move slow tests to nightly

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,5 +1,5 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug on Diffusers
+description: Report a bug on diffusers
 labels: [ "bug" ]
 body:
   - type: markdown
@@ -10,16 +10,15 @@ body:
         Thus, issues are of the same importance as pull requests when contributing to this library ❤️.
         In order to make your issue as **useful for the community as possible**, let's try to stick to some simple guidelines:
         - 1. Please try to be as precise and concise as possible.
-             *Give your issue a fitting title. Assume that someone which very limited knowledge of Diffusers can understand your issue. Add links to the source code, documentation other issues, pull requests etc...*
+             *Give your issue a fitting title. Assume that someone which very limited knowledge of diffusers can understand your issue. Add links to the source code, documentation other issues, pull requests etc...*
         - 2. If your issue is about something not working, **always** provide a reproducible code snippet. The reader should be able to reproduce your issue by **only copy-pasting your code snippet into a Python shell**.
              *The community cannot solve your issue if it cannot reproduce it. If your bug is related to training, add your training script and make everything needed to train public. Otherwise, just add a simple Python code snippet.*
-        - 3. Add the **minimum** amount of code / context that is needed to understand, reproduce your issue.
+        - 3. Add the **minimum amount of code / context that is needed to understand, reproduce your issue**.
              *Make the life of maintainers easy. `diffusers` is getting many issues every day. Make sure your issue is about one bug and one bug only. Make sure you add only the context, code needed to understand your issues - nothing more. Generally, every issue is a way of documenting this library, try to make it a good documentation entry.*
-        - 4. For issues related to community pipelines (i.e., the pipelines located in the `examples/community` folder), please tag the author of the pipeline in your issue thread as those pipelines are not maintained.
   - type: markdown
     attributes:
       value: |
-        For more in-detail information on how to write good issues you can have a look [here](https://huggingface.co/course/chapter8/5?fw=pt).
+        For more in-detail information on how to write good issues you can have a look [here](https://huggingface.co/course/chapter8/5?fw=pt)
   - type: textarea
     id: bug-description
     attributes:
@@ -47,7 +46,7 @@ body:
     attributes:
       label: System Info
       description: Please share your system info with us. You can run the command `diffusers-cli env` and copy-paste its output below.
-      placeholder: Diffusers version, platform, Python version, ...
+      placeholder: diffusers version, platform, python version, ...
     validations:
       required: true
   - type: textarea
@@ -55,56 +54,27 @@ body:
     attributes:
       label: Who can help?
       description: |
-        Your issue will be replied to more quickly if you can figure out the right person to tag with @.
+        Your issue will be replied to more quickly if you can figure out the right person to tag with @
         If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
-
+        
         All issues are read by one of the core maintainers, so if you don't know who to tag, just leave this blank and
         a core maintainer will ping the right person.
+        
+        Please tag fewer than 3 people.
+        
+        General library related questions: @patrickvonplaten and @sayakpaul
 
-        Please tag a maximum of 2 people.
+        Questions on the training examples: @williamberman, @sayakpaul, @yiyixuxu
 
-        Questions on DiffusionPipeline (Saving, Loading, From pretrained, ...): @sayakpaul @DN6
+        Questions on memory optimizations, LoRA, float16, etc.: @williamberman, @patrickvonplaten, and @sayakpaul
 
-        Questions on pipelines:
-        - Stable Diffusion @yiyixuxu @asomoza
-        - Stable Diffusion XL @yiyixuxu @sayakpaul @DN6
-        - Stable Diffusion 3: @yiyixuxu @sayakpaul @DN6 @asomoza
-        - Kandinsky @yiyixuxu
-        - ControlNet @sayakpaul @yiyixuxu @DN6
-        - T2I Adapter @sayakpaul @yiyixuxu @DN6
-        - IF @DN6
-        - Text-to-Video / Video-to-Video @DN6 @a-r-r-o-w
-        - Wuerstchen @DN6
-        - Other: @yiyixuxu @DN6
-        - Improving generation quality: @asomoza
+        Questions on schedulers: @patrickvonplaten and @williamberman
 
-        Questions on models:
-        - UNet @DN6 @yiyixuxu @sayakpaul
-        - VAE @sayakpaul @DN6 @yiyixuxu
-        - Transformers/Attention @DN6 @yiyixuxu @sayakpaul
-
-        Questions on single file checkpoints: @DN6
-
-        Questions on Schedulers: @yiyixuxu
-
-        Questions on LoRA: @sayakpaul
-
-        Questions on Textual Inversion: @sayakpaul
-
-        Questions on Training:
-        - DreamBooth @sayakpaul
-        - Text-to-Image Fine-tuning @sayakpaul
-        - Textual Inversion @sayakpaul
-        - ControlNet @sayakpaul
-
-        Questions on Tests: @DN6 @sayakpaul @yiyixuxu
-
-        Questions on Documentation: @stevhliu
+        Questions on models and pipelines: @patrickvonplaten, @sayakpaul, and @williamberman
 
         Questions on JAX- and MPS-related things: @pcuenca
 
-        Questions on audio pipelines: @sanchit-gandhi
-
-
-
+        Questions on audio pipelines: @patrickvonplaten, @kashif, and @sanchit-gandhi 
+        
+        Documentation: @stevhliu and @yiyixuxu
       placeholder: "@Username ..."

.github/ISSUE_TEMPLATE/config.yml

@@ -1,4 +1,7 @@
 contact_links:
-  - name: Questions / Discussions
-    url: https://github.com/huggingface/diffusers/discussions
-    about: General usage questions and community discussions
+  - name: Blank issue
+    url: https://github.com/huggingface/diffusers/issues/new
+    about: Other
+  - name: Forum
+    url: https://discuss.huggingface.co/
+    about: General usage questions and community discussions

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,5 +1,5 @@
 ---
-name: "\U0001F680 Feature Request"
+name: "\U0001F680 Feature request"
 about: Suggest an idea for this project
 title: ''
 labels: ''
@@ -8,13 +8,13 @@ assignees: ''
 ---
 
 **Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...].
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
 
-**Describe the solution you'd like.**
+**Describe the solution you'd like**
 A clear and concise description of what you want to happen.
 
-**Describe alternatives you've considered.**
+**Describe alternatives you've considered**
 A clear and concise description of any alternative solutions or features you've considered.
 
-**Additional context.**
+**Additional context**
 Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/new-model-addition.yml

@@ -1,5 +1,5 @@
-name: "\U0001F31F New Model/Pipeline/Scheduler Addition"
-description: Submit a proposal/request to implement a new diffusion model/pipeline/scheduler
+name: "\U0001F31F New model/pipeline/scheduler addition"
+description: Submit a proposal/request to implement a new diffusion model / pipeline / scheduler
 labels: [ "New model/pipeline/scheduler" ]
 
 body:
@@ -19,7 +19,7 @@ body:
       description: |
           Please note that if the model implementation isn't available or if the weights aren't open-source, we are less likely to implement it in `diffusers`.
       options:
-        - label: "The model implementation is available."
+        - label: "The model implementation is available"
         - label: "The model weights are available (Only relevant if addition is not a scheduler)."
 
   - type: textarea

.github/ISSUE_TEMPLATE/remote-vae-pilot-feedback.yml

@@ -1,38 +0,0 @@
-name: "\U0001F31F Remote VAE"
-description: Feedback for remote VAE pilot
-labels: [ "Remote VAE" ]
-
-body:
-  - type: textarea
-    id: positive
-    validations:
-      required: true
-    attributes:
-      label: Did you like the remote VAE solution?
-      description: |
-        If you liked it, we would appreciate it if you could elaborate what you liked.
-
-  - type: textarea
-    id: feedback
-    validations:
-      required: true
-    attributes:
-      label: What can be improved about the current solution?
-      description: |
-        Let us know the things you would like to see improved. Note that we will work optimizing the solution once the pilot is over and we have usage.
-
-  - type: textarea
-    id: others
-    validations:
-      required: true
-    attributes:
-      label: What other VAEs you would like to see if the pilot goes well?
-      description: |
-        Provide a list of the VAEs you would like to see in the future if the pilot goes well.
-
-  - type: textarea
-    id: additional-info
-    attributes:
-      label: Notify the members of the team
-      description: |
-        Tag the following folks when submitting this feedback: @hlky @sayakpaul

.github/ISSUE_TEMPLATE/translate.md

@@ -1,29 +0,0 @@
----
-name: 🌐 Translating a New Language?
-about: Start a new translation effort in your language
-title: '[<languageCode>] Translating docs to <languageName>'
-labels: WIP
-assignees: ''
-
----
-
-<!--
-Note: Please search to see if an issue already exists for the language you are trying to translate.
--->
-
-Hi!
-
-Let's bring the documentation to all the <languageName>-speaking community 🌐.
-
-Who would want to translate? Please follow the 🤗 [TRANSLATING guide](https://github.com/huggingface/diffusers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.
-
-Some notes:
-
-* Please translate using an informal tone (imagine you are talking with a friend about Diffusers 🤗).
-* Please translate in a gender-neutral way.
-* Add your translations to the folder called `<languageCode>` inside the [source folder](https://github.com/huggingface/diffusers/tree/main/docs/source).
-* Register your translation in `<languageCode>/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml).
-* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu for review.
-* 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63).
-
-Thank you so much for your help! 🤗

.github/PULL_REQUEST_TEMPLATE.md

@@ -19,10 +19,10 @@ Fixes # (issue)
 - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
 - [ ] Did you read the [contributor guideline](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md)?
 - [ ] Did you read our [philosophy doc](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md) (important for complex PRs)?
-- [ ] Was this discussed/approved via a GitHub issue or the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)? Please add a link to it if that's the case.
+- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.
 - [ ] Did you make sure to update the documentation with your changes? Here are the
       [documentation guidelines](https://github.com/huggingface/diffusers/tree/main/docs), and
-      [here are tips on formatting docstrings](https://github.com/huggingface/diffusers/tree/main/docs#writing-source-documentation).
+      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
 - [ ] Did you write any new necessary tests?
 
 
@@ -31,25 +31,24 @@ Fixes # (issue)
 Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
 members/contributors who may be interested in your PR.
 
-<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @.
+<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
 
  If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
  Please tag fewer than 3 people.
 
 Core library:
 
-- Schedulers: @yiyixuxu
-- Pipelines and pipeline callbacks: @yiyixuxu and @asomoza
-- Training examples: @sayakpaul
-- Docs: @stevhliu and @sayakpaul
+- Schedulers: @williamberman and @patrickvonplaten
+- Pipelines:  @patrickvonplaten and @sayakpaul
+- Training examples: @sayakpaul and @patrickvonplaten
+- Docs: @stevenliu and @yiyixu
 - JAX and MPS: @pcuenca
 - Audio: @sanchit-gandhi
-- General functionalities: @sayakpaul @yiyixuxu @DN6
+- General functionalities: @patrickvonplaten and @sayakpaul
 
 Integrations:
 
-- deepspeed: HF Trainer/Accelerate: @SunMarc
-- PEFT: @sayakpaul @BenjaminBossan
+- deepspeed: HF Trainer/Accelerate: @pacman100
 
 HF projects:
 

.github/actions/setup-miniconda/action.yml

@@ -4,7 +4,7 @@ description: Sets up miniconda in your ${RUNNER_TEMP} environment and gives you
 
 inputs:
   python-version:
-    description: If set to any value, don't use sudo to clean the workspace
+    description: If set to any value, dont use sudo to clean the workspace
     required: false
     type: string
     default: "3.9"

.github/workflows/benchmark.yml

@@ -1,89 +0,0 @@
-name: Benchmarking tests
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron: "30 1 1,15 * *" # every 2 weeks on the 1st and the 15th of every month at 1:30 AM
-
-env:
-  DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
-  HF_HOME: /mnt/cache
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  BASE_PATH: benchmark_outputs
-
-jobs:
-  torch_models_cuda_benchmark_tests:
-    env:
-      SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_BENCHMARK }}
-    name: Torch Core Models CUDA Benchmarking Tests
-    strategy:
-      fail-fast: false
-      max-parallel: 1
-    runs-on:
-      group: aws-g6e-4xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-      - name: Install dependencies
-        run: |
-          apt update
-          apt install -y libpq-dev postgresql-client
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install -r benchmarks/requirements.txt
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Diffusers Benchmarking
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        run: |
-          cd benchmarks && python run_all.py
-
-      - name: Push results to the Hub
-        env: 
-          HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
-        run: |
-          cd benchmarks && python push_results.py
-          mkdir $BASE_PATH && cp *.csv $BASE_PATH
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: benchmark_test_reports
-          path: benchmarks/${{ env.BASE_PATH }}
-      
-      # TODO: enable this once the connection problem has been resolved.
-      - name: Update benchmarking results to DB
-        env:
-          PGDATABASE: metrics
-          PGHOST: ${{ secrets.DIFFUSERS_BENCHMARKS_PGHOST }}
-          PGUSER: transformers_benchmarks
-          PGPASSWORD: ${{ secrets.DIFFUSERS_BENCHMARKS_PGPASSWORD }}
-          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
-        run: |
-          git config --global --add safe.directory /__w/diffusers/diffusers
-          commit_id=$GITHUB_SHA
-          commit_msg=$(git show -s --format=%s "$commit_id" | cut -c1-70)
-          cd benchmarks && python populate_into_db.py "$BRANCH_NAME" "$commit_id" "$commit_msg"
-
-      - name: Report success status
-        if: ${{ success() }}
-        run: |
-          pip install requests && python utils/notify_benchmarking_status.py --status=success
-
-      - name: Report failure status
-        if: ${{ failure() }}
-        run: |
-          pip install requests && python utils/notify_benchmarking_status.py --status=failure

.github/workflows/build_docker_images.yml

@@ -1,66 +1,20 @@
-name: Test, build, and push Docker images
+name: Build Docker images (nightly)
 
 on:
-  pull_request: # During PRs, we just check if the changes Dockerfiles can be successfully built
-    branches:
-      - main
-    paths:
-      - "docker/**"
   workflow_dispatch:
   schedule:
     - cron: "0 0 * * *" # every day at midnight
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
+  group: docker-image-builds
+  cancel-in-progress: false
 
 env:
   REGISTRY: diffusers
-  CI_SLACK_CHANNEL: ${{ secrets.CI_DOCKER_CHANNEL }}
 
 jobs:
-  test-build-docker-images:
-    runs-on:
-      group: aws-general-8-plus
-    if: github.event_name == 'pull_request'
-    steps:
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v1
-
-      - name: Check out code
-        uses: actions/checkout@v3
-
-      - name: Find Changed Dockerfiles
-        id: file_changes
-        uses: jitterbit/get-changed-files@v1
-        with:
-          format: "space-delimited"
-          token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Build Changed Docker Images
-        env: 
-          CHANGED_FILES: ${{ steps.file_changes.outputs.all }}
-        run: |
-          echo "$CHANGED_FILES"
-          for FILE in $CHANGED_FILES; do 
-            # skip anything that isn't still on disk
-            if [[ ! -f "$FILE" ]]; then
-              echo "Skipping removed file $FILE"
-              continue
-            fi           
-            if [[ "$FILE" == docker/*Dockerfile ]]; then
-              DOCKER_PATH="${FILE%/Dockerfile}"
-              DOCKER_TAG=$(basename "$DOCKER_PATH")
-              echo "Building Docker image for $DOCKER_TAG"
-              docker build -t "$DOCKER_TAG" "$DOCKER_PATH"
-            fi
-          done
-        if: steps.file_changes.outputs.all != ''
-
-  build-and-push-docker-images:
-    runs-on:
-      group: aws-general-8-plus
-    if: github.event_name != 'pull_request'
+  build-docker-images:
+    runs-on: ubuntu-latest
 
     permissions:
       contents: read
@@ -72,21 +26,21 @@ jobs:
         image-name:
           - diffusers-pytorch-cpu
           - diffusers-pytorch-cuda
-          - diffusers-pytorch-cuda
-          - diffusers-pytorch-xformers-cuda
-          - diffusers-pytorch-minimum-cuda
-          - diffusers-doc-builder
+          - diffusers-flax-cpu
+          - diffusers-flax-tpu
+          - diffusers-onnxruntime-cpu
+          - diffusers-onnxruntime-cuda
 
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v1
+
       - name: Login to Docker Hub
         uses: docker/login-action@v2
         with:
           username: ${{ env.REGISTRY }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
+
       - name: Build and push
         uses: docker/build-push-action@v3
         with:
@@ -94,14 +48,3 @@ jobs:
           context: ./docker/${{ matrix.image-name }}
           push: true
           tags: ${{ env.REGISTRY }}/${{ matrix.image-name }}:latest
-
-      - name: Post to a Slack channel
-        id: slack
-        uses: huggingface/hf-workflows/.github/actions/post-slack@main
-        with:
-          # Slack channel id, channel name, or user id to post message.
-          # See also: https://api.slack.com/methods/chat.postMessage#channels
-          slack_channel: ${{ env.CI_SLACK_CHANNEL }}
-          title: "🤗 Results of the ${{ matrix.image-name }} Docker Image build"
-          status: ${{ job.status }}
-          slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}

.github/workflows/build_documentation.yml

@@ -7,10 +7,6 @@ on:
       - doc-builder*
       - v*-release
       - v*-patch
-    paths:
-      - "src/diffusers/**.py"
-      - "examples/**"
-      - "docs/**"
 
 jobs:
   build:
@@ -20,8 +16,8 @@ jobs:
       install_libgl1: true
       package: diffusers
       notebook_folder: diffusers_doc
-      languages: en ko zh ja pt
-      custom_container: diffusers/diffusers-doc-builder
+      languages: en ko zh
+
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}
       hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/build_pr_documentation.yml

@@ -2,10 +2,6 @@ name: Build PR Documentation
 
 on:
   pull_request:
-    paths:
-      - "src/diffusers/**.py"
-      - "examples/**"
-      - "docs/**"
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
@@ -19,5 +15,4 @@ jobs:
       pr_number: ${{ github.event.number }}
       install_libgl1: true
       package: diffusers
-      languages: en ko zh ja pt
-      custom_container: diffusers/diffusers-doc-builder
+      languages: en ko zh

.github/workflows/delete_doc_comment.yml

@@ -0,0 +1,14 @@
+name: Delete doc comment
+
+on:
+  workflow_run:
+    workflows: ["Delete doc comment trigger"]
+    types:
+      - completed
+
+
+jobs:
+  delete:
+    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment.yml@main
+    secrets:
+      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

.github/workflows/delete_doc_comment_trigger.yml

@@ -0,0 +1,12 @@
+name: Delete doc comment trigger
+
+on:
+  pull_request:
+    types: [ closed ]
+
+
+jobs:
+  delete:
+    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment_trigger.yml@main
+    with:
+      pr_number: ${{ github.event.number }}

.github/workflows/mirror_community_pipeline.yml

@@ -1,102 +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:
-    env:
-      SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_COMMUNITY_MIRROR }}
-
-    runs-on: ubuntu-22.04
-    steps:
-      # Checkout to correct ref
-      #   If workflow dispatch
-      #     If ref is 'main', set:
-      #       CHECKOUT_REF=refs/heads/main
-      #       PATH_IN_REPO=main
-      #     Else it must be a tag. Set:
-      #       CHECKOUT_REF=refs/tags/{tag}
-      #       PATH_IN_REPO={tag}
-      #   If not workflow dispatch
-      #     If ref is 'refs/heads/main' => set 'main'
-      #     Else it must be a tag => set {tag}
-      - name: Set checkout_ref and path_in_repo
-        run: |
-          if [ "${{ 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 }}" >> $GITHUB_ENV
-            echo "PATH_IN_REPO=$(echo ${{ github.ref }} | sed 's/^refs\/tags\///')" >> $GITHUB_ENV
-          fi
-      - name: Print env vars
-        run: |
-          echo "CHECKOUT_REF: ${{ env.CHECKOUT_REF }}"
-          echo "PATH_IN_REPO: ${{ env.PATH_IN_REPO }}"
-      - 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 --upgrade pip
-          pip install --upgrade huggingface_hub
-
-      # Check secret is set
-      - name: whoami
-        run: hf auth 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: hf 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 }}
-
-      - name: Report success status
-        if: ${{ success() }}
-        run: |
-          pip install requests && python utils/notify_community_pipelines_mirror.py --status=success
-
-      - name: Report failure status
-        if: ${{ failure() }}
-        run: |
-          pip install requests && python utils/notify_community_pipelines_mirror.py --status=failure

.github/workflows/nightly_tests.yml

@@ -1,615 +1,162 @@
-name: Nightly and release tests on main/release branch
+name: Nightly tests on main
 
 on:
-  workflow_dispatch:
   schedule:
     - cron: "0 0 * * *" # every day at midnight
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
+  HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 600
   RUN_SLOW: yes
   RUN_NIGHTLY: yes
-  PIPELINE_USAGE_CUTOFF: 0
-  SLACK_API_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
-  CONSOLIDATED_REPORT_PATH: consolidated_test_report.md
 
