A robot pokemon, 4k photo" \
  --report_to="wandb" \
  --push_to_hub \
-  --output_dir="kandi2-decoder-naruto-model"
+  --output_dir="kandi2-decoder-pokemon-model" 
 ```


@@ -227,10 +227,10 @@ on consumer GPUs like Tesla T4, Tesla V100.

 ### Training

-First, you need to set up your development environment as explained in the [installation](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder) and the [Narutos dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).
+First, you need to set up your development environment as explained in the [installation](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).  


-#### Train decoder
+#### Train decoder 

 ```bash
 export DATASET_NAME="lambdalabs/naruto-blip-captions"
@@ -244,7 +244,7 @@ accelerate launch --mixed_precision="fp16" train_text_to_image_decoder_lora.py \
  --seed=42 \
  --rank=4 \
  --gradient_checkpointing \
-  --output_dir="kandi22-decoder-naruto-lora" \
+  --output_dir="kandi22-decoder-pokemon-lora" \
  --validation_prompt="cute dragon creature" --report_to="wandb" \
  --push_to_hub \
 ```
@@ -262,7 +262,7 @@ accelerate launch --mixed_precision="fp16" train_text_to_image_prior_lora.py \
  --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
  --seed=42 \
  --rank=4 \
-  --output_dir="kandi22-prior-naruto-lora" \
+  --output_dir="kandi22-prior-pokemon-lora" \
  --validation_prompt="cute dragon creature" --report_to="wandb" \
  --push_to_hub \
 ```
@@ -274,7 +274,7 @@ accelerate launch --mixed_precision="fp16" train_text_to_image_prior_lora.py \

 #### Inference using fine-tuned LoRA checkpoint for decoder

-Once you have trained a Kandinsky decoder model using the above command, inference can be done with the `AutoPipelineForText2Image` after loading the trained LoRA weights.  You need to pass the `output_dir` for loading the LoRA weights, which in this case is `kandi22-decoder-naruto-lora`.
+Once you have trained a Kandinsky decoder model using the above command, inference can be done with the `AutoPipelineForText2Image` after loading the trained LoRA weights.  You need to pass the `output_dir` for loading the LoRA weights, which in this case is `kandi22-decoder-pokemon-lora`.


 ```python
@@ -285,9 +285,9 @@ pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-
 pipe.unet.load_attn_procs(output_dir)
 pipe.enable_model_cpu_offload()

-prompt='A robot naruto, 4k photo'
+prompt='A robot pokemon, 4k photo'
 image = pipe(prompt=prompt).images[0]
-image.save("robot_naruto.png")
+image.save("robot_pokemon.png")
 ```

 #### Inference using fine-tuned LoRA checkpoint for prior
@@ -300,9 +300,9 @@ pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-
 pipe.prior_prior.load_attn_procs(output_dir)
 pipe.enable_model_cpu_offload()