 jobs:
-  setup_torch_cuda_pipeline_matrix:
-    name: Setup Torch Pipelines CUDA Slow Tests Matrix
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-    outputs:
-      pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: Install dependencies
-        run: |
-          pip install -e .[test]
-          pip install huggingface_hub
-      - name: Fetch Pipeline Matrix
-        id: fetch_pipeline_matrix
-        run: |
-          matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)
-          echo $matrix
-          echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
-
-      - name: Pipeline Tests Artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: test-pipelines.json
-          path: reports
-
-  run_nightly_tests_for_torch_pipelines:
-    name: Nightly Torch Pipelines CUDA Tests
-    needs: setup_torch_cuda_pipeline_matrix
+  run_nightly_tests:
     strategy:
       fail-fast: false
-      max-parallel: 8
-      matrix:
-        module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: nvidia-smi
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          python -m uv pip install pytest-reportlog
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Pipeline CUDA Test
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-            --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \
-            tests/pipelines/${{ matrix.module }}
-      - 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: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: pipeline_${{ matrix.module }}_test_reports
-          path: reports
-
-  run_nightly_tests_for_other_torch_modules:
-    name: Nightly Torch CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    defaults:
-      run:
-        shell: bash
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-      matrix:
-        module: [models, schedulers, lora, others, single_file, examples]
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        python -m uv pip install pytest-reportlog
-    - name: Environment
-      run: python utils/print_env.py
-
-    - name: Run nightly PyTorch CUDA tests for non-pipeline modules
-      if: ${{ matrix.module != 'examples'}}
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_torch_${{ matrix.module }}_cuda \
-          --report-log=tests_torch_${{ matrix.module }}_cuda.log \
-          tests/${{ matrix.module }}
-
-    - name: Run nightly example tests with Torch
-      if: ${{ matrix.module == 'examples' }}
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v --make-reports=examples_torch_cuda \
-          --report-log=examples_torch_cuda.log \
-          examples/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_torch_${{ matrix.module }}_cuda_stats.txt
-        cat reports/tests_torch_${{ matrix.module }}_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_${{ matrix.module }}_cuda_test_reports
-        path: reports
-
-  run_torch_compile_tests:
-    name: PyTorch Compile CUDA tests
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Run torch compile tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        RUN_COMPILE: yes
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_torch_compile_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_compile_test_reports
-        path: reports
-
-  run_big_gpu_torch_tests:
-    name: Torch tests on big GPU
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-    runs-on:
-      group: aws-g6e-xlarge-plus
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: nvidia-smi
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          python -m uv pip install pytest-reportlog
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Selected Torch CUDA Test on big GPU
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-          BIG_GPU_MEMORY: 40
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -m "big_accelerator" \
-            --make-reports=tests_big_gpu_torch_cuda \
-            --report-log=tests_big_gpu_torch_cuda.log \
-            tests/
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_big_gpu_torch_cuda_stats.txt
-          cat reports/tests_big_gpu_torch_cuda_failures_short.txt
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: torch_cuda_big_gpu_test_reports
-          path: reports
-
-  torch_minimum_version_cuda_tests:
-    name: Torch Minimum Version CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    defaults:
-      run:
-        shell: bash
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-      - name: Environment
-        run: |
-          python utils/print_env.py
-
-      - name: Run PyTorch CUDA tests
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_torch_minimum_version_cuda \
-            tests/models/test_modeling_common.py \
-            tests/pipelines/test_pipelines_common.py \
-            tests/pipelines/test_pipeline_utils.py \
-            tests/pipelines/test_pipelines.py \
-            tests/pipelines/test_pipelines_auto.py \
-            tests/schedulers/test_schedulers.py \
-            tests/others
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_torch_minimum_version_cuda_stats.txt
-          cat reports/tests_torch_minimum_version_cuda_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: torch_minimum_version_cuda_test_reports
-          path: reports
-
-  run_nightly_quantization_tests:
-    name: Torch quantization nightly tests
-    strategy:
-      fail-fast: false
-      max-parallel: 2
       matrix:
         config:
-          - backend: "bitsandbytes"
-            test_location: "bnb"
-            additional_deps: ["peft"]
-          - backend: "gguf"
-            test_location: "gguf"
-            additional_deps: ["peft", "kernels"]
-          - backend: "torchao"
-            test_location: "torchao"
-            additional_deps: []
-          - backend: "optimum_quanto"
-            test_location: "quanto"
-            additional_deps: []
-          - backend: "nvidia_modelopt"
-            test_location: "modelopt"
-            additional_deps: []
-    runs-on:
-      group: aws-g6e-xlarge-plus
+          - name: Nightly PyTorch CUDA tests on Ubuntu
+            framework: pytorch
+            runner: docker-gpu
+            image: diffusers/diffusers-pytorch-cuda
+            report: torch_cuda
+          - name: Nightly Flax TPU tests on Ubuntu
+            framework: flax
+            runner: docker-tpu
+            image: diffusers/diffusers-flax-tpu
+            report: flax_tpu
+          - name: Nightly ONNXRuntime CUDA tests on Ubuntu
+            framework: onnxruntime
+            runner: docker-gpu
+            image: diffusers/diffusers-onnxruntime-cuda
+            report: onnx_cuda
+
+    name: ${{ matrix.config.name }}
+
+    runs-on: ${{ matrix.config.runner }}
+
     container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus all
+      image: ${{ matrix.config.image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ ${{ matrix.config.runner == 'docker-tpu' && '--privileged' || '--gpus 0'}}
+
+    defaults:
+      run:
+        shell: bash
+
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
         with:
           fetch-depth: 2
+
       - name: NVIDIA-SMI
-        run: nvidia-smi
+        if: ${{ matrix.config.runner == 'docker-gpu' }}
+        run: |
+          nvidia-smi
+
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install -U ${{ matrix.config.backend }}
-          if [ "${{ join(matrix.config.additional_deps, ' ') }}" != "" ]; then
-              python -m uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
-          fi
-          python -m uv pip install pytest-reportlog
+          python -m pip install -e .[quality,test]
+          python -m pip install -U git+https://github.com/huggingface/transformers
+          python -m pip install git+https://github.com/huggingface/accelerate
+
       - name: Environment
         run: |
           python utils/print_env.py
-      - name: ${{ matrix.config.backend }} quantization tests on GPU
+
+      - name: Run nightly PyTorch CUDA tests
+        if: ${{ matrix.config.framework == 'pytorch' }}
         env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-          BIG_GPU_MEMORY: 40
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
         run: |
           python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            --make-reports=tests_${{ matrix.config.backend }}_torch_cuda \
-            --report-log=tests_${{ matrix.config.backend }}_torch_cuda.log \
-            tests/quantization/${{ matrix.config.test_location }}
+            -s -v -k "not Flax and not Onnx" \
+            --make-reports=tests_${{ matrix.config.report }} \
+            tests/
+
+      - name: Run nightly Flax TPU tests
+        if: ${{ matrix.config.framework == 'flax' }}
+        env:
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+        run: |
+          python -m pytest -n 0 \
+            -s -v -k "Flax" \
+            --make-reports=tests_${{ matrix.config.report }} \
+            tests/
+
+      - name: Run nightly ONNXRuntime CUDA tests
+        if: ${{ matrix.config.framework == 'onnxruntime' }}
+        env:
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+        run: |
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "Onnx" \
+            --make-reports=tests_${{ matrix.config.report }} \
+            tests/
+
       - name: Failure short reports
         if: ${{ failure() }}
-        run: |
-          cat reports/tests_${{ matrix.config.backend }}_torch_cuda_stats.txt
-          cat reports/tests_${{ matrix.config.backend }}_torch_cuda_failures_short.txt
+        run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v2
         with:
-          name: torch_cuda_${{ matrix.config.backend }}_reports
+          name: ${{ matrix.config.report }}_test_reports
           path: reports
-          
-  run_nightly_pipeline_level_quantization_tests:
-    name: Torch quantization nightly tests
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-    runs-on:
-      group: aws-g6e-xlarge-plus
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus all
+
+  run_nightly_tests_apple_m1:
+    name: Nightly PyTorch MPS tests on MacOS
+    runs-on: [ self-hosted, apple-m1 ]
+
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
         with:
           fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: nvidia-smi
-      - name: Install dependencies
+
+      - name: Clean checkout
+        shell: arch -arch arm64 bash {0}
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install -U bitsandbytes optimum_quanto
-          python -m uv pip install pytest-reportlog
+          git clean -fxd
+
+      - name: Setup miniconda
+        uses: ./.github/actions/setup-miniconda
+        with:
+          python-version: 3.9
+
+      - name: Install dependencies
+        shell: arch -arch arm64 bash {0}
+        run: |
+          ${CONDA_RUN} python -m pip install --upgrade pip
+          ${CONDA_RUN} python -m pip install -e .[quality,test]
+          ${CONDA_RUN} python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+          ${CONDA_RUN} python -m pip install git+https://github.com/huggingface/accelerate
+
       - name: Environment
+        shell: arch -arch arm64 bash {0}
         run: |
-          python utils/print_env.py
-      - name: Pipeline-level quantization tests on GPU
+          ${CONDA_RUN} python utils/print_env.py
+
+      - name: Run nightly PyTorch tests on M1 (MPS)
+        shell: arch -arch arm64 bash {0}
         env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-          BIG_GPU_MEMORY: 40
+          HF_HOME: /System/Volumes/Data/mnt/cache
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
         run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            --make-reports=tests_pipeline_level_quant_torch_cuda \
-            --report-log=tests_pipeline_level_quant_torch_cuda.log \
-            tests/quantization/test_pipeline_level_quantization.py
+          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps tests/
+
       - name: Failure short reports
         if: ${{ failure() }}
-        run: |
-          cat reports/tests_pipeline_level_quant_torch_cuda_stats.txt
-          cat reports/tests_pipeline_level_quant_torch_cuda_failures_short.txt
+        run: cat reports/tests_torch_mps_failures_short.txt
+
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v2
         with:
-          name: torch_cuda_pipeline_level_quant_reports
+          name: torch_mps_test_reports
           path: reports
-
-  generate_consolidated_report:
-    name: Generate Consolidated Test Report
-    needs: [
-      run_nightly_tests_for_torch_pipelines,
-      run_nightly_tests_for_other_torch_modules,
-      run_torch_compile_tests,
-      run_big_gpu_torch_tests,
-      run_nightly_quantization_tests,
-      run_nightly_pipeline_level_quantization_tests,
-      # run_nightly_onnx_tests,
-      torch_minimum_version_cuda_tests,
-      # run_flax_tpu_tests
-    ]
-    if: always()
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: Create reports directory
-        run: mkdir -p combined_reports
-
-      - name: Download all test reports
-        uses: actions/download-artifact@v4
-        with:
-          path: artifacts
-
-      - name: Prepare reports
-        run: |
-          # Move all report files to a single directory for processing
-          find artifacts -name "*.txt" -exec cp {} combined_reports/ \;
-
-      - name: Install dependencies
-        run: |
-          pip install -e .[test]
-          pip install slack_sdk tabulate
-
-      - name: Generate consolidated report
-        run: |
-          python utils/consolidated_test_report.py \
-            --reports_dir combined_reports \
-            --output_file $CONSOLIDATED_REPORT_PATH \
-            --slack_channel_name diffusers-ci-nightly
-
-      - name: Show consolidated report
-        run: |
-          cat $CONSOLIDATED_REPORT_PATH >> $GITHUB_STEP_SUMMARY
-
-      - name: Upload consolidated report
-        uses: actions/upload-artifact@v4
-        with:
-          name: consolidated_test_report
-          path: ${{ env.CONSOLIDATED_REPORT_PATH }}
-
-# M1 runner currently not well supported
-# TODO: (Dhruv) add these back when we setup better testing for Apple Silicon
-#  run_nightly_tests_apple_m1:
-#    name: Nightly PyTorch MPS tests on MacOS
-#    runs-on: [ self-hosted, apple-m1 ]
-#    if: github.event_name == 'schedule'
-#
-#    steps:
-#      - name: Checkout diffusers
-#        uses: actions/checkout@v3
-#        with:
-#          fetch-depth: 2
-#
-#      - name: Clean checkout
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          git clean -fxd
-#      - name: Setup miniconda
-#        uses: ./.github/actions/setup-miniconda
-#        with:
-#          python-version: 3.9
-#
-#      - name: Install dependencies
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          ${CONDA_RUN} python -m pip install --upgrade pip uv
-#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
-#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
-#      - name: Environment
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          ${CONDA_RUN} python utils/print_env.py
-#      - name: Run nightly PyTorch tests on M1 (MPS)
-#        shell: arch -arch arm64 bash {0}
-#        env:
-#          HF_HOME: /System/Volumes/Data/mnt/cache
-#          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-#        run: |
-#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
-#            --report-log=tests_torch_mps.log \
-#            tests/
-#      - name: Failure short reports
-#        if: ${{ failure() }}
-#        run: cat reports/tests_torch_mps_failures_short.txt
-#
-#      - name: Test suite reports artifacts
-#        if: ${{ always() }}
-#        uses: actions/upload-artifact@v4
-#        with:
-#          name: torch_mps_test_reports
-#          path: reports
-#
-#      - name: Generate Report and Notify Channel
-#        if: always()
-#        run: |
-#          pip install slack_sdk tabulate
-#          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY  run_nightly_tests_apple_m1:
-#    name: Nightly PyTorch MPS tests on MacOS
-#    runs-on: [ self-hosted, apple-m1 ]
-#    if: github.event_name == 'schedule'
-#
-#    steps:
-#      - name: Checkout diffusers
-#        uses: actions/checkout@v3
-#        with:
-#          fetch-depth: 2
-#
-#      - name: Clean checkout
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          git clean -fxd
-#      - name: Setup miniconda
-#        uses: ./.github/actions/setup-miniconda
-#        with:
-#          python-version: 3.9
-#
-#      - name: Install dependencies
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          ${CONDA_RUN} python -m pip install --upgrade pip uv
-#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
-#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
-#      - name: Environment
-#        shell: arch -arch arm64 bash {0}
-#        run: |
-#          ${CONDA_RUN} python utils/print_env.py
-#      - name: Run nightly PyTorch tests on M1 (MPS)
-#        shell: arch -arch arm64 bash {0}
-#        env:
-#          HF_HOME: /System/Volumes/Data/mnt/cache
-#          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-#        run: |
-#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
-#            --report-log=tests_torch_mps.log \
-#            tests/
-#      - name: Failure short reports
-#        if: ${{ failure() }}
-#        run: cat reports/tests_torch_mps_failures_short.txt
-#
-#      - name: Test suite reports artifacts
-#        if: ${{ always() }}
-#        uses: actions/upload-artifact@v4
-#        with:
-#          name: torch_mps_test_reports
-#          path: reports
-#
-#      - name: Generate Report and Notify Channel
-#        if: always()
-#        run: |
-#          pip install slack_sdk tabulate
-#          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

.github/workflows/notify_slack_about_release.yml

@@ -1,23 +0,0 @@
-name: Notify Slack about a release
-
-on:
-  workflow_dispatch:
-  release:
-    types: [published]
-
-jobs:
-  build:
-    runs-on: ubuntu-22.04
-
-    steps:
-    - uses: actions/checkout@v3
-
-    - name: Setup Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.8'
-
-    - name: Notify Slack about the release
-      env:
-        SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}
-      run: pip install requests && python utils/notify_slack_about_release.py

.github/workflows/pr_dependency_test.yml

@@ -4,8 +4,6 @@ on:
   pull_request:
     branches:
       - main
-    paths:
-      - "src/diffusers/**.py"
   push:
     branches:
       - main
@@ -16,20 +14,19 @@ concurrency:
 
 jobs:
   check_dependencies:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.7"
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m pip install --upgrade pip uv
-          python -m uv pip install -e .
-          python -m uv pip install pytest
+          python -m pip install --upgrade pip
+          pip install -e .
+          pip install pytest
       - name: Check for soft dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
           pytest tests/others/test_dependencies.py
+      

.github/workflows/pr_modular_tests.yml

@@ -1,141 +0,0 @@
-name: Fast PR tests for Modular
-
-on:
-  pull_request:
-    branches: [main]
-    paths:
-      - "src/diffusers/modular_pipelines/**.py"
-      - "src/diffusers/models/modeling_utils.py"
-      - "src/diffusers/models/model_loading_utils.py"
-      - "src/diffusers/pipelines/pipeline_utils.py"
-      - "src/diffusers/pipeline_loading_utils.py"
-      - "src/diffusers/loaders/lora_base.py"
-      - "src/diffusers/loaders/lora_pipeline.py"
-      - "src/diffusers/loaders/peft.py"
-      - "tests/modular_pipelines/**.py"
-      - ".github/**.yml"
-      - "utils/**.py"
-      - "setup.py"
-  push:
-    branches:
-      - ci-*
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
-  OMP_NUM_THREADS: 4
-  MKL_NUM_THREADS: 4
-  PYTEST_TIMEOUT: 60
-
-jobs:
-  check_code_quality:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check quality
-        run: make quality
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
-
-  check_repository_consistency:
-    needs: check_code_quality
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check repo consistency
-        run: |
-          python utils/check_copies.py
-          python utils/check_dummies.py
-          python utils/check_support_list.py
-          make deps_table_check_updated
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
-
-  run_fast_tests:
-    needs: [check_code_quality, check_repository_consistency]
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - name: Fast PyTorch Modular Pipeline CPU tests
-            framework: pytorch_pipelines
-            runner: aws-highmemory-32-plus
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_cpu_modular_pipelines
-
-    name: ${{ matrix.config.name }}
-
-    runs-on:
-      group: ${{ matrix.config.runner }}
-
-    container:
-      image: ${{ matrix.config.image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-
-    defaults:
-      run:
-        shell: bash
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run fast PyTorch Pipeline CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/modular_pipelines
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
-        path: reports
-
-

.github/workflows/pr_quality.yml

@@ -0,0 +1,50 @@
+name: Run code quality checks
+
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - main
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  check_code_quality:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.7"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check quality
+        run: |
+          black --check examples tests src utils scripts
+          ruff examples tests src utils scripts
+          doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
+
+  check_repository_consistency:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.7"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check quality
+        run: |
+          python utils/check_copies.py
+          python utils/check_dummies.py
+          make deps_table_check_updated

.github/workflows/pr_style_bot.yml

@@ -1,17 +0,0 @@
-name: PR Style Bot
-
-on:
-  issue_comment:
-    types: [created]
-
-permissions:
-  contents: write
-  pull-requests: write
-
-jobs:
-  style:
-    uses: huggingface/huggingface_hub/.github/workflows/style-bot-action.yml@main
-    with:
-      python_quality_dependencies: "[quality]"
-    secrets:
-      bot_token: ${{ secrets.HF_STYLE_BOT_ACTION }}

.github/workflows/pr_test_fetcher.yml

@@ -1,177 +0,0 @@
-name: Fast tests for PRs - Test Fetcher
-
-on: workflow_dispatch
-
-env:
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 4
-  MKL_NUM_THREADS: 4
-  PYTEST_TIMEOUT: 60
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  setup_pr_tests:
-    name: Setup PR Tests
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-    defaults:
-      run:
-        shell: bash
-    outputs:
-      matrix: ${{ steps.set_matrix.outputs.matrix }}
-      test_map: ${{ steps.set_matrix.outputs.test_map }}
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 0
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-        echo $(git --version)
-    - name: Fetch Tests
-      run: |
-        python utils/tests_fetcher.py | tee test_preparation.txt
-    - name: Report fetched tests
-      uses: actions/upload-artifact@v3
-      with:
-        name: test_fetched
-        path: test_preparation.txt
-    - id: set_matrix
-      name: Create Test Matrix
-      # The `keys` is used as GitHub actions matrix for jobs, i.e. `models`, `pipelines`, etc.
-      # The `test_map` is used to get the actual identified test files under each key.
-      # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail)
-      run: |
-        if [ -f test_map.json ]; then
-            keys=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); d = list(test_map.keys()); print(json.dumps(d))')
-            test_map=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); print(json.dumps(test_map))')
-        else
-            keys=$(python3 -c 'keys = ["dummy"]; print(keys)')
-            test_map=$(python3 -c 'test_map = {"dummy": []}; print(test_map)')
-        fi
-        echo $keys
-        echo $test_map
-        echo "matrix=$keys" >> $GITHUB_OUTPUT
-        echo "test_map=$test_map" >> $GITHUB_OUTPUT
-
-  run_pr_tests:
-    name: Run PR Tests
-    needs: setup_pr_tests
-    if: contains(fromJson(needs.setup_pr_tests.outputs.matrix), 'dummy') != true
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-      matrix:
-        modules: ${{ fromJson(needs.setup_pr_tests.outputs.matrix) }}
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pip install -e [quality,test]
-        python -m pip install accelerate
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run all selected tests on CPU
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.modules }}_tests_cpu ${{ fromJson(needs.setup_pr_tests.outputs.test_map)[matrix.modules] }}
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      continue-on-error: true
-      run: |
-        cat reports/${{ matrix.modules }}_tests_cpu_stats.txt
-        cat reports/${{ matrix.modules }}_tests_cpu_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v3
-      with:
-          name: ${{ matrix.modules }}_test_reports
-          path: reports
-
-  run_staging_tests:
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - name: Hub tests for models, schedulers, and pipelines
-            framework: hub_tests_pytorch
-            runner: aws-general-8-plus
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_hub
-
-    name: ${{ matrix.config.name }}
-    runs-on:
-      group: ${{ matrix.config.runner }}
-    container:
-      image: ${{ matrix.config.image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-
-    defaults:
-      run:
-        shell: bash
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pip install -e [quality,test]
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run Hub tests for models, schedulers, and pipelines on a staging env
-      if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        HUGGINGFACE_CO_STAGING=true python -m pytest \
-          -m "is_staging_test" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: pr_${{ matrix.config.report }}_test_reports
-        path: reports

.github/workflows/pr_tests.yml

@@ -2,16 +2,8 @@ name: Fast tests for PRs
 
 on:
   pull_request:
-    branches: [main]
-    paths:
-      - "src/diffusers/**.py"
-      - "benchmarks/**.py"
-      - "examples/**.py"
-      - "scripts/**.py"
-      - "tests/**.py"
-      - ".github/**.yml"
-      - "utils/**.py"
-      - "setup.py"
+    branches:
+      - main
   push:
     branches:
       - ci-*
@@ -22,161 +14,39 @@ concurrency:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60
 
 jobs:
-  check_code_quality:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check quality
-        run: make quality
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
-
-  check_repository_consistency:
-    needs: check_code_quality
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check repo consistency
-        run: |
-          python utils/check_copies.py
-          python utils/check_dummies.py
-          python utils/check_support_list.py
-          make deps_table_check_updated
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
-
   run_fast_tests:
-    needs: [check_code_quality, check_repository_consistency]
     strategy:
       fail-fast: false
       matrix:
         config:
           - name: Fast PyTorch Pipeline CPU tests
             framework: pytorch_pipelines
-            runner: aws-highmemory-32-plus
+            runner: docker-cpu
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu_pipelines
           - name: Fast PyTorch Models & Schedulers CPU tests
             framework: pytorch_models
-            runner: aws-general-8-plus
+            runner: docker-cpu
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu_models_schedulers
+          - name: Fast Flax CPU tests
+            framework: flax
+            runner: docker-cpu
+            image: diffusers/diffusers-flax-cpu
+            report: flax_cpu
           - name: PyTorch Example CPU tests
             framework: pytorch_examples
-            runner: aws-general-8-plus
+            runner: docker-cpu
             image: diffusers/diffusers-pytorch-cpu
             report: torch_example_cpu
 
     name: ${{ matrix.config.name }}
 
-    runs-on:
-      group: ${{ matrix.config.runner }}
-
-    container:
-      image: ${{ matrix.config.image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-
-    defaults:
-      run:
-        shell: bash
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run fast PyTorch Pipeline CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/pipelines
-
-    - name: Run fast PyTorch Model Scheduler CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_models' }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx and not Dependency" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/models tests/schedulers tests/others
-
-    - name: Run example PyTorch CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_examples' }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install peft timm
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          --make-reports=tests_${{ matrix.config.report }} \
-          examples
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
-        path: reports
-
-  run_staging_tests:
-    needs: [check_code_quality, check_repository_consistency]
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - name: Hub tests for models, schedulers, and pipelines
-            framework: hub_tests_pytorch
-            runner:
-              group: aws-general-8-plus
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_hub
-
-    name: ${{ matrix.config.name }}
-
     runs-on: ${{ matrix.config.runner }}
 
     container:
@@ -195,95 +65,51 @@ jobs:
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
+        apt-get update && apt-get install libsndfile1-dev libgl1 -y
+        python -m pip install -e .[quality,test]
 
     - name: Environment
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
-    - name: Run Hub tests for models, schedulers, and pipelines on a staging env
-      if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}
+    - name: Run fast PyTorch Pipeline CPU tests
+      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        HUGGINGFACE_CO_STAGING=true python -m pytest \
-          -m "is_staging_test" \
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/pipelines
+
+    - name: Run fast PyTorch Model Scheduler CPU tests
+      if: ${{ matrix.config.framework == 'pytorch_models' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx and not Dependency" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/models tests/schedulers tests/others
+
+    - name: Run fast Flax TPU tests
+      if: ${{ matrix.config.framework == 'flax' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Flax" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests
 
+    - name: Run example PyTorch CPU tests
+      if: ${{ matrix.config.framework == 'pytorch_examples' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          --make-reports=tests_${{ matrix.config.report }} \
+          examples/test_examples.py 
+
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports
-
-  run_lora_tests:
-    needs: [check_code_quality, check_repository_consistency]
-    strategy:
-      fail-fast: false
-
-    name: LoRA tests with PEFT main
-
-    runs-on:
-      group: aws-general-8-plus
-
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-
-    defaults:
-      run:
-        shell: bash
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        # TODO (sayakpaul, DN6): revisit `--no-deps`
-        python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
-        python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-        python -m uv pip install -U tokenizers
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run fast PyTorch LoRA tests with PEFT
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -s -v \
-          --make-reports=tests_peft_main \
-          tests/lora/
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -s -v \
-          --make-reports=tests_models_lora_peft_main \
-          tests/models/ -k "lora"
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_peft_main_failures_short.txt
-        cat reports/tests_models_lora_peft_main_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: pr_main_test_reports
-        path: reports
-

.github/workflows/pr_tests_gpu.yml

@@ -1,297 +0,0 @@
-name: Fast GPU Tests on PR 
-
-on:
-  pull_request:
-    branches: main
-    paths:
-      - "src/diffusers/models/modeling_utils.py"
-      - "src/diffusers/models/model_loading_utils.py"
-      - "src/diffusers/pipelines/pipeline_utils.py"
-      - "src/diffusers/pipeline_loading_utils.py"
-      - "src/diffusers/loaders/lora_base.py"
-      - "src/diffusers/loaders/lora_pipeline.py"
-      - "src/diffusers/loaders/peft.py"
-      - "tests/pipelines/test_pipelines_common.py"
-      - "tests/models/test_modeling_common.py"
-      - "examples/**/*.py"
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
-  PYTEST_TIMEOUT: 600
-  PIPELINE_USAGE_CUTOFF: 1000000000 # set high cutoff so that only always-test pipelines run
-
-jobs:
-  check_code_quality:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check quality
-        run: make quality
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
-
-  check_repository_consistency:
-    needs: check_code_quality
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check repo consistency
-        run: |
-          python utils/check_copies.py
-          python utils/check_dummies.py
-          python utils/check_support_list.py
-          make deps_table_check_updated
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
-  
-  setup_torch_cuda_pipeline_matrix:
-    needs: [check_code_quality, check_repository_consistency]
-    name: Setup Torch Pipelines CUDA Slow Tests Matrix
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-    outputs:
-      pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Fetch Pipeline Matrix
-        id: fetch_pipeline_matrix
-        run: |
-          matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)
-          echo $matrix
-          echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
-      - name: Pipeline Tests Artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: test-pipelines.json
-          path: reports
-
-  torch_pipelines_cuda_tests:
-    name: Torch Pipelines CUDA Tests
-    needs: setup_torch_cuda_pipeline_matrix
-    strategy:
-      fail-fast: false
-      max-parallel: 8
-      matrix:
-        module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Extract tests
-        id: extract_tests
-        run: |
-          pattern=$(python utils/extract_tests_from_mixin.py --type pipeline)
-          echo "$pattern" > /tmp/test_pattern.txt
-          echo "pattern_file=/tmp/test_pattern.txt" >> $GITHUB_OUTPUT
-
-      - name: PyTorch CUDA checkpoint tests on Ubuntu
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          if [ "${{ matrix.module }}" = "ip_adapters" ]; then 
-              python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -s -v -k "not Flax and not Onnx" \
-              --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-              tests/pipelines/${{ matrix.module }}
-          else 
-              pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
-              python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -s -v -k "not Flax and not Onnx and $pattern" \
-              --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-              tests/pipelines/${{ matrix.module }}
-          fi 
-
-      - 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: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: pipeline_${{ matrix.module }}_test_reports
-          path: reports
-
-  torch_cuda_tests:
-    name: Torch CUDA Tests
-    needs: [check_code_quality, check_repository_consistency]
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    defaults:
-      run:
-        shell: bash
-    strategy:
-      fail-fast: false
-      max-parallel: 4
-      matrix:
-        module: [models, schedulers, lora, others]
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Extract tests
-      id: extract_tests
-      run: |
-        pattern=$(python utils/extract_tests_from_mixin.py --type ${{ matrix.module }})
-        echo "$pattern" > /tmp/test_pattern.txt
-        echo "pattern_file=/tmp/test_pattern.txt" >> $GITHUB_OUTPUT
-
-    - name: Run PyTorch CUDA tests
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
-      run: |
-        pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
-        if [ -z "$pattern" ]; then
-          python -m pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx" tests/${{ matrix.module }} \
-          --make-reports=tests_torch_cuda_${{ matrix.module }}  
-        else
-          python -m pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
-          --make-reports=tests_torch_cuda_${{ matrix.module }}  
-        fi
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_torch_cuda_${{ matrix.module }}_stats.txt
-        cat reports/tests_torch_cuda_${{ matrix.module }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_cuda_test_reports_${{ matrix.module }}
-        path: reports
-
-  run_examples_tests:
-    name: Examples PyTorch CUDA tests on Ubuntu
-    needs: [check_code_quality, check_repository_consistency]
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-        python -m uv pip install -e [quality,test,training]
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run example tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install timm
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/examples_torch_cuda_stats.txt
-        cat reports/examples_torch_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: examples_test_reports
-        path: reports
-

.github/workflows/pr_torch_dependency_test.yml

@@ -1,36 +0,0 @@
-name: Run Torch dependency tests
-
-on:
-  pull_request:
-    branches:
-      - main
-    paths:
-      - "src/diffusers/**.py"
-  push:
-    branches:
-      - main
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  check_torch_dependencies:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m pip install --upgrade pip uv
-          python -m uv pip install -e .
-          python -m uv pip install torch torchvision torchaudio
-          python -m uv pip install pytest
-      - name: Check for soft dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          pytest tests/others/test_dependencies.py

.github/workflows/push_tests.yml

@@ -1,258 +1,126 @@
-name: Fast GPU Tests on main
+name: Slow tests on main
 
 on:
-  workflow_dispatch:
   push:
     branches:
       - main
-    paths:
-      - "src/diffusers/**.py"
-      - "examples/**.py"
-      - "tests/**.py"
 
 env:
   DIFFUSERS_IS_CI: yes
+  HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
-  PIPELINE_USAGE_CUTOFF: 50000
+  RUN_SLOW: yes
 
 jobs:
-  setup_torch_cuda_pipeline_matrix:
-    name: Setup Torch Pipelines CUDA Slow Tests Matrix
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-    outputs:
-      pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Fetch Pipeline Matrix
-        id: fetch_pipeline_matrix
-        run: |
-          matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)
-          echo $matrix
-          echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
-      - name: Pipeline Tests Artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: test-pipelines.json
-          path: reports
-
-  torch_pipelines_cuda_tests:
-    name: Torch Pipelines CUDA Tests
-    needs: setup_torch_cuda_pipeline_matrix
+  run_slow_tests:
     strategy:
       fail-fast: false
-      max-parallel: 8
+      max-parallel: 1
       matrix:
-        module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: PyTorch CUDA checkpoint tests on Ubuntu
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-            tests/pipelines/${{ matrix.module }}
-      - 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: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: pipeline_${{ matrix.module }}_test_reports
-          path: reports
+        config:
+          - name: Slow PyTorch CUDA tests on Ubuntu
+            framework: pytorch
+            runner: docker-gpu
+            image: diffusers/diffusers-pytorch-cuda
+            report: torch_cuda
+          - name: Slow Flax TPU tests on Ubuntu
+            framework: flax
+            runner: docker-tpu
+            image: diffusers/diffusers-flax-tpu
+            report: flax_tpu
+          - name: Slow ONNXRuntime CUDA tests on Ubuntu
+            framework: onnxruntime
+            runner: docker-gpu
+            image: diffusers/diffusers-onnxruntime-cuda
+            report: onnx_cuda
+
+    name: ${{ matrix.config.name }}
+
+    runs-on: ${{ matrix.config.runner }}
 
-  torch_cuda_tests:
-    name: Torch CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
     container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      image: ${{ matrix.config.image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ ${{ matrix.config.runner == 'docker-tpu' && '--privileged' || '--gpus 0'}}
+
     defaults:
       run:
         shell: bash
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-      matrix:
-        module: [models, schedulers, lora, others, single_file]
+
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
       with:
         fetch-depth: 2
 
+    - name: NVIDIA-SMI
+      if : ${{ matrix.config.runner == 'docker-gpu' }}
+      run: |
+        nvidia-smi
+
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        apt-get update && apt-get install libsndfile1-dev libgl1 -y
+        python -m pip install -e .[quality,test]
 
     - name: Environment
       run: |
         python utils/print_env.py
 
-    - name: Run PyTorch CUDA tests
+    - name: Run slow PyTorch CUDA tests
+      if: ${{ matrix.config.framework == 'pytorch' }}
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
+        CUBLAS_WORKSPACE_CONFIG: :16:8 
+
       run: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_torch_cuda_${{ matrix.module }} \
-          tests/${{ matrix.module }}
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
 