-prompt='A robot naruto, 4k photo'
+prompt='A robot pokemon, 4k photo'
 image = pipe(prompt=prompt).images[0]
-image.save("robot_naruto.png")
+image.save("robot_pokemon.png")
 image
 ```

@@ -52,7 +52,7 @@ if is_wandb_available():


 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__, log_level="INFO")

@@ -896,6 +896,7 @@ def main():
        images = []
        if args.validation_prompts is not None:
            logger.info("Running inference for collecting generated images...")
+            pipeline = pipeline.to(accelerator.device)
            pipeline.torch_dtype = weight_dtype
            pipeline.set_progress_bar_config(disable=True)
            pipeline.enable_model_cpu_offload()
@@ -46,7 +46,7 @@ from diffusers.utils import check_min_version, is_wandb_available


 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__, log_level="INFO")

@@ -46,7 +46,7 @@ from diffusers.utils import check_min_version, is_wandb_available


 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__, log_level="INFO")

@@ -51,7 +51,7 @@ if is_wandb_available():


 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__, log_level="INFO")

@@ -1,16 +1,16 @@
 # Overview

-These examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers.
+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`.

-The key option is a change of the variable `n_guide_steps`.
+The key option is a change of the variable `n_guide_steps`. 
 When `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not fine-tuned to maximize reward in the environment.
 By default, `n_guide_steps=2` to match the original implementation.
-
+ 

 You will need some RL specific requirements to run the examples:

-```sh
+```
 pip install -f https://download.pytorch.org/whl/torch_stable.html \
                free-mujoco-py \
                einops \
@@ -6,7 +6,7 @@ Updating them to the most recent version of the library will require some work.

 To use any of them, just run the command

-```sh
+```
 pip install -r requirements.txt
 ```
 inside the folder of your choice.
@@ -1,156 +0,0 @@
-# GLIGEN: Open-Set Grounded Text-to-Image Generation
-
-These scripts contain the code to prepare the grounding data and train the GLIGEN model on COCO dataset.
-
-### Install the requirements
-
-```bash
-conda create -n diffusers python==3.10
-conda activate diffusers
-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()
-```
-
-### Prepare the training data
-
-If you want to make your own grounding data, you need to install the requirements.
-
-I used [RAM](https://github.com/xinyu1205/recognize-anything) to tag
-images, [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO/issues?q=refer) to detect objects,
-and [BLIP2](https://huggingface.co/docs/transformers/en/model_doc/blip-2) to caption instances.
-
-Only RAM needs to be installed manually:
-
-```bash
-pip install git+https://github.com/xinyu1205/recognize-anything.git --no-deps
-```
-
-Download the pre-trained model:
-
-```bash
-huggingface-cli download --resume-download xinyu1205/recognize_anything_model ram_swin_large_14m.pth
-huggingface-cli download --resume-download IDEA-Research/grounding-dino-base
-huggingface-cli download --resume-download Salesforce/blip2-flan-t5-xxl
-huggingface-cli download --resume-download clip-vit-large-patch14
-huggingface-cli download --resume-download masterful/gligen-1-4-generation-text-box
-```
-
-Make the training data on 8 GPUs:
-
-```bash
-torchrun --master_port 17673 --nproc_per_node=8 make_datasets.py \
-    --data_root /mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017 \
-    --save_root /root/gligen_data \
-    --ram_checkpoint /root/.cache/huggingface/hub/models--xinyu1205--recognize_anything_model/snapshots/ebc52dc741e86466202a5ab8ab22eae6e7d48bf1/ram_swin_large_14m.pth
-```
-
-You can download the COCO training data from
-
-```bash
-huggingface-cli download --resume-download Hzzone/GLIGEN_COCO coco_train2017.pth
-```
-
-It's in the format of
-
-```json
-[
-  ...
-  {
-    'file_path': Path,
-    'annos': [
-      {
-        'caption': Instance
-        Caption,
-        'bbox': bbox
-        in
-        xyxy,
-        'text_embeddings_before_projection': CLIP
-        text
-        embedding
-        before
-        linear
-        projection
-      }
-    ]
-  }
-  ...
-]
-```
-
-### Training commands
-
-The training script is heavily based
-on https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet.py
-
-```bash
-accelerate launch train_gligen_text.py \
-    --data_path /root/data/zhizhonghuang/coco_train2017.pth \
-    --image_path /mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017 \
-    --train_batch_size 8 \
-    --max_train_steps 100000 \
-    --checkpointing_steps 1000 \
-    --checkpoints_total_limit 10 \
-    --learning_rate 5e-5 \
-    --dataloader_num_workers 16 \
-    --mixed_precision fp16 \
-    --report_to wandb \
-    --tracker_project_name gligen \
-    --output_dir /root/data/zhizhonghuang/ckpt/GLIGEN_Text_Retrain_COCO
-```
-
-I trained the model on 8 A100 GPUs for about 11 hours (at least 24GB GPU memory). The generated images will follow the
-layout possibly at 50k iterations.
-
-Note that although the pre-trained GLIGEN model has been loaded, the parameters of `fuser` and `position_net` have been reset (see line 420 in `train_gligen_text.py`)
-
-The trained model can be downloaded from
-
-```bash
-huggingface-cli download --resume-download Hzzone/GLIGEN_COCO config.json diffusion_pytorch_model.safetensors
-```
-
-You can run `demo.ipynb` to visualize the generated images.
-
-Example prompts:
-
-```python
-prompt = 'A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky'
-boxes = [[0.041015625, 0.548828125, 0.453125, 0.859375],
-         [0.525390625, 0.552734375, 0.93359375, 0.865234375],
-         [0.12890625, 0.015625, 0.412109375, 0.279296875],
-         [0.578125, 0.08203125, 0.857421875, 0.27734375]]
-gligen_phrases = ['a green car', 'a blue truck', 'a red air balloon', 'a bird']
-```
-
-Example images:
-![alt text](generated-images-100000-00.png)
-
-### Citation
-
-```
-@article{li2023gligen,
-  title={GLIGEN: Open-Set Grounded Text-to-Image Generation},
-  author={Li, Yuheng and Liu, Haotian and Wu, Qingyang and Mu, Fangzhou and Yang, Jianwei and Gao, Jianfeng and Li, Chunyuan and Lee, Yong Jae},