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_torch_cuda_${{ matrix.module }}_stats.txt
-        cat reports/tests_torch_cuda_${{ matrix.module }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_cuda_test_reports_${{ matrix.module }}
-        path: reports
-
-  run_torch_compile_tests:
-    name: PyTorch Compile CUDA tests
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Run example tests on GPU
+    - name: Run slow Flax TPU tests
+      if: ${{ matrix.config.framework == 'flax' }}
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        RUN_COMPILE: yes
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
       run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_torch_compile_cuda_failures_short.txt
+        python -m pytest -n 0 \
+          -s -v -k "Flax" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
 
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_compile_test_reports
-        path: reports
-
-  run_xformers_tests:
-    name: PyTorch xformers CUDA tests
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Run example tests on GPU
+    - name: Run slow ONNXRuntime CUDA tests
+      if: ${{ matrix.config.framework == 'onnxruntime' }}
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
       run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
     - name: Failure short reports
       if: ${{ failure() }}
-      run: cat reports/tests_torch_xformers_cuda_failures_short.txt
+      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
-        name: torch_xformers_test_reports
+        name: ${{ matrix.config.report }}_test_reports
         path: reports
 
   run_examples_tests:
     name: Examples PyTorch CUDA tests on Ubuntu
 
-    runs-on:
-      group: aws-g4dn-2xlarge
+    runs-on: docker-gpu
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
@@ -262,33 +130,28 @@ jobs:
     - name: NVIDIA-SMI
       run: |
         nvidia-smi
+
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
+        python -m pip install -e .[quality,test,training]
 
     - name: Environment
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install timm
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}
-      run: |
-        cat reports/examples_torch_cuda_stats.txt
-        cat reports/examples_torch_cuda_failures_short.txt
+      run: cat reports/examples_torch_cuda_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: examples_test_reports
         path: reports

.github/workflows/push_tests_fast.yml

@@ -4,21 +4,12 @@ on:
   push:
     branches:
       - main
-    paths:
-      - "src/diffusers/**.py"
-      - "examples/**.py"
-      - "tests/**.py"
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
 
 env:
   DIFFUSERS_IS_CI: yes
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
@@ -30,19 +21,28 @@ jobs:
         config:
           - name: Fast PyTorch CPU tests on Ubuntu
             framework: pytorch
-            runner: aws-general-8-plus
+            runner: docker-cpu
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu
+          - name: Fast Flax CPU tests on Ubuntu
+            framework: flax
+            runner: docker-cpu
+            image: diffusers/diffusers-flax-cpu
+            report: flax_cpu
+          - name: Fast ONNXRuntime CPU tests on Ubuntu
+            framework: onnxruntime
+            runner: docker-cpu
+            image: diffusers/diffusers-onnxruntime-cpu
+            report: onnx_cpu
           - name: PyTorch Example CPU tests on Ubuntu
             framework: pytorch_examples
-            runner: aws-general-8-plus
+            runner: docker-cpu
             image: diffusers/diffusers-pytorch-cpu
             report: torch_example_cpu
 
     name: ${{ matrix.config.name }}
 
-    runs-on:
-      group: ${{ matrix.config.runner }}
+    runs-on: ${{ matrix.config.runner }}
 
     container:
       image: ${{ matrix.config.image }}
@@ -60,31 +60,43 @@ jobs:
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
+        apt-get update && apt-get install libsndfile1-dev libgl1 -y
+        python -m pip install -e .[quality,test]
 
     - name: Environment
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run fast PyTorch CPU tests
       if: ${{ matrix.config.framework == 'pytorch' }}
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
           -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/
 
+    - name: Run fast Flax TPU tests
+      if: ${{ matrix.config.framework == 'flax' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Flax" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run fast ONNXRuntime CPU tests
+      if: ${{ matrix.config.framework == 'onnxruntime' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
     - name: Run example PyTorch CPU tests
       if: ${{ matrix.config.framework == 'pytorch_examples' }}
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install peft timm
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
-          examples
+          examples/test_examples.py 
 
     - name: Failure short reports
       if: ${{ failure() }}
@@ -92,7 +104,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports

.github/workflows/push_tests_mps.yml

@@ -1,25 +1,22 @@
 name: Fast mps tests on main
 
 on:
-  workflow_dispatch:
+  push:
+    branches:
+      - main
 
 env:
   DIFFUSERS_IS_CI: yes
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
 jobs:
   run_fast_tests_apple_m1:
     name: Fast PyTorch MPS tests on MacOS
-    runs-on: macos-13-xlarge
+    runs-on: [ self-hosted, apple-m1 ]
 
     steps:
     - name: Checkout diffusers
@@ -40,11 +37,11 @@ jobs:
     - name: Install dependencies
       shell: arch -arch arm64 bash {0}
       run: |
-        ${CONDA_RUN} python -m pip install --upgrade pip uv
-        ${CONDA_RUN} python -m uv pip install -e ".[quality,test]"
-        ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio
-        ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
-        ${CONDA_RUN} python -m uv pip install transformers --upgrade
+        ${CONDA_RUN} python -m pip install --upgrade pip
+        ${CONDA_RUN} python -m pip install -e .[quality,test]
+        ${CONDA_RUN} python -m pip install torch torchvision torchaudio
+        ${CONDA_RUN} python -m pip install accelerate --upgrade
+        ${CONDA_RUN} python -m pip install transformers --upgrade
 
     - name: Environment
       shell: arch -arch arm64 bash {0}
@@ -55,7 +52,7 @@ jobs:
       shell: arch -arch arm64 bash {0}
       env:
         HF_HOME: /System/Volumes/Data/mnt/cache
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
       run: |
         ${CONDA_RUN} python -m pytest -n 0 -s -v --make-reports=tests_torch_mps tests/
 
@@ -65,7 +62,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v2
       with:
         name: pr_torch_mps_test_reports
         path: reports

.github/workflows/pypi_publish.yaml

@@ -1,81 +0,0 @@
-# Adapted from https://blog.deepjyoti30.dev/pypi-release-github-action
-
-name: PyPI release
-
-on:
-  workflow_dispatch:
-  push:
-    tags:
-      - "*"
-
-jobs:
-  find-and-checkout-latest-branch:
-    runs-on: ubuntu-22.04
-    outputs:
-      latest_branch: ${{ steps.set_latest_branch.outputs.latest_branch }}
-    steps:
-      - name: Checkout Repo
-        uses: actions/checkout@v3
-
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: '3.8'
-
-      - name: Fetch latest branch
-        id: fetch_latest_branch
-        run: |
-          pip install -U requests packaging
-          LATEST_BRANCH=$(python utils/fetch_latest_release_branch.py)
-          echo "Latest branch: $LATEST_BRANCH"
-          echo "latest_branch=$LATEST_BRANCH" >> $GITHUB_ENV
-
-      - name: Set latest branch output
-        id: set_latest_branch
-        run: echo "::set-output name=latest_branch::${{ env.latest_branch }}"
-
-  release:
-    needs: find-and-checkout-latest-branch
-    runs-on: ubuntu-22.04
-
-    steps:
-      - name: Checkout Repo
-        uses: actions/checkout@v3
-        with:
-          ref: ${{ needs.find-and-checkout-latest-branch.outputs.latest_branch }}
-
-      - name: Setup Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install -U setuptools wheel twine
-          pip install -U torch --index-url https://download.pytorch.org/whl/cpu
-          pip install -U transformers
-
-      - name: Build the dist files
-        run: python setup.py bdist_wheel && python setup.py sdist
-
-      - name: Publish to the test PyPI
-        env:
-          TWINE_USERNAME: ${{ secrets.TEST_PYPI_USERNAME }}
-          TWINE_PASSWORD: ${{ secrets.TEST_PYPI_PASSWORD }}
-        run: twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/
-
-      - name: Test installing diffusers and importing
-        run: |
-          pip install diffusers && pip uninstall diffusers -y
-          pip install -i https://test.pypi.org/simple/ diffusers
-          python -c "from diffusers import __version__; print(__version__)"
-          python -c "from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('fusing/unet-ldm-dummy-update'); pipe()"
-          python -c "from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=None); pipe('ah suh du')"
-          python -c "from diffusers import *"
-
-      - name: Publish to PyPI
-        env:
-          TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-          TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
-        run: twine upload dist/* -r pypi

.github/workflows/release_tests_fast.yml

@@ -1,351 +0,0 @@
-# Duplicate workflow to push_tests.yml that is meant to run on release/patch branches as a final check
-# Creating a duplicate workflow here is simpler than adding complex path/branch parsing logic to push_tests.yml
-# Needs to be updated if push_tests.yml updated
-name: (Release) Fast GPU Tests on main
-
-on:
-  push:
-    branches:
-      - "v*.*.*-release"
-      - "v*.*.*-patch"
-
-env:
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  PYTEST_TIMEOUT: 600
-  PIPELINE_USAGE_CUTOFF: 50000
-
-jobs:
-  setup_torch_cuda_pipeline_matrix:
-    name: Setup Torch Pipelines CUDA Slow Tests Matrix
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-    outputs:
-      pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Fetch Pipeline Matrix
-        id: fetch_pipeline_matrix
-        run: |
-          matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)
-          echo $matrix
-          echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
-      - name: Pipeline Tests Artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: test-pipelines.json
-          path: reports
-
-  torch_pipelines_cuda_tests:
-    name: Torch Pipelines CUDA Tests
-    needs: setup_torch_cuda_pipeline_matrix
-    strategy:
-      fail-fast: false
-      max-parallel: 8
-      matrix:
-        module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Slow PyTorch CUDA checkpoint tests on Ubuntu
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-            tests/pipelines/${{ matrix.module }}
-      - 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: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: pipeline_${{ matrix.module }}_test_reports
-          path: reports
-
-  torch_cuda_tests:
-    name: Torch CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    defaults:
-      run:
-        shell: bash
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-      matrix:
-        module: [models, schedulers, lora, others, single_file]
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run PyTorch CUDA tests
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_torch_${{ matrix.module }}_cuda \
-          tests/${{ matrix.module }}
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_torch_${{ matrix.module }}_cuda_stats.txt
-        cat reports/tests_torch_${{ matrix.module }}_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_cuda_${{ matrix.module }}_test_reports
-        path: reports
-
-  torch_minimum_version_cuda_tests:
-    name: Torch Minimum Version CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    defaults:
-      run:
-        shell: bash
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-      - name: Environment
-        run: |
-          python utils/print_env.py
-
-      - name: Run PyTorch CUDA tests
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_torch_minimum_cuda \
-            tests/models/test_modeling_common.py \
-            tests/pipelines/test_pipelines_common.py \
-            tests/pipelines/test_pipeline_utils.py \
-            tests/pipelines/test_pipelines.py \
-            tests/pipelines/test_pipelines_auto.py \
-            tests/schedulers/test_schedulers.py \
-            tests/others
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_torch_minimum_version_cuda_stats.txt
-          cat reports/tests_torch_minimum_version_cuda_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: torch_minimum_version_cuda_test_reports
-          path: reports
-
-  run_torch_compile_tests:
-    name: PyTorch Compile CUDA tests
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Run torch compile tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        RUN_COMPILE: yes
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_torch_compile_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_compile_test_reports
-        path: reports
-
-  run_xformers_tests:
-    name: PyTorch xformers CUDA tests
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Run example tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_torch_xformers_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_xformers_test_reports
-        path: reports
-
-  run_examples_tests:
-    name: Examples PyTorch CUDA tests on Ubuntu
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run example tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install timm
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/examples_torch_cuda_stats.txt
-        cat reports/examples_torch_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: examples_test_reports
-        path: reports

.github/workflows/run_tests_from_a_pr.yml

@@ -1,74 +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
-      pr_number:
-        description: 'PR number 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:
-      group: aws-g4dn-2xlarge
-    container:
-      image: ${{ github.event.inputs.docker_image }}
-      options: --gpus all --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 [[ ! "$PY_TEST" =~ ^tests/(models|pipelines|lora) ]]; then
-            echo "Error: The input string must contain either 'models', 'pipelines', or 'lora' after 'tests/'."
-            exit 1
-          fi
-
-          if [[ "$PY_TEST" == *";"* ]]; then
-            echo "Error: The input string must not contain ';'."
-            exit 1
-          fi
-          echo "$PY_TEST"
-        
-        shell: bash -e {0}
-
-      - name: Checkout PR branch
-        uses: actions/checkout@v4
-        with:
-          ref: refs/pull/${{ inputs.pr_number }}/head
-
-      - 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"

.github/workflows/ssh-pr-runner.yml

@@ -1,40 +0,0 @@
-name: SSH into PR runners
-
-on:
-  workflow_dispatch:
-    inputs:
-      docker_image:
-        description: 'Name of the Docker image'
-        required: true
-
-env:
-  IS_GITHUB_CI: "1"
-  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-  HF_HOME: /mnt/cache
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  RUN_SLOW: yes
-
-jobs:
-  ssh_runner:
-    name: "SSH"
-    runs-on:
-      group: aws-highmemory-32-plus
-    container:
-      image: ${{ github.event.inputs.docker_image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --privileged
-
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: Tailscale # In order to be able to SSH when a test fails
-        uses: huggingface/tailscale-action@main
-        with:
-          authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
-          slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
-          slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
-          waitForSSH: true

.github/workflows/ssh-runner.yml

@@ -1,52 +0,0 @@
-name: SSH into GPU runners
-
-on:
-  workflow_dispatch:
-    inputs:
-      runner_type:
-        description: 'Type of runner to test (aws-g6-4xlarge-plus: a10, aws-g4dn-2xlarge: t4, aws-g6e-xlarge-plus: L40)'
-        type: choice
-        required: true
-        options:
-          - aws-g6-4xlarge-plus
-          - aws-g4dn-2xlarge
-          - aws-g6e-xlarge-plus
-      docker_image:
-        description: 'Name of the Docker image'
-        required: true
-
-env:
-  IS_GITHUB_CI: "1"
-  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-  HF_HOME: /mnt/cache
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  RUN_SLOW: yes
-
-jobs:
-  ssh_runner:
-    name: "SSH"
-    runs-on:
-      group: "${{ github.event.inputs.runner_type }}"
-    container:
-      image: ${{ github.event.inputs.docker_image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus all --privileged
-
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-
-      - name: Tailscale # In order to be able to SSH when a test fails
-        uses: huggingface/tailscale-action@main
-        with:
-          authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
-          slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
-          slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
-          waitForSSH: true

.github/workflows/stale.yml

@@ -8,10 +8,7 @@ jobs:
   close_stale_issues:
     name: Close Stale Issues
     if: github.repository == 'huggingface/diffusers'
-    runs-on: ubuntu-22.04
-    permissions:
-      issues: write
-      pull-requests: write
+    runs-on: ubuntu-latest
     env:
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:
@@ -20,7 +17,7 @@ jobs:
     - name: Setup Python
       uses: actions/setup-python@v1
       with:
-        python-version: 3.8
+        python-version: 3.7
 
     - name: Install requirements
       run: |

.github/workflows/trufflehog.yml

@@ -1,18 +0,0 @@
-on:
-  push:
-
-name: Secret Leaks
-
-jobs:
-  trufflehog:
-    runs-on: ubuntu-22.04
-    steps:
-    - name: Checkout code
-      uses: actions/checkout@v4
-      with:
-        fetch-depth: 0
-    - name: Secret Scanning
-      uses: trufflesecurity/trufflehog@main
-      with:
-        extra_args: --results=verified,unknown
-

.github/workflows/typos.yml

@@ -5,7 +5,7 @@ on:
 
 jobs:
   build:
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v3

.github/workflows/update_metadata.yml

@@ -1,30 +0,0 @@
-name: Update Diffusers metadata
-
-on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
-      - update_diffusers_metadata*
-
-jobs:
-  update_metadata:
-    runs-on: ubuntu-22.04
-    defaults:
-      run:
-        shell: bash -l {0}
-
-    steps:
-      - uses: actions/checkout@v3
-
-      - name: Setup environment
-        run: |
-          pip install --upgrade pip
-          pip install datasets pandas
-          pip install .[torch]
-
-      - name: Update metadata
-        env:
-          HF_TOKEN: ${{ secrets.SAYAK_HF_TOKEN }}
-        run: |
-          python utils/update_metadata.py --commit_sha ${{ github.sha }}

.gitignore

@@ -1,4 +1,4 @@
-# Initially taken from GitHub's Python gitignore file
+# Initially taken from Github's Python gitignore file
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -34,7 +34,7 @@ wheels/
 MANIFEST
 
 # PyInstaller
-#  Usually these files are written by a Python script from a template
+#  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 *.manifest
 *.spec
@@ -153,7 +153,7 @@ debug.env
 # vim
 .*.swp
 
-# ctags
+#ctags
 tags
 
 # pre-commit
@@ -164,7 +164,6 @@ tags
 
 # DS_Store (MacOS)
 .DS_Store
-
 # RL pipelines may produce mp4 outputs
 *.mp4
 
@@ -174,5 +173,4 @@ tags
 # ruff
 .ruff_cache
 
-# wandb
 wandb

CITATION.cff

@@ -19,16 +19,6 @@ authors:
     family-names: Rasul
   - given-names: Mishig
     family-names: Davaadorj
-  - given-names: Dhruv
-    family-names: Nair
-  - given-names: Sayak
-    family-names: Paul
-  - given-names: Steven
-    family-names: Liu
-  - given-names: William
-    family-names: Berman
-  - given-names: Yiyi
-    family-names: Xu
   - given-names: Thomas
     family-names: Wolf
 repository-code: 'https://github.com/huggingface/diffusers'
@@ -41,12 +31,10 @@ keywords:
   - deep-learning
   - pytorch
   - image-generation
-  - hacktoberfest
   - diffusion
   - text2image
   - image2image
   - score-based-generative-modeling
   - stable-diffusion
-  - stable-diffusion-diffusers
 license: Apache-2.0
 version: 0.12.1

CODE_OF_CONDUCT.md

@@ -7,7 +7,7 @@ We as members, contributors, and leaders pledge to make participation in our
 community a harassment-free experience for everyone, regardless of age, body
 size, visible or invisible disability, ethnicity, sex characteristics, gender
 identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, caste, color, religion, or sexual identity
+nationality, personal appearance, race, religion, or sexual identity
 and orientation.
 
 We pledge to act and interact in ways that contribute to an open, welcoming,
@@ -24,7 +24,7 @@ community include:
 * Accepting responsibility and apologizing to those affected by our mistakes,
   and learning from the experience
 * Focusing on what is best not just for us as individuals, but for the
-  overall Diffusers community
+  overall diffusers community
 
 Examples of unacceptable behavior include:
 
@@ -117,8 +117,8 @@ the community.
 ## Attribution
 
 This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.1, available at
-https://www.contributor-covenant.org/version/2/1/code_of_conduct.html.
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
 
 Community Impact Guidelines were inspired by [Mozilla's code of conduct
 enforcement ladder](https://github.com/mozilla/diversity).

CONTRIBUTING.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 We ❤️ contributions from the open-source community! Everyone is welcome, and all types of participation –not just code– are valued and appreciated. Answering questions, helping others, reaching out, and improving the documentation are all immensely valuable to the community, so don't be afraid and get involved if you're up for it!
 
-Everyone is encouraged to start by saying 👋 in our public Discord channel. We discuss the latest trends in diffusion models, ask questions, show off personal projects, help each other with contributions, or just hang out ☕. <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=Discord&logoColor=white"></a>
+Everyone is encouraged to start by saying 👋 in our public Discord channel. We discuss the latest trends in diffusion models, ask questions, show off personal projects, help each other with contributions, or just hang out ☕. <a href="https://Discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/Discord/823813159592001537?color=5865F2&logo=Discord&logoColor=white"></a>
 
 Whichever way you choose to contribute, we strive to be part of an open, welcoming, and kind community. Please, read our [code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md) and be mindful to respect it during your interactions. We also recommend you become familiar with the [ethical guidelines](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines) that guide our project and ask you to adhere to the same principles of transparency and responsibility.
 
@@ -28,11 +28,11 @@ the core library.
 In the following, we give an overview of different ways to contribute, ranked by difficulty in ascending order. All of them are valuable to the community.
 
 * 1. Asking and answering questions on [the Diffusers discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers) or on [Discord](https://discord.gg/G7tWnz98XR).
-* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose).
-* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues).
+* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose)
+* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues)
 * 4. Fix a simple issue, marked by the "Good first issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).
 * 5. Contribute to the [documentation](https://github.com/huggingface/diffusers/tree/main/docs/source).
-* 6. Contribute a [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples).
+* 6. Contribute a [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples)
 * 7. Contribute to the [examples](https://github.com/huggingface/diffusers/tree/main/examples).
 * 8. Fix a more difficult issue, marked by the "Good second issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22).
 * 9. Add a new pipeline, model, or scheduler, see ["New Pipeline/Model"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) and ["New scheduler"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) issues. For this contribution, please have a look at [Design Philosophy](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md).
@@ -40,7 +40,7 @@ In the following, we give an overview of different ways to contribute, ranked by
 As said before, **all contributions are valuable to the community**.
 In the following, we will explain each contribution a bit more in detail.
 
-For all contributions 4-9, you will need to open a PR. It is explained in detail how to do so in [Opening a pull request](#how-to-open-a-pr).
+For all contributions 4.-9. you will need to open a PR. It is explained in detail how to do so in [Opening a pull requst](#how-to-open-a-pr)
 
 ### 1. Asking and answering questions on the Diffusers discussion forum or on the Diffusers Discord
 
@@ -57,13 +57,13 @@ Any question or comment related to the Diffusers library can be asked on the [di
 - ...
 
 Every question that is asked on the forum or on Discord actively encourages the community to publicly
-share knowledge and might very well help a beginner in the future who has the same question you're
+share knowledge and might very well help a beginner in the future that has the same question you're
 having. Please do pose any questions you might have.
 In the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from.
 
 **Please** keep in mind that the more effort you put into asking or answering a question, the higher
 the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database.
-In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formatted/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
+In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accesible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
 
 **NOTE about channels**:
 [*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago.
@@ -91,12 +91,12 @@ open a new issue nevertheless and link to the related issue.
 
 New issues usually include the following.
 
-#### 2.1. Reproducible, minimal bug reports
+#### 2.1. Reproducible, minimal bug reports.
 
 A bug report should always have a reproducible code snippet and be as minimal and concise as possible.
 This means in more detail:
-- Narrow the bug down as much as you can, **do not just dump your whole code file**.
-- Format your code.
+- Narrow the bug down as much as you can, **do not just dump your whole code file**
+- Format your code
 - Do not include any external libraries except for Diffusers depending on them.
 - **Always** provide all necessary information about your environment; for this, you can run: `diffusers-cli env` in your shell and copy-paste the displayed information to the issue.
 - Explain the issue. If the reader doesn't know what the issue is and why it is an issue, she cannot solve it.
@@ -105,9 +105,9 @@ This means in more detail:
 
 For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
 
-You can open a bug report [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&projects=&template=bug-report.yml).
+You can open a bug report [here](https://github.com/huggingface/diffusers/issues/new/choose).
 
-#### 2.2. Feature requests
+#### 2.2. Feature requests.
 
 A world-class feature request addresses the following points:
 
@@ -125,21 +125,21 @@ Awesome! Tell us what problem it solved for you.
 
 You can open a feature request [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=).
 
-#### 2.3 Feedback
+#### 2.3 Feedback.
 
 Feedback about the library design and why it is good or not good helps the core maintainers immensely to build a user-friendly library. To understand the philosophy behind the current design philosophy, please have a look [here](https://huggingface.co/docs/diffusers/conceptual/philosophy). If you feel like a certain design choice does not fit with the current design philosophy, please explain why and how it should be changed. If a certain design choice follows the design philosophy too much, hence restricting use cases, explain why and how it should be changed.
 If a certain design choice is very useful for you, please also leave a note as this is great feedback for future design decisions.
 
 You can open an issue about feedback [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).
 
-#### 2.4 Technical questions
+#### 2.4 Technical questions.
 
 Technical questions are mainly about why certain code of the library was written in a certain way, or what a certain part of the code does. Please make sure to link to the code in question and please provide detail on
 why this part of the code is difficult to understand.
 
 You can open an issue about a technical question [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml).
 
-#### 2.5 Proposal to add a new model, scheduler, or pipeline
+#### 2.5 Proposal to add a new model, scheduler, or pipeline.
 
 If the diffusion model community released a new model, pipeline, or scheduler that you would like to see in the Diffusers library, please provide the following information:
 
@@ -156,19 +156,19 @@ You can open a request for a model/pipeline/scheduler [here](https://github.com/
 
 Answering issues on GitHub might require some technical knowledge of Diffusers, but we encourage everybody to give it a try even if you are not 100% certain that your answer is correct.
 Some tips to give a high-quality answer to an issue:
-- Be as concise and minimal as possible.
+- Be as concise and minimal as possible
 - Stay on topic. An answer to the issue should concern the issue and only the issue.
 - Provide links to code, papers, or other sources that prove or encourage your point.
 - Answer in code. If a simple code snippet is the answer to the issue or shows how the issue can be solved, please provide a fully reproducible code snippet.
 
 Also, many issues tend to be simply off-topic, duplicates of other issues, or irrelevant. It is of great
 help to the maintainers if you can answer such issues, encouraging the author of the issue to be
-more precise, provide the link to a duplicated issue or redirect them to [the forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR).
+more precise, provide the link to a duplicated issue or redirect them to [the forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR)
 
 If you have verified that the issued bug report is correct and requires a correction in the source code,
 please have a look at the next sections.
 
-For all of the following contributions, you will need to open a PR. It is explained in detail how to do so in the [Opening a pull request](#how-to-open-a-pr) section.
+For all of the following contributions, you will need to open a PR. It is explained in detail how to do so in the [Opening a pull requst](#how-to-open-a-pr) section.
 
 ### 4. Fixing a "Good first issue"
 
@@ -202,7 +202,7 @@ Please have a look at [this page](https://github.com/huggingface/diffusers/tree/
 ### 6. Contribute a community pipeline
 
 [Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) are usually the first point of contact between the Diffusers library and the user.
-Pipelines are examples of how to use Diffusers [models](https://huggingface.co/docs/diffusers/api/models/overview) and [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview).
+Pipelines are examples of how to use Diffusers [models](https://huggingface.co/docs/diffusers/api/models) and [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview).
 We support two types of pipelines:
 
 - Official Pipelines
@@ -242,28 +242,27 @@ We support two types of training examples:
 
 Research training examples are located in [examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects) whereas official training examples include all folders under [examples](https://github.com/huggingface/diffusers/tree/main/examples) except the `research_projects` and `community` folders.
 The official training examples are maintained by the Diffusers' core maintainers whereas the research training examples are maintained by the community.
-This is because of the same reasons put forward in [6. Contribute a community pipeline](#6-contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models.
+This is because of the same reasons put forward in [6. Contribute a community pipeline](#contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models.
 If the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the `research_projects` folder and maintained by the author.
 
-Both official training and research examples consist of a directory that contains one or more training scripts, a `requirements.txt` file, and a `README.md` file. In order for the user to make use of the
+Both official training and research examples consist of a directory that contains one or more training scripts, a requirements.txt file, and a README.md file. In order for the user to make use of the
 training examples, it is required to clone the repository:
 
-```bash
+```
 git clone https://github.com/huggingface/diffusers
 ```
 
 as well as to install all additional dependencies required for training:
 
-```bash
-cd diffusers
-pip install -r examples/<your-example-folder>/requirements.txt
+```
+pip install -r /examples/<your-example-folder>/requirements.txt
 ```
 
 Therefore when adding an example, the `requirements.txt` file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the [DreamBooth `requirements.txt` file](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt).
 