-  journal={CVPR},
-  year={2023}
-}
-```
@@ -1,110 +0,0 @@
-import os
-import random
-
-import torch
-import torchvision.transforms as transforms
-from PIL import Image
-
-
-def recalculate_box_and_verify_if_valid(x, y, w, h, image_size, original_image_size, min_box_size):
-    scale = image_size / min(original_image_size)
-    crop_y = (original_image_size[1] * scale - image_size) // 2
-    crop_x = (original_image_size[0] * scale - image_size) // 2
-    x0 = max(x * scale - crop_x, 0)
-    y0 = max(y * scale - crop_y, 0)
-    x1 = min((x + w) * scale - crop_x, image_size)
-    y1 = min((y + h) * scale - crop_y, image_size)
-    if (x1 - x0) * (y1 - y0) / (image_size * image_size) < min_box_size:
-        return False, (None, None, None, None)
-    return True, (x0, y0, x1, y1)
-
-
-class COCODataset(torch.utils.data.Dataset):
-    def __init__(
-        self,
-        data_path,
-        image_path,
-        image_size=512,
-        min_box_size=0.01,
-        max_boxes_per_data=8,
-        tokenizer=None,
-    ):
-        super().__init__()
-        self.min_box_size = min_box_size
-        self.max_boxes_per_data = max_boxes_per_data
-        self.image_size = image_size
-        self.image_path = image_path
-        self.tokenizer = tokenizer
-        self.transforms = transforms.Compose(
-            [
-                transforms.Resize(image_size, interpolation=transforms.InterpolationMode.BILINEAR),
-                transforms.CenterCrop(image_size),
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-        )
-
-        self.data_list = torch.load(data_path, map_location="cpu")
-
-    def __getitem__(self, index):
-        if self.max_boxes_per_data > 99:
-            assert False, "Are you sure setting such large number of boxes per image?"
-
-        out = {}
-
-        data = self.data_list[index]
-        image = Image.open(os.path.join(self.image_path, data["file_path"])).convert("RGB")
-        original_image_size = image.size
-        out["pixel_values"] = self.transforms(image)
-
-        annos = data["annos"]
-
-        areas, valid_annos = [], []
-        for anno in annos:
-            # x, y, w, h = anno['bbox']
-            x0, y0, x1, y1 = anno["bbox"]
-            x, y, w, h = x0, y0, x1 - x0, y1 - y0
-            valid, (x0, y0, x1, y1) = recalculate_box_and_verify_if_valid(
-                x, y, w, h, self.image_size, original_image_size, self.min_box_size
-            )
-            if valid:
-                anno["bbox"] = [x0, y0, x1, y1]
-                areas.append((x1 - x0) * (y1 - y0))
-                valid_annos.append(anno)
-
-        # Sort according to area and choose the largest N objects
-        wanted_idxs = torch.tensor(areas).sort(descending=True)[1]
-        wanted_idxs = wanted_idxs[: self.max_boxes_per_data]
-        valid_annos = [valid_annos[i] for i in wanted_idxs]
-
-        out["boxes"] = torch.zeros(self.max_boxes_per_data, 4)
-        out["masks"] = torch.zeros(self.max_boxes_per_data)
-        out["text_embeddings_before_projection"] = torch.zeros(self.max_boxes_per_data, 768)
-
-        for i, anno in enumerate(valid_annos):
-            out["boxes"][i] = torch.tensor(anno["bbox"]) / self.image_size
-            out["masks"][i] = 1
-            out["text_embeddings_before_projection"][i] = anno["text_embeddings_before_projection"]
-
-        prob_drop_boxes = 0.1
-        if random.random() < prob_drop_boxes:
-            out["masks"][:] = 0
-
-        caption = random.choice(data["captions"])
-
-        prob_drop_captions = 0.5
-        if random.random() < prob_drop_captions:
-            caption = ""
-        caption = self.tokenizer(
-            caption,
-            max_length=self.tokenizer.model_max_length,
-            padding="max_length",
-            truncation=True,
-            return_tensors="pt",
-        )
-        out["caption"] = caption
-
-        return out
-
-    def __len__(self):
-        return len(self.data_list)
@@ -1,201 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda/envs/densecaption/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import torch\n",
-    "from diffusers import StableDiffusionGLIGENTextImagePipeline, StableDiffusionGLIGENPipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import diffusers\n",
-    "from diffusers import (\n",
-    "    AutoencoderKL,\n",
-    "    DDPMScheduler,\n",
-    "    UNet2DConditionModel,\n",
-    "    UniPCMultistepScheduler,\n",
-    "    EulerDiscreteScheduler,\n",
-    ")\n",
-    "from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n",
-    "# pretrained_model_name_or_path = 'masterful/gligen-1-4-generation-text-box'\n",
-    "\n",
-    "pretrained_model_name_or_path = '/root/data/zhizhonghuang/checkpoints/models--masterful--gligen-1-4-generation-text-box/snapshots/d2820dc1e9ba6ca082051ce79cfd3eb468ae2c83'\n",
-    "\n",
-    "tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder=\"tokenizer\")\n",
-    "noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder=\"scheduler\")\n",
-    "text_encoder = CLIPTextModel.from_pretrained(\n",
-    "    pretrained_model_name_or_path, subfolder=\"text_encoder\"\n",
-    ")\n",
-    "vae = AutoencoderKL.from_pretrained(\n",
-    "    pretrained_model_name_or_path, subfolder=\"vae\"\n",
-    ")\n",
-    "# unet = UNet2DConditionModel.from_pretrained(\n",
-    "#     pretrained_model_name_or_path, subfolder=\"unet\"\n",
-    "# )\n",
-    "\n",
-    "noise_scheduler = EulerDiscreteScheduler.from_config(noise_scheduler.config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unet = UNet2DConditionModel.from_pretrained(\n",
-    "    '/root/data/zhizhonghuang/ckpt/GLIGEN_Text_Retrain_COCO'\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion_gligen.pipeline_stable_diffusion_gligen.StableDiffusionGLIGENPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\n"
-     ]
-    }
-   ],
-   "source": [
-    "pipe = StableDiffusionGLIGENPipeline(\n",
-    "    vae,\n",
-    "    text_encoder,\n",
-    "    tokenizer,\n",
-    "    unet,\n",
-    "    noise_scheduler,\n",
-    "    safety_checker=None,\n",
-    "    feature_extractor=None,\n",
-    ")\n",
-    "pipe = pipe.to(\"cuda\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# prompt = 'A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky'\n",
-    "# gen_boxes = [('a green car', [21, 281, 211, 159]), ('a blue truck', [269, 283, 209, 160]), ('a red air balloon', [66, 8, 145, 135]), ('a bird', [296, 42, 143, 100])]\n",
-    "\n",
-    "# prompt = 'A realistic top-down view of a wooden table with two apples on it'\n",
-    "# gen_boxes = [('a wooden table', [20, 148, 472, 216]), ('an apple', [150, 226, 100, 100]), ('an apple', [280, 226, 100, 100])]\n",
-    "\n",
-    "# prompt = 'A realistic scene of three skiers standing in a line on the snow near a palm tree'\n",
-    "# gen_boxes = [('a skier', [5, 152, 139, 168]), ('a skier', [278, 192, 121, 158]), ('a skier', [148, 173, 124, 155]), ('a palm tree', [404, 105, 103, 251])]\n",
-    "\n",
-    "prompt = 'An oil painting of a pink dolphin jumping on the left of a steam boat on the sea'\n",
-    "gen_boxes = [('a steam boat', [232, 225, 257, 149]), ('a jumping pink dolphin', [21, 249, 189, 123])]\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "boxes = np.array([x[1] for x in gen_boxes])\n",
-    "boxes = boxes / 512\n",
-    "boxes[:, 2] = boxes[:, 0] + boxes[:, 2]\n",
-    "boxes[:, 3] = boxes[:, 1] + boxes[:, 3]\n",
-    "boxes = boxes.tolist()\n",
-    "gligen_phrases = [x[0] for x in gen_boxes]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/miniconda/envs/densecaption/lib/python3.11/site-packages/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py:683: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n",
-      "  num_channels_latents = self.unet.in_channels\n",
-      "/root/miniconda/envs/densecaption/lib/python3.11/site-packages/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py:716: FutureWarning: Accessing config attribute `cross_attention_dim` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'cross_attention_dim' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.cross_attention_dim'.\n",
-      "  max_objs, self.unet.cross_attention_dim, device=device, dtype=self.text_encoder.dtype\n",
-      "100%|██████████| 50/50 [01:21<00:00,  1.64s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "images = pipe(\n",
-    "    prompt=prompt,\n",
-    "    gligen_phrases=gligen_phrases,\n",
-    "    gligen_boxes=boxes,\n",
-    "    gligen_scheduled_sampling_beta=1.0,\n",
-    "    output_type=\"pil\",\n",
-    "    num_inference_steps=50,\n",
-    "    negative_prompt=\"artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate\",\n",
-    "    num_images_per_prompt=16,\n",
-    ").images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diffusers.utils.make_image_grid(images, 4, len(images)//4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "densecaption",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
@@ -1,119 +0,0 @@
-import argparse
-import os
-import random
-
-import torch
-import torchvision
-import torchvision.transforms as TS
-from PIL import Image
-from ram import inference_ram
-from ram.models import ram
-from tqdm import tqdm
-from transformers import (
-    AutoModelForZeroShotObjectDetection,
-    AutoProcessor,
-    Blip2ForConditionalGeneration,
-    Blip2Processor,
-    CLIPTextModel,
-    CLIPTokenizer,
-)
-
-
-torch.autograd.set_grad_enabled(False)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser("Caption Generation script", add_help=False)
-    parser.add_argument("--data_root", type=str, required=True, help="path to COCO")
-    parser.add_argument("--save_root", type=str, required=True, help="path to save")
-    parser.add_argument("--ram_checkpoint", type=str, required=True, help="path to save")
-    args = parser.parse_args()
-
-    # ram_checkpoint = '/root/.cache/huggingface/hub/models--xinyu1205--recognize_anything_model/snapshots/ebc52dc741e86466202a5ab8ab22eae6e7d48bf1/ram_swin_large_14m.pth'
-    # data_root = '/mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017'
-    # save_root = '/root/gligen_data'
-    box_threshold = 0.25
-    text_threshold = 0.2
-
-    import torch.distributed as dist
-
-    dist.init_process_group(backend="nccl", init_method="env://")
-    local_rank = torch.distributed.get_rank() % torch.cuda.device_count()
-    device = f"cuda:{local_rank}"
-    torch.cuda.set_device(local_rank)
-
-    ram_model = ram(pretrained=args.ram_checkpoint, image_size=384, vit="swin_l").cuda().eval()
-    ram_processor = TS.Compose(
-        [TS.Resize((384, 384)), TS.ToTensor(), TS.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]
-    )
-
-    grounding_dino_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
-    grounding_dino_model = AutoModelForZeroShotObjectDetection.from_pretrained(
-        "IDEA-Research/grounding-dino-base"
-    ).cuda()
-
-    blip2_processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
-    blip2_model = Blip2ForConditionalGeneration.from_pretrained(
-        "Salesforce/blip2-flan-t5-xxl", torch_dtype=torch.float16
-    ).cuda()
-
-    clip_text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").cuda()
-    clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-
-    image_paths = [os.path.join(args.data_root, x) for x in os.listdir(args.data_root)]
-    random.shuffle(image_paths)
-
-    for image_path in tqdm.tqdm(image_paths):
-        pth_path = os.path.join(args.save_root, os.path.basename(image_path))
-        if os.path.exists(pth_path):
-            continue
-
-        sample = {"file_path": os.path.basename(image_path), "annos": []}
-
-        raw_image = Image.open(image_path).convert("RGB")
-
-        res = inference_ram(ram_processor(raw_image).unsqueeze(0).cuda(), ram_model)
-
-        text = res[0].replace(" |", ".")
-
-        inputs = grounding_dino_processor(images=raw_image, text=text, return_tensors="pt")
-        inputs = {k: v.cuda() for k, v in inputs.items()}
-        outputs = grounding_dino_model(**inputs)
-
-        results = grounding_dino_processor.post_process_grounded_object_detection(
-            outputs,
-            inputs["input_ids"],
-            box_threshold=box_threshold,
-            text_threshold=text_threshold,
-            target_sizes=[raw_image.size[::-1]],
-        )
-        boxes = results[0]["boxes"]
-        labels = results[0]["labels"]