 Training examples of the Diffusers library should adhere to the following philosophy:
-- All the code necessary to run the examples should be found in a single Python file.
-- One should be able to run the example from the command line with `python <your-example>.py --args`.
+- All the code necessary to run the examples should be found in a single Python file
+- One should be able to run the example from the command line with `python <your-example>.py --args`
 - Examples should be kept simple and serve as **an example** on how to use Diffusers for training. The purpose of example scripts is **not** to create state-of-the-art diffusion models, but rather to reproduce known training schemes without adding too much custom logic. As a byproduct of this point, our examples also strive to serve as good educational materials.
 
 To contribute an example, it is highly recommended to look at already existing examples such as [dreambooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) to get an idea of how they should look like.
@@ -282,7 +281,7 @@ If you are contributing to the official training examples, please also make sure
 usually more complicated to solve than [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).
 The issue description usually gives less guidance on how to fix the issue and requires
 a decent understanding of the library by the interested contributor.
-If you are interested in tackling a good second issue, feel free to open a PR to fix it and link the PR to the issue. If you see that a PR has already been opened for this issue but did not get merged, have a look to understand why it wasn't merged and try to open an improved PR.
+If you are interested in tackling a second good issue, feel free to open a PR to fix it and link the PR to the issue. If you see that a PR has already been opened for this issue but did not get merged, have a look to understand why it wasn't merged and try to open an improved PR.
 Good second issues are usually more difficult to get merged compared to good first issues, so don't hesitate to ask for help from the core maintainers. If your PR is almost finished the core maintainers can also jump into your PR and commit to it in order to get it merged.
 
 ### 9. Adding pipelines, models, schedulers
@@ -338,8 +337,8 @@ to be merged;
 9. Add high-coverage tests. No quality testing = no merge.
 - If you are adding new `@slow` tests, make sure they pass using
 `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.
-CircleCI does not run the slow tests, but GitHub Actions does every night!
-10. All public methods must have informative docstrings that work nicely with markdown. See [`pipeline_latent_diffusion.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py) for an example.
+CircleCI does not run the slow tests, but GitHub actions does every night!
+10. All public methods must have informative docstrings that work nicely with markdown. See `[pipeline_latent_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py)` for an example.
 11. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
 [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) or [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images) to place these files.
 If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
@@ -356,7 +355,7 @@ You will need basic `git` proficiency to be able to contribute to
 manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
 Git](https://git-scm.com/book/en/v2) is a very good reference.
 
-Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/42f25d601a910dceadaee6c44345896b4cfa9928/setup.py#L270)):
+Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/main/setup.py#L244)):
 
 1. Fork the [repository](https://github.com/huggingface/diffusers) by
 clicking on the 'Fork' button on the repository's page. This creates a copy of the code
@@ -365,7 +364,7 @@ under your GitHub user account.
 2. Clone your fork to your local disk, and add the base repository as a remote:
 
  ```bash
- $ git clone git@github.com:<your GitHub handle>/diffusers.git
+ $ git clone git@github.com:<your Github handle>/diffusers.git
  $ cd diffusers
  $ git remote add upstream https://github.com/huggingface/diffusers.git
  ```
@@ -403,7 +402,7 @@ with this command:
  $ pip install -e ".[test]"
  ```
 
-You can also run the full test suite with the following command, but it takes
+You can run the full test suite with the following command, but it takes
 a beefy machine to produce a result in a decent amount of time now that
 Diffusers has grown a lot. Here is the command for it:
 
@@ -411,7 +410,7 @@ Diffusers has grown a lot. Here is the command for it:
  $ make test
  ```
 
-🧨 Diffusers relies on `ruff` and `isort` to format its source code
+🧨 Diffusers relies on `black` and `isort` to format its source code
 consistently. After you make changes, apply automatic style corrections and code verifications
 that can't be automated in one go with:
 
@@ -431,7 +430,7 @@ make a commit with `git commit` to record your changes locally:
 
  ```bash
  $ git add modified_file.py
- $ git commit -m "A descriptive message about your changes."
+ $ git commit
  ```
 
 It is a good idea to sync your copy of the code with the original
@@ -494,7 +493,7 @@ To avoid pinging the upstream repository which adds reference notes to each upst
 when syncing the main branch of a forked repository, please, follow these steps:
 1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main.
 2. If a PR is absolutely necessary, use the following steps after checking out your branch:
-```bash
+```
 $ git checkout -b your-branch-for-syncing
 $ git pull --squash --no-commit upstream main
 $ git commit -m '<your message without GitHub references>'
@@ -503,4 +502,4 @@ $ git push --set-upstream origin your-branch-for-syncing
 
 ### Style guide
 
-For documentation strings, 🧨 Diffusers follows the [Google style](https://google.github.io/styleguide/pyguide.html).
+For documentation strings, 🧨 Diffusers follows the [google style](https://google.github.io/styleguide/pyguide.html).

Makefile

@@ -3,14 +3,14 @@
 # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
 export PYTHONPATH = src
 
-check_dirs := examples scripts src tests utils benchmarks
+check_dirs := examples scripts src tests utils
 
 modified_only_fixup:
 	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))
 	@if test -n "$(modified_py_files)"; then \
 		echo "Checking/fixing $(modified_py_files)"; \
-		ruff check $(modified_py_files) --fix; \
-		ruff format $(modified_py_files);\
+		black $(modified_py_files); \
+		ruff $(modified_py_files); \
 	else \
 		echo "No library .py files were modified"; \
 	fi
@@ -40,23 +40,23 @@ repo-consistency:
 # this target runs checks on all files
 
 quality:
-	ruff check $(check_dirs) setup.py
-	ruff format --check $(check_dirs) setup.py
-	doc-builder style src/diffusers docs/source --max_len 119 --check_only
+	black --check $(check_dirs)
+	ruff $(check_dirs)
+	doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
 	python utils/check_doc_toc.py
 
 # Format source code automatically and check is there are any problems left that need manual fixing
 
 extra_style_checks:
 	python utils/custom_init_isort.py
+	doc-builder style src/diffusers docs/source --max_len 119 --path_to_docs docs/source
 	python utils/check_doc_toc.py --fix_and_overwrite
 
 # this target runs checks on all files and potentially modifies some of them
 
 style:
-	ruff check $(check_dirs) setup.py --fix
-	ruff format $(check_dirs) setup.py
-	doc-builder style src/diffusers docs/source --max_len 119
+	black $(check_dirs)
+	ruff $(check_dirs) --fix
 	${MAKE} autogenerate_code
 	${MAKE} extra_style_checks
 
@@ -78,7 +78,7 @@ test:
 # Run tests for examples
 
 test-examples:
-	python -m pytest -n auto --dist=loadfile -s -v ./examples/
+	python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/
 
 
 # Release stuff

PHILOSOPHY.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -15,14 +15,14 @@ specific language governing permissions and limitations under the License.
 🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities.
 Its purpose is to serve as a **modular toolbox** for both inference and training.
 
-We aim to build a library that stands the test of time and therefore take API design very seriously.
+We aim at building a library that stands the test of time and therefore take API design very seriously.
 
 In a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones:
 
 ## Usability over Performance
 
 - While Diffusers has many built-in performance-enhancing features (see [Memory and Speed](https://huggingface.co/docs/diffusers/optimization/fp16)), models are always loaded with the highest precision and lowest optimization. Therefore, by default diffusion pipelines are always instantiated on CPU with float32 precision if not otherwise defined by the user. This ensures usability across different platforms and accelerators and means that no complex installations are required to run the library.
-- Diffusers aims to be a **light-weight** package and therefore has very few required dependencies, but many soft dependencies that can improve performance (such as `accelerate`, `safetensors`, `onnx`, etc...). We strive to keep the library as lightweight as possible so that it can be added without much concern as a dependency on other packages.
+- Diffusers aim at being a **light-weight** package and therefore has very few required dependencies, but many soft dependencies that can improve performance (such as `accelerate`, `safetensors`, `onnx`, etc...). We strive to keep the library as lightweight as possible so that it can be added without much concern as a dependency on other packages.
 - Diffusers prefers simple, self-explainable code over condensed, magic code. This means that short-hand code syntaxes such as lambda functions, and advanced PyTorch operators are often not desired.
 
 ## Simple over easy
@@ -31,13 +31,13 @@ As PyTorch states, **explicit is better than implicit** and **simple is better t
 - We follow PyTorch's API with methods like [`DiffusionPipeline.to`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.to) to let the user handle device management.
 - Raising concise error messages is preferred to silently correct erroneous input. Diffusers aims at teaching the user, rather than making the library as easy to use as possible.
 - Complex model vs. scheduler logic is exposed instead of magically handled inside. Schedulers/Samplers are separated from diffusion models with minimal dependencies on each other. This forces the user to write the unrolled denoising loop. However, the separation allows for easier debugging and gives the user more control over adapting the denoising process or switching out diffusion models or schedulers.
-- Separately trained components of the diffusion pipeline, *e.g.* the text encoder, the UNet, and the variational autoencoder, each has their own model class. This forces the user to handle the interaction between the different model components, and the serialization format separates the model components into different files. However, this allows for easier debugging and customization. DreamBooth or Textual Inversion training
-is very simple thanks to Diffusers' ability to separate single components of the diffusion pipeline.
+- Separately trained components of the diffusion pipeline, *e.g.* the text encoder, the unet, and the variational autoencoder, each have their own model class. This forces the user to handle the interaction between the different model components, and the serialization format separates the model components into different files. However, this allows for easier debugging and customization. Dreambooth or textual inversion training
+is very simple thanks to diffusers' ability to separate single components of the diffusion pipeline.
 
 ## Tweakable, contributor-friendly over abstraction
 
 For large parts of the library, Diffusers adopts an important design principle of the [Transformers library](https://github.com/huggingface/transformers), which is to prefer copy-pasted code over hasty abstractions. This design principle is very opinionated and stands in stark contrast to popular design principles such as [Don't repeat yourself (DRY)](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself).
-In short, just like Transformers does for modeling files, Diffusers prefers to keep an extremely low level of abstraction and very self-contained code for pipelines and schedulers.
+In short, just like Transformers does for modeling files, diffusers prefers to keep an extremely low level of abstraction and very self-contained code for pipelines and schedulers.
 Functions, long code blocks, and even classes can be copied across multiple files which at first can look like a bad, sloppy design choice that makes the library unmaintainable.
 **However**, this design has proven to be extremely successful for Transformers and makes a lot of sense for community-driven, open-source machine learning libraries because:
 - Machine Learning is an extremely fast-moving field in which paradigms, model architectures, and algorithms are changing rapidly, which therefore makes it very difficult to define long-lasting code abstractions.
@@ -47,30 +47,30 @@ Functions, long code blocks, and even classes can be copied across multiple file
 At Hugging Face, we call this design the **single-file policy** which means that almost all of the code of a certain class should be written in a single, self-contained file. To read more about the philosophy, you can have a look
 at [this blog post](https://huggingface.co/blog/transformers-design-philosophy).
 
-In Diffusers, we follow this philosophy for both pipelines and schedulers, but only partly for diffusion models. The reason we don't follow this design fully for diffusion models is because almost all diffusion pipelines, such
-as [DDPM](https://huggingface.co/docs/diffusers/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [unCLIP (DALL·E 2)](https://huggingface.co/docs/diffusers/api/pipelines/unclip) and [Imagen](https://imagen.research.google/) all rely on the same diffusion model, the [UNet](https://huggingface.co/docs/diffusers/api/models/unet2d-cond).
+In diffusers, we follow this philosophy for both pipelines and schedulers, but only partly for diffusion models. The reason we don't follow this design fully for diffusion models is because almost all diffusion pipelines, such
+as [DDPM](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [UnCLIP (Dalle-2)](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/unclip#overview) and [Imagen](https://imagen.research.google/) all rely on the same diffusion model, the [UNet](https://huggingface.co/docs/diffusers/api/models#diffusers.UNet2DConditionModel).
 
 Great, now you should have generally understood why 🧨 Diffusers is designed the way it is 🤗.
 We try to apply these design principles consistently across the library. Nevertheless, there are some minor exceptions to the philosophy or some unlucky design choices. If you have feedback regarding the design, we would ❤️  to hear it [directly on GitHub](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).
 
 ## Design Philosophy in Details
 
-Now, let's look a bit into the nitty-gritty details of the design philosophy. Diffusers essentially consists of three major classes: [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), and [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).
-Let's walk through more detailed design decisions for each class.
+Now, let's look a bit into the nitty-gritty details of the design philosophy. Diffusers essentially consist of three major classes, [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), and [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).
+Let's walk through more in-detail design decisions for each class.
 
 ### Pipelines
 
-Pipelines are designed to be easy to use (therefore do not follow [*Simple over easy*](#simple-over-easy) 100%), are not feature complete, and should loosely be seen as examples of how to use [models](#models) and [schedulers](#schedulers) for inference.
+Pipelines are designed to be easy to use (therefore do not follow [*Simple over easy*](#simple-over-easy) 100%)), are not feature complete, and should loosely be seen as examples of how to use [models](#models) and [schedulers](#schedulers) for inference.
 
 The following design principles are followed:
-- Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [# Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).
-- Pipelines all inherit from [`DiffusionPipeline`].
-- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
+- Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [#Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).
+- Pipelines all inherit from [`DiffusionPipeline`]
+- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
 - Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function.
 - 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.
@@ -81,16 +81,16 @@ Models are designed as configurable toolboxes that are natural extensions of [Py
 
 The following design principles are followed:
 - Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.
-- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unets/unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_2d_condition.py), [`transformers/transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_2d.py), etc...
+- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...
 - Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.
-- Models intend to expose complexity, just like PyTorch's `Module` class, and give clear error messages.
+- Models intend to expose complexity, just like PyTorch's module does, and give clear error messages.
 - Models all inherit from `ModelMixin` and `ConfigMixin`.
-- Models can be optimized for performance when it doesn’t demand major code changes, keep backward compatibility, and give significant memory or compute gain.
+- Models can be optimized for performance when it doesn’t demand major code changes, keeps backward compatibility, and gives significant memory or compute gain.
 - Models should by default have the highest precision and lowest performance setting.
 - To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different.
 - Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and "foreseeing" future changes, *e.g.* it is usually better to add `string` "...type" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work.
 - The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and
-readable long-term, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+readable longterm, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
 
 ### Schedulers
 
@@ -99,12 +99,12 @@ Schedulers are responsible to guide the denoising process for inference as well
 The following design principles are followed:
 - All schedulers are found in [`src/diffusers/schedulers`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).
 - Schedulers are **not** allowed to import from large utils files and shall be kept very self-contained.
-- One scheduler Python file corresponds to one scheduler algorithm (as might be defined in a paper).
-- If schedulers share similar functionalities, we can make use of the `# Copied from` mechanism.
+- One scheduler python file corresponds to one scheduler algorithm (as might be defined in a paper).
+- If schedulers share similar functionalities, we can make use of the `#Copied from` mechanism.
 - Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.
-- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./docs/source/en/using-diffusers/schedulers.md).
+- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.md).
 - Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.
-- Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon.
+- Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon
 - The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).
 - Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a "black box".
 - In almost all cases, novel schedulers shall be implemented in a new scheduling file.

README.md

@@ -1,30 +1,18 @@
-<!---
-Copyright 2022 - 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.
--->
-
 <p align="center">
     <br>
     <img src="https://raw.githubusercontent.com/huggingface/diffusers/main/docs/source/en/imgs/diffusers_library.jpg" width="400"/>
     <br>
 <p>
 <p align="center">
-    <a href="https://github.com/huggingface/diffusers/blob/main/LICENSE"><img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue"></a>
-    <a href="https://github.com/huggingface/diffusers/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg"></a>
-    <a href="https://pepy.tech/project/diffusers"><img alt="GitHub release" src="https://static.pepy.tech/badge/diffusers/month"></a>
-    <a href="CODE_OF_CONDUCT.md"><img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg"></a>
-    <a href="https://twitter.com/diffuserslib"><img alt="X account" src="https://img.shields.io/twitter/url/https/twitter.com/diffuserslib.svg?style=social&label=Follow%20%40diffuserslib"></a>
+    <a href="https://github.com/huggingface/diffusers/blob/main/LICENSE">
+        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue">
+    </a>
+    <a href="https://github.com/huggingface/diffusers/releases">
+        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg">
+    </a>
+    <a href="CODE_OF_CONDUCT.md">
+        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg">
+    </a>
 </p>
 
 🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
@@ -33,11 +21,11 @@ limitations under the License.
 
 - State-of-the-art [diffusion pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) that can be run in inference with just a few lines of code.
 - Interchangeable noise [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview) for different diffusion speeds and output quality.
-- Pretrained [models](https://huggingface.co/docs/diffusers/api/models/overview) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
+- Pretrained [models](https://huggingface.co/docs/diffusers/api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
 
 ## Installation
 
-We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/), please refer to their official documentation.
+We recommend installing 🤗 Diffusers in a virtual environment from PyPi or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.
 
 ### PyTorch
 
@@ -53,19 +41,27 @@ With `conda` (maintained by the community):
 conda install -c conda-forge diffusers
 ```
 
+### Flax
+
+With `pip` (official package):
+
+```bash
+pip install --upgrade diffusers[flax]
+```
+
 ### Apple Silicon (M1/M2) support
 
 Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.
 
 ## 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 30,000+ 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 4000+ checkpoints):
 
 ```python
 from diffusers import DiffusionPipeline
 import torch
 
-pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
 pipeline.to("cuda")
 pipeline("An image of a squirrel in Picasso style").images[0]
 ```
@@ -76,13 +72,14 @@ You can also dig into the models and schedulers toolbox to build your own diffus
 from diffusers import DDPMScheduler, UNet2DModel
 from PIL import Image
 import torch
+import numpy as np
 
 scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
 model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
 scheduler.set_timesteps(50)
 
 sample_size = model.config.sample_size
-noise = torch.randn((1, 3, sample_size, sample_size), device="cuda")
+noise = torch.randn((1, 3, sample_size, sample_size)).to("cuda")
 input = noise
 
 for t in scheduler.timesteps:
@@ -104,9 +101,9 @@ Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to l
 | **Documentation**                                                   | **What can I learn?**                                                                                                                                                                           |
 |---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview)                                                            | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model.  |
-| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading)                                                             | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers.                                         |
-| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/overview_techniques)                                             | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library.               |
-| [Optimization](https://huggingface.co/docs/diffusers/optimization/fp16)                                                        | Guides for how to optimize your diffusion model to run faster and consume less memory.                                                                                                          |
+| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview)                                                             | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers.                                         |
+| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview)                                             | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library.               |
+| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview)                                                        | Guides for how to optimize your diffusion model to run faster and consume less memory.                                                                                                          |
 | [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques.                                                                                               |
 ## Contribution
 
@@ -117,7 +114,8 @@ You can look out for [issues](https://github.com/huggingface/diffusers/issues) y
 - See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines
 - See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)
 
-Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out ☕.
+Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or
+just hang out ☕.
 
 
 ## Popular Tasks & Pipelines
@@ -136,16 +134,16 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
   <tr style="border-top: 2px solid black">
     <td>Text-to-Image</td>
     <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img">Stable Diffusion Text-to-Image</a></td>
-      <td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5"> stable-diffusion-v1-5/stable-diffusion-v1-5 </a></td>
+      <td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
   </tr>
   <tr>
     <td>Text-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/unclip">unCLIP</a></td>
+    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/unclip">unclip</a></td>
       <td><a href="https://huggingface.co/kakaobrain/karlo-v1-alpha"> kakaobrain/karlo-v1-alpha </a></td>
   </tr>
   <tr>
     <td>Text-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/deepfloyd_if">DeepFloyd IF</a></td>
+    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/if">DeepFloyd IF</a></td>
       <td><a href="https://huggingface.co/DeepFloyd/IF-I-XL-v1.0"> DeepFloyd/IF-I-XL-v1.0 </a></td>
   </tr>
   <tr>
@@ -155,22 +153,22 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
   </tr>
   <tr style="border-top: 2px solid black">
     <td>Text-guided Image-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/controlnet">ControlNet</a></td>
+    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet">Controlnet</a></td>
       <td><a href="https://huggingface.co/lllyasviel/sd-controlnet-canny"> lllyasviel/sd-controlnet-canny </a></td>
   </tr>
   <tr>
     <td>Text-guided Image-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/pix2pix">InstructPix2Pix</a></td>
+    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/pix2pix">Instruct Pix2Pix</a></td>
       <td><a href="https://huggingface.co/timbrooks/instruct-pix2pix"> timbrooks/instruct-pix2pix </a></td>
   </tr>
   <tr>
     <td>Text-guided Image-to-Image</td>
     <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img">Stable Diffusion Image-to-Image</a></td>
-      <td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5"> stable-diffusion-v1-5/stable-diffusion-v1-5 </a></td>
+      <td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
   </tr>
   <tr style="border-top: 2px solid black">
     <td>Text-guided Image Inpainting</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpainting</a></td>
+    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpaint</a></td>
       <td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
   </tr>
   <tr style="border-top: 2px solid black">
@@ -194,7 +192,6 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 
 - https://github.com/microsoft/TaskMatrix
 - https://github.com/invoke-ai/InvokeAI
-- https://github.com/InstantID/InstantID
 - https://github.com/apple/ml-stable-diffusion
 - https://github.com/Sanster/lama-cleaner
 - https://github.com/IDEA-Research/Grounded-Segment-Anything
@@ -202,9 +199,9 @@ 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
-- +14,000 other amazing GitHub repositories 💪
+- +3000 other amazing GitHub repositories 💪
 
-Thank you for using us ❤️.
+Thank you for using us ❤️
 
 ## Credits
 
@@ -221,7 +218,7 @@ We also want to thank @heejkoo for the very helpful overview of papers, code and
 
 ```bibtex
 @misc{von-platen-etal-2022-diffusers,
-  author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Dhruv Nair and Sayak Paul and William Berman and Yiyi Xu and Steven Liu and Thomas Wolf},
+  author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
   title = {Diffusers: State-of-the-art diffusion models},
   year = {2022},
   publisher = {GitHub},

benchmarks/README.md

@@ -1,69 +0,0 @@
-# Diffusers Benchmarks
-
-Welcome to Diffusers Benchmarks. These benchmarks are use to obtain latency and memory information of the most popular models across different scenarios such as:
-
-* Base case i.e., when using `torch.bfloat16` and `torch.nn.functional.scaled_dot_product_attention`.
-* Base + `torch.compile()`
-* NF4 quantization
-* Layerwise upcasting
-
-Instead of full diffusion pipelines, only the forward pass of the respective model classes (such as `FluxTransformer2DModel`) is tested with the real checkpoints (such as `"black-forest-labs/FLUX.1-dev"`). 
-
-The entrypoint to running all the currently available benchmarks is in `run_all.py`. However, one can run the individual benchmarks, too, e.g., `python benchmarking_flux.py`. It should produce a CSV file containing various information about the benchmarks run.
-
-The benchmarks are run on a weekly basis and the CI is defined in [benchmark.yml](../.github/workflows/benchmark.yml).
-
-## Running the benchmarks manually
-
-First set up `torch` and install `diffusers` from the root of the directory:
-
-```py
-pip install -e ".[quality,test]"
-```
-
-Then make sure the other dependencies are installed:
-
-```sh
-cd benchmarks/
-pip install -r requirements.txt
-```
-
-We need to be authenticated to access some of the checkpoints used during benchmarking:
-
-```sh
-hf auth login
-```
-
-We use an L40 GPU with 128GB RAM to run the benchmark CI. As such, the benchmarks are configured to run on NVIDIA GPUs. So, make sure you have access to a similar machine (or modify the benchmarking scripts accordingly).
-
-Then you can either launch the entire benchmarking suite by running:
-
-```sh
-python run_all.py
-```
-
-Or, you can run the individual benchmarks.
-
-## Customizing the benchmarks
-
-We define "scenarios" to cover the most common ways in which these models are used. You can
-define a new scenario, modifying an existing benchmark file:
-
-```py
-BenchmarkScenario(
-    name=f"{CKPT_ID}-bnb-8bit",
-    model_cls=FluxTransformer2DModel,
-    model_init_kwargs={
-        "pretrained_model_name_or_path": CKPT_ID,
-        "torch_dtype": torch.bfloat16,
-        "subfolder": "transformer",
-        "quantization_config": BitsAndBytesConfig(load_in_8bit=True),
-    },
-    get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-    model_init_fn=model_init_fn,
-)
-```
-
-You can also configure a new model-level benchmark and add it to the existing suite. To do so, just defining a valid benchmarking file like `benchmarking_flux.py` should be enough.
-
-Happy benchmarking 🧨

benchmarks/benchmarking_flux.py

@@ -1,98 +0,0 @@
-from functools import partial
-
-import torch
-from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
-
-from diffusers import BitsAndBytesConfig, FluxTransformer2DModel
-from diffusers.utils.testing_utils import torch_device
-
-
-CKPT_ID = "black-forest-labs/FLUX.1-dev"
-RESULT_FILENAME = "flux.csv"
-
-
-def get_input_dict(**device_dtype_kwargs):
-    # resolution: 1024x1024
-    # maximum sequence length 512
-    hidden_states = torch.randn(1, 4096, 64, **device_dtype_kwargs)
-    encoder_hidden_states = torch.randn(1, 512, 4096, **device_dtype_kwargs)
-    pooled_prompt_embeds = torch.randn(1, 768, **device_dtype_kwargs)
-    image_ids = torch.ones(512, 3, **device_dtype_kwargs)
-    text_ids = torch.ones(4096, 3, **device_dtype_kwargs)
-    timestep = torch.tensor([1.0], **device_dtype_kwargs)
-    guidance = torch.tensor([1.0], **device_dtype_kwargs)
-
-    return {
-        "hidden_states": hidden_states,
-        "encoder_hidden_states": encoder_hidden_states,
-        "img_ids": image_ids,
-        "txt_ids": text_ids,
-        "pooled_projections": pooled_prompt_embeds,
-        "timestep": timestep,
-        "guidance": guidance,
-    }
-
-
-if __name__ == "__main__":
-    scenarios = [
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-bf16",
-            model_cls=FluxTransformer2DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=model_init_fn,
-            compile_kwargs={"fullgraph": True},
-        ),
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-bnb-nf4",
-            model_cls=FluxTransformer2DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-                "quantization_config": BitsAndBytesConfig(
-                    load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type="nf4"
-                ),
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=model_init_fn,
-        ),
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-layerwise-upcasting",
-            model_cls=FluxTransformer2DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
-        ),
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-group-offload-leaf",
-            model_cls=FluxTransformer2DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=partial(
-                model_init_fn,
-                group_offload_kwargs={
-                    "onload_device": torch_device,
-                    "offload_device": torch.device("cpu"),
-                    "offload_type": "leaf_level",
-                    "use_stream": True,
-                    "non_blocking": True,
-                },
-            ),
-        ),
-    ]
-
-    runner = BenchmarkMixin()
-    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