-        scores = results[0]["scores"]
-        indices = torchvision.ops.nms(boxes, scores, 0.5)
-        boxes = boxes[indices]
-        category_names = [labels[i] for i in indices]
-
-        for i, bbox in enumerate(boxes):
-            bbox = bbox.tolist()
-            inputs = blip2_processor(images=raw_image.crop(bbox), return_tensors="pt")
-            inputs = {k: v.cuda().to(torch.float16) for k, v in inputs.items()}
-            outputs = blip2_model.generate(**inputs)
-            caption = blip2_processor.decode(outputs[0], skip_special_tokens=True)
-            inputs = clip_tokenizer(
-                caption,
-                padding="max_length",
-                max_length=clip_tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            inputs = {k: v.cuda() for k, v in inputs.items()}
-            text_embeddings_before_projection = clip_text_encoder(**inputs).pooler_output.squeeze(0)
-
-            sample["annos"].append(
-                {
-                    "caption": caption,
-                    "bbox": bbox,
-                    "text_embeddings_before_projection": text_embeddings_before_projection,
-                }
-            )
-        torch.save(sample, pth_path)
@@ -1,11 +0,0 @@
-accelerate>=0.16.0
-torchvision
-transformers>=4.25.1
-ftfy
-tensorboard
-Jinja2
-diffusers
-scipy
-timm
-fairscale
-wandb
@@ -1,715 +0,0 @@
-# from accelerate.utils import write_basic_config
-#
-# write_basic_config()
-import argparse
-import logging
-import math
-import os
-import shutil
-from pathlib import Path
-
-import accelerate
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration, set_seed
-from packaging import version
-from tqdm.auto import tqdm
-
-import diffusers
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    EulerDiscreteScheduler,
-    StableDiffusionGLIGENPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.optimization import get_scheduler
-from diffusers.utils import is_wandb_available, make_image_grid
-from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.torch_utils import is_compiled_module
-
-
-if is_wandb_available():
-    pass
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-# check_min_version("0.28.0.dev0")
-
-logger = get_logger(__name__)
-
-
-@torch.no_grad()
-def log_validation(vae, text_encoder, tokenizer, unet, noise_scheduler, args, accelerator, step, weight_dtype):
-    if accelerator.is_main_process:
-        print("generate test images...")
-    unet = accelerator.unwrap_model(unet)
-    vae.to(accelerator.device, dtype=torch.float32)
-
-    pipeline = StableDiffusionGLIGENPipeline(
-        vae,
-        text_encoder,
-        tokenizer,
-        unet,
-        EulerDiscreteScheduler.from_config(noise_scheduler.config),
-        safety_checker=None,
-        feature_extractor=None,
-    )
-    pipeline = pipeline.to(accelerator.device)
-    pipeline.set_progress_bar_config(disable=not accelerator.is_main_process)
-    if args.enable_xformers_memory_efficient_attention:
-        pipeline.enable_xformers_memory_efficient_attention()
-
-    if args.seed is None:
-        generator = None
-    else:
-        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
-
-    prompt = "A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky"
-    boxes = [
-        [0.041015625, 0.548828125, 0.453125, 0.859375],
-        [0.525390625, 0.552734375, 0.93359375, 0.865234375],
-        [0.12890625, 0.015625, 0.412109375, 0.279296875],
-        [0.578125, 0.08203125, 0.857421875, 0.27734375],
-    ]
-    gligen_phrases = ["a green car", "a blue truck", "a red air balloon", "a bird"]
-    images = pipeline(
-        prompt=prompt,
-        gligen_phrases=gligen_phrases,
-        gligen_boxes=boxes,
-        gligen_scheduled_sampling_beta=1.0,
-        output_type="pil",
-        num_inference_steps=50,
-        negative_prompt="artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate",
-        num_images_per_prompt=4,
-        generator=generator,
-    ).images
-    os.makedirs(os.path.join(args.output_dir, "images"), exist_ok=True)
-    make_image_grid(images, 1, 4).save(
-        os.path.join(args.output_dir, "images", f"generated-images-{step:06d}-{accelerator.process_index:02d}.png")
-    )
-
-    vae.to(accelerator.device, dtype=weight_dtype)
-
-
-def parse_args(input_args=None):
-    parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.")
-    parser.add_argument(
-        "--data_path",
-        type=str,
-        default="coco_train2017.pth",
-        help="Path to training dataset.",
-    )
-    parser.add_argument(
-        "--image_path",
-        type=str,
-        default="coco_train2017.pth",
-        help="Path to training images.",
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="controlnet-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=1)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
-            "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
-            "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
-            "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
-            "instructions."
-        ),
-    )
-    parser.add_argument(
-        "--checkpoints_total_limit",
-        type=int,
-        default=None,
-        help=("Max number of checkpoints to store."),
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--lr_num_cycles",
-        type=int,
-        default=1,
-        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
-    )
-    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
-    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("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument(
-        "--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(
-        "--allow_tf32",
-        action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    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(
-        "--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(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-    parser.add_argument(
-        "--set_grads_to_none",
-        action="store_true",
-        help=(
-            "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
-            " behaviors, so disable this argument if it causes any problems. More info:"