benchmarks/benchmarking_ltx.py

@@ -1,80 +0,0 @@
-from functools import partial
-
-import torch
-from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
-
-from diffusers import LTXVideoTransformer3DModel
-from diffusers.utils.testing_utils import torch_device
-
-
-CKPT_ID = "Lightricks/LTX-Video-0.9.7-dev"
-RESULT_FILENAME = "ltx.csv"
-
-
-def get_input_dict(**device_dtype_kwargs):
-    # 512x704 (161 frames)
-    # `max_sequence_length`: 256
-    hidden_states = torch.randn(1, 7392, 128, **device_dtype_kwargs)
-    encoder_hidden_states = torch.randn(1, 256, 4096, **device_dtype_kwargs)
-    encoder_attention_mask = torch.ones(1, 256, **device_dtype_kwargs)
-    timestep = torch.tensor([1.0], **device_dtype_kwargs)
-    video_coords = torch.randn(1, 3, 7392, **device_dtype_kwargs)
-
-    return {
-        "hidden_states": hidden_states,
-        "encoder_hidden_states": encoder_hidden_states,
-        "encoder_attention_mask": encoder_attention_mask,
-        "timestep": timestep,
-        "video_coords": video_coords,
-    }
-
-
-if __name__ == "__main__":
-    scenarios = [
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-bf16",
-            model_cls=LTXVideoTransformer3DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=model_init_fn,
-            compile_kwargs={"fullgraph": True},
-        ),
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-layerwise-upcasting",
-            model_cls=LTXVideoTransformer3DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
-        ),
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-group-offload-leaf",
-            model_cls=LTXVideoTransformer3DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=partial(
-                model_init_fn,
-                group_offload_kwargs={
-                    "onload_device": torch_device,
-                    "offload_device": torch.device("cpu"),
-                    "offload_type": "leaf_level",
-                    "use_stream": True,
-                    "non_blocking": True,
-                },
-            ),
-        ),
-    ]
-
-    runner = BenchmarkMixin()
-    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

benchmarks/benchmarking_sdxl.py

@@ -1,82 +0,0 @@
-from functools import partial
-
-import torch
-from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
-
-from diffusers import UNet2DConditionModel
-from diffusers.utils.testing_utils import torch_device
-
-
-CKPT_ID = "stabilityai/stable-diffusion-xl-base-1.0"
-RESULT_FILENAME = "sdxl.csv"
-
-
-def get_input_dict(**device_dtype_kwargs):
-    # height: 1024
-    # width: 1024
-    # max_sequence_length: 77
-    hidden_states = torch.randn(1, 4, 128, 128, **device_dtype_kwargs)
-    encoder_hidden_states = torch.randn(1, 77, 2048, **device_dtype_kwargs)
-    timestep = torch.tensor([1.0], **device_dtype_kwargs)
-    added_cond_kwargs = {
-        "text_embeds": torch.randn(1, 1280, **device_dtype_kwargs),
-        "time_ids": torch.ones(1, 6, **device_dtype_kwargs),
-    }
-
-    return {
-        "sample": hidden_states,
-        "encoder_hidden_states": encoder_hidden_states,
-        "timestep": timestep,
-        "added_cond_kwargs": added_cond_kwargs,
-    }
-
-
-if __name__ == "__main__":
-    scenarios = [
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-bf16",
-            model_cls=UNet2DConditionModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "unet",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=model_init_fn,
-            compile_kwargs={"fullgraph": True},
-        ),
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-layerwise-upcasting",
-            model_cls=UNet2DConditionModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "unet",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
-        ),
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-group-offload-leaf",
-            model_cls=UNet2DConditionModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "unet",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=partial(
-                model_init_fn,
-                group_offload_kwargs={
-                    "onload_device": torch_device,
-                    "offload_device": torch.device("cpu"),
-                    "offload_type": "leaf_level",
-                    "use_stream": True,
-                    "non_blocking": True,
-                },
-            ),
-        ),
-    ]
-
-    runner = BenchmarkMixin()
-    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

benchmarks/benchmarking_utils.py

@@ -1,244 +0,0 @@
-import gc
-import inspect
-import logging
-import os
-import queue
-import threading
-from contextlib import nullcontext
-from dataclasses import dataclass
-from typing import Any, Callable, Dict, Optional, Union
-
-import pandas as pd
-import torch
-import torch.utils.benchmark as benchmark
-
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.utils.testing_utils import require_torch_gpu, torch_device
-
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-logger = logging.getLogger(__name__)
-
-NUM_WARMUP_ROUNDS = 5
-
-
-def benchmark_fn(f, *args, **kwargs):
-    t0 = benchmark.Timer(
-        stmt="f(*args, **kwargs)",
-        globals={"args": args, "kwargs": kwargs, "f": f},
-        num_threads=1,
-    )
-    return float(f"{(t0.blocked_autorange().mean):.3f}")
-
-
-def flush():
-    gc.collect()
-    torch.cuda.empty_cache()
-    torch.cuda.reset_max_memory_allocated()
-    torch.cuda.reset_peak_memory_stats()
-
-
-# Adapted from https://github.com/lucasb-eyer/cnn_vit_benchmarks/blob/15b665ff758e8062131353076153905cae00a71f/main.py
-def calculate_flops(model, input_dict):
-    try:
-        from torchprofile import profile_macs
-    except ModuleNotFoundError:
-        raise
-
-    # This is a hacky way to convert the kwargs to args as `profile_macs` cries about kwargs.
-    sig = inspect.signature(model.forward)
-    param_names = [
-        p.name
-        for p in sig.parameters.values()
-        if p.kind
-        in (
-            inspect.Parameter.POSITIONAL_ONLY,
-            inspect.Parameter.POSITIONAL_OR_KEYWORD,
-        )
-        and p.name != "self"
-    ]
-    bound = sig.bind_partial(**input_dict)
-    bound.apply_defaults()
-    args = tuple(bound.arguments[name] for name in param_names)
-
-    model.eval()
-    with torch.no_grad():
-        macs = profile_macs(model, args)
-    flops = 2 * macs  # 1 MAC operation = 2 FLOPs (1 multiplication + 1 addition)
-    return flops
-
-
-def calculate_params(model):
-    return sum(p.numel() for p in model.parameters())
-
-
-# Users can define their own in case this doesn't suffice. For most cases,
-# it should be sufficient.
-def model_init_fn(model_cls, group_offload_kwargs=None, layerwise_upcasting=False, **init_kwargs):
-    model = model_cls.from_pretrained(**init_kwargs).eval()
-    if group_offload_kwargs and isinstance(group_offload_kwargs, dict):
-        model.enable_group_offload(**group_offload_kwargs)
-    else:
-        model.to(torch_device)
-    if layerwise_upcasting:
-        model.enable_layerwise_casting(
-            storage_dtype=torch.float8_e4m3fn, compute_dtype=init_kwargs.get("torch_dtype", torch.bfloat16)
-        )
-    return model
-
-
-@dataclass
-class BenchmarkScenario:
-    name: str
-    model_cls: ModelMixin
-    model_init_kwargs: Dict[str, Any]
-    model_init_fn: Callable
-    get_model_input_dict: Callable
-    compile_kwargs: Optional[Dict[str, Any]] = None
-
-
-@require_torch_gpu
-class BenchmarkMixin:
-    def pre_benchmark(self):
-        flush()
-        torch.compiler.reset()
-
-    def post_benchmark(self, model):
-        model.cpu()
-        flush()
-        torch.compiler.reset()
-
-    @torch.no_grad()
-    def run_benchmark(self, scenario: BenchmarkScenario):
-        # 0) Basic stats
-        logger.info(f"Running scenario: {scenario.name}.")
-        try:
-            model = model_init_fn(scenario.model_cls, **scenario.model_init_kwargs)
-            num_params = round(calculate_params(model) / 1e9, 2)
-            try:
-                flops = round(calculate_flops(model, input_dict=scenario.get_model_input_dict()) / 1e9, 2)
-            except Exception as e:
-                logger.info(f"Problem in calculating FLOPs:\n{e}")
-                flops = None
-            model.cpu()
-            del model
-        except Exception as e:
-            logger.info(f"Error while initializing the model and calculating FLOPs:\n{e}")
-            return {}
-        self.pre_benchmark()
-
-        # 1) plain stats
-        results = {}
-        plain = None
-        try:
-            plain = self._run_phase(
-                model_cls=scenario.model_cls,
-                init_fn=scenario.model_init_fn,
-                init_kwargs=scenario.model_init_kwargs,
-                get_input_fn=scenario.get_model_input_dict,
-                compile_kwargs=None,
-            )
-        except Exception as e:
-            logger.info(f"Benchmark could not be run with the following error:\n{e}")
-            return results
-
-        # 2) compiled stats (if any)
-        compiled = {"time": None, "memory": None}
-        if scenario.compile_kwargs:
-            try:
-                compiled = self._run_phase(
-                    model_cls=scenario.model_cls,
-                    init_fn=scenario.model_init_fn,
-                    init_kwargs=scenario.model_init_kwargs,
-                    get_input_fn=scenario.get_model_input_dict,
-                    compile_kwargs=scenario.compile_kwargs,
-                )
-            except Exception as e:
-                logger.info(f"Compilation benchmark could not be run with the following error\n: {e}")
-                if plain is None:
-                    return results
-
-        # 3) merge
-        result = {
-            "scenario": scenario.name,
-            "model_cls": scenario.model_cls.__name__,
-            "num_params_B": num_params,
-            "flops_G": flops,
-            "time_plain_s": plain["time"],
-            "mem_plain_GB": plain["memory"],
-            "time_compile_s": compiled["time"],
-            "mem_compile_GB": compiled["memory"],
-        }
-        if scenario.compile_kwargs:
-            result["fullgraph"] = scenario.compile_kwargs.get("fullgraph", False)
-            result["mode"] = scenario.compile_kwargs.get("mode", "default")
-        else:
-            result["fullgraph"], result["mode"] = None, None
-        return result
-
-    def run_bencmarks_and_collate(self, scenarios: Union[BenchmarkScenario, list[BenchmarkScenario]], filename: str):
-        if not isinstance(scenarios, list):
-            scenarios = [scenarios]
-        record_queue = queue.Queue()
-        stop_signal = object()
-
-        def _writer_thread():
-            while True:
-                item = record_queue.get()
-                if item is stop_signal:
-                    break
-                df_row = pd.DataFrame([item])
-                write_header = not os.path.exists(filename)
-                df_row.to_csv(filename, mode="a", header=write_header, index=False)
-                record_queue.task_done()
-
-            record_queue.task_done()
-
-        writer = threading.Thread(target=_writer_thread, daemon=True)
-        writer.start()
-
-        for s in scenarios:
-            try:
-                record = self.run_benchmark(s)
-                if record:
-                    record_queue.put(record)
-                else:
-                    logger.info(f"Record empty from scenario: {s.name}.")
-            except Exception as e:
-                logger.info(f"Running scenario ({s.name}) led to error:\n{e}")
-        record_queue.put(stop_signal)
-        logger.info(f"Results serialized to {filename=}.")
-
-    def _run_phase(
-        self,
-        *,
-        model_cls: ModelMixin,
-        init_fn: Callable,
-        init_kwargs: Dict[str, Any],
-        get_input_fn: Callable,
-        compile_kwargs: Optional[Dict[str, Any]],
-    ) -> Dict[str, float]:
-        # setup
-        self.pre_benchmark()
-
-        # init & (optional) compile
-        model = init_fn(model_cls, **init_kwargs)
-        if compile_kwargs:
-            model.compile(**compile_kwargs)
-
-        # build inputs
-        inp = get_input_fn()
-
-        # measure
-        run_ctx = torch._inductor.utils.fresh_inductor_cache() if compile_kwargs else nullcontext()
-        with run_ctx:
-            for _ in range(NUM_WARMUP_ROUNDS):
-                _ = model(**inp)
-            time_s = benchmark_fn(lambda m, d: m(**d), model, inp)
-        mem_gb = torch.cuda.max_memory_allocated() / (1024**3)
-        mem_gb = round(mem_gb, 2)
-
-        # teardown
-        self.post_benchmark(model)
-        del model
-        return {"time": time_s, "memory": mem_gb}

benchmarks/benchmarking_wan.py

@@ -1,74 +0,0 @@
-from functools import partial
-
-import torch
-from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
-
-from diffusers import WanTransformer3DModel
-from diffusers.utils.testing_utils import torch_device
-
-
-CKPT_ID = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
-RESULT_FILENAME = "wan.csv"
-
-
-def get_input_dict(**device_dtype_kwargs):
-    # height: 480
-    # width: 832
-    # num_frames: 81
-    # max_sequence_length: 512
-    hidden_states = torch.randn(1, 16, 21, 60, 104, **device_dtype_kwargs)
-    encoder_hidden_states = torch.randn(1, 512, 4096, **device_dtype_kwargs)
-    timestep = torch.tensor([1.0], **device_dtype_kwargs)
-
-    return {"hidden_states": hidden_states, "encoder_hidden_states": encoder_hidden_states, "timestep": timestep}
-
-
-if __name__ == "__main__":
-    scenarios = [
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-bf16",
-            model_cls=WanTransformer3DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=model_init_fn,
-            compile_kwargs={"fullgraph": True},
-        ),
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-layerwise-upcasting",
-            model_cls=WanTransformer3DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
-        ),
-        BenchmarkScenario(
-            name=f"{CKPT_ID}-group-offload-leaf",
-            model_cls=WanTransformer3DModel,
-            model_init_kwargs={
-                "pretrained_model_name_or_path": CKPT_ID,
-                "torch_dtype": torch.bfloat16,
-                "subfolder": "transformer",
-            },
-            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
-            model_init_fn=partial(
-                model_init_fn,
-                group_offload_kwargs={
-                    "onload_device": torch_device,
-                    "offload_device": torch.device("cpu"),
-                    "offload_type": "leaf_level",
-                    "use_stream": True,
-                    "non_blocking": True,
-                },
-            ),
-        ),
-    ]
-
-    runner = BenchmarkMixin()
-    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

benchmarks/populate_into_db.py

@@ -1,166 +0,0 @@
-import argparse
-import os
-import sys
-
-import gpustat
-import pandas as pd
-import psycopg2
-import psycopg2.extras
-from psycopg2.extensions import register_adapter
-from psycopg2.extras import Json
-
-
-register_adapter(dict, Json)
-
-FINAL_CSV_FILENAME = "collated_results.csv"
-# https://github.com/huggingface/transformers/blob/593e29c5e2a9b17baec010e8dc7c1431fed6e841/benchmark/init_db.sql#L27
-BENCHMARKS_TABLE_NAME = "benchmarks"
-MEASUREMENTS_TABLE_NAME = "model_measurements"
-
-
-def _init_benchmark(conn, branch, commit_id, commit_msg):
-    gpu_stats = gpustat.GPUStatCollection.new_query()
-    metadata = {"gpu_name": gpu_stats[0]["name"]}
-    repository = "huggingface/diffusers"
-    with conn.cursor() as cur:
-        cur.execute(
-            f"INSERT INTO {BENCHMARKS_TABLE_NAME} (repository, branch, commit_id, commit_message, metadata) VALUES (%s, %s, %s, %s, %s) RETURNING benchmark_id",
-            (repository, branch, commit_id, commit_msg, metadata),
-        )
-        benchmark_id = cur.fetchone()[0]
-        print(f"Initialised benchmark #{benchmark_id}")
-        return benchmark_id
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "branch",
-        type=str,
-        help="The branch name on which the benchmarking is performed.",
-    )
-
-    parser.add_argument(
-        "commit_id",
-        type=str,
-        help="The commit hash on which the benchmarking is performed.",
-    )
-
-    parser.add_argument(
-        "commit_msg",
-        type=str,
-        help="The commit message associated with the commit, truncated to 70 characters.",
-    )
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    try:
-        conn = psycopg2.connect(
-            host=os.getenv("PGHOST"),
-            database=os.getenv("PGDATABASE"),
-            user=os.getenv("PGUSER"),
-            password=os.getenv("PGPASSWORD"),
-        )
-        print("DB connection established successfully.")
-    except Exception as e:
-        print(f"Problem during DB init: {e}")
-        sys.exit(1)
-
-    try:
-        benchmark_id = _init_benchmark(
-            conn=conn,
-            branch=args.branch,
-            commit_id=args.commit_id,
-            commit_msg=args.commit_msg,
-        )
-    except Exception as e:
-        print(f"Problem during initializing benchmark: {e}")
-        sys.exit(1)
-
-    cur = conn.cursor()
-
-    df = pd.read_csv(FINAL_CSV_FILENAME)
-
-    # Helper to cast values (or None) given a dtype
-    def _cast_value(val, dtype: str):
-        if pd.isna(val):
-            return None
-
-        if dtype == "text":
-            return str(val).strip()
-
-        if dtype == "float":
-            try:
-                return float(val)
-            except ValueError:
-                return None
-
-        if dtype == "bool":
-            s = str(val).strip().lower()
-            if s in ("true", "t", "yes", "1"):
-                return True
-            if s in ("false", "f", "no", "0"):
-                return False
-            if val in (1, 1.0):
-                return True
-            if val in (0, 0.0):
-                return False
-            return None
-
-        return val
-
-    try:
-        rows_to_insert = []
-        for _, row in df.iterrows():
-            scenario = _cast_value(row.get("scenario"), "text")
-            model_cls = _cast_value(row.get("model_cls"), "text")
-            num_params_B = _cast_value(row.get("num_params_B"), "float")
-            flops_G = _cast_value(row.get("flops_G"), "float")
-            time_plain_s = _cast_value(row.get("time_plain_s"), "float")
-            mem_plain_GB = _cast_value(row.get("mem_plain_GB"), "float")
-            time_compile_s = _cast_value(row.get("time_compile_s"), "float")
-            mem_compile_GB = _cast_value(row.get("mem_compile_GB"), "float")
-            fullgraph = _cast_value(row.get("fullgraph"), "bool")
-            mode = _cast_value(row.get("mode"), "text")
-
-            # If "github_sha" column exists in the CSV, cast it; else default to None
-            if "github_sha" in df.columns:
-                github_sha = _cast_value(row.get("github_sha"), "text")
-            else:
-                github_sha = None
-
-            measurements = {
-                "scenario": scenario,
-                "model_cls": model_cls,
-                "num_params_B": num_params_B,
-                "flops_G": flops_G,
-                "time_plain_s": time_plain_s,
-                "mem_plain_GB": mem_plain_GB,
-                "time_compile_s": time_compile_s,
-                "mem_compile_GB": mem_compile_GB,
-                "fullgraph": fullgraph,
-                "mode": mode,
-                "github_sha": github_sha,
-            }
-            rows_to_insert.append((benchmark_id, measurements))
-
-        # Batch-insert all rows
-        insert_sql = f"""
-        INSERT INTO {MEASUREMENTS_TABLE_NAME} (
-            benchmark_id,
-            measurements
-        )
-        VALUES (%s, %s);
-        """
-
-        psycopg2.extras.execute_batch(cur, insert_sql, rows_to_insert)
-        conn.commit()
-
-        cur.close()
-        conn.close()
-    except Exception as e:
-        print(f"Exception: {e}")
-        sys.exit(1)

benchmarks/push_results.py

@@ -1,76 +0,0 @@
-import os
-
-import pandas as pd
-from huggingface_hub import hf_hub_download, upload_file
-from huggingface_hub.utils import EntryNotFoundError
-
-
-REPO_ID = "diffusers/benchmarks"
-
-
-def has_previous_benchmark() -> str:
-    from run_all import FINAL_CSV_FILENAME
-
-    csv_path = None
-    try:
-        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILENAME)
-    except EntryNotFoundError:
-        csv_path = None
-    return csv_path
-
-
-def filter_float(value):
-    if isinstance(value, str):
-        return float(value.split()[0])
-    return value
-
-
-def push_to_hf_dataset():
-    from run_all import FINAL_CSV_FILENAME, GITHUB_SHA
-
-    csv_path = has_previous_benchmark()
-    if csv_path is not None:
-        current_results = pd.read_csv(FINAL_CSV_FILENAME)
-        previous_results = pd.read_csv(csv_path)
-
-        numeric_columns = current_results.select_dtypes(include=["float64", "int64"]).columns
-
-        for column in numeric_columns:
-            # get previous values as floats, aligned to current index
-            prev_vals = previous_results[column].map(filter_float).reindex(current_results.index)
-
-            # get current values as floats
-            curr_vals = current_results[column].astype(float)
-
-            # stringify the current values
-            curr_str = curr_vals.map(str)
-
-            # build an appendage only when prev exists and differs
-            append_str = prev_vals.where(prev_vals.notnull() & (prev_vals != curr_vals), other=pd.NA).map(
-                lambda x: f" ({x})" if pd.notnull(x) else ""
-            )
-
-            # combine
-            current_results[column] = curr_str + append_str
-        os.remove(FINAL_CSV_FILENAME)
-        current_results.to_csv(FINAL_CSV_FILENAME, index=False)
-
-    commit_message = f"upload from sha: {GITHUB_SHA}" if GITHUB_SHA is not None else "upload benchmark results"
-    upload_file(
-        repo_id=REPO_ID,
-        path_in_repo=FINAL_CSV_FILENAME,
-        path_or_fileobj=FINAL_CSV_FILENAME,
-        repo_type="dataset",
-        commit_message=commit_message,
-    )
-    upload_file(
-        repo_id="diffusers/benchmark-analyzer",
-        path_in_repo=FINAL_CSV_FILENAME,
-        path_or_fileobj=FINAL_CSV_FILENAME,
-        repo_type="space",
-        commit_message=commit_message,
-    )
-
-
-if __name__ == "__main__":
-    push_to_hf_dataset()

benchmarks/requirements.txt

@@ -1,6 +0,0 @@
-pandas 
-psutil
-gpustat
-torchprofile
-bitsandbytes
-psycopg2==2.9.9

benchmarks/run_all.py

@@ -1,84 +0,0 @@
-import glob
-import logging
-import os
-import subprocess
-
-import pandas as pd
-
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-logger = logging.getLogger(__name__)
-
-PATTERN = "benchmarking_*.py"
-FINAL_CSV_FILENAME = "collated_results.csv"
-GITHUB_SHA = os.getenv("GITHUB_SHA", None)
-
-
-class SubprocessCallException(Exception):
-    pass
-
-
-def run_command(command: list[str], return_stdout=False):
-    try:
-        output = subprocess.check_output(command, stderr=subprocess.STDOUT)
-        if return_stdout and hasattr(output, "decode"):
-            return output.decode("utf-8")
-    except subprocess.CalledProcessError as e:
-        raise SubprocessCallException(f"Command `{' '.join(command)}` failed with:\n{e.output.decode()}") from e
-
-
-def merge_csvs(final_csv: str = "collated_results.csv"):
-    all_csvs = glob.glob("*.csv")
-    all_csvs = [f for f in all_csvs if f != final_csv]
-    if not all_csvs:
-        logger.info("No result CSVs found to merge.")
-        return
-
-    df_list = []
-    for f in all_csvs:
-        try:
-            d = pd.read_csv(f)
-        except pd.errors.EmptyDataError:
-            # If a file existed but was zero‐bytes or corrupted, skip it
-            continue
-        df_list.append(d)
-
-    if not df_list:
-        logger.info("All result CSVs were empty or invalid; nothing to merge.")
-        return
-
-    final_df = pd.concat(df_list, ignore_index=True)
-    if GITHUB_SHA is not None:
-        final_df["github_sha"] = GITHUB_SHA
-    final_df.to_csv(final_csv, index=False)
-    logger.info(f"Merged {len(all_csvs)} partial CSVs → {final_csv}.")
-
-
-def run_scripts():
-    python_files = sorted(glob.glob(PATTERN))
-    python_files = [f for f in python_files if f != "benchmarking_utils.py"]
-
-    for file in python_files:
-        script_name = file.split(".py")[0].split("_")[-1]  # example: benchmarking_foo.py -> foo
-        logger.info(f"\n****** Running file: {file} ******")
-
-        partial_csv = f"{script_name}.csv"
-        if os.path.exists(partial_csv):
-            logger.info(f"Found {partial_csv}. Removing for safer numbers and duplication.")
-            os.remove(partial_csv)
-
-        command = ["python", file]
-        try:
-            run_command(command)
-            logger.info(f"→ {file} finished normally.")
-        except SubprocessCallException as e:
-            logger.info(f"Error running {file}:\n{e}")
-        finally:
-            logger.info(f"→ Merging partial CSVs after {file} …")
-            merge_csvs(final_csv=FINAL_CSV_FILENAME)
-
-    logger.info(f"\nAll scripts attempted. Final collated CSV: {FINAL_CSV_FILENAME}")
-
-
-if __name__ == "__main__":
-    run_scripts()

docker/diffusers-doc-builder/Dockerfile

@@ -1,56 +0,0 @@
-FROM ubuntu:20.04
-LABEL maintainer="Hugging Face"
-LABEL repository="diffusers"
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
-
-RUN apt install -y bash \
-                   build-essential \
-                   git \
-                   git-lfs \
-                   curl \
-                   ca-certificates \
-                   libsndfile1-dev \
-                   python3.10 \
-                   python3-pip \
-                   libgl1 \
-                   zip \
-                   wget \
-                   python3.10-venv && \
-    rm -rf /var/lib/apt/lists
-
-# make sure to use venv
-RUN python3.10 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio \
-        invisible_watermark \
-        --extra-index-url https://download.pytorch.org/whl/cpu && \
-    python3.10 -m uv pip install --no-cache-dir \
-        accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        Jinja2 \
-        librosa \
-        numpy==1.26.4 \
-        scipy \
-        tensorboard \
-        transformers \
-        matplotlib \
-        setuptools==69.5.1 \
-        bitsandbytes \
-        torchao \
-        gguf \
-        optimum-quanto
-
-CMD ["/bin/bash"]

docker/diffusers-flax-cpu/Dockerfile

@@ -0,0 +1,44 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --upgrade --no-cache-dir \
+        clu \
+        "jax[cpu]>=0.2.16,!=0.3.2" \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

docker/diffusers-flax-tpu/Dockerfile

@@ -0,0 +1,46 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        "jax[tpu]>=0.2.16,!=0.3.2" \
+        -f https://storage.googleapis.com/jax-releases/libtpu_releases.html && \
+    python3 -m pip install --upgrade --no-cache-dir \
+        clu \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \        
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -4,46 +4,41 @@ LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
-
-RUN apt install -y bash \
+RUN apt update && \
+    apt install -y bash \
                    build-essential \
                    git \
                    git-lfs \
                    curl \
                    ca-certificates \
                    libsndfile1-dev \
-                   libgl1 \
-                   python3.10 \
+                   python3.8 \
                    python3-pip \
-                   python3.10-venv && \
+                   python3.8-venv && \
     rm -rf /var/lib/apt/lists
 
 # make sure to use venv
-RUN python3.10 -m venv /opt/venv
+RUN python3 -m venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3 -m uv pip install --no-cache-dir \
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
         torch \
         torchvision \
-        torchaudio\
+        torchaudio \
         onnxruntime \
         --extra-index-url https://download.pytorch.org/whl/cpu && \
-    python3 -m uv pip install --no-cache-dir \
+    python3 -m pip install --no-cache-dir \
         accelerate \
         datasets \
         hf-doc-builder \
         huggingface-hub \
         Jinja2 \
         librosa \
-        numpy==1.26.4 \
+        numpy \
         scipy \
         tensorboard \
-        transformers \
-        hf_transfer
+        transformers
 
 CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -1,50 +1,44 @@
-FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
+FROM nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
-
-RUN apt install -y bash \
+RUN apt update && \
+    apt install -y bash \
                    build-essential \
                    git \
                    git-lfs \
                    curl \
                    ca-certificates \
                    libsndfile1-dev \
-                   libgl1 \
-                   python3.10 \
+                   python3.8 \
                    python3-pip \
-                   python3.10-venv && \
+                   python3.8-venv && \
     rm -rf /var/lib/apt/lists
 
 # make sure to use venv
-RUN python3.10 -m venv /opt/venv
+RUN python3 -m venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
         torch \
         torchvision \
         torchaudio \
         "onnxruntime-gpu>=1.13.1" \
         --extra-index-url https://download.pytorch.org/whl/cu117 && \
-    python3.10 -m uv pip install --no-cache-dir \
+    python3 -m pip install --no-cache-dir \
         accelerate \
         datasets \
         hf-doc-builder \
         huggingface-hub \
-        hf_transfer \
         Jinja2 \
         librosa \
-        numpy==1.26.4 \
+        numpy \
         scipy \
         tensorboard \
-        transformers \
-        hf_transfer
+        transformers
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cpu/Dockerfile

@@ -4,47 +4,42 @@ LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
-
-RUN apt install -y bash \
+RUN apt update && \
+    apt install -y bash \
                    build-essential \
                    git \
                    git-lfs \
                    curl \
                    ca-certificates \
                    libsndfile1-dev \
-                   python3.10 \
-                   python3.10-dev \
+                   python3.8 \
                    python3-pip \
                    libgl1 \
-                   python3.10-venv && \
+                   python3.8-venv && \
     rm -rf /var/lib/apt/lists
 
 # make sure to use venv
-RUN python3.10 -m venv /opt/venv
+RUN python3 -m venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
         torch \
         torchvision \
         torchaudio \
         invisible_watermark \
         --extra-index-url https://download.pytorch.org/whl/cpu && \
-    python3.10 -m uv pip install --no-cache-dir \
+    python3 -m pip install --no-cache-dir \
         accelerate \
         datasets \
         hf-doc-builder \
         huggingface-hub \
         Jinja2 \
         librosa \
-        numpy==1.26.4 \
+        numpy \
         scipy \
         tensorboard \
-        transformers matplotlib  \
-        hf_transfer
+        transformers
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cuda/Dockerfile

@@ -1,51 +1,47 @@
-FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
+FROM nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
-
-RUN apt install -y bash \
-    build-essential \
-    git \
-    git-lfs \
-    curl \
-    ca-certificates \
-    libsndfile1-dev \
-    libgl1 \
-    python3.10 \
-    python3.10-dev \
-    python3-pip \
-    python3.10-venv && \
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   libgl1 \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
     rm -rf /var/lib/apt/lists
 
 # make sure to use venv
-RUN python3.10 -m venv /opt/venv
+RUN python3 -m venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
-    torch \
-    torchvision \
-    torchaudio \
-    invisible_watermark && \
-    python3.10 -m pip install --no-cache-dir \
-    accelerate \
-    datasets \
-    hf-doc-builder \
-    huggingface-hub \
-    hf_transfer \
-    Jinja2 \
-    librosa \
-    numpy==1.26.4 \
-    scipy \
-    tensorboard \
-    transformers \
-    pytorch-lightning  \
-    hf_transfer
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        invisible_watermark && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers \
+        omegaconf \
+        pytorch-lightning \
+        xformers
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-minimum-cuda/Dockerfile

@@ -1,53 +0,0 @@
-FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
-LABEL maintainer="Hugging Face"
-LABEL repository="diffusers"
-
-ENV DEBIAN_FRONTEND=noninteractive
-ENV MINIMUM_SUPPORTED_TORCH_VERSION="2.1.0"
-ENV MINIMUM_SUPPORTED_TORCHVISION_VERSION="0.16.0"
-ENV MINIMUM_SUPPORTED_TORCHAUDIO_VERSION="2.1.0"
-
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
-
-RUN apt install -y bash \
-    build-essential \
-    git \
-    git-lfs \
-    curl \
-    ca-certificates \
-    libsndfile1-dev \
-    libgl1 \
-    python3.10 \
-    python3.10-dev \
-    python3-pip \
-    python3.10-venv && \
-    rm -rf /var/lib/apt/lists
-
-# make sure to use venv
-RUN python3.10 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
-    torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
-    torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
-    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION \
-    invisible_watermark && \
-    python3.10 -m pip install --no-cache-dir \
-    accelerate \
-    datasets \
-    hf-doc-builder \
-    huggingface-hub \
-    hf_transfer \
-    Jinja2 \
-    librosa \
-    numpy==1.26.4 \
-    scipy \
-    tensorboard \
-    transformers \
-    hf_transfer
-
-CMD ["/bin/bash"]

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -1,51 +0,0 @@
-FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
-LABEL maintainer="Hugging Face"
-LABEL repository="diffusers"
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
-
-RUN apt install -y bash \
-                   build-essential \
-                   git \
-                   git-lfs \
-                   curl \
-                   ca-certificates \
-                   libsndfile1-dev \
-                   libgl1 \
-                   python3.10 \
-                   python3.10-dev \
-                   python3-pip \
-                   python3.10-venv && \
-    rm -rf /var/lib/apt/lists
-
-# make sure to use venv
-RUN python3.10 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio \
-        invisible_watermark && \
-    python3.10 -m uv pip install --no-cache-dir \
-        accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        hf_transfer \
-        Jinja2 \
-        librosa \
-        numpy==1.26.4 \
-        scipy \
-        tensorboard \
-        transformers \
-        xformers  \
-        hf_transfer
-
-CMD ["/bin/bash"]

docs/README.md

@@ -1,5 +1,5 @@
 <!---
-Copyright 2024- The HuggingFace Team. All rights reserved.
+Copyright 2023- The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -16,7 +16,7 @@ limitations under the License.
 
 # Generating the documentation
 
-To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
+To generate the documentation, you first have to build it. Several packages are necessary to build the doc, 
 you can install them with the following command, at the root of the code repository:
 
 ```bash
@@ -71,7 +71,7 @@ The `preview` command only works with existing doc files. When you add a complet
 Accepted files are Markdown (.md).
 
 Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
-the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml) file.
+the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/diffusers/blob/main/docs/source/_toctree.yml) file.
 
 ## Renaming section headers and moving sections
 
@@ -81,14 +81,14 @@ Therefore, we simply keep a little map of moved sections at the end of the docum
 
 So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
 
-```md
+```
 Sections that were moved:
 
 [ <a href="#section-b">Section A</a><a id="section-a"></a> ]
 ```
 and of course, if you moved it to another file, then:
 
-```md
+```
 Sections that were moved:
 
 [ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
@@ -109,8 +109,8 @@ although we can write them directly in Markdown.
 
 Adding a new tutorial or section is done in two steps:
 
-- Add a new Markdown (.md) file under `docs/source/<languageCode>`.
-- Link that file in `docs/source/<languageCode>/_toctree.yml` on the correct toc-tree.
+- Add a new file under `docs/source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
+- Link that file in `docs/source/_toctree.yml` on the correct toc-tree.
 
 Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
 depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or four.
@@ -119,7 +119,7 @@ depending on the intended targets (beginners, more advanced users, or researcher
 
 When adding a new pipeline:
 
-- Create a file `xxx.md` under `docs/source/<languageCode>/api/pipelines` (don't hesitate to copy an existing file as template).
+- create a file `xxx.md` under `docs/source/api/pipelines` (don't hesitate to copy an existing file as template).
 - Link that file in (*Diffusers Summary*) section in `docs/source/api/pipelines/overview.md`, along with the link to the paper, and a colab notebook (if available).
 - Write a short overview of the diffusion model:
     - Overview with paper & authors
@@ -129,6 +129,8 @@ When adding a new pipeline:
 - Add all the pipeline classes that should be linked in the diffusion model. These classes should be added using our Markdown syntax. By default as follows:
 
 ```
+## XXXPipeline
+
 [[autodoc]] XXXPipeline
     - all
 	- __call__
@@ -142,11 +144,11 @@ This will include every public method of the pipeline that is documented, as wel
 	- __call__
 	- enable_attention_slicing
 	- disable_attention_slicing
-    - enable_xformers_memory_efficient_attention
+    - enable_xformers_memory_efficient_attention 
     - disable_xformers_memory_efficient_attention
 ```
 
-You can follow the same process to create a new scheduler under the `docs/source/<languageCode>/api/schedulers` folder.
+You can follow the same process to create a new scheduler under the `docs/source/api/schedulers` folder
 
 ### Writing source documentation
 
@@ -154,7 +156,7 @@ Values that should be put in `code` should either be surrounded by backticks: \`
 and objects like True, None, or any strings should usually be put in `code`.
 
 When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
-adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or
+adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or 
 function to be in the main package.
 
 If you want to create a link to some internal class or function, you need to
@@ -162,7 +164,7 @@ provide its path. For instance: \[\`pipelines.ImagePipelineOutput\`\]. This will
 `pipelines.ImagePipelineOutput` in the description. To get rid of the path and only keep the name of the object you are
 linking to in the description, add a ~: \[\`~pipelines.ImagePipelineOutput\`\] will generate a link with `ImagePipelineOutput` in the description.
 
-The same works for methods so you can either use \[\`XXXClass.method\`\] or \[\`~XXXClass.method\`\].
+The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
 
 #### Defining arguments in a method
 
@@ -194,8 +196,8 @@ Here's an example showcasing everything so far:
 For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
 following signature:
 
-```py
-def my_function(x: str=None, a: float=3.14):
+```
+def my_function(x: str = None, a: float = 1):
 ```
 
 then its documentation should look like this:
@@ -204,7 +206,7 @@ then its documentation should look like this:
     Args:
         x (`str`, *optional*):
             This argument controls ...
-        a (`float`, *optional*, defaults to `3.14`):
+        a (`float`, *optional*, defaults to 1):
             This argument is used to ...
 ```
 
@@ -242,10 +244,10 @@ Here's an example of a tuple return, comprising several objects:
 
 ```
     Returns:
-        `tuple(torch.Tensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
-        - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.Tensor` of shape `(1,)` --
+        `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
+        - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
           Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
-        - **prediction_scores** (`torch.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
+        - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
           Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
 ```
 
@@ -266,3 +268,4 @@ We have an automatic script running with the `make style` command that will make
 This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's
 recommended to commit your changes before running `make style`, so you can revert the changes done by that script
 easily.
+

docs/TRANSLATING.md

@@ -1,22 +1,10 @@
-<!--Copyright 2025 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.
--->
-
 ### Translating the Diffusers documentation into your language
 
 As part of our mission to democratize machine learning, we'd love to make the Diffusers library available in many more languages! Follow the steps below if you want to help translate the documentation into your language 🙏.
 
 **🗞️ Open an issue**
 
-To get started, navigate to the [Issues](https://github.com/huggingface/diffusers/issues) page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the "🌐 Translating a New Language?" from the "New issue" button.
+To get started, navigate to the [Issues](https://github.com/huggingface/diffusers/issues) page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the "Translation template" from the "New issue" button.
 
 Once an issue exists, post a comment to indicate which chapters you'd like to work on, and we'll add your name to the list.
 
@@ -28,7 +16,7 @@ First, you'll need to [fork the Diffusers repo](https://docs.github.com/en/get-s
 Once you've forked the repo, you'll want to get the files on your local machine for editing. You can do that by cloning the fork with Git as follows:
 
 ```bash
-git clone https://github.com/<YOUR-USERNAME>/diffusers.git
+git clone https://github.com/YOUR-USERNAME/diffusers.git
 ```
 
 **📋 Copy-paste the English version with a new language code**
@@ -41,18 +29,18 @@ You'll only need to copy the files in the [`docs/source/en`](https://github.com/
 
 ```bash
 cd ~/path/to/diffusers/docs
-cp -r source/en source/<LANG-ID>
+cp -r source/en source/LANG-ID
 ```
 
-Here, `<LANG-ID>` should be one of the ISO 639-1 or ISO 639-2 language codes -- see [here](https://www.loc.gov/standards/iso639-2/php/code_list.php) for a handy table.
+Here, `LANG-ID` should be one of the ISO 639-1 or ISO 639-2 language codes -- see [here](https://www.loc.gov/standards/iso639-2/php/code_list.php) for a handy table.
 
 **✍️ Start translating**
 
 The fun part comes - translating the text!
 
-The first thing we recommend is translating the part of the `_toctree.yml` file that corresponds to your doc chapter. This file is used to render the table of contents on the website.
+The first thing we recommend is translating the part of the `_toctree.yml` file that corresponds to your doc chapter. This file is used to render the table of contents on the website. 
 
-> 🙋 If the `_toctree.yml` file doesn't yet exist for your language, you can create one by copy-pasting from the English version and deleting the sections unrelated to your chapter. Just make sure it exists in the `docs/source/<LANG-ID>/` directory!
+> 🙋 If the `_toctree.yml` file doesn't yet exist for your language, you can create one by copy-pasting from the English version and deleting the sections unrelated to your chapter. Just make sure it exists in the `docs/source/LANG-ID/` directory!
 
 The fields you should add are `local` (with the name of the file containing the translation; e.g. `autoclass_tutorial`), and `title` (with the title of the doc in your language; e.g. `Load pretrained instances with an AutoClass`) -- as a reference, here is the `_toctree.yml` for [English](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml):
 