-            " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
-        ),
-    )
-    parser.add_argument(
-        "--tracker_project_name",
-        type=str,
-        default="train_controlnet",
-        help=(
-            "The `project_name` argument passed to Accelerator.init_trackers for"
-            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
-        ),
-    )
-    args = parser.parse_args()
-    return args
-
-
-def main(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(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
-        project_config=accelerator_project_config,
-    )
-
-    # Disable AMP for MPS.
-    if torch.backends.mps.is_available():
-        accelerator.native_amp = False
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-    # import correct text encoder class
-    # text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
-    # Load scheduler and models
-    from transformers import CLIPTextModel, CLIPTokenizer
-
-    pretrained_model_name_or_path = "masterful/gligen-1-4-generation-text-box"
-    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer")
-    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler")
-    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder="text_encoder")
-
-    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae")
-    unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet")
-
-    # Taken from [Sayak Paul's Diffusers PR #6511](https://github.com/huggingface/diffusers/pull/6511/files)
-    def unwrap_model(model):
-        model = accelerator.unwrap_model(model)
-        model = model._orig_mod if is_compiled_module(model) else model
-        return model
-
-    # `accelerate` 0.16.0 will have better support for customized saving
-    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
-        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
-        def save_model_hook(models, weights, output_dir):
-            if accelerator.is_main_process:
-                i = len(weights) - 1
-
-                while len(weights) > 0:
-                    weights.pop()
-                    model = models[i]
-
-                    sub_dir = "unet"
-                    model.save_pretrained(os.path.join(output_dir, sub_dir))
-
-                    i -= 1
-
-        def load_model_hook(models, input_dir):
-            while len(models) > 0:
-                # pop models so that they are not loaded again
-                model = models.pop()
-
-                # load diffusers style into model
-                load_model = unet.from_pretrained(input_dir, subfolder="unet")
-                model.register_to_config(**load_model.config)
-
-                model.load_state_dict(load_model.state_dict())
-                del load_model
-
-        accelerator.register_save_state_pre_hook(save_model_hook)
-        accelerator.register_load_state_pre_hook(load_model_hook)
-
-    vae.requires_grad_(False)
-    unet.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            import xformers
-
-            xformers_version = version.parse(xformers.__version__)
-            if xformers_version == version.parse("0.0.16"):
-                logger.warning(
-                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
-                )
-            unet.enable_xformers_memory_efficient_attention()
-            # controlnet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    # if args.gradient_checkpointing:
-    #     controlnet.enable_gradient_checkpointing()
-
-    # Check that all trainable models are in full precision
-    low_precision_error_string = (
-        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
-        " doing mixed precision training, copy of the weights should still be float32."
-    )
-
-    if unwrap_model(unet).dtype != torch.float32:
-        raise ValueError(f"Controlnet loaded as datatype {unwrap_model(unet).dtype}. {low_precision_error_string}")
-
-    # Enable TF32 for faster training on Ampere GPUs,
-    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    if args.allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    optimizer_class = torch.optim.AdamW
-    # Optimizer creation
-    for n, m in unet.named_modules():
-        if ("fuser" in n) or ("position_net" in n):
-            import torch.nn as nn
-
-            if isinstance(m, (nn.Linear, nn.LayerNorm)):
-                m.reset_parameters()
-    params_to_optimize = []
-    for n, p in unet.named_parameters():
-        if ("fuser" in n) or ("position_net" in n):
-            p.requires_grad = True
-            params_to_optimize.append(p)
-    optimizer = optimizer_class(
-        params_to_optimize,
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    from dataset import COCODataset
-
-    train_dataset = COCODataset(
-        data_path=args.data_path,
-        image_path=args.image_path,
-        tokenizer=tokenizer,
-        image_size=args.resolution,
-        max_boxes_per_data=30,
-    )
-
-    print("num samples: ", len(train_dataset))
-
-    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,
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
-        num_training_steps=args.max_train_steps * accelerator.num_processes,
-        num_cycles=args.lr_num_cycles,
-        power=args.lr_power,
-    )
-
-    # Prepare everything with our `accelerator`.
-    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet, optimizer, train_dataloader, lr_scheduler
-    )
-
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move vae, unet and text_encoder to device and cast to weight_dtype
-    vae.to(accelerator.device, dtype=weight_dtype)
-    # unet.to(accelerator.device, dtype=weight_dtype)
-    unet.to(accelerator.device, dtype=torch.float32)
-    text_encoder.to(accelerator.device, dtype=weight_dtype)
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        tracker_config = dict(vars(args))
-
-        # tensorboard cannot handle list types for config
-        # tracker_config.pop("validation_prompt")
-        # tracker_config.pop("validation_image")
-
-        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
-
-    # Train!
-    # total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    # logger.info("***** Running training *****")
-    # logger.info(f"  Num examples = {len(train_dataset)}")
-    # logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
-    # logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    # logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    # logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    # logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    # logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1] if len(dirs) > 0 else None
-
-        if path is None:
-            accelerator.print(
-                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
-            )
-            args.resume_from_checkpoint = None
-            initial_global_step = 0
-        else:
-            accelerator.print(f"Resuming from checkpoint {path}")
-            accelerator.load_state(os.path.join(args.output_dir, path))
-            global_step = int(path.split("-")[1])
-
-            initial_global_step = global_step
-            first_epoch = global_step // num_update_steps_per_epoch
-    else:
-        initial_global_step = 0
-
-    progress_bar = tqdm(
-        range(0, args.max_train_steps),
-        initial=initial_global_step,
-        desc="Steps",
-        # Only show the progress bar once on each machine.
-        disable=not accelerator.is_local_main_process,
-    )
-
-    log_validation(
-        vae,
-        text_encoder,
-        tokenizer,
-        unet,
-        noise_scheduler,
-        args,
-        accelerator,
-        global_step,
-        weight_dtype,
-    )
-
-    # image_logs = None
-    for epoch in range(first_epoch, args.num_train_epochs):
-        for step, batch in enumerate(train_dataloader):
-            with accelerator.accumulate(unet):
-                # Convert images to latent space
-                latents = vae.encode(batch["pixel_values"].to(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.config.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():
-                    # Get the text embedding for conditioning
-                    encoder_hidden_states = text_encoder(
-                        batch["caption"]["input_ids"].squeeze(1),
-                        # batch['caption']['attention_mask'].squeeze(1),
-                        return_dict=False,
-                    )[0]
-
-                cross_attention_kwargs = {}
-                cross_attention_kwargs["gligen"] = {
-                    "boxes": batch["boxes"],
-                    "positive_embeddings": batch["text_embeddings_before_projection"],
-                    "masks": batch["masks"],
-                }
-                # Predict the noise residual
-                model_pred = unet(
-                    noisy_latents,
-                    timesteps,
-                    encoder_hidden_states=encoder_hidden_states,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad(set_to_none=args.set_grads_to_none)
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-                if global_step % args.checkpointing_steps == 0:
-                    if accelerator.is_main_process:
-                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
-                        if args.checkpoints_total_limit is not None:
-                            checkpoints = os.listdir(args.output_dir)
-                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
-                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
-
-                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
-                            if len(checkpoints) >= args.checkpoints_total_limit:
-                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
-                                removing_checkpoints = checkpoints[0:num_to_remove]
-
-                                logger.info(
-                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
-                                )
-                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
-
-                                for removing_checkpoint in removing_checkpoints:
-                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
-                                    shutil.rmtree(removing_checkpoint)
-
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step:06d}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-                    # if args.validation_prompt is not None and global_step % args.validation_steps == 0:
-                    log_validation(
-                        vae,
-                        text_encoder,
-                        tokenizer,
-                        unet,
-                        noise_scheduler,
-                        args,
-                        accelerator,
-                        global_step,
-                        weight_dtype,
-                    )
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-    # Create the pipeline using using the trained modules and save it.
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        unet = unwrap_model(unet)
-        unet.save_pretrained(args.output_dir)
-    #
-    #     # Run a final round of validation.
-    #     image_logs = None
-    #     if args.validation_prompt is not None:
-    #         image_logs = log_validation(
-    #             vae=vae,
-    #             text_encoder=text_encoder,
-    #             tokenizer=tokenizer,
-    #             unet=unet,
-    #             controlnet=None,
-    #             args=args,
-    #             accelerator=accelerator,
-    #             weight_dtype=weight_dtype,
-    #             step=global_step,
-    #             is_final_validation=True,
-    #         )
-    #
-    #     if args.push_to_hub:
-    #         save_model_card(
-    #             repo_id,
-    #             image_logs=image_logs,
-    #             base_model=args.pretrained_model_name_or_path,
-    #             repo_folder=args.output_dir,
-    #         )
-    #         upload_folder(
-    #             repo_id=repo_id,
-    #             folder_path=args.output_dir,
-    #             commit_message="End of training",
-    #             ignore_patterns=["step_*", "epoch_*"],
-    #         )
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
@@ -19,7 +19,7 @@ on consumer GPUs like Tesla T4, Tesla V100.

 ### Training

-First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Narutos dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).
+First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).  

 **___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**

@@ -30,7 +30,7 @@ export MODEL_NAME="CompVis/stable-diffusion-v1-4"
 export DATASET_NAME="lambdalabs/naruto-blip-captions"
 ```

-For this example we want to directly store the trained LoRA embeddings on the Hub, so
+For this example we want to directly store the trained LoRA embeddings on the Hub, so 
 we need to be logged in and add the `--push_to_hub` flag.

 ```bash
@@ -48,7 +48,7 @@ accelerate launch --mixed_precision="fp16" train_text_to_image_lora.py \
  --num_train_epochs=100 --checkpointing_steps=5000 \
  --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
  --seed=42 \
-  --output_dir="sd-naruto-model-lora" \
+  --output_dir="sd-pokemon-model-lora" \
  --validation_prompt="cute dragon creature" --report_to="wandb"
  --use_peft \
  --lora_r=4 --lora_alpha=32 \
@@ -61,12 +61,12 @@ The above command will also run inference as fine-tuning progresses and log the

 The final LoRA embedding weights have been uploaded to [sayakpaul/sd-model-finetuned-lora-t4](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4). **___Note: [The final weights](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/pytorch_lora_weights.bin) are only 3 MB in size, which is orders of magnitudes smaller than the original model.___**

-You can check some inference samples that were logged during the course of the fine-tuning process [here](https://wandb.ai/sayakpaul/text2image-fine-tune/runs/q4lc0xsw).
+You can check some inference samples that were logged during the course of the fine-tuning process [here](https://wandb.ai/sayakpaul/text2image-fine-tune/runs/q4lc0xsw). 

 ### Inference

-Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline` after loading the trained LoRA weights.  You
-need to pass the `output_dir` for loading the LoRA weights which, in this case, is `sd-naruto-model-lora`.
+Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline` after loading the trained LoRA weights.  You 
+need to pass the `output_dir` for loading the LoRA weights which, in this case, is `sd-pokemon-model-lora`.

 ```python
 from diffusers import StableDiffusionPipeline
@@ -77,7 +77,7 @@ pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4",
 pipe.unet.load_attn_procs(model_path)
 pipe.to("cuda")

-prompt = "A naruto with green eyes and red legs."
+prompt = "A pokemon with green eyes and red legs."
 image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
-image.save("naruto.png")
+image.save("pokemon.png")
 ```
@@ -32,7 +32,7 @@ And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) e
 accelerate config
 ```

-### Naruto example
+### Pokemon example

 You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-4`, so you'll need to visit [its card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree.

@@ -51,7 +51,7 @@ If you have already cloned the repo, then you won't need to go through these ste
 ## Use ONNXRuntime to accelerate training
 In order to leverage onnxruntime to accelerate training, please use train_text_to_image.py

-The command to train a DDPM UNetCondition model on the Naruto dataset with onnxruntime:
+The command to train a DDPM UNetCondition model on the Pokemon dataset with onnxruntime:

 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
@@ -68,7 +68,7 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-naruto-model"
+  --output_dir="sd-pokemon-model"
 ```

 Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
@@ -18,13 +18,13 @@

 Upon having access to a TPU VM (TPUs higher than version 3), you should first install
 a TPU-compatible version of JAX:
-```sh
+```
 pip install jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
 ```

 Next, we can install [flax](https://github.com/google/flax) and the diffusers library:

-```sh
+```
 pip install flax diffusers transformers
 ```

@@ -60,7 +60,7 @@ if is_wandb_available():
    import wandb

 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.29.0.dev0")
+check_min_version("0.28.0.dev0")

 logger = get_logger(__name__)

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Dhruv Nair	8426bf7142	update	2024-05-23 15:40:23 +00:00
Dhruv Nair	4149262362	update	2024-05-23 15:26:54 +00:00