docs/source/en/_toctree.yml

@@ -1,195 +1,106 @@
-- title: Get started
-  sections:
+- sections:
   - local: index
-    title: Diffusers
+    title: 🧨 Diffusers
+  - local: quicktour
+    title: Quicktour
+  - local: stable_diffusion
+    title: Effective and efficient diffusion
   - local: installation
     title: Installation
-  - local: quicktour
-    title: Quickstart
-  - local: stable_diffusion
-    title: Basic performance
-
-- title: Pipelines
-  isExpanded: false
-  sections:
-  - local: using-diffusers/loading
-    title: DiffusionPipeline
-  - local: tutorials/autopipeline
-    title: AutoPipeline
-  - local: using-diffusers/custom_pipeline_overview
-    title: Community pipelines and components
-  - local: using-diffusers/callback
-    title: Pipeline callbacks
-  - local: using-diffusers/reusing_seeds
-    title: Reproducibility
-  - local: using-diffusers/schedulers
-    title: Schedulers
-  - local: using-diffusers/other-formats
-    title: Model files and layouts
-  - local: using-diffusers/push_to_hub
-    title: Sharing pipelines and models
-
-- title: Adapters
-  isExpanded: false
-  sections:
-  - local: tutorials/using_peft_for_inference
-    title: LoRA
-  - local: using-diffusers/ip_adapter
-    title: IP-Adapter
-  - local: using-diffusers/controlnet
-    title: ControlNet
-  - local: using-diffusers/t2i_adapter
-    title: T2I-Adapter
-  - local: using-diffusers/dreambooth
-    title: DreamBooth
-  - local: using-diffusers/textual_inversion_inference
-    title: Textual inversion
-
-- title: Inference
-  isExpanded: false
-  sections:
-  - local: using-diffusers/weighted_prompts
-    title: Prompt techniques
-  - local: using-diffusers/create_a_server
-    title: Create a server
-  - local: using-diffusers/batched_inference
-    title: Batch inference
-  - local: training/distributed_inference
-    title: Distributed inference
-
-- title: Inference optimization
-  isExpanded: false
-  sections:
-  - local: optimization/fp16
-    title: Accelerate inference
-  - local: optimization/cache
-    title: Caching
-  - local: optimization/attention_backends
-    title: Attention backends
-  - local: optimization/memory
-    title: Reduce memory usage
-  - local: optimization/speed-memory-optims
-    title: Compiling and offloading quantized models
-  - title: Community optimizations
-    sections:
-    - local: optimization/pruna
-      title: Pruna
-    - local: optimization/xformers
-      title: xFormers
-    - local: optimization/tome
-      title: Token merging
-    - local: optimization/deepcache
-      title: DeepCache
-    - local: optimization/tgate
-      title: TGATE
-    - local: optimization/xdit
-      title: xDiT
-    - local: optimization/para_attn
-      title: ParaAttention
-    - local: using-diffusers/image_quality
-      title: FreeU
-
-- title: Hybrid Inference
-  isExpanded: false
-  sections:
-  - local: hybrid_inference/overview
+  title: Get started
+- sections:
+  - local: tutorials/tutorial_overview
     title: Overview
-  - local: hybrid_inference/vae_decode
-    title: VAE Decode
-  - local: hybrid_inference/vae_encode
-    title: VAE Encode
-  - local: hybrid_inference/api_reference
-    title: API Reference
-
-- title: Modular Diffusers
-  isExpanded: false
-  sections:
-  - local: modular_diffusers/overview
-    title: Overview
-  - local: modular_diffusers/quickstart
-    title: Quickstart
-  - local: modular_diffusers/modular_diffusers_states
-    title: States
-  - local: modular_diffusers/pipeline_block
-    title: ModularPipelineBlocks
-  - local: modular_diffusers/sequential_pipeline_blocks
-    title: SequentialPipelineBlocks
-  - local: modular_diffusers/loop_sequential_pipeline_blocks
-    title: LoopSequentialPipelineBlocks
-  - local: modular_diffusers/auto_pipeline_blocks
-    title: AutoPipelineBlocks
-  - local: modular_diffusers/modular_pipeline
-    title: ModularPipeline
-  - local: modular_diffusers/components_manager
-    title: ComponentsManager
-  - local: modular_diffusers/guiders
-    title: Guiders
-
-- title: Training
-  isExpanded: false
-  sections:
-  - local: training/overview
-    title: Overview
-  - local: training/create_dataset
-    title: Create a dataset for training
-  - local: training/adapt_a_model
-    title: Adapt a model to a new task
+  - local: using-diffusers/write_own_pipeline
+    title: Understanding models and schedulers
   - local: tutorials/basic_training
     title: Train a diffusion model
-  - title: Models
-    sections:
+  title: Tutorials
+- sections:
+  - sections:
+    - local: using-diffusers/loading_overview
+      title: Overview
+    - local: using-diffusers/loading
+      title: Load pipelines, models, and schedulers
+    - local: using-diffusers/schedulers
+      title: Load and compare different schedulers
+    - local: using-diffusers/custom_pipeline_overview
+      title: Load community pipelines
+    - local: using-diffusers/using_safetensors
+      title: Load safetensors
+    - local: using-diffusers/other-formats
+      title: Load different Stable Diffusion formats
+    title: Loading & Hub
+  - sections:
+    - local: using-diffusers/pipeline_overview
+      title: Overview
+    - local: using-diffusers/unconditional_image_generation
+      title: Unconditional image generation
+    - local: using-diffusers/conditional_image_generation
+      title: Text-to-image generation
+    - local: using-diffusers/img2img
+      title: Text-guided image-to-image
+    - local: using-diffusers/inpaint
+      title: Text-guided image-inpainting
+    - local: using-diffusers/depth2img
+      title: Text-guided depth-to-image
+    - local: using-diffusers/textual_inversion_inference
+      title: Textual inversion
+    - local: training/distributed_inference
+      title: Distributed inference with multiple GPUs
+    - local: using-diffusers/reusing_seeds
+      title: Improve image quality with deterministic generation
+    - local: using-diffusers/control_brightness
+      title: Control image brightness
+    - local: using-diffusers/reproducibility
+      title: Create reproducible pipelines
+    - local: using-diffusers/custom_pipeline_examples
+      title: Community pipelines
+    - local: using-diffusers/contribute_pipeline
+      title: How to contribute a community pipeline
+    - local: using-diffusers/stable_diffusion_jax_how_to
+      title: Stable Diffusion in JAX/Flax
+    - local: using-diffusers/weighted_prompts
+      title: Weighting Prompts
+    title: Pipelines for Inference
+  - sections:
+    - local: training/overview
+      title: Overview
+    - local: training/create_dataset
+      title: Create a dataset for training
+    - local: training/adapt_a_model
+      title: Adapt a model to a new task
     - local: training/unconditional_training
       title: Unconditional image generation
-    - local: training/text2image
-      title: Text-to-image
-    - local: training/sdxl
-      title: Stable Diffusion XL
-    - local: training/kandinsky
-      title: Kandinsky 2.2
-    - local: training/wuerstchen
-      title: Wuerstchen
-    - local: training/controlnet
-      title: ControlNet
-    - local: training/t2i_adapters
-      title: T2I-Adapters
-    - local: training/instructpix2pix
-      title: InstructPix2Pix
-    - local: training/cogvideox
-      title: CogVideoX
-  - title: Methods
-    sections:
     - local: training/text_inversion
       title: Textual Inversion
     - local: training/dreambooth
       title: DreamBooth
+    - local: training/text2image
+      title: Text-to-image
     - local: training/lora
-      title: LoRA
+      title: Low-Rank Adaptation of Large Language Models (LoRA)
+    - local: training/controlnet
+      title: ControlNet
+    - local: training/instructpix2pix
+      title: InstructPix2Pix Training
     - local: training/custom_diffusion
       title: Custom Diffusion
-    - local: training/lcm_distill
-      title: Latent Consistency Distillation
-    - local: training/ddpo
-      title: Reinforcement learning training with DDPO
-
-- title: Quantization
-  isExpanded: false
-  sections:
-  - local: quantization/overview
-    title: Getting started
-  - local: quantization/bitsandbytes
-    title: bitsandbytes
-  - local: quantization/gguf
-    title: gguf
-  - local: quantization/torchao
-    title: torchao
-  - local: quantization/quanto
-    title: quanto
-  - local: quantization/modelopt
-    title: NVIDIA ModelOpt
-
-- title: Model accelerators and hardware
-  isExpanded: false
-  sections:
+    title: Training
+  - sections:
+    - local: using-diffusers/other-modalities
+      title: Other Modalities
+    title: Taking Diffusers Beyond Images
+  title: Using Diffusers
+- sections:
+  - local: optimization/opt_overview
+    title: Overview
+  - local: optimization/fp16
+    title: Memory and Speed
+  - local: optimization/torch2.0
+    title: Torch2.0 support
+  - local: optimization/xformers
+    title: xFormers
   - local: optimization/onnx
     title: ONNX
   - local: optimization/open_vino
@@ -197,63 +108,13 @@
   - local: optimization/coreml
     title: Core ML
   - local: optimization/mps
-    title: Metal Performance Shaders (MPS)
+    title: MPS
   - local: optimization/habana
-    title: Intel Gaudi
-  - local: optimization/neuron
-    title: AWS Neuron
-
-- title: Specific pipeline examples
-  isExpanded: false
-  sections:
-  - local: using-diffusers/consisid
-    title: ConsisID
-  - local: using-diffusers/sdxl
-    title: Stable Diffusion XL
-  - local: using-diffusers/sdxl_turbo
-    title: SDXL Turbo
-  - local: using-diffusers/kandinsky
-    title: Kandinsky
-  - local: using-diffusers/omnigen
-    title: OmniGen
-  - local: using-diffusers/pag
-    title: PAG
-  - local: using-diffusers/inference_with_lcm
-    title: Latent Consistency Model
-  - local: using-diffusers/shap-e
-    title: Shap-E
-  - local: using-diffusers/diffedit
-    title: DiffEdit
-  - local: using-diffusers/inference_with_tcd_lora
-    title: Trajectory Consistency Distillation-LoRA
-  - local: using-diffusers/svd
-    title: Stable Video Diffusion
-  - local: using-diffusers/marigold_usage
-    title: Marigold Computer Vision
-
-- title: Resources
-  isExpanded: false
-  sections:
-  - title: Task recipes
-    sections:
-    - local: using-diffusers/unconditional_image_generation
-      title: Unconditional image generation
-    - local: using-diffusers/conditional_image_generation
-      title: Text-to-image
-    - local: using-diffusers/img2img
-      title: Image-to-image
-    - local: using-diffusers/inpaint
-      title: Inpainting
-    - local: advanced_inference/outpaint
-      title: Outpainting
-    - local: using-diffusers/text-img2vid
-      title: Video generation
-    - local: using-diffusers/depth2img
-      title: Depth-to-image
-  - local: using-diffusers/write_own_pipeline
-    title: Understanding pipelines, models and schedulers
-  - local: community_projects
-    title: Projects built with Diffusers
+    title: Habana Gaudi
+  - local: optimization/tome
+    title: Token Merging
+  title: Optimization/Special Hardware
+- sections:
   - local: conceptual/philosophy
     title: Philosophy
   - local: using-diffusers/controlling_generation
@@ -264,240 +125,71 @@
     title: Diffusers' Ethical Guidelines
   - local: conceptual/evaluation
     title: Evaluating Diffusion Models
-
-- title: API
-  isExpanded: false
-  sections:
-  - title: Main Classes
-    sections:
-    - local: api/configuration
-      title: Configuration
+  title: Conceptual Guides
+- sections:
+  - sections:
+    - local: api/attnprocessor
+      title: Attention Processor
+    - local: api/diffusion_pipeline
+      title: Diffusion Pipeline
     - local: api/logging
       title: Logging
+    - local: api/configuration
+      title: Configuration
     - local: api/outputs
       title: Outputs
-    - local: api/quantization
-      title: Quantization
-  - title: Modular
-    sections:
-    - local: api/modular_diffusers/pipeline
-      title: Pipeline
-    - local: api/modular_diffusers/pipeline_blocks
-      title: Blocks
-    - local: api/modular_diffusers/pipeline_states
-      title: States
-    - local: api/modular_diffusers/pipeline_components
-      title: Components and configs
-    - local: api/modular_diffusers/guiders
-      title: Guiders
-  - title: Loaders
-    sections:
-    - local: api/loaders/ip_adapter
-      title: IP-Adapter
-    - local: api/loaders/lora
-      title: LoRA
-    - local: api/loaders/single_file
-      title: Single files
-    - local: api/loaders/textual_inversion
-      title: Textual Inversion
-    - local: api/loaders/unet
-      title: UNet
-    - local: api/loaders/transformer_sd3
-      title: SD3Transformer2D
-    - local: api/loaders/peft
-      title: PEFT
-  - title: Models
-    sections:
+    - local: api/loaders
+      title: Loaders
+    - local: api/utilities
+      title: Utilities
+    - local: api/image_processor
+      title: VAE Image Processor
+    title: Main Classes
+  - sections:
     - local: api/models/overview
       title: Overview
-    - local: api/models/auto_model
-      title: AutoModel
-    - title: ControlNets
-      sections:
-      - local: api/models/controlnet
-        title: ControlNetModel
-      - local: api/models/controlnet_union
-        title: ControlNetUnionModel
-      - local: api/models/controlnet_flux
-        title: FluxControlNetModel
-      - local: api/models/controlnet_hunyuandit
-        title: HunyuanDiT2DControlNetModel
-      - local: api/models/controlnet_sana
-        title: SanaControlNetModel
-      - local: api/models/controlnet_sd3
-        title: SD3ControlNetModel
-      - local: api/models/controlnet_sparsectrl
-        title: SparseControlNetModel
-    - title: Transformers
-      sections:
-      - local: api/models/allegro_transformer3d
-        title: AllegroTransformer3DModel
-      - local: api/models/aura_flow_transformer2d
-        title: AuraFlowTransformer2DModel
-      - local: api/models/bria_transformer
-        title: BriaTransformer2DModel
-      - local: api/models/chroma_transformer
-        title: ChromaTransformer2DModel
-      - local: api/models/cogvideox_transformer3d
-        title: CogVideoXTransformer3DModel
-      - local: api/models/cogview3plus_transformer2d
-        title: CogView3PlusTransformer2DModel
-      - local: api/models/cogview4_transformer2d
-        title: CogView4Transformer2DModel
-      - local: api/models/consisid_transformer3d
-        title: ConsisIDTransformer3DModel
-      - local: api/models/cosmos_transformer3d
-        title: CosmosTransformer3DModel
-      - local: api/models/dit_transformer2d
-        title: DiTTransformer2DModel
-      - local: api/models/easyanimate_transformer3d
-        title: EasyAnimateTransformer3DModel
-      - local: api/models/flux_transformer
-        title: FluxTransformer2DModel
-      - local: api/models/hidream_image_transformer
-        title: HiDreamImageTransformer2DModel
-      - local: api/models/hunyuan_transformer2d
-        title: HunyuanDiT2DModel
-      - local: api/models/hunyuan_video_transformer_3d
-        title: HunyuanVideoTransformer3DModel
-      - local: api/models/latte_transformer3d
-        title: LatteTransformer3DModel
-      - local: api/models/ltx_video_transformer3d
-        title: LTXVideoTransformer3DModel
-      - local: api/models/lumina2_transformer2d
-        title: Lumina2Transformer2DModel
-      - local: api/models/lumina_nextdit2d
-        title: LuminaNextDiT2DModel
-      - local: api/models/mochi_transformer3d
-        title: MochiTransformer3DModel
-      - local: api/models/omnigen_transformer
-        title: OmniGenTransformer2DModel
-      - local: api/models/pixart_transformer2d
-        title: PixArtTransformer2DModel
-      - local: api/models/prior_transformer
-        title: PriorTransformer
-      - local: api/models/qwenimage_transformer2d
-        title: QwenImageTransformer2DModel
-      - local: api/models/sana_transformer2d
-        title: SanaTransformer2DModel
-      - local: api/models/sd3_transformer2d
-        title: SD3Transformer2DModel
-      - local: api/models/skyreels_v2_transformer_3d
-        title: SkyReelsV2Transformer3DModel
-      - local: api/models/stable_audio_transformer
-        title: StableAudioDiTModel
-      - local: api/models/transformer2d
-        title: Transformer2DModel
-      - local: api/models/transformer_temporal
-        title: TransformerTemporalModel
-      - local: api/models/wan_transformer_3d
-        title: WanTransformer3DModel
-    - title: UNets
-      sections:
-      - local: api/models/stable_cascade_unet
-        title: StableCascadeUNet
-      - local: api/models/unet
-        title: UNet1DModel
-      - local: api/models/unet2d-cond
-        title: UNet2DConditionModel
-      - local: api/models/unet2d
-        title: UNet2DModel
-      - local: api/models/unet3d-cond
-        title: UNet3DConditionModel
-      - local: api/models/unet-motion
-        title: UNetMotionModel
-      - local: api/models/uvit2d
-        title: UViT2DModel
-    - title: VAEs
-      sections:
-      - local: api/models/asymmetricautoencoderkl
-        title: AsymmetricAutoencoderKL
-      - local: api/models/autoencoder_dc
-        title: AutoencoderDC
-      - local: api/models/autoencoderkl
-        title: AutoencoderKL
-      - local: api/models/autoencoderkl_allegro
-        title: AutoencoderKLAllegro
-      - local: api/models/autoencoderkl_cogvideox
-        title: AutoencoderKLCogVideoX
-      - local: api/models/autoencoderkl_cosmos
-        title: AutoencoderKLCosmos
-      - local: api/models/autoencoder_kl_hunyuan_video
-        title: AutoencoderKLHunyuanVideo
-      - local: api/models/autoencoderkl_ltx_video
-        title: AutoencoderKLLTXVideo
-      - local: api/models/autoencoderkl_magvit
-        title: AutoencoderKLMagvit
-      - local: api/models/autoencoderkl_mochi
-        title: AutoencoderKLMochi
-      - local: api/models/autoencoderkl_qwenimage
-        title: AutoencoderKLQwenImage
-      - local: api/models/autoencoder_kl_wan
-        title: AutoencoderKLWan
-      - local: api/models/consistency_decoder_vae
-        title: ConsistencyDecoderVAE
-      - local: api/models/autoencoder_oobleck
-        title: Oobleck AutoEncoder
-      - local: api/models/autoencoder_tiny
-        title: Tiny AutoEncoder
-      - local: api/models/vq
-        title: VQModel
-  - title: Pipelines
-    sections:
+    - local: api/models/unet
+      title: UNet1DModel
+    - local: api/models/unet2d
+      title: UNet2DModel
+    - local: api/models/unet2d-cond
+      title: UNet2DConditionModel
+    - local: api/models/unet3d-cond
+      title: UNet3DConditionModel
+    - local: api/models/vq
+      title: VQModel
+    - local: api/models/autoencoderkl
+      title: AutoencoderKL
+    - local: api/models/asymmetricautoencoderkl
+      title: AsymmetricAutoencoderKL
+    - local: api/models/transformer2d
+      title: Transformer2D
+    - local: api/models/transformer_temporal
+      title: Transformer Temporal
+    - local: api/models/prior_transformer
+      title: Prior Transformer
+    - local: api/models/controlnet
+      title: ControlNet
+    title: Models
+  - sections:
     - local: api/pipelines/overview
       title: Overview
-    - local: api/pipelines/allegro
-      title: Allegro
-    - local: api/pipelines/amused
-      title: aMUSEd
-    - local: api/pipelines/animatediff
-      title: AnimateDiff
+    - local: api/pipelines/alt_diffusion
+      title: AltDiffusion
     - local: api/pipelines/attend_and_excite
       title: Attend-and-Excite
+    - local: api/pipelines/audio_diffusion
+      title: Audio Diffusion
     - local: api/pipelines/audioldm
       title: AudioLDM
-    - local: api/pipelines/audioldm2
-      title: AudioLDM 2
-    - local: api/pipelines/aura_flow
-      title: AuraFlow
     - local: api/pipelines/auto_pipeline
       title: AutoPipeline
-    - local: api/pipelines/blip_diffusion
-      title: BLIP-Diffusion
-    - local: api/pipelines/bria_3_2
-      title: Bria 3.2
-    - local: api/pipelines/chroma
-      title: Chroma
-    - local: api/pipelines/cogvideox
-      title: CogVideoX
-    - local: api/pipelines/cogview3
-      title: CogView3
-    - local: api/pipelines/cogview4
-      title: CogView4
-    - local: api/pipelines/consisid
-      title: ConsisID
     - local: api/pipelines/consistency_models
       title: Consistency Models
     - local: api/pipelines/controlnet
       title: ControlNet
-    - local: api/pipelines/controlnet_flux
-      title: ControlNet with Flux.1
-    - local: api/pipelines/controlnet_hunyuandit
-      title: ControlNet with Hunyuan-DiT
-    - local: api/pipelines/controlnet_sd3
-      title: ControlNet with Stable Diffusion 3
-    - local: api/pipelines/controlnet_sdxl
-      title: ControlNet with Stable Diffusion XL
-    - local: api/pipelines/controlnet_sana
-      title: ControlNet-Sana
-    - local: api/pipelines/controlnetxs
-      title: ControlNet-XS
-    - local: api/pipelines/controlnetxs_sdxl
-      title: ControlNet-XS with Stable Diffusion XL
-    - local: api/pipelines/controlnet_union
-      title: ControlNetUnion
-    - local: api/pipelines/cosmos
-      title: Cosmos
+    - local: api/pipelines/cycle_diffusion
+      title: Cycle Diffusion
     - local: api/pipelines/dance_diffusion
       title: Dance Diffusion
     - local: api/pipelines/ddim
@@ -510,225 +202,135 @@
       title: DiffEdit
     - local: api/pipelines/dit
       title: DiT
-    - local: api/pipelines/easyanimate
-      title: EasyAnimate
-    - local: api/pipelines/flux
-      title: Flux
-    - local: api/pipelines/control_flux_inpaint
-      title: FluxControlInpaint
-    - local: api/pipelines/framepack
-      title: Framepack
-    - local: api/pipelines/hidream
-      title: HiDream-I1
-    - local: api/pipelines/hunyuandit
-      title: Hunyuan-DiT
-    - local: api/pipelines/hunyuan_video
-      title: HunyuanVideo
-    - local: api/pipelines/i2vgenxl
-      title: I2VGen-XL
     - local: api/pipelines/pix2pix
       title: InstructPix2Pix
     - local: api/pipelines/kandinsky
-      title: Kandinsky 2.1
+      title: Kandinsky
     - local: api/pipelines/kandinsky_v22
       title: Kandinsky 2.2
-    - local: api/pipelines/kandinsky3
-      title: Kandinsky 3
-    - local: api/pipelines/kolors
-      title: Kolors
-    - local: api/pipelines/latent_consistency_models
-      title: Latent Consistency Models
     - local: api/pipelines/latent_diffusion
       title: Latent Diffusion
-    - local: api/pipelines/latte
-      title: Latte
-    - local: api/pipelines/ledits_pp
-      title: LEDITS++
-    - local: api/pipelines/ltx_video
-      title: LTXVideo
-    - local: api/pipelines/lumina2
-      title: Lumina 2.0
-    - local: api/pipelines/lumina
-      title: Lumina-T2X
-    - local: api/pipelines/marigold
-      title: Marigold
-    - local: api/pipelines/mochi
-      title: Mochi
     - local: api/pipelines/panorama
       title: MultiDiffusion
-    - local: api/pipelines/musicldm
-      title: MusicLDM
-    - local: api/pipelines/omnigen
-      title: OmniGen
-    - local: api/pipelines/pag
-      title: PAG
     - local: api/pipelines/paint_by_example
-      title: Paint by Example
-    - local: api/pipelines/pia
-      title: Personalized Image Animator (PIA)
-    - local: api/pipelines/pixart
-      title: PixArt-α
-    - local: api/pipelines/pixart_sigma
-      title: PixArt-Σ
-    - local: api/pipelines/qwenimage
-      title: QwenImage
-    - local: api/pipelines/sana
-      title: Sana
-    - local: api/pipelines/sana_sprint
-      title: Sana Sprint
+      title: PaintByExample
+    - local: api/pipelines/paradigms
+      title: Parallel Sampling of Diffusion Models
+    - local: api/pipelines/pix2pix_zero
+      title: Pix2Pix Zero
+    - local: api/pipelines/pndm
+      title: PNDM
+    - local: api/pipelines/repaint
+      title: RePaint
+    - local: api/pipelines/score_sde_ve
+      title: Score SDE VE
     - local: api/pipelines/self_attention_guidance
       title: Self-Attention Guidance
     - local: api/pipelines/semantic_stable_diffusion
       title: Semantic Guidance
     - local: api/pipelines/shap_e
       title: Shap-E
-    - local: api/pipelines/skyreels_v2
-      title: SkyReels-V2
-    - local: api/pipelines/stable_audio
-      title: Stable Audio
-    - local: api/pipelines/stable_cascade
-      title: Stable Cascade
-    - title: Stable Diffusion
-      sections:
+    - local: api/pipelines/spectrogram_diffusion
+      title: Spectrogram Diffusion
+    - sections:
       - local: api/pipelines/stable_diffusion/overview
         title: Overview
-      - local: api/pipelines/stable_diffusion/depth2img
-        title: Depth-to-image
-      - local: api/pipelines/stable_diffusion/gligen
-        title: GLIGEN (Grounded Language-to-Image Generation)
-      - local: api/pipelines/stable_diffusion/image_variation
-        title: Image variation
-      - local: api/pipelines/stable_diffusion/img2img
-        title: Image-to-image
-      - local: api/pipelines/stable_diffusion/svd
-        title: Image-to-video
-      - local: api/pipelines/stable_diffusion/inpaint
-        title: Inpainting
-      - local: api/pipelines/stable_diffusion/k_diffusion
-        title: K-Diffusion
-      - local: api/pipelines/stable_diffusion/latent_upscale
-        title: Latent upscaler
-      - local: api/pipelines/stable_diffusion/ldm3d_diffusion
-        title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
-      - local: api/pipelines/stable_diffusion/stable_diffusion_safe
-        title: Safe Stable Diffusion
-      - local: api/pipelines/stable_diffusion/sdxl_turbo
-        title: SDXL Turbo
-      - local: api/pipelines/stable_diffusion/stable_diffusion_2
-        title: Stable Diffusion 2
-      - local: api/pipelines/stable_diffusion/stable_diffusion_3
-        title: Stable Diffusion 3
-      - local: api/pipelines/stable_diffusion/stable_diffusion_xl
-        title: Stable Diffusion XL
-      - local: api/pipelines/stable_diffusion/upscale
-        title: Super-resolution
-      - local: api/pipelines/stable_diffusion/adapter
-        title: T2I-Adapter
       - local: api/pipelines/stable_diffusion/text2img
         title: Text-to-image
+      - local: api/pipelines/stable_diffusion/img2img
+        title: Image-to-image
+      - local: api/pipelines/stable_diffusion/inpaint
+        title: Inpainting
+      - local: api/pipelines/stable_diffusion/depth2img
+        title: Depth-to-image
+      - local: api/pipelines/stable_diffusion/image_variation
+        title: Image variation
+      - local: api/pipelines/stable_diffusion/stable_diffusion_safe
+        title: Safe Stable Diffusion
+      - local: api/pipelines/stable_diffusion/stable_diffusion_2
+        title: Stable Diffusion 2
+      - local: api/pipelines/stable_diffusion/stable_diffusion_xl
+        title: Stable Diffusion XL
+      - local: api/pipelines/stable_diffusion/latent_upscale
+        title: Latent upscaler
+      - local: api/pipelines/stable_diffusion/upscale
+        title: Super-resolution
+      - local: api/pipelines/stable_diffusion/ldm3d_diffusion
+        title: LDM3D Text-to-(RGB, Depth)
+      - local: api/pipelines/stable_diffusion/adapter
+        title: Stable Diffusion T2I-adapter
+      title: Stable Diffusion
     - local: api/pipelines/stable_unclip
       title: Stable unCLIP
+    - local: api/pipelines/stochastic_karras_ve
+      title: Stochastic Karras VE
+    - local: api/pipelines/model_editing
+      title: Text-to-image model editing
     - local: api/pipelines/text_to_video
       title: Text-to-video
     - local: api/pipelines/text_to_video_zero
       title: Text2Video-Zero
     - local: api/pipelines/unclip
-      title: unCLIP
+      title: UnCLIP
+    - local: api/pipelines/latent_diffusion_uncond
+      title: Unconditional Latent Diffusion
     - local: api/pipelines/unidiffuser
       title: UniDiffuser
     - local: api/pipelines/value_guided_sampling
       title: Value-guided sampling
-    - local: api/pipelines/visualcloze
-      title: VisualCloze
-    - local: api/pipelines/wan
-      title: Wan
-    - local: api/pipelines/wuerstchen
-      title: Wuerstchen
-  - title: Schedulers
-    sections:
+    - local: api/pipelines/versatile_diffusion
+      title: Versatile Diffusion
+    - local: api/pipelines/vq_diffusion
+      title: VQ Diffusion
+    title: Pipelines
+  - sections:
     - local: api/schedulers/overview
       title: Overview
     - local: api/schedulers/cm_stochastic_iterative
-      title: CMStochasticIterativeScheduler
-    - local: api/schedulers/ddim_cogvideox
-      title: CogVideoXDDIMScheduler
-    - local: api/schedulers/multistep_dpm_solver_cogvideox
-      title: CogVideoXDPMScheduler
-    - local: api/schedulers/consistency_decoder
-      title: ConsistencyDecoderScheduler
-    - local: api/schedulers/cosine_dpm
-      title: CosineDPMSolverMultistepScheduler
-    - local: api/schedulers/ddim_inverse
-      title: DDIMInverseScheduler
+      title: Consistency Model Multistep Scheduler
     - local: api/schedulers/ddim
-      title: DDIMScheduler
+      title: DDIM
+    - local: api/schedulers/ddim_inverse
+      title: DDIMInverse
     - local: api/schedulers/ddpm
-      title: DDPMScheduler
+      title: DDPM
     - local: api/schedulers/deis
-      title: DEISMultistepScheduler
-    - local: api/schedulers/multistep_dpm_solver_inverse
-      title: DPMSolverMultistepInverse
-    - local: api/schedulers/multistep_dpm_solver
-      title: DPMSolverMultistepScheduler
+      title: DEIS
+    - local: api/schedulers/dpm_discrete
+      title: DPM Discrete Scheduler
+    - local: api/schedulers/dpm_discrete_ancestral
+      title: DPM Discrete Scheduler with ancestral sampling
     - local: api/schedulers/dpm_sde
       title: DPMSolverSDEScheduler
-    - local: api/schedulers/singlestep_dpm_solver
-      title: DPMSolverSinglestepScheduler
-    - local: api/schedulers/edm_multistep_dpm_solver
-      title: EDMDPMSolverMultistepScheduler
-    - local: api/schedulers/edm_euler
-      title: EDMEulerScheduler
     - local: api/schedulers/euler_ancestral
-      title: EulerAncestralDiscreteScheduler
+      title: Euler Ancestral Scheduler
     - local: api/schedulers/euler
-      title: EulerDiscreteScheduler
-    - local: api/schedulers/flow_match_euler_discrete
-      title: FlowMatchEulerDiscreteScheduler
-    - local: api/schedulers/flow_match_heun_discrete
-      title: FlowMatchHeunDiscreteScheduler
+      title: Euler scheduler
     - local: api/schedulers/heun
-      title: HeunDiscreteScheduler
+      title: Heun Scheduler
+    - local: api/schedulers/multistep_dpm_solver_inverse
+      title: Inverse Multistep DPM-Solver
     - local: api/schedulers/ipndm
-      title: IPNDMScheduler
-    - local: api/schedulers/stochastic_karras_ve
-      title: KarrasVeScheduler
-    - local: api/schedulers/dpm_discrete_ancestral
-      title: KDPM2AncestralDiscreteScheduler
-    - local: api/schedulers/dpm_discrete
-      title: KDPM2DiscreteScheduler
-    - local: api/schedulers/lcm
-      title: LCMScheduler
+      title: IPNDM
     - local: api/schedulers/lms_discrete
-      title: LMSDiscreteScheduler
+      title: Linear Multistep
+    - local: api/schedulers/multistep_dpm_solver
+      title: Multistep DPM-Solver
     - local: api/schedulers/pndm
-      title: PNDMScheduler
+      title: PNDM
     - local: api/schedulers/repaint
-      title: RePaintScheduler
-    - local: api/schedulers/score_sde_ve
-      title: ScoreSdeVeScheduler
-    - local: api/schedulers/score_sde_vp
-      title: ScoreSdeVpScheduler
-    - local: api/schedulers/tcd
-      title: TCDScheduler
+      title: RePaint Scheduler
+    - local: api/schedulers/singlestep_dpm_solver
+      title: Singlestep DPM-Solver
+    - local: api/schedulers/stochastic_karras_ve
+      title: Stochastic Kerras VE
     - local: api/schedulers/unipc
       title: UniPCMultistepScheduler
+    - local: api/schedulers/score_sde_ve
+      title: VE-SDE
+    - local: api/schedulers/score_sde_vp
+      title: VP-SDE
     - local: api/schedulers/vq_diffusion
       title: VQDiffusionScheduler
-  - title: Internal classes
-    sections:
-    - local: api/internal_classes_overview
-      title: Overview
-    - local: api/attnprocessor
-      title: Attention Processor
-    - local: api/activations
-      title: Custom activation functions
-    - local: api/cache
-      title: Caching methods
-    - local: api/normalization
-      title: Custom normalization layers
-    - local: api/utilities
-      title: Utilities
-    - local: api/image_processor
-      title: VAE Image Processor
-    - local: api/video_processor
-      title: Video Processor
+    title: Schedulers
+  title: API

docs/source/en/advanced_inference/outpaint.md

@@ -1,231 +0,0 @@
-<!--Copyright 2025 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>

docs/source/en/api/activations.md

@@ -1,40 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# Activation functions
-
-Customized activation functions for supporting various models in 🤗 Diffusers.
-
-## GELU
-
-[[autodoc]] models.activations.GELU
-
-## GEGLU
-
-[[autodoc]] models.activations.GEGLU
-
-## ApproximateGELU
-
-[[autodoc]] models.activations.ApproximateGELU
-
-
-## SwiGLU
-
-[[autodoc]] models.activations.SwiGLU
-
-## FP32SiLU
-
-[[autodoc]] models.activations.FP32SiLU
-
-## LinearActivation
-
-[[autodoc]] models.activations.LinearActivation

docs/source/en/api/attnprocessor.md

@@ -1,166 +1,42 @@
-<!--Copyright 2025 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.
--->
-
 # Attention Processor
 
 An attention processor is a class for applying different types of attention mechanisms.
 
 ## AttnProcessor
-
 [[autodoc]] models.attention_processor.AttnProcessor
 
+## AttnProcessor2_0
 [[autodoc]] models.attention_processor.AttnProcessor2_0
 
-[[autodoc]] models.attention_processor.AttnAddedKVProcessor
-
-[[autodoc]] models.attention_processor.AttnAddedKVProcessor2_0
-
-[[autodoc]] models.attention_processor.AttnProcessorNPU
-
-[[autodoc]] models.attention_processor.FusedAttnProcessor2_0
-
-## Allegro
-
-[[autodoc]] models.attention_processor.AllegroAttnProcessor2_0
-
-## AuraFlow
-
-[[autodoc]] models.attention_processor.AuraFlowAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedAuraFlowAttnProcessor2_0
-
-## CogVideoX
-
-[[autodoc]] models.attention_processor.CogVideoXAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedCogVideoXAttnProcessor2_0
-
-## CrossFrameAttnProcessor
-
-[[autodoc]] pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor
-
-## Custom Diffusion
-
-[[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor
-
-[[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.CustomDiffusionXFormersAttnProcessor
-
-## Flux
-
-[[autodoc]] models.attention_processor.FluxAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedFluxAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FluxSingleAttnProcessor2_0
-
-## Hunyuan
-
-[[autodoc]] models.attention_processor.HunyuanAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedHunyuanAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGHunyuanAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGCFGHunyuanAttnProcessor2_0
-
-## IdentitySelfAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGIdentitySelfAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0
-
-## IP-Adapter
-
-[[autodoc]] models.attention_processor.IPAdapterAttnProcessor
-
-[[autodoc]] models.attention_processor.IPAdapterAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.SD3IPAdapterJointAttnProcessor2_0
-
-## JointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.JointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGJointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGCFGJointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedJointAttnProcessor2_0
-
-## LoRA
-
+## LoRAAttnProcessor
 [[autodoc]] models.attention_processor.LoRAAttnProcessor
 
+## LoRAAttnProcessor2_0
 [[autodoc]] models.attention_processor.LoRAAttnProcessor2_0
 
+## CustomDiffusionAttnProcessor
+[[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor
+
+## AttnAddedKVProcessor
+[[autodoc]] models.attention_processor.AttnAddedKVProcessor
+
+## AttnAddedKVProcessor2_0
+[[autodoc]] models.attention_processor.AttnAddedKVProcessor2_0
+
+## LoRAAttnAddedKVProcessor
 [[autodoc]] models.attention_processor.LoRAAttnAddedKVProcessor
 
-[[autodoc]] models.attention_processor.LoRAXFormersAttnProcessor
-
-## Lumina-T2X
-
-[[autodoc]] models.attention_processor.LuminaAttnProcessor2_0
-
-## Mochi
-
-[[autodoc]] models.attention_processor.MochiAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.MochiVaeAttnProcessor2_0
-
-## Sana
-
-[[autodoc]] models.attention_processor.SanaLinearAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.SanaMultiscaleAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGCFGSanaLinearAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGIdentitySanaLinearAttnProcessor2_0
-
-## Stable Audio
-
-[[autodoc]] models.attention_processor.StableAudioAttnProcessor2_0
-
-## SlicedAttnProcessor
-
-[[autodoc]] models.attention_processor.SlicedAttnProcessor
-
-[[autodoc]] models.attention_processor.SlicedAttnAddedKVProcessor
-
 ## XFormersAttnProcessor
-
 [[autodoc]] models.attention_processor.XFormersAttnProcessor
 
-[[autodoc]] models.attention_processor.XFormersAttnAddedKVProcessor
+## LoRAXFormersAttnProcessor
+[[autodoc]] models.attention_processor.LoRAXFormersAttnProcessor
 
-## XLAFlashAttnProcessor2_0
+## CustomDiffusionXFormersAttnProcessor
+[[autodoc]] models.attention_processor.CustomDiffusionXFormersAttnProcessor
 
-[[autodoc]] models.attention_processor.XLAFlashAttnProcessor2_0
+## SlicedAttnProcessor
+[[autodoc]] models.attention_processor.SlicedAttnProcessor
 
-## XFormersJointAttnProcessor
-
-[[autodoc]] models.attention_processor.XFormersJointAttnProcessor
-
-## IPAdapterXFormersAttnProcessor
-
-[[autodoc]] models.attention_processor.IPAdapterXFormersAttnProcessor
-
-## FluxIPAdapterJointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FluxIPAdapterJointAttnProcessor2_0
-
-
-## XLAFluxFlashAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.XLAFluxFlashAttnProcessor2_0
+## SlicedAttnAddedKVProcessor
+[[autodoc]] models.attention_processor.SlicedAttnAddedKVProcessor

docs/source/en/api/cache.md

@@ -1,36 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# Caching methods
-
-Cache methods speedup diffusion transformers by storing and reusing intermediate outputs of specific layers, such as attention and feedforward layers, instead of recalculating them at each inference step.
-
-## CacheMixin
-
-[[autodoc]] CacheMixin
-
-## PyramidAttentionBroadcastConfig
-
-[[autodoc]] PyramidAttentionBroadcastConfig
-
-[[autodoc]] apply_pyramid_attention_broadcast
-
-## FasterCacheConfig
-
-[[autodoc]] FasterCacheConfig
-
-[[autodoc]] apply_faster_cache
-
-### FirstBlockCacheConfig
-
-[[autodoc]] FirstBlockCacheConfig
-
-[[autodoc]] apply_first_block_cache

docs/source/en/api/configuration.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -16,7 +16,7 @@ Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from
 
 <Tip>
 
-To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
+To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
 
 </Tip>
 

docs/source/en/api/diffusion_pipeline.md

@@ -0,0 +1,36 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Pipelines
+
+The [`DiffusionPipeline`] is the quickest way to load any pretrained diffusion pipeline from the [Hub](https://huggingface.co/models?library=diffusers) for inference.
+
+<Tip>
+
+You shouldn't use the [`DiffusionPipeline`] class for training or finetuning a diffusion model. Individual 
+components (for example, [`UNet2DModel`] and [`UNet2DConditionModel`]) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.
+
+</Tip>
+
+The pipeline type (for example [`StableDiffusionPipeline`]) of any diffusion pipeline loaded with [`~DiffusionPipeline.from_pretrained`] is automatically 
+detected and pipeline components are loaded and passed to the `__init__` function of the pipeline.
+
+Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrained`].
+
+## DiffusionPipeline
+
+[[autodoc]] DiffusionPipeline
+	- all
+	- __call__
+	- device
+	- to
+	- components

docs/source/en/api/image_processor.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -12,30 +12,16 @@ specific language governing permissions and limitations under the License.
 
 # VAE Image Processor
 
-The [`VaeImageProcessor`] provides a unified API for [`StableDiffusionPipeline`]s to prepare image inputs for VAE encoding and post-processing outputs once they're decoded. This includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays.
+The [`VaeImageProcessor`] provides a unified API for [`StableDiffusionPipeline`]'s to prepare image inputs for VAE encoding and post-processing outputs once they're decoded. This includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays. 
 
-All pipelines with [`VaeImageProcessor`] accept PIL Image, PyTorch tensor, or NumPy arrays as image inputs and return outputs based on the `output_type` argument by the user. You can pass encoded image latents directly to the pipeline and return latents from the pipeline as a specific output with the `output_type` argument (for example `output_type="latent"`). This allows you to take the generated latents from one pipeline and pass it to another pipeline as input without leaving the latent space. It also makes it much easier to use multiple pipelines together by passing PyTorch tensors directly between different pipelines.
+All pipelines with [`VaeImageProcessor`] accepts PIL Image, PyTorch tensor, or NumPy arrays as image inputs and returns outputs based on the `output_type` argument by the user. You can pass encoded image latents directly to the pipeline and return latents from the pipeline as a specific output with the `output_type` argument (for example `output_type="pt"`). This allows you to take the generated latents from one pipeline and pass it to another pipeline as input without leaving the latent space. It also makes it much easier to use multiple pipelines together by passing PyTorch tensors directly between different pipelines. 
 
 ## VaeImageProcessor
 
 [[autodoc]] image_processor.VaeImageProcessor
 
-## InpaintProcessor
-
-The [`InpaintProcessor`] accepts `mask` and `image` inputs and process them together. Optionally, it can accept padding_mask_crop and apply mask overlay.
-
-[[autodoc]] image_processor.InpaintProcessor
-
 ## VaeImageProcessorLDM3D
 
 The [`VaeImageProcessorLDM3D`] accepts RGB and depth inputs and returns RGB and depth outputs.
 
-[[autodoc]] image_processor.VaeImageProcessorLDM3D
-
-## PixArtImageProcessor
-
-[[autodoc]] image_processor.PixArtImageProcessor
-
-## IPAdapterMaskProcessor
-
-[[autodoc]] image_processor.IPAdapterMaskProcessor
+[[autodoc]] image_processor.VaeImageProcessorLDM3D

docs/source/en/api/internal_classes_overview.md

@@ -1,15 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# Overview
-
-The APIs in this section are more experimental and prone to breaking changes. Most of them are used internally for development, but they may also be useful to you if you're interested in building a diffusion model with some custom parts or if you're interested in some of our helper utilities for working with 🤗 Diffusers.

docs/source/en/api/loaders.md

@@ -0,0 +1,45 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Loaders
+
+Adapters (textual inversion, LoRA, hypernetworks) allow you to modify a diffusion model to generate images in a specific style without training or finetuning the entire model. The adapter weights are typically only a tiny fraction of the pretrained model's which making them very portable. 🤗 Diffusers provides an easy-to-use `LoaderMixin` API to load adapter weights.
+
+<Tip warning={true}>
+
+🧪 The `LoaderMixins` are highly experimental and prone to future changes. To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
+
+</Tip>
+
+## UNet2DConditionLoadersMixin
+
+[[autodoc]] loaders.UNet2DConditionLoadersMixin
+
+## TextualInversionLoaderMixin
+
+[[autodoc]] loaders.TextualInversionLoaderMixin
+
+## LoraLoaderMixin
+
+[[autodoc]] loaders.LoraLoaderMixin
+
+## FromSingleFileMixin
+
+[[autodoc]] loaders.FromSingleFileMixin
+
+## FromOriginalControlnetMixin
+
+[[autodoc]] loaders.FromOriginalControlnetMixin
+
+## FromOriginalVAEMixin
+
+[[autodoc]] loaders.FromOriginalVAEMixin

docs/source/en/api/loaders/ip_adapter.md

@@ -1,35 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# IP-Adapter
-
-[IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.
-
-<Tip>
-
-Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading](../../using-diffusers/loading_adapters#ip-adapter) guide, and you can see how to use it in the [usage](../../using-diffusers/ip_adapter) guide.
-
-</Tip>
-
-## IPAdapterMixin
-
-[[autodoc]] loaders.ip_adapter.IPAdapterMixin
-
-## SD3IPAdapterMixin
-
-[[autodoc]] loaders.ip_adapter.SD3IPAdapterMixin
-    - all
-    - is_ip_adapter_active
-
-## IPAdapterMaskProcessor
-
-[[autodoc]] image_processor.IPAdapterMaskProcessor

docs/source/en/api/loaders/lora.md

@@ -1,115 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# LoRA
-
-LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text encoder or both. The denoiser usually corresponds to a UNet ([`UNet2DConditionModel`], for example) or a Transformer ([`SD3Transformer2DModel`], for example). There are several classes for loading LoRA weights:
-
-- [`StableDiffusionLoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
-- [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`StableDiffusionLoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.
-- [`SD3LoraLoaderMixin`] provides similar functions for [Stable Diffusion 3](https://huggingface.co/blog/sd3).
-- [`FluxLoraLoaderMixin`] provides similar functions for [Flux](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux).
-- [`CogVideoXLoraLoaderMixin`] provides similar functions for [CogVideoX](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox).
-- [`Mochi1LoraLoaderMixin`] provides similar functions for [Mochi](https://huggingface.co/docs/diffusers/main/en/api/pipelines/mochi).
-- [`AuraFlowLoraLoaderMixin`] provides similar functions for [AuraFlow](https://huggingface.co/fal/AuraFlow).
-- [`LTXVideoLoraLoaderMixin`] provides similar functions for [LTX-Video](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
-- [`SanaLoraLoaderMixin`] provides similar functions for [Sana](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana).
-- [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
-- [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
-- [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
-- [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
-- [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
-- [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
-- [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
-- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen)
-- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
-
-<Tip>
-
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
-
-## LoraBaseMixin
-
-[[autodoc]] loaders.lora_base.LoraBaseMixin
-
-## StableDiffusionLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.StableDiffusionLoraLoaderMixin
-
-## StableDiffusionXLLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin
-
-## SD3LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.SD3LoraLoaderMixin
-
-## FluxLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.FluxLoraLoaderMixin
-
-## CogVideoXLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.CogVideoXLoraLoaderMixin
-
-## Mochi1LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.Mochi1LoraLoaderMixin
-## AuraFlowLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.AuraFlowLoraLoaderMixin
-
-## LTXVideoLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.LTXVideoLoraLoaderMixin
-
-## SanaLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.SanaLoraLoaderMixin
-
-## HunyuanVideoLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.HunyuanVideoLoraLoaderMixin
-
-## Lumina2LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.Lumina2LoraLoaderMixin
-
-## CogView4LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.CogView4LoraLoaderMixin
-
-## WanLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
-
-## SkyReelsV2LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
-
-## AmusedLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
-
-## HiDreamImageLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
-
-## QwenImageLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
-
-## LoraBaseMixin
-
-[[autodoc]] loaders.lora_base.LoraBaseMixin

docs/source/en/api/loaders/peft.md

@@ -1,25 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# PEFT
-
-Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
-
-<Tip>
-
-Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
-
-</Tip>
-
-## PeftAdapterMixin
-
-[[autodoc]] loaders.peft.PeftAdapterMixin

docs/source/en/api/loaders/single_file.md

@@ -1,62 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# Single files
-
-The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
-
-* 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
-
-> [!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
-
-- [`StableDiffusionPipeline`]
-- [`StableDiffusionImg2ImgPipeline`]
-- [`StableDiffusionInpaintPipeline`]
-- [`StableDiffusionControlNetPipeline`]
-- [`StableDiffusionControlNetImg2ImgPipeline`]
-- [`StableDiffusionControlNetInpaintPipeline`]
-- [`StableDiffusionUpscalePipeline`]
-- [`StableDiffusionXLPipeline`]
-- [`StableDiffusionXLImg2ImgPipeline`]
-- [`StableDiffusionXLInpaintPipeline`]
-- [`StableDiffusionXLInstructPix2PixPipeline`]
-- [`StableDiffusionXLControlNetPipeline`]
-- [`StableDiffusionXLKDiffusionPipeline`]
-- [`StableDiffusion3Pipeline`]
-- [`LatentConsistencyModelPipeline`]
-- [`LatentConsistencyModelImg2ImgPipeline`]
-- [`StableDiffusionControlNetXSPipeline`]
-- [`StableDiffusionXLControlNetXSPipeline`]
-- [`LEditsPPPipelineStableDiffusion`]
-- [`LEditsPPPipelineStableDiffusionXL`]
-- [`PIAPipeline`]
-
-## Supported models
-
-- [`UNet2DConditionModel`]
-- [`StableCascadeUNet`]
-- [`AutoencoderKL`]
-- [`ControlNetModel`]
-- [`SD3Transformer2DModel`]
-- [`FluxTransformer2DModel`]
-
-## FromSingleFileMixin
-
-[[autodoc]] loaders.single_file.FromSingleFileMixin
-
-## FromOriginalModelMixin
-
-[[autodoc]] loaders.single_file_model.FromOriginalModelMixin

docs/source/en/api/loaders/textual_inversion.md

@@ -1,27 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# Textual Inversion
-
-Textual Inversion is a training method for personalizing models by learning new text embeddings from a few example images. The file produced from training is extremely small (a few KBs) and the new embeddings can be loaded into the text encoder.
-
-[`TextualInversionLoaderMixin`] provides a function for loading Textual Inversion embeddings from Diffusers and Automatic1111 into the text encoder and loading a special token to activate the embeddings.
-
-<Tip>
-
-To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
-
-</Tip>
-
-## TextualInversionLoaderMixin
-
-[[autodoc]] loaders.textual_inversion.TextualInversionLoaderMixin

docs/source/en/api/loaders/transformer_sd3.md

@@ -1,29 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# SD3Transformer2D
-
-This class is useful when *only* loading weights into a [`SD3Transformer2DModel`]. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check [`SD3LoraLoaderMixin`](lora#diffusers.loaders.SD3LoraLoaderMixin) class instead.
-
-The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
-
-<Tip>
-
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
-
-## SD3Transformer2DLoadersMixin
-
-[[autodoc]] loaders.transformer_sd3.SD3Transformer2DLoadersMixin
-    - all
-    - _load_ip_adapter_weights

docs/source/en/api/loaders/unet.md

@@ -1,27 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# UNet
-
-Some training methods - like LoRA and Custom Diffusion - typically target the UNet's attention layers, but these training methods can also target other non-attention layers. Instead of training all of a model's parameters, only a subset of the parameters are trained, which is faster and more efficient. This class is useful if you're *only* loading weights into a UNet. If you need to load weights into the text encoder or a text encoder and UNet, try using the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] function instead.
-
-The [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.
-
-<Tip>
-
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
-
-## UNet2DConditionLoadersMixin
-
-[[autodoc]] loaders.unet.UNet2DConditionLoadersMixin

docs/source/en/api/logging.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -51,7 +51,7 @@ logger.warning("WARN")
 
 All methods of the logging module are documented below. The main methods are
 [`logging.get_verbosity`] to get the current level of verbosity in the logger and
-[`logging.set_verbosity`] to set the verbosity to the level of your choice.
+[`logging.set_verbosity`] to set the verbosity to the level of your choice. 
 
 In order from the least verbose to the most verbose:
 
@@ -67,30 +67,30 @@ By default, `tqdm` progress bars are displayed during model download. [`logging.
 
 ## Base setters
 
-[[autodoc]] utils.logging.set_verbosity_error
+[[autodoc]] logging.set_verbosity_error
 
-[[autodoc]] utils.logging.set_verbosity_warning
+[[autodoc]] logging.set_verbosity_warning
 
-[[autodoc]] utils.logging.set_verbosity_info
+[[autodoc]] logging.set_verbosity_info
 
-[[autodoc]] utils.logging.set_verbosity_debug
+[[autodoc]] logging.set_verbosity_debug
 
 ## Other functions
 
-[[autodoc]] utils.logging.get_verbosity
+[[autodoc]] logging.get_verbosity
 
-[[autodoc]] utils.logging.set_verbosity
+[[autodoc]] logging.set_verbosity
 
-[[autodoc]] utils.logging.get_logger
+[[autodoc]] logging.get_logger
 
-[[autodoc]] utils.logging.enable_default_handler
+[[autodoc]] logging.enable_default_handler
 
-[[autodoc]] utils.logging.disable_default_handler
+[[autodoc]] logging.disable_default_handler
 
-[[autodoc]] utils.logging.enable_explicit_format
+[[autodoc]] logging.enable_explicit_format
 
-[[autodoc]] utils.logging.reset_format
+[[autodoc]] logging.reset_format
 
-[[autodoc]] utils.logging.enable_progress_bar
+[[autodoc]] logging.enable_progress_bar
 
-[[autodoc]] utils.logging.disable_progress_bar
+[[autodoc]] logging.disable_progress_bar

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

@@ -1,30 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AllegroTransformer3DModel
-
-A Diffusion Transformer model for 3D data from [Allegro](https://github.com/rhymes-ai/Allegro) was introduced in [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) by RhymesAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AllegroTransformer3DModel
-
-transformer = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
-```
-
-## AllegroTransformer3DModel
-
-[[autodoc]] AllegroTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -1,24 +1,12 @@
-<!--Copyright 2025 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.
--->
-
 # AsymmetricAutoencoderKL
 
-Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://huggingface.co/papers/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
+Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://arxiv.org/abs/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
 
 The abstract from the paper is:
 
 *StableDiffusion is a revolutionary text-to-image generator that is causing a stir in the world of image generation and editing. Unlike traditional methods that learn a diffusion model in pixel space, StableDiffusion learns a diffusion model in the latent space via a VQGAN, ensuring both efficiency and quality. It not only supports image generation tasks, but also enables image editing for real images, such as image inpainting and local editing. However, we have observed that the vanilla VQGAN used in StableDiffusion leads to significant information loss, causing distortion artifacts even in non-edited image regions. To this end, we propose a new asymmetric VQGAN with two simple designs. Firstly, in addition to the input from the encoder, the decoder contains a conditional branch that incorporates information from task-specific priors, such as the unmasked image region in inpainting. Secondly, the decoder is much heavier than the encoder, allowing for more detailed recovery while only slightly increasing the total inference cost. The training cost of our asymmetric VQGAN is cheap, and we only need to retrain a new asymmetric decoder while keeping the vanilla VQGAN encoder and StableDiffusion unchanged. Our asymmetric VQGAN can be widely used in StableDiffusion-based inpainting and local editing methods. Extensive experiments demonstrate that it can significantly improve the inpainting and editing performance, while maintaining the original text-to-image capability. The code is available at https://github.com/buxiangzhiren/Asymmetric_VQGAN*
 
-Evaluation results can be found in section 4.1 of the original paper.
+Evaluation results can be found in section 4.1 of the original paper. 
 
 ## Available checkpoints
 
@@ -28,33 +16,40 @@ Evaluation results can be found in section 4.1 of the original paper.
 ## Example Usage
 
 ```python
+from io import BytesIO
+from PIL import Image
+import requests
 from diffusers import AsymmetricAutoencoderKL, StableDiffusionInpaintPipeline
-from diffusers.utils import load_image, make_image_grid
 
 
-prompt = "a photo of a person with beard"
+def download_image(url: str) -> Image.Image:
+    response = requests.get(url)
+    return Image.open(BytesIO(response.content)).convert("RGB")
+
+
+prompt = "a photo of a person"
 img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
 mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
 
-original_image = load_image(img_url).resize((512, 512))
-mask_image = load_image(mask_url).resize((512, 512))
+image = download_image(img_url).resize((256, 256))
+mask_image = download_image(mask_url).resize((256, 256))
 
 pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
 pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
 pipe.to("cuda")
 
-image = pipe(prompt=prompt, image=original_image, mask_image=mask_image).images[0]
-make_image_grid([original_image, mask_image, image], rows=1, cols=3)
+image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
+image.save("image.jpeg")
 ```
 
 ## AsymmetricAutoencoderKL
 
-[[autodoc]] models.autoencoders.autoencoder_asym_kl.AsymmetricAutoencoderKL
+[[autodoc]] models.autoencoder_asym_kl.AsymmetricAutoencoderKL
 
 ## AutoencoderKLOutput
 
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
+[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
 
 ## DecoderOutput
 
-[[autodoc]] models.autoencoders.vae.DecoderOutput
+[[autodoc]] models.vae.DecoderOutput

docs/source/en/api/models/aura_flow_transformer2d.md

@@ -1,19 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# AuraFlowTransformer2DModel
-
-A Transformer model for image-like data from [AuraFlow](https://blog.fal.ai/auraflow/).
-
-## AuraFlowTransformer2DModel
-
-[[autodoc]] AuraFlowTransformer2DModel

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

@@ -1,29 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# AutoModel
-
-The `AutoModel` is designed to make it easy to load a checkpoint without needing to know the specific model class. `AutoModel` automatically retrieves the correct model class from the checkpoint `config.json` file.
-
-```python
-from diffusers import AutoModel, AutoPipelineForText2Image
-
-unet = AutoModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
-pipe = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", unet=unet)
-```
-
-
-## AutoModel
-
-[[autodoc]] AutoModel
-	- all
-	- from_pretrained

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

@@ -1,72 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# AutoencoderDC
-
-The 2D Autoencoder model used in [SANA](https://huggingface.co/papers/2410.10629) and introduced in [DCAE](https://huggingface.co/papers/2410.10733) by authors Junyu Chen\*, Han Cai\*, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, Song Han from MIT HAN Lab.
-
-The abstract from the paper is:
-
-*We present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated impressive results at a moderate spatial compression ratio (e.g., 8x), but fail to maintain satisfactory reconstruction accuracy for high spatial compression ratios (e.g., 64x). We address this challenge by introducing two key techniques: (1) Residual Autoencoding, where we design our models to learn residuals based on the space-to-channel transformed features to alleviate the optimization difficulty of high spatial-compression autoencoders; (2) Decoupled High-Resolution Adaptation, an efficient decoupled three-phases training strategy for mitigating the generalization penalty of high spatial-compression autoencoders. With these designs, we improve the autoencoder's spatial compression ratio up to 128 while maintaining the reconstruction quality. Applying our DC-AE to latent diffusion models, we achieve significant speedup without accuracy drop. For example, on ImageNet 512x512, our DC-AE provides 19.1x inference speedup and 17.9x training speedup on H100 GPU for UViT-H while achieving a better FID, compared with the widely used SD-VAE-f8 autoencoder. Our code is available at [this https URL](https://github.com/mit-han-lab/efficientvit).*
-
-The following DCAE models are released and supported in Diffusers.
-
-| Diffusers format | Original format |
-|:----------------:|:---------------:|
-| [`mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers) | [`mit-han-lab/dc-ae-f32c32-sana-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0)
-| [`mit-han-lab/dc-ae-f32c32-in-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0-diffusers) | [`mit-han-lab/dc-ae-f32c32-in-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0)
-| [`mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers) | [`mit-han-lab/dc-ae-f32c32-mix-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-mix-1.0)
-| [`mit-han-lab/dc-ae-f64c128-in-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-in-1.0-diffusers) | [`mit-han-lab/dc-ae-f64c128-in-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-in-1.0)
-| [`mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers) | [`mit-han-lab/dc-ae-f64c128-mix-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-mix-1.0)
-| [`mit-han-lab/dc-ae-f128c512-in-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0-diffusers) | [`mit-han-lab/dc-ae-f128c512-in-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0)
-| [`mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers) | [`mit-han-lab/dc-ae-f128c512-mix-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-mix-1.0)
-
-This model was contributed by [lawrence-cj](https://github.com/lawrence-cj).
-
-Load a model in Diffusers format with [`~ModelMixin.from_pretrained`].
-
-```python
-from diffusers import AutoencoderDC
-
-ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers", torch_dtype=torch.float32).to("cuda")
-```
-
-## Load a model in Diffusers via `from_single_file`
-
-```python
-from difusers import AutoencoderDC
-
-ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0/blob/main/model.safetensors"
-model = AutoencoderDC.from_single_file(ckpt_path) 
-
-```
-
-The `AutoencoderDC` model has `in` and `mix` single file checkpoint variants that have matching checkpoint keys, but use different scaling factors. It is not possible for Diffusers to automatically infer the correct config file to use with the model based on just the checkpoint and will default to configuring the model using the `mix` variant config file. To override the automatically determined config, please use the `config` argument when using single file loading with `in` variant checkpoints. 
-
-```python
-from diffusers import AutoencoderDC
-
-ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0/blob/main/model.safetensors"
-model = AutoencoderDC.from_single_file(ckpt_path, config="mit-han-lab/dc-ae-f128c512-in-1.0-diffusers")
-```
-
-
-## AutoencoderDC
-
-[[autodoc]] AutoencoderDC
-  - encode
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput
-

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

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# AutoencoderKLHunyuanVideo
-
-The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanVideo](https://github.com/Tencent/HunyuanVideo/), which was introduced in [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://huggingface.co/papers/2412.03603) by Tencent.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLHunyuanVideo
-
-vae = AutoencoderKLHunyuanVideo.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder="vae", torch_dtype=torch.float16)
-```
-
-## AutoencoderKLHunyuanVideo
-
-[[autodoc]] AutoencoderKLHunyuanVideo
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# AutoencoderKLWan
-
-The 3D variational autoencoder (VAE) model with KL loss used in [Wan 2.1](https://github.com/Wan-Video/Wan2.1) by the Alibaba Wan Team.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLWan
-
-vae = AutoencoderKLWan.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32)
-```
-
-## AutoencoderKLWan
-
-[[autodoc]] AutoencoderKLWan
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,38 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# AutoencoderOobleck
-
-The Oobleck variational autoencoder (VAE) model with KL loss was introduced in [Stability-AI/stable-audio-tools](https://github.com/Stability-AI/stable-audio-tools) and [Stable Audio Open](https://huggingface.co/papers/2407.14358) by Stability AI. The model is used in 🤗 Diffusers to encode audio waveforms into latents and to decode latent representations into audio waveforms.
-
-The abstract from the paper is:
-
-*Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.*
-
-## AutoencoderOobleck
-
-[[autodoc]] AutoencoderOobleck
-    - decode
-    - encode
-    - all
-
-## OobleckDecoderOutput
-
-[[autodoc]] models.autoencoders.autoencoder_oobleck.OobleckDecoderOutput
-
-## OobleckDecoderOutput
-
-[[autodoc]] models.autoencoders.autoencoder_oobleck.OobleckDecoderOutput
-
-## AutoencoderOobleckOutput
-
-[[autodoc]] models.autoencoders.autoencoder_oobleck.AutoencoderOobleckOutput

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

@@ -1,57 +0,0 @@
-<!--Copyright 2025 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.
--->
-
-# Tiny AutoEncoder
-
-Tiny AutoEncoder for Stable Diffusion (TAESD) was introduced in [madebyollin/taesd](https://github.com/madebyollin/taesd) by Ollin Boer Bohan. It is a tiny distilled version of Stable Diffusion's VAE that can quickly decode the latents in a [`StableDiffusionPipeline`] or [`StableDiffusionXLPipeline`] almost instantly.
-
-To use with Stable Diffusion v-2.1:
-
-```python
-import torch
-from diffusers import DiffusionPipeline, AutoencoderTiny
-
-pipe = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1-base", torch_dtype=torch.float16
-)
-pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd", torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
-
-prompt = "slice of delicious New York-style berry cheesecake"
-image = pipe(prompt, num_inference_steps=25).images[0]
-image
-```
-
-To use with Stable Diffusion XL 1.0
-
-```python
-import torch
-from diffusers import DiffusionPipeline, AutoencoderTiny
-
-pipe = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
-)
-pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
-
-prompt = "slice of delicious New York-style berry cheesecake"
-image = pipe(prompt, num_inference_steps=25).images[0]
-image
-```
-
-## AutoencoderTiny
-
-[[autodoc]] AutoencoderTiny
-
-## AutoencoderTinyOutput
-
-[[autodoc]] models.autoencoders.autoencoder_tiny.AutoencoderTinyOutput

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

@@ -1,18 +1,6 @@
-<!--Copyright 2025 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.
--->
-
 # AutoencoderKL
 
-The variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://huggingface.co/papers/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
+The variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
 
 The abstract from the paper is:
 
@@ -21,26 +9,35 @@ The abstract from the paper is:
 ## Loading from the original format
 
 By default the [`AutoencoderKL`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded
-from the original format using [`FromOriginalModelMixin.from_single_file`] as follows:
+from the original format using [`FromOriginalVAEMixin.from_single_file`] as follows:
 
 ```py
 from diffusers import AutoencoderKL
 
-url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"  # can also be a local file
+url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"  # can also be local file
 model = AutoencoderKL.from_single_file(url)
 ```
 
 ## AutoencoderKL
 
 [[autodoc]] AutoencoderKL
-    - decode
-    - encode
-    - all
 
 ## AutoencoderKLOutput
 
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
+[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
 
 ## DecoderOutput
 
-[[autodoc]] models.autoencoders.vae.DecoderOutput
+[[autodoc]] models.vae.DecoderOutput
+
+## FlaxAutoencoderKL
+
+[[autodoc]] FlaxAutoencoderKL
+
+## FlaxAutoencoderKLOutput
+
+[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
+
+## FlaxDecoderOutput
+
+[[autodoc]] models.vae_flax.FlaxDecoderOutput

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

@@ -1,37 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLAllegro
-
-The 3D variational autoencoder (VAE) model with KL loss used in [Allegro](https://github.com/rhymes-ai/Allegro) was introduced in [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) by RhymesAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLAllegro
-
-vae = AutoencoderKLAllegro.from_pretrained("rhymes-ai/Allegro", subfolder="vae", torch_dtype=torch.float32).to("cuda")
-```
-
-## AutoencoderKLAllegro
-
-[[autodoc]] AutoencoderKLAllegro
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,37 +0,0 @@
-<!--Copyright 2025 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. -->
-
-# AutoencoderKLCogVideoX
-
-The 3D variational autoencoder (VAE) model with KL loss used in [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLCogVideoX
-
-vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-2b", subfolder="vae", torch_dtype=torch.float16).to("cuda")
-```
-
-## AutoencoderKLCogVideoX
-
-[[autodoc]] AutoencoderKLCogVideoX
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,40 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# AutoencoderKLCosmos
-
-[Cosmos Tokenizers](https://github.com/NVIDIA/Cosmos-Tokenizer).
-
-Supported models:
-- [nvidia/Cosmos-1.0-Tokenizer-CV8x8x8](https://huggingface.co/nvidia/Cosmos-1.0-Tokenizer-CV8x8x8)
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLCosmos
-
-vae = AutoencoderKLCosmos.from_pretrained("nvidia/Cosmos-1.0-Tokenizer-CV8x8x8", subfolder="vae")
-```
-
-## AutoencoderKLCosmos
-
-[[autodoc]] AutoencoderKLCosmos
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,37 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# AutoencoderKLLTXVideo
-
-The 3D variational autoencoder (VAE) model with KL loss used in [LTX](https://huggingface.co/Lightricks/LTX-Video) was introduced by Lightricks.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLLTXVideo
-
-vae = AutoencoderKLLTXVideo.from_pretrained("Lightricks/LTX-Video", subfolder="vae", torch_dtype=torch.float32).to("cuda")
-```
-
-## AutoencoderKLLTXVideo
-
-[[autodoc]] AutoencoderKLLTXVideo
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,37 +0,0 @@
-<!--Copyright 2025 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. -->
-
-# AutoencoderKLMagvit
-
-The 3D variational autoencoder (VAE) model with KL loss used in [EasyAnimate](https://github.com/aigc-apps/EasyAnimate) was introduced by Alibaba PAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLMagvit
-
-vae = AutoencoderKLMagvit.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="vae", torch_dtype=torch.float16).to("cuda")
-```
-
-## AutoencoderKLMagvit
-
-[[autodoc]] AutoencoderKLMagvit
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# AutoencoderKLMochi
-
-The 3D variational autoencoder (VAE) model with KL loss used in [Mochi](https://github.com/genmoai/models) was introduced in [Mochi 1 Preview](https://huggingface.co/genmo/mochi-1-preview) by Tsinghua University & ZhipuAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLMochi
-
-vae = AutoencoderKLMochi.from_pretrained("genmo/mochi-1-preview", subfolder="vae", torch_dtype=torch.float32).to("cuda")
-```
-
-## AutoencoderKLMochi
-
-[[autodoc]] AutoencoderKLMochi
-    - decode
-    - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,35 +0,0 @@
-<!-- Copyright 2025 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. -->
-
-# AutoencoderKLQwenImage
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLQwenImage
-
-vae = AutoencoderKLQwenImage.from_pretrained("Qwen/QwenImage-20B", subfolder="vae")
-```
-
-## AutoencoderKLQwenImage
-
-[[autodoc]] AutoencoderKLQwenImage
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput