up

* update

* `disable_mmap` in `from_pretrained`

---------

Co-authored-by: DN6 <dhruv.nair@gmail.com>

.github/workflows/benchmark.yml

@@ -7,7 +7,7 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
+  HF_XET_HIGH_PERFORMANCE: 1
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
@@ -28,7 +28,7 @@ jobs:
       options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: NVIDIA-SMI
@@ -38,9 +38,8 @@ jobs:
         run: |
           apt update
           apt install -y libpq-dev postgresql-client
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip install -r benchmarks/requirements.txt
-          python -m uv pip install -r benchmarks/requirements.txt
       - name: Environment
         run: |
           python utils/print_env.py
@@ -59,7 +58,7 @@ jobs:
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: benchmark_test_reports
           path: benchmarks/${{ env.BASE_PATH }}

.github/workflows/build_docker_images.yml

@@ -28,7 +28,7 @@ jobs:
         uses: docker/setup-buildx-action@v1
 
       - name: Check out code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
 
       - name: Find Changed Dockerfiles
         id: file_changes
@@ -42,19 +42,40 @@ jobs:
           CHANGED_FILES: ${{ steps.file_changes.outputs.all }}
         run: |
           echo "$CHANGED_FILES"
+          ALLOWED_IMAGES=(
+            diffusers-pytorch-cpu
+            diffusers-pytorch-cuda
+            diffusers-pytorch-xformers-cuda
+            diffusers-pytorch-minimum-cuda
+            diffusers-doc-builder
+          )
+
+          declare -A IMAGES_TO_BUILD=()
+
           for FILE in $CHANGED_FILES; do
             # skip anything that isn't still on disk
-            if [[ ! -f "$FILE" ]]; then
+            if [[ ! -e "$FILE" ]]; then
               echo "Skipping removed file $FILE"
               continue
             fi
-            if [[ "$FILE" == docker/*Dockerfile ]]; then
+
-              DOCKER_PATH="${FILE%/Dockerfile}"
+            for IMAGE in "${ALLOWED_IMAGES[@]}"; do
-              DOCKER_TAG=$(basename "$DOCKER_PATH")
+              if [[ "$FILE" == docker/${IMAGE}/* ]]; then
-              echo "Building Docker image for $DOCKER_TAG"
+                IMAGES_TO_BUILD["$IMAGE"]=1
-              docker build -t "$DOCKER_TAG" "$DOCKER_PATH"
               fi
             done
+          done
+
+          if [[ ${#IMAGES_TO_BUILD[@]} -eq 0 ]]; then
+            echo "No relevant Docker changes detected."
+            exit 0
+          fi
+
+          for IMAGE in "${!IMAGES_TO_BUILD[@]}"; do
+            DOCKER_PATH="docker/${IMAGE}"
+            echo "Building Docker image for $IMAGE"
+            docker build -t "$IMAGE" "$DOCKER_PATH"
+          done
         if: steps.file_changes.outputs.all != ''
 
   build-and-push-docker-images:
@@ -72,14 +93,13 @@ jobs:
         image-name:
           - diffusers-pytorch-cpu
           - diffusers-pytorch-cuda
-          - diffusers-pytorch-cuda
           - diffusers-pytorch-xformers-cuda
           - diffusers-pytorch-minimum-cuda
           - diffusers-doc-builder
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v1
       - name: Login to Docker Hub

.github/workflows/build_pr_documentation.yml

@@ -12,7 +12,33 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  check-links:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: '3.10'
+
+      - name: Install uv
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+
+      - name: Install doc-builder
+        run: |
+          uv pip install --system git+https://github.com/huggingface/doc-builder.git@main
+
+      - name: Check documentation links
+        run: |
+          uv run doc-builder check-links docs/source/en
+
   build:
+    needs: check-links
     uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
     with:
       commit_sha: ${{ github.event.pull_request.head.sha }}

.github/workflows/codeql.yml

@@ -0,0 +1,22 @@
+---
+name: CodeQL Security Analysis For Github Actions
+
+on:
+  push:
+    branches: ["main"]
+  workflow_dispatch:
+  # pull_request:
+
+jobs:
+  codeql:
+    name: CodeQL Analysis
+    uses: huggingface/security-workflows/.github/workflows/codeql-reusable.yml@v1
+    permissions:
+      security-events: write
+      packages: read
+      actions: read
+      contents: read
+    with:
+      languages: '["actions","python"]'
+      queries: 'security-extended,security-and-quality'
+      runner: 'ubuntu-latest' #optional if need custom runner

.github/workflows/mirror_community_pipeline.yml

@@ -24,7 +24,6 @@ jobs:
   mirror_community_pipeline:
     env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_COMMUNITY_MIRROR }}
-
     runs-on: ubuntu-22.04
     steps:
       # Checkout to correct ref
@@ -39,42 +38,46 @@ jobs:
       #     If ref is 'refs/heads/main' => set 'main'
       #     Else it must be a tag => set {tag}
       - name: Set checkout_ref and path_in_repo
+        env:
+          EVENT_NAME: ${{ github.event_name }}
+          EVENT_INPUT_REF: ${{ github.event.inputs.ref }}
+          GITHUB_REF: ${{ github.ref }}
         run: |
-          if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
+          if [ "$EVENT_NAME" == "workflow_dispatch" ]; then
-            if [ -z "${{ github.event.inputs.ref }}" ]; then
+            if [ -z "$EVENT_INPUT_REF" ]; then
               echo "Error: Missing ref input"
               exit 1
-            elif [ "${{ github.event.inputs.ref }}" == "main" ]; then
+            elif [ "$EVENT_INPUT_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 }}" >> $GITHUB_ENV
+              echo "CHECKOUT_REF=refs/tags/$EVENT_INPUT_REF" >> $GITHUB_ENV
-              echo "PATH_IN_REPO=${{ github.event.inputs.ref }}" >> $GITHUB_ENV
+              echo "PATH_IN_REPO=$EVENT_INPUT_REF" >> $GITHUB_ENV
             fi
-          elif [ "${{ github.ref }}" == "refs/heads/main" ]; then
+          elif [ "$GITHUB_REF" == "refs/heads/main" ]; then
-            echo "CHECKOUT_REF=${{ github.ref }}" >> $GITHUB_ENV
+            echo "CHECKOUT_REF=$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 }}" >> $GITHUB_ENV
+            echo "CHECKOUT_REF=$GITHUB_REF" >> $GITHUB_ENV
-            echo "PATH_IN_REPO=$(echo ${{ github.ref }} | sed 's/^refs\/tags\///')" >> $GITHUB_ENV
+            echo "PATH_IN_REPO=$(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
+      - uses: actions/checkout@v6
         with:
           ref: ${{ env.CHECKOUT_REF }}
 
       # Setup + install dependencies
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
           python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
+          pip install --upgrade pip
           pip install --upgrade huggingface_hub
 
       # Check secret is set

.github/workflows/nightly_tests.yml

@@ -7,7 +7,7 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
+  HF_XET_HIGH_PERFORMANCE: 1
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 600
@@ -28,7 +28,7 @@ jobs:
       pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: Install dependencies
@@ -44,7 +44,7 @@ jobs:
 
       - name: Pipeline Tests Artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: test-pipelines.json
           path: reports
@@ -64,17 +64,18 @@ jobs:
       options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: NVIDIA-SMI
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          python -m uv pip install pytest-reportlog
+          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+          uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -84,8 +85,8 @@ jobs:
           # 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 \
+          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
+             -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \
             tests/pipelines/${{ matrix.module }}
@@ -96,7 +97,7 @@ jobs:
           cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: pipeline_${{ matrix.module }}_test_reports
           path: reports
@@ -118,17 +119,18 @@ jobs:
         module: [models, schedulers, lora, others, single_file, examples]
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m uv pip install -e [quality,test]
+        uv pip install peft@git+https://github.com/huggingface/peft.git
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        python -m uv pip install pytest-reportlog
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+        uv pip install pytest-reportlog
     - name: Environment
       run: python utils/print_env.py
 
@@ -139,8 +141,8 @@ jobs:
         # 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 \
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
+          -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           --report-log=tests_torch_${{ matrix.module }}_cuda.log \
           tests/${{ matrix.module }}
@@ -152,8 +154,8 @@ jobs:
         # 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 \
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v --make-reports=examples_torch_cuda \
+          --make-reports=examples_torch_cuda \
           --report-log=examples_torch_cuda.log \
           examples/
 
@@ -165,7 +167,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: torch_${{ matrix.module }}_cuda_test_reports
         path: reports
@@ -182,7 +184,7 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
@@ -191,8 +193,9 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality,training]"
-        python -m uv pip install -e [quality,test,training]
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
     - name: Environment
       run: |
         python utils/print_env.py
@@ -201,14 +204,14 @@ jobs:
         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/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -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: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: torch_compile_test_reports
         path: reports
@@ -225,18 +228,19 @@ jobs:
       options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: NVIDIA-SMI
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip install peft@git+https://github.com/huggingface/peft.git
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          python -m uv pip install pytest-reportlog
+          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+          uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -247,7 +251,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
           BIG_GPU_MEMORY: 40
         run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          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 \
@@ -259,7 +263,7 @@ jobs:
           cat reports/tests_big_gpu_torch_cuda_failures_short.txt
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: torch_cuda_big_gpu_test_reports
           path: reports
@@ -276,16 +280,17 @@ jobs:
         shell: bash
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
 
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip install peft@git+https://github.com/huggingface/peft.git
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
 
       - name: Environment
         run: |
@@ -297,8 +302,8 @@ jobs:
           # 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 \
+          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
+            -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 \
@@ -316,7 +321,7 @@ jobs:
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: torch_minimum_version_cuda_test_reports
           path: reports
@@ -340,6 +345,9 @@ jobs:
           - backend: "optimum_quanto"
             test_location: "quanto"
             additional_deps: []
+          - backend: "nvidia_modelopt"
+            test_location: "modelopt"
+            additional_deps: []
     runs-on:
       group: aws-g6e-xlarge-plus
     container:
@@ -347,20 +355,21 @@ jobs:
       options: --shm-size "20gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: NVIDIA-SMI
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip install -U ${{ matrix.config.backend }}
-          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, ' ') }}
+              uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
           fi
-          python -m uv pip install pytest-reportlog
+          uv pip install pytest-reportlog
+          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
       - name: Environment
         run: |
           python utils/print_env.py
@@ -371,7 +380,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
           BIG_GPU_MEMORY: 40
         run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
             --make-reports=tests_${{ matrix.config.backend }}_torch_cuda \
             --report-log=tests_${{ matrix.config.backend }}_torch_cuda.log \
             tests/quantization/${{ matrix.config.test_location }}
@@ -382,7 +391,7 @@ jobs:
           cat reports/tests_${{ matrix.config.backend }}_torch_cuda_failures_short.txt
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: torch_cuda_${{ matrix.config.backend }}_reports
           path: reports
@@ -399,17 +408,18 @@ jobs:
       options: --shm-size "20gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         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"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip install -U bitsandbytes optimum_quanto
-          python -m uv pip install -U bitsandbytes optimum_quanto
+          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          python -m uv pip install pytest-reportlog
+          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+          uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -420,7 +430,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
           BIG_GPU_MEMORY: 40
         run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          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
@@ -431,7 +441,7 @@ jobs:
           cat reports/tests_pipeline_level_quant_torch_cuda_failures_short.txt
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: torch_cuda_pipeline_level_quant_reports
           path: reports
@@ -456,7 +466,7 @@ jobs:
       image: diffusers/diffusers-pytorch-cpu
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
 
@@ -464,7 +474,7 @@ jobs:
         run: mkdir -p combined_reports
 
       - name: Download all test reports
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v7
         with:
           path: artifacts
 
@@ -490,7 +500,7 @@ jobs:
           cat $CONSOLIDATED_REPORT_PATH >> $GITHUB_STEP_SUMMARY
 
       - name: Upload consolidated report
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: consolidated_test_report
           path: ${{ env.CONSOLIDATED_REPORT_PATH }}
@@ -504,7 +514,7 @@ jobs:
 #
 #    steps:
 #      - name: Checkout diffusers
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v6
 #        with:
 #          fetch-depth: 2
 #
@@ -520,11 +530,11 @@ jobs:
 #      - name: Install dependencies
 #        shell: arch -arch arm64 bash {0}
 #        run: |
-#          ${CONDA_RUN} python -m pip install --upgrade pip uv
+#          ${CONDA_RUN} pip install --upgrade pip uv
-#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
+#          ${CONDA_RUN} uv pip install -e ".[quality]"
-#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+#          ${CONDA_RUN} 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} uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
+#          ${CONDA_RUN} uv pip install pytest-reportlog
 #      - name: Environment
 #        shell: arch -arch arm64 bash {0}
 #        run: |
@@ -535,7 +545,7 @@ jobs:
 #          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 \
+#          ${CONDA_RUN} pytest -n 1  --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
 #            tests/
 #      - name: Failure short reports
@@ -544,7 +554,7 @@ jobs:
 #
 #      - name: Test suite reports artifacts
 #        if: ${{ always() }}
-#        uses: actions/upload-artifact@v4
+#        uses: actions/upload-artifact@v6
 #        with:
 #          name: torch_mps_test_reports
 #          path: reports
@@ -560,7 +570,7 @@ jobs:
 #
 #    steps:
 #      - name: Checkout diffusers
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v6
 #        with:
 #          fetch-depth: 2
 #
@@ -576,11 +586,11 @@ jobs:
 #      - name: Install dependencies
 #        shell: arch -arch arm64 bash {0}
 #        run: |
-#          ${CONDA_RUN} python -m pip install --upgrade pip uv
+#          ${CONDA_RUN} pip install --upgrade pip uv
-#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
+#          ${CONDA_RUN} uv pip install -e ".[quality]"
-#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+#          ${CONDA_RUN} 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} uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
+#          ${CONDA_RUN} uv pip install pytest-reportlog
 #      - name: Environment
 #        shell: arch -arch arm64 bash {0}
 #        run: |
@@ -591,7 +601,7 @@ jobs:
 #          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 \
+#          ${CONDA_RUN} pytest -n 1  --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
 #            tests/
 #      - name: Failure short reports
@@ -600,7 +610,7 @@ jobs:
 #
 #      - name: Test suite reports artifacts
 #        if: ${{ always() }}
-#        uses: actions/upload-artifact@v4
+#        uses: actions/upload-artifact@v6
 #        with:
 #          name: torch_mps_test_reports
 #          path: reports

.github/workflows/notify_slack_about_release.yml

@@ -10,12 +10,12 @@ jobs:
     runs-on: ubuntu-22.04
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v6
 
     - name: Setup Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v6
       with:
-        python-version: '3.8'
+        python-version: '3.10'
 
     - name: Notify Slack about the release
       env:

.github/workflows/pr_dependency_test.yml

@@ -18,18 +18,15 @@ jobs:
   check_dependencies:
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          pip install -e .
-          python -m pip install --upgrade pip uv
+          pip install pytest
-          python -m uv pip install -e .
-          python -m uv pip install pytest
       - name: Check for soft dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
             pytest tests/others/test_dependencies.py

.github/workflows/pr_modular_tests.yml

@@ -1,3 +1,4 @@
+
 name: Fast PR tests for Modular
 
 on:
@@ -26,7 +27,7 @@ concurrency:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
+  HF_XET_HIGH_PERFORMANCE: 1
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60
@@ -35,14 +36,14 @@ jobs:
   check_code_quality:
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
           python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
+          pip install --upgrade pip
           pip install .[quality]
       - name: Check quality
         run: make quality
@@ -55,14 +56,14 @@ jobs:
     needs: check_code_quality
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
           python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
+          pip install --upgrade pip
           pip install .[quality]
       - name: Check repo consistency
         run: |
@@ -77,23 +78,13 @@ jobs:
 
   run_fast_tests:
     needs: [check_code_quality, check_repository_consistency]
-    strategy:
+    name: Fast PyTorch Modular Pipeline CPU tests
-      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 }}
+      group: aws-highmemory-32-plus
 
     container:
-      image: ${{ matrix.config.image }}
+      image: diffusers/diffusers-pytorch-cpu
       options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
 
     defaults:
@@ -102,40 +93,35 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m uv pip install -e [quality,test]
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        uv pip uninstall accelerate && 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"
+        pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
+          -k "not Flax and not Onnx" \
-          -s -v -k "not Flax and not Onnx" \
+          --make-reports=tests_torch_cpu_modular_pipelines \
-          --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
+      run: cat reports/tests_torch_cpu_modular_pipelines_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
-        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
+        name: pr_pytorch_pipelines_torch_cpu_modular_pipelines_test_reports
         path: reports
-
-

.github/workflows/pr_test_fetcher.yml

@@ -28,13 +28,12 @@ jobs:
       test_map: ${{ steps.set_matrix.outputs.test_map }}
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 0
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m uv pip install -e [quality,test]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -43,7 +42,7 @@ jobs:
       run: |
         python utils/tests_fetcher.py | tee test_preparation.txt
     - name: Report fetched tests
-      uses: actions/upload-artifact@v3
+      uses: actions/upload-artifact@v6
       with:
         name: test_fetched
         path: test_preparation.txt
@@ -84,25 +83,22 @@ jobs:
         shell: bash
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m pip install -e [quality,test]
+        uv pip install accelerate
-        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:$PATH"
+        pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.modules }}_tests_cpu ${{ fromJson(needs.setup_pr_tests.outputs.test_map)[matrix.modules] }}
-        python -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.modules }}_tests_cpu ${{ fromJson(needs.setup_pr_tests.outputs.test_map)[matrix.modules] }}
 
     - name: Failure short reports
       if: ${{ failure() }}
@@ -113,7 +109,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v3
+      uses: actions/upload-artifact@v6
       with:
           name: ${{ matrix.modules }}_test_reports
           path: reports
@@ -142,25 +138,22 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        pip install -e [quality]
-        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 pytest \
-        HUGGINGFACE_CO_STAGING=true python -m pytest \
           -m "is_staging_test" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests
@@ -171,7 +164,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports

.github/workflows/pr_tests.yml

@@ -22,7 +22,7 @@ concurrency:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HUB_ENABLE_HF_TRANSFER: 1
+  HF_XET_HIGH_PERFORMANCE: 1
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60
@@ -31,14 +31,14 @@ jobs:
   check_code_quality:
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
+          pip install --upgrade pip
           pip install .[quality]
       - name: Check quality
         run: make quality
@@ -51,14 +51,14 @@ jobs:
     needs: check_code_quality
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
+          pip install --upgrade pip
           pip install .[quality]
       - name: Check repo consistency
         run: |
@@ -108,46 +108,42 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m uv pip install -e [quality,test]
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        uv pip uninstall accelerate && 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"
+        pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
+          -k "not Flax and not Onnx" \
-          -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"
+        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -k "not Flax and not Onnx and not Dependency" \
-          -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"
+        uv pip install ".[training]"
-        python -m uv pip install peft timm
+        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
           examples
 
@@ -157,7 +153,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
         path: reports
@@ -189,25 +185,22 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m uv 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 pytest \
-        HUGGINGFACE_CO_STAGING=true python -m pytest \
           -m "is_staging_test" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests
@@ -218,7 +211,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports
@@ -243,34 +236,32 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        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
+        uv 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
+        uv pip install -U tokenizers
-        python -m uv pip install -U tokenizers
+        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
 
     - 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"
+        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-        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 \
+        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -s -v \
+          \
           --make-reports=tests_models_lora_peft_main \
           tests/models/ -k "lora"
 
@@ -282,7 +273,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: pr_main_test_reports
         path: reports

.github/workflows/pr_tests_gpu.yml

@@ -24,7 +24,7 @@ env:
   DIFFUSERS_IS_CI: yes
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
+  HF_XET_HIGH_PERFORMANCE: 1
   PYTEST_TIMEOUT: 600
   PIPELINE_USAGE_CUTOFF: 1000000000 # set high cutoff so that only always-test pipelines run
 
@@ -32,14 +32,14 @@ jobs:
   check_code_quality:
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
+          pip install --upgrade pip
           pip install .[quality]
       - name: Check quality
         run: make quality
@@ -52,14 +52,14 @@ jobs:
     needs: check_code_quality
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
+          pip install --upgrade pip
           pip install .[quality]
       - name: Check repo consistency
         run: |
@@ -83,13 +83,12 @@ jobs:
       pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -101,7 +100,7 @@ jobs:
           echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
       - name: Pipeline Tests Artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: test-pipelines.json
           path: reports
@@ -121,7 +120,7 @@ jobs:
       options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
 
@@ -130,10 +129,10 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
 
       - name: Environment
         run: |
@@ -152,14 +151,14 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
           if [ "${{ matrix.module }}" = "ip_adapters" ]; then
-              python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+              pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -s -v -k "not Flax and not Onnx" \
+              -k "not Flax and not Onnx" \
               --make-reports=tests_pipeline_${{ 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 \
+              pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -s -v -k "not Flax and not Onnx and $pattern" \
+              -k "not Flax and not Onnx and $pattern" \
               --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
               tests/pipelines/${{ matrix.module }}
           fi
@@ -171,7 +170,7 @@ jobs:
           cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: pipeline_${{ matrix.module }}_test_reports
           path: reports
@@ -194,17 +193,17 @@ jobs:
         module: [models, schedulers, lora, others]
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m uv pip install -e [quality,test]
+        uv pip install peft@git+https://github.com/huggingface/peft.git
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
 
     - name: Environment
       run: |
@@ -225,10 +224,10 @@ jobs:
       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/${{ matrix.module }} \
+          pytest -n 1  --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx" tests/${{ 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 }} \
+          pytest -n 1  --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
 
@@ -240,7 +239,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: torch_cuda_test_reports_${{ matrix.module }}
         path: reports
@@ -256,7 +255,7 @@ jobs:
       options: --gpus all --shm-size "16gb" --ipc host
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
@@ -265,22 +264,20 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
-        python -m uv pip install -e [quality,test,training]
+        uv pip install -e ".[quality,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"
+        uv pip install ".[training]"
-        python -m uv pip install timm
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
-        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() }}
@@ -290,7 +287,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: examples_test_reports
         path: reports

.github/workflows/pr_torch_dependency_test.yml

@@ -18,19 +18,15 @@ jobs:
   check_torch_dependencies:
     runs-on: ubuntu-22.04
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          pip install -e .
-          python -m pip install --upgrade pip uv
+          pip install torch torchvision torchaudio pytest
-          python -m uv pip install -e .
-          python -m uv pip install torch torchvision torchaudio
-          python -m uv pip install pytest
       - name: Check for soft dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
             pytest tests/others/test_dependencies.py

.github/workflows/push_tests.yml

@@ -14,7 +14,7 @@ env:
   DIFFUSERS_IS_CI: yes
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
+  HF_XET_HIGH_PERFORMANCE: 1
   PYTEST_TIMEOUT: 600
   PIPELINE_USAGE_CUTOFF: 50000
 
@@ -29,13 +29,12 @@ jobs:
       pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -47,7 +46,7 @@ jobs:
           echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
       - name: Pipeline Tests Artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: test-pipelines.json
           path: reports
@@ -67,7 +66,7 @@ jobs:
       options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: NVIDIA-SMI
@@ -75,9 +74,10 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
       - name: Environment
         run: |
           python utils/print_env.py
@@ -87,8 +87,8 @@ jobs:
           # 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 \
+          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
+            -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -98,7 +98,7 @@ jobs:
           cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: pipeline_${{ matrix.module }}_test_reports
           path: reports
@@ -120,16 +120,17 @@ jobs:
         module: [models, schedulers, lora, others, single_file]
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m uv pip install -e [quality,test]
+        uv pip install peft@git+https://github.com/huggingface/peft.git
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
 
     - name: Environment
       run: |
@@ -141,8 +142,8 @@ jobs:
         # 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 \
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
+          -k "not Flax and not Onnx" \
           --make-reports=tests_torch_cuda_${{ matrix.module }} \
           tests/${{ matrix.module }}
 
@@ -154,7 +155,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: torch_cuda_test_reports_${{ matrix.module }}
         path: reports
@@ -171,7 +172,7 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
@@ -180,8 +181,9 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality,training]"
-        python -m uv pip install -e [quality,test,training]
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
     - name: Environment
       run: |
         python utils/print_env.py
@@ -190,14 +192,14 @@ jobs:
         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/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -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
+      uses: actions/upload-artifact@v6
       with:
         name: torch_compile_test_reports
         path: reports
@@ -214,7 +216,7 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
@@ -223,8 +225,7 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality,training]"
-        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -232,14 +233,14 @@ jobs:
       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/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -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
+      uses: actions/upload-artifact@v6
       with:
         name: torch_xformers_test_reports
         path: reports
@@ -255,7 +256,7 @@ jobs:
       options: --gpus all --shm-size "16gb" --ipc host
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
@@ -264,21 +265,18 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality,training]"
-        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"
+        uv pip install ".[training]"
-        python -m uv pip install timm
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
-        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() }}
@@ -288,7 +286,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: examples_test_reports
         path: reports

.github/workflows/push_tests_fast.yml

@@ -18,7 +18,7 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
+  HF_XET_HIGH_PERFORMANCE: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
@@ -54,35 +54,31 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m uv 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"
+        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -k "not Flax and not Onnx" \
-          -s -v -k "not Flax and not 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"
+        uv pip install ".[training]"
-        python -m uv pip install peft timm
+        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
           examples
 
@@ -92,7 +88,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports

.github/workflows/push_tests_mps.yml

@@ -8,7 +8,7 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
+  HF_XET_HIGH_PERFORMANCE: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
@@ -23,7 +23,7 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
@@ -57,7 +57,7 @@ jobs:
         HF_HOME: /System/Volumes/Data/mnt/cache
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
-        ${CONDA_RUN} python -m pytest -n 0 -s -v --make-reports=tests_torch_mps tests/
+        ${CONDA_RUN} python -m pytest -n 0 --make-reports=tests_torch_mps tests/
 
     - name: Failure short reports
       if: ${{ failure() }}
@@ -65,7 +65,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: pr_torch_mps_test_reports
         path: reports

.github/workflows/pypi_publish.yaml

@@ -15,12 +15,12 @@ jobs:
       latest_branch: ${{ steps.set_latest_branch.outputs.latest_branch }}
     steps:
       - name: Checkout Repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
 
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
-          python-version: '3.8'
+          python-version: '3.10'
 
       - name: Fetch latest branch
         id: fetch_latest_branch
@@ -40,14 +40,14 @@ jobs:
 
     steps:
       - name: Checkout Repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           ref: ${{ needs.find-and-checkout-latest-branch.outputs.latest_branch }}
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
-          python-version: "3.8"
+          python-version: "3.10"
 
       - name: Install dependencies
         run: |

.github/workflows/release_tests_fast.yml

@@ -27,13 +27,12 @@ jobs:
       pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -45,7 +44,7 @@ jobs:
           echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
       - name: Pipeline Tests Artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: test-pipelines.json
           path: reports
@@ -65,7 +64,7 @@ jobs:
       options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
       - name: NVIDIA-SMI
@@ -73,9 +72,8 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.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
@@ -85,8 +83,8 @@ jobs:
           # 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 \
+          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
+            -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -96,7 +94,7 @@ jobs:
           cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: pipeline_${{ matrix.module }}_test_reports
           path: reports
@@ -118,16 +116,15 @@ jobs:
         module: [models, schedulers, lora, others, single_file]
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality]"
-        python -m uv pip install -e [quality,test]
+        uv pip install peft@git+https://github.com/huggingface/peft.git
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
     - name: Environment
       run: |
@@ -139,8 +136,8 @@ jobs:
         # 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 \
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
+          -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           tests/${{ matrix.module }}
 
@@ -152,7 +149,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: torch_cuda_${{ matrix.module }}_test_reports
         path: reports
@@ -169,16 +166,15 @@ jobs:
         shell: bash
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
 
       - name: Install dependencies
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip install peft@git+https://github.com/huggingface/peft.git
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
       - name: Environment
         run: |
@@ -190,8 +186,8 @@ jobs:
           # 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 \
+          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
+            -k "not Flax and not Onnx" \
             --make-reports=tests_torch_minimum_cuda \
             tests/models/test_modeling_common.py \
             tests/pipelines/test_pipelines_common.py \
@@ -209,7 +205,7 @@ jobs:
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: torch_minimum_version_cuda_test_reports
           path: reports
@@ -226,7 +222,7 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
@@ -235,8 +231,7 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality,training]"
-        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -245,14 +240,14 @@ jobs:
         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/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -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
+      uses: actions/upload-artifact@v6
       with:
         name: torch_compile_test_reports
         path: reports
@@ -269,7 +264,7 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
@@ -278,8 +273,7 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality,training]"
-        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -287,14 +281,14 @@ jobs:
       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/
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile -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
+      uses: actions/upload-artifact@v6
       with:
         name: torch_xformers_test_reports
         path: reports
@@ -311,7 +305,7 @@ jobs:
 
     steps:
     - name: Checkout diffusers
-      uses: actions/checkout@v3
+      uses: actions/checkout@v6
       with:
         fetch-depth: 2
 
@@ -321,21 +315,18 @@ jobs:
 
     - name: Install dependencies
       run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        uv pip install -e ".[quality,training]"
-        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"
+        uv pip install ".[training]"
-        python -m uv pip install timm
+        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
-        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() }}
@@ -345,7 +336,7 @@ jobs:
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
-      uses: actions/upload-artifact@v4
+      uses: actions/upload-artifact@v6
       with:
         name: examples_test_reports
         path: reports

.github/workflows/run_tests_from_a_pr.yml

@@ -57,15 +57,14 @@ jobs:
         shell: bash -e {0}
 
       - name: Checkout PR branch
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           ref: refs/pull/${{ inputs.pr_number }}/head
 
       - name: Install pytest
         run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          uv pip install -e ".[quality]"
-          python -m uv pip install -e [quality,test]
+          uv pip install peft
-          python -m uv pip install peft
 
       - name: Run tests
         env:

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

@@ -27,7 +27,7 @@ jobs:
 
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
 

.github/workflows/ssh-runner.yml

@@ -35,7 +35,7 @@ jobs:
 
     steps:
       - name: Checkout diffusers
-        uses: actions/checkout@v3
+        uses: actions/checkout@v6
         with:
           fetch-depth: 2
 

.github/workflows/stale.yml

@@ -15,12 +15,12 @@ jobs:
     env:
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v6
 
     - name: Setup Python
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v6
       with:
-        python-version: 3.8
+        python-version: 3.10
 
     - name: Install requirements
       run: |

.github/workflows/trufflehog.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-22.04
     steps:
     - name: Checkout code
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         fetch-depth: 0
     - name: Secret Scanning

.github/workflows/typos.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-22.04
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
 
       - name: typos-action
         uses: crate-ci/typos@v1.12.4

.github/workflows/update_metadata.yml

@@ -15,7 +15,7 @@ jobs:
         shell: bash -l {0}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v6
 
       - name: Setup environment
         run: |

.gitignore

@@ -125,6 +125,9 @@ dmypy.json
 .vs
 .vscode
 
+# Cursor
+.cursor
+
 # Pycharm
 .idea
 

LICENSE

@@ -144,7 +144,7 @@
       agreed to in writing, Licensor provides the Work (and each
       Contributor provides its Contributions) on an "AS IS" BASIS,
       WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
+      implied, including, without limitation, Any warranties or conditions
       of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
       PARTICULAR PURPOSE. You are solely responsible for determining the
       appropriateness of using or redistributing the Work and assume any

README.md

@@ -171,7 +171,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
   <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/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
+      <td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting"> stable-diffusion-v1-5/stable-diffusion-inpainting </a></td>
   </tr>
   <tr style="border-top: 2px solid black">
     <td>Image Variation</td>

benchmarks/benchmarking_utils.py

@@ -6,7 +6,7 @@ import queue
 import threading
 from contextlib import nullcontext
 from dataclasses import dataclass
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Callable
 
 import pandas as pd
 import torch
@@ -91,10 +91,10 @@ def model_init_fn(model_cls, group_offload_kwargs=None, layerwise_upcasting=Fals
 class BenchmarkScenario:
     name: str
     model_cls: ModelMixin
-    model_init_kwargs: Dict[str, Any]
+    model_init_kwargs: dict[str, Any]
     model_init_fn: Callable
     get_model_input_dict: Callable
-    compile_kwargs: Optional[Dict[str, Any]] = None
+    compile_kwargs: dict[str, Any] | None = None
 
 
 @require_torch_gpu
@@ -176,7 +176,7 @@ class BenchmarkMixin:
             result["fullgraph"], result["mode"] = None, None
         return result
 
-    def run_bencmarks_and_collate(self, scenarios: Union[BenchmarkScenario, list[BenchmarkScenario]], filename: str):
+    def run_bencmarks_and_collate(self, scenarios: BenchmarkScenario | list[BenchmarkScenario], filename: str):
         if not isinstance(scenarios, list):
             scenarios = [scenarios]
         record_queue = queue.Queue()
@@ -214,10 +214,10 @@ class BenchmarkMixin:
         *,
         model_cls: ModelMixin,
         init_fn: Callable,
-        init_kwargs: Dict[str, Any],
+        init_kwargs: dict[str, Any],
         get_input_fn: Callable,
-        compile_kwargs: Optional[Dict[str, Any]],
+        compile_kwargs: dict[str, Any] | None = None,
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         # setup
         self.pre_benchmark()
 

docker/diffusers-doc-builder/Dockerfile

@@ -1,56 +1,45 @@
-FROM ubuntu:20.04
+FROM python:3.10-slim
+ENV PYTHONDONTWRITEBYTECODE=1
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update \
+RUN apt-get -y update && apt-get install -y bash \
-    && 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 \
+    libglib2.0-0 \
     libsndfile1-dev \
-                   python3.10 \
-                   python3-pip \
     libgl1 \
     zip \
-                   wget \
+    wget
-                   python3.10-venv && \
-    rm -rf /var/lib/apt/lists
 
-# make sure to use venv
+ENV UV_PYTHON=/usr/local/bin/python
-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 && \
+RUN pip install uv
-    python3.10 -m uv pip install --no-cache-dir \
+RUN uv pip install --no-cache-dir \
     torch \
     torchvision \
     torchaudio \
-        invisible_watermark \
+    --extra-index-url https://download.pytorch.org/whl/cpu
-        --extra-index-url https://download.pytorch.org/whl/cpu && \
+
-    python3.10 -m uv pip install --no-cache-dir \
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
     accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        Jinja2 \
-        librosa \
     numpy==1.26.4 \
-        scipy \
+    hf_xet \
-        tensorboard \
-        transformers \
-        matplotlib \
     setuptools==69.5.1 \
     bitsandbytes \
     torchao \
     gguf \
     optimum-quanto
 
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
+
 CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -44,6 +44,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         scipy \
         tensorboard \
         transformers \
-        hf_transfer
+        hf_xet
 
 CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -38,13 +38,12 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
-        hf_transfer \
+        hf_xet \
         Jinja2 \
         librosa \
         numpy==1.26.4 \
         scipy \
         tensorboard \
-        transformers \
+        transformers
-        hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cpu/Dockerfile

@@ -1,50 +1,38 @@
-FROM ubuntu:20.04
+FROM python:3.10-slim
+ENV PYTHONDONTWRITEBYTECODE=1
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update \
+RUN apt-get -y update && apt-get install -y bash \
-    && 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 \
+    libglib2.0-0 \
     libsndfile1-dev \
-                   python3.10 \
+    libgl1
-                   python3.10-dev \
-                   python3-pip \
-                   libgl1 \
-                   python3.10-venv && \
-    rm -rf /var/lib/apt/lists
 
-# make sure to use venv
+ENV UV_PYTHON=/usr/local/bin/python
-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 && \
+RUN pip install uv
-    python3.10 -m uv pip install --no-cache-dir \
+RUN uv pip install --no-cache-dir \
     torch \
     torchvision \
     torchaudio \
-        invisible_watermark \
+    --extra-index-url https://download.pytorch.org/whl/cpu
-        --extra-index-url https://download.pytorch.org/whl/cpu && \
+
-    python3.10 -m uv pip install --no-cache-dir \
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
     accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        Jinja2 \
-        librosa \
     numpy==1.26.4 \
-        scipy \
+    hf_xet
-        tensorboard \
+
-        transformers matplotlib  \
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
-        hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cuda/Dockerfile

@@ -2,11 +2,13 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
+ARG PYTHON_VERSION=3.12
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
     && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
+    && add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update
 
 RUN apt install -y bash \
     build-essential \
@@ -14,38 +16,34 @@ RUN apt install -y bash \
     git-lfs \
     curl \
     ca-certificates \
+    libglib2.0-0 \
     libsndfile1-dev \
     libgl1 \
-    python3.10 \
+    python3 \
-    python3.10-dev \
     python3-pip \
-    python3.10-venv && \
+    && apt-get clean \
-    rm -rf /var/lib/apt/lists
+    && rm -rf /var/lib/apt/lists/*
 
-# make sure to use venv
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-RUN python3.10 -m venv /opt/venv
+ENV PATH="/root/.local/bin:$PATH"
-ENV PATH="/opt/venv/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/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 && \
+RUN uv pip install --no-cache-dir \
-    python3.10 -m uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio \
+    torchaudio
-    invisible_watermark && \
+
-    python3.10 -m pip install --no-cache-dir \
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv 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
+    hf_xet
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-minimum-cuda/Dockerfile

@@ -2,6 +2,7 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
+ARG PYTHON_VERSION=3.10
 ENV DEBIAN_FRONTEND=noninteractive
 ENV MINIMUM_SUPPORTED_TORCH_VERSION="2.1.0"
 ENV MINIMUM_SUPPORTED_TORCHVISION_VERSION="0.16.0"
@@ -9,7 +10,8 @@ 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
+    && add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update
 
 RUN apt install -y bash \
     build-essential \
@@ -17,37 +19,34 @@ RUN apt install -y bash \
     git-lfs \
     curl \
     ca-certificates \
+    libglib2.0-0 \
     libsndfile1-dev \
     libgl1 \
-    python3.10 \
+    python3 \
-    python3.10-dev \
     python3-pip \
-    python3.10-venv && \
+    && apt-get clean \
-    rm -rf /var/lib/apt/lists
+    && rm -rf /var/lib/apt/lists/*
 
-# make sure to use venv
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-RUN python3.10 -m venv /opt/venv
+ENV PATH="/root/.local/bin:$PATH"
-ENV PATH="/opt/venv/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/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 && \
+RUN uv pip install --no-cache-dir \
-    python3.10 -m uv pip install --no-cache-dir \
     torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
     torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
-    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION \
+    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION
-    invisible_watermark && \
+
-    python3.10 -m pip install --no-cache-dir \
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
     accelerate \
-    datasets \
-    hf-doc-builder \
-    huggingface-hub \
-    hf_transfer \
-    Jinja2 \
-    librosa \
     numpy==1.26.4 \
-    scipy \
+    pytorch-lightning \
-    tensorboard \
+    hf_xet
-    transformers \
-    hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -2,11 +2,13 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
+ARG PYTHON_VERSION=3.12
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
     && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
+    && add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update
 
 RUN apt install -y bash \
     build-essential \
@@ -14,38 +16,35 @@ RUN apt install -y bash \
     git-lfs \
     curl \
     ca-certificates \
+    libglib2.0-0 \
     libsndfile1-dev \
     libgl1 \
-                   python3.10 \
+    python3 \
-                   python3.10-dev \
     python3-pip \
-                   python3.10-venv && \
+    && apt-get clean \
-    rm -rf /var/lib/apt/lists
+    && rm -rf /var/lib/apt/lists/*
 
-# make sure to use venv
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-RUN python3.10 -m venv /opt/venv
+ENV PATH="/root/.local/bin:$PATH"
-ENV PATH="/opt/venv/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/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 && \
+RUN uv pip install --no-cache-dir \
-    python3.10 -m pip install --no-cache-dir \
     torch \
     torchvision \
-        torchaudio \
+    torchaudio
-        invisible_watermark && \
+
-    python3.10 -m uv pip install --no-cache-dir \
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
     accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        hf_transfer \
-        Jinja2 \
-        librosa \
     numpy==1.26.4 \
-        scipy \
+    pytorch-lightning \
-        tensorboard \
+    hf_xet \
-        transformers \
+    xformers
-        xformers  \
-        hf_transfer
 
 CMD ["/bin/bash"]

docs/source/en/_toctree.yml

@@ -1,5 +1,4 @@
-- title: Get started
+- sections:
-  sections:
   - local: index
     title: Diffusers
   - local: installation
@@ -8,9 +7,8 @@
     title: Quickstart
   - local: stable_diffusion
     title: Basic performance
-
+  title: Get started
-- title: Pipelines
+- isExpanded: false
-  isExpanded: false
   sections:
   - local: using-diffusers/loading
     title: DiffusionPipeline
@@ -23,16 +21,15 @@
   - local: using-diffusers/reusing_seeds
     title: Reproducibility
   - local: using-diffusers/schedulers
-    title: Load schedulers and models
+    title: Schedulers
-  - local: using-diffusers/scheduler_features
+  - local: using-diffusers/automodel
-    title: Scheduler features
+    title: AutoModel
   - local: using-diffusers/other-formats
-    title: Model files and layouts
+    title: Model formats
   - local: using-diffusers/push_to_hub
-    title: Push files to the Hub
+    title: Sharing pipelines and models
-
+  title: Pipelines
-- title: Adapters
+- isExpanded: false
-  isExpanded: false
   sections:
   - local: tutorials/using_peft_for_inference
     title: LoRA
@@ -46,38 +43,33 @@
     title: DreamBooth
   - local: using-diffusers/textual_inversion_inference
     title: Textual inversion
-
+  title: Adapters
-- title: Inference
+- isExpanded: false
-  isExpanded: false
   sections:
   - local: using-diffusers/weighted_prompts
-    title: Prompt techniques
+    title: Prompting
   - 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
-  - local: using-diffusers/scheduler_features
+  - local: hybrid_inference/overview
-    title: Scheduler features
+    title: Remote inference
-  - local: using-diffusers/callback
+  title: Inference
-    title: Pipeline callbacks
+- isExpanded: false
-  - local: using-diffusers/image_quality
-    title: Controlling image quality
-
-- 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:
-    sections:
     - local: optimization/pruna
       title: Pruna
     - local: optimization/xformers
@@ -86,27 +78,19 @@
       title: Token merging
     - local: optimization/deepcache
       title: DeepCache
+    - local: optimization/cache_dit
+      title: CacheDiT
     - local: optimization/tgate
       title: TGATE
     - local: optimization/xdit
       title: xDiT
     - local: optimization/para_attn
       title: ParaAttention
-
+    - local: using-diffusers/image_quality
-- title: Hybrid Inference
+      title: FreeU
-  isExpanded: false
+    title: Community optimizations
-  sections:
+  title: Inference optimization
-  - local: hybrid_inference/overview
+- isExpanded: false
-    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
@@ -128,9 +112,10 @@
     title: ComponentsManager
   - local: modular_diffusers/guiders
     title: Guiders
-
+  - local: modular_diffusers/custom_blocks
-- title: Training
+    title: Building Custom Blocks
-  isExpanded: false
+  title: Modular Diffusers
+- isExpanded: false
   sections:
   - local: training/overview
     title: Overview
@@ -140,8 +125,7 @@
     title: Adapt a model to a new task
   - local: tutorials/basic_training
     title: Train a diffusion model
-  - title: Models
+  - sections:
-    sections:
     - local: training/unconditional_training
       title: Unconditional image generation
     - local: training/text2image
@@ -160,8 +144,8 @@
       title: InstructPix2Pix
     - local: training/cogvideox
       title: CogVideoX
-  - title: Methods
+    title: Models
-    sections:
+  - sections:
     - local: training/text_inversion
       title: Textual Inversion
     - local: training/dreambooth
@@ -174,9 +158,9 @@
       title: Latent Consistency Distillation
     - local: training/ddpo
       title: Reinforcement learning training with DDPO
-
+    title: Methods
-- title: Quantization
+  title: Training
-  isExpanded: false
+- isExpanded: false
   sections:
   - local: quantization/overview
     title: Getting started
@@ -188,9 +172,10 @@
     title: torchao
   - local: quantization/quanto
     title: quanto
-
+  - local: quantization/modelopt
-- title: Model accelerators and hardware
+    title: NVIDIA ModelOpt
-  isExpanded: false
+  title: Quantization
+- isExpanded: false
   sections:
   - local: optimization/onnx
     title: ONNX
@@ -204,9 +189,8 @@
     title: Intel Gaudi
   - local: optimization/neuron
     title: AWS Neuron
-
+  title: Model accelerators and hardware
-- title: Specific pipeline examples
+- isExpanded: false
-  isExpanded: false
   sections:
   - local: using-diffusers/consisid
     title: ConsisID
@@ -232,12 +216,10 @@
     title: Stable Video Diffusion
   - local: using-diffusers/marigold_usage
     title: Marigold Computer Vision
-
+  title: Specific pipeline examples
-- title: Resources
+- isExpanded: false
-  isExpanded: false
-  sections:
-  - title: Task recipes
   sections:
+  - sections:
     - local: using-diffusers/unconditional_image_generation
       title: Unconditional image generation
     - local: using-diffusers/conditional_image_generation
@@ -252,6 +234,7 @@
       title: Video generation
     - local: using-diffusers/depth2img
       title: Depth-to-image
+    title: Task recipes
   - local: using-diffusers/write_own_pipeline
     title: Understanding pipelines, models and schedulers
   - local: community_projects
@@ -266,12 +249,10 @@
     title: Diffusers' Ethical Guidelines
   - local: conceptual/evaluation
     title: Evaluating Diffusion Models
-
+  title: Resources
-- title: API
+- isExpanded: false
-  isExpanded: false
-  sections:
-  - title: Main Classes
   sections:
+  - sections:
     - local: api/configuration
       title: Configuration
     - local: api/logging
@@ -280,8 +261,12 @@
       title: Outputs
     - local: api/quantization
       title: Quantization
-  - title: Modular
+    - local: hybrid_inference/api_reference
-    sections:
+      title: Remote inference
+    - local: api/parallel
+      title: Parallel inference
+    title: Main Classes
+  - sections:
     - local: api/modular_diffusers/pipeline
       title: Pipeline
     - local: api/modular_diffusers/pipeline_blocks
@@ -292,8 +277,8 @@
       title: Components and configs
     - local: api/modular_diffusers/guiders
       title: Guiders
-  - title: Loaders
+    title: Modular
-    sections:
+  - sections:
     - local: api/loaders/ip_adapter
       title: IP-Adapter
     - local: api/loaders/lora
@@ -308,14 +293,13 @@
       title: SD3Transformer2D
     - local: api/loaders/peft
       title: PEFT
-  - title: Models
+    title: Loaders
-    sections:
+  - sections:
     - local: api/models/overview
       title: Overview
     - local: api/models/auto_model
       title: AutoModel
-    - title: ControlNets
+    - sections:
-      sections:
       - local: api/models/controlnet
         title: ControlNetModel
       - local: api/models/controlnet_union
@@ -330,16 +314,20 @@
         title: SD3ControlNetModel
       - local: api/models/controlnet_sparsectrl
         title: SparseControlNetModel
-    - title: Transformers
+      title: ControlNets
-      sections:
+    - sections:
       - local: api/models/allegro_transformer3d
         title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
+      - local: api/models/transformer_bria_fibo
+        title: BriaFiboTransformer2DModel
       - local: api/models/bria_transformer
         title: BriaTransformer2DModel
       - local: api/models/chroma_transformer
         title: ChromaTransformer2DModel
+      - local: api/models/chronoedit_transformer_3d
+        title: ChronoEditTransformer3DModel
       - local: api/models/cogvideox_transformer3d
         title: CogVideoXTransformer3DModel
       - local: api/models/cogview3plus_transformer2d
@@ -354,16 +342,28 @@
         title: DiTTransformer2DModel
       - local: api/models/easyanimate_transformer3d
         title: EasyAnimateTransformer3DModel
+      - local: api/models/flux2_transformer
+        title: Flux2Transformer2DModel
       - local: api/models/flux_transformer
         title: FluxTransformer2DModel
+      - local: api/models/glm_image_transformer2d
+        title: GlmImageTransformer2DModel
       - local: api/models/hidream_image_transformer
         title: HiDreamImageTransformer2DModel
       - local: api/models/hunyuan_transformer2d
         title: HunyuanDiT2DModel
+      - local: api/models/hunyuanimage_transformer_2d
+        title: HunyuanImageTransformer2DModel
+      - local: api/models/hunyuan_video15_transformer_3d
+        title: HunyuanVideo15Transformer3DModel
       - local: api/models/hunyuan_video_transformer_3d
         title: HunyuanVideoTransformer3DModel
       - local: api/models/latte_transformer3d
         title: LatteTransformer3DModel
+      - local: api/models/longcat_image_transformer2d
+        title: LongCatImageTransformer2DModel
+      - local: api/models/ltx2_video_transformer3d
+        title: LTX2VideoTransformer3DModel
       - local: api/models/ltx_video_transformer3d
         title: LTXVideoTransformer3DModel
       - local: api/models/lumina2_transformer2d
@@ -374,6 +374,8 @@
         title: MochiTransformer3DModel
       - local: api/models/omnigen_transformer
         title: OmniGenTransformer2DModel
+      - local: api/models/ovisimage_transformer2d
+        title: OvisImageTransformer2DModel
       - local: api/models/pixart_transformer2d
         title: PixArtTransformer2DModel
       - local: api/models/prior_transformer
@@ -382,6 +384,8 @@
         title: QwenImageTransformer2DModel
       - local: api/models/sana_transformer2d
         title: SanaTransformer2DModel
+      - local: api/models/sana_video_transformer3d
+        title: SanaVideoTransformer3DModel
       - local: api/models/sd3_transformer2d
         title: SD3Transformer2DModel
       - local: api/models/skyreels_v2_transformer_3d
@@ -392,10 +396,14 @@
         title: Transformer2DModel
       - local: api/models/transformer_temporal
         title: TransformerTemporalModel
+      - local: api/models/wan_animate_transformer_3d
+        title: WanAnimateTransformer3DModel
       - local: api/models/wan_transformer_3d
         title: WanTransformer3DModel
-    - title: UNets
+      - local: api/models/z_image_transformer2d
-      sections:
+        title: ZImageTransformer2DModel
+      title: Transformers
+    - sections:
       - local: api/models/stable_cascade_unet
         title: StableCascadeUNet
       - local: api/models/unet
@@ -410,8 +418,8 @@
         title: UNetMotionModel
       - local: api/models/uvit2d
         title: UViT2DModel
-    - title: VAEs
+      title: UNets
-      sections:
+    - sections:
       - local: api/models/asymmetricautoencoderkl
         title: AsymmetricAutoencoderKL
       - local: api/models/autoencoder_dc
@@ -424,8 +432,18 @@
         title: AutoencoderKLCogVideoX
       - local: api/models/autoencoderkl_cosmos
         title: AutoencoderKLCosmos
+      - local: api/models/autoencoder_kl_hunyuanimage
+        title: AutoencoderKLHunyuanImage
+      - local: api/models/autoencoder_kl_hunyuanimage_refiner
+        title: AutoencoderKLHunyuanImageRefiner
       - local: api/models/autoencoder_kl_hunyuan_video
         title: AutoencoderKLHunyuanVideo
+      - local: api/models/autoencoder_kl_hunyuan_video15
+        title: AutoencoderKLHunyuanVideo15
+      - local: api/models/autoencoderkl_audio_ltx_2
+        title: AutoencoderKLLTX2Audio
+      - local: api/models/autoencoderkl_ltx_2
+        title: AutoencoderKLLTX2Video
       - local: api/models/autoencoderkl_ltx_video
         title: AutoencoderKLLTXVideo
       - local: api/models/autoencoderkl_magvit
@@ -444,40 +462,46 @@
         title: Tiny AutoEncoder
       - local: api/models/vq
         title: VQModel
-  - title: Pipelines
+      title: VAEs
-    sections:
+    title: Models
+  - sections:
     - local: api/pipelines/overview
       title: Overview
-    - local: api/pipelines/allegro
+    - local: api/pipelines/auto_pipeline
-      title: Allegro
+      title: AutoPipeline
+    - sections:
+      - local: api/pipelines/audioldm
+        title: AudioLDM
+      - local: api/pipelines/audioldm2
+        title: AudioLDM 2
+      - local: api/pipelines/dance_diffusion
+        title: Dance Diffusion
+      - local: api/pipelines/musicldm
+        title: MusicLDM
+      - local: api/pipelines/stable_audio
+        title: Stable Audio
+      title: Audio
+    - sections:
       - local: api/pipelines/amused
         title: aMUSEd
       - local: api/pipelines/animatediff
         title: AnimateDiff
       - local: api/pipelines/attend_and_excite
         title: Attend-and-Excite
-    - 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/bria_fibo
+        title: Bria Fibo
       - 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
@@ -500,8 +524,6 @@
         title: ControlNetUnion
       - local: api/pipelines/cosmos
         title: Cosmos
-    - local: api/pipelines/dance_diffusion
-      title: Dance Diffusion
       - local: api/pipelines/ddim
         title: DDIM
       - local: api/pipelines/ddpm
@@ -516,18 +538,18 @@
         title: EasyAnimate
       - local: api/pipelines/flux
         title: Flux
+      - local: api/pipelines/flux2
+        title: Flux2
       - local: api/pipelines/control_flux_inpaint
         title: FluxControlInpaint
-    - local: api/pipelines/framepack
+      - local: api/pipelines/glm_image
-      title: Framepack
+        title: GLM-Image
       - local: api/pipelines/hidream
         title: HiDream-I1
       - local: api/pipelines/hunyuandit
         title: Hunyuan-DiT
-    - local: api/pipelines/hunyuan_video
+      - local: api/pipelines/hunyuanimage21
-      title: HunyuanVideo
+        title: HunyuanImage2.1
-    - local: api/pipelines/i2vgenxl
-      title: I2VGen-XL
       - local: api/pipelines/pix2pix
         title: InstructPix2Pix
       - local: api/pipelines/kandinsky
@@ -536,62 +558,57 @@
         title: Kandinsky 2.2
       - local: api/pipelines/kandinsky3
         title: Kandinsky 3
+      - local: api/pipelines/kandinsky5_image
+        title: Kandinsky 5.0 Image
       - 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
+      - local: api/pipelines/longcat_image
-      title: LTXVideo
+        title: LongCat-Image
       - 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/ovis_image
+        title: Ovis-Image
       - 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/prx
+        title: PRX
       - local: api/pipelines/qwenimage
         title: QwenImage
       - local: api/pipelines/sana
         title: Sana
       - local: api/pipelines/sana_sprint
         title: Sana Sprint
+      - local: api/pipelines/sana_video
+        title: Sana Video
       - 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:
-      sections:
         - local: api/pipelines/stable_diffusion/overview
           title: Overview
         - local: api/pipelines/stable_diffusion/depth2img
@@ -602,8 +619,6 @@
           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
@@ -611,7 +626,8 @@
         - 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
+          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
@@ -628,12 +644,9 @@
           title: T2I-Adapter
         - local: api/pipelines/stable_diffusion/text2img
           title: Text-to-image
+        title: Stable Diffusion
       - local: api/pipelines/stable_unclip
         title: Stable unCLIP
-    - 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
       - local: api/pipelines/unidiffuser
@@ -642,12 +655,53 @@
         title: Value-guided sampling
       - local: api/pipelines/visualcloze
         title: VisualCloze
-    - local: api/pipelines/wan
-      title: Wan
       - local: api/pipelines/wuerstchen
         title: Wuerstchen
-  - title: Schedulers
+      - local: api/pipelines/z_image
-    sections:
+        title: Z-Image
+      title: Image
+    - sections:
+      - local: api/pipelines/allegro
+        title: Allegro
+      - local: api/pipelines/chronoedit
+        title: ChronoEdit
+      - local: api/pipelines/cogvideox
+        title: CogVideoX
+      - local: api/pipelines/consisid
+        title: ConsisID
+      - local: api/pipelines/framepack
+        title: Framepack
+      - local: api/pipelines/hunyuan_video
+        title: HunyuanVideo
+      - local: api/pipelines/hunyuan_video15
+        title: HunyuanVideo1.5
+      - local: api/pipelines/i2vgenxl
+        title: I2VGen-XL
+      - local: api/pipelines/kandinsky5_video
+        title: Kandinsky 5.0 Video
+      - local: api/pipelines/latte
+        title: Latte
+      - local: api/pipelines/ltx2
+        title: LTX-2
+      - local: api/pipelines/ltx_video
+        title: LTXVideo
+      - local: api/pipelines/mochi
+        title: Mochi
+      - local: api/pipelines/pia
+        title: Personalized Image Animator (PIA)
+      - local: api/pipelines/skyreels_v2
+        title: SkyReels-V2
+      - local: api/pipelines/stable_diffusion/svd
+        title: Stable Video Diffusion
+      - local: api/pipelines/text_to_video
+        title: Text-to-video
+      - local: api/pipelines/text_to_video_zero
+        title: Text2Video-Zero
+      - local: api/pipelines/wan
+        title: Wan
+      title: Video
+    title: Pipelines
+  - sections:
     - local: api/schedulers/overview
       title: Overview
     - local: api/schedulers/cm_stochastic_iterative
@@ -716,8 +770,8 @@
       title: UniPCMultistepScheduler
     - local: api/schedulers/vq_diffusion
       title: VQDiffusionScheduler
-  - title: Internal classes
+    title: Schedulers
-    sections:
+  - sections:
     - local: api/internal_classes_overview
       title: Overview
     - local: api/attnprocessor
@@ -734,3 +788,5 @@
       title: VAE Image Processor
     - local: api/video_processor
       title: Video Processor
+    title: Internal classes
+  title: API

docs/source/en/api/cache.md

@@ -29,8 +29,14 @@ Cache methods speedup diffusion transformers by storing and reusing intermediate
 
 [[autodoc]] apply_faster_cache
 
-### FirstBlockCacheConfig
+## FirstBlockCacheConfig
 
 [[autodoc]] FirstBlockCacheConfig
 
 [[autodoc]] apply_first_block_cache
+
+### TaylorSeerCacheConfig
+
+[[autodoc]] TaylorSeerCacheConfig
+
+[[autodoc]] apply_taylorseer_cache

docs/source/en/api/configuration.md

@@ -14,11 +14,8 @@ specific language governing permissions and limitations under the License.
 
 Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which stores all the parameters that are passed to their respective `__init__` methods in a JSON-configuration file.
 
-<Tip>
+> [!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 `hf auth login`.
-
-</Tip>
 
 ## ConfigMixin
 

docs/source/en/api/image_processor.md

@@ -20,6 +20,12 @@ All pipelines with [`VaeImageProcessor`] accept PIL Image, PyTorch tensor, or Nu
 
 [[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.

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

@@ -14,11 +14,8 @@ specific language governing permissions and limitations under the License.
 
 [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>
+> [!TIP]
-
+> Learn how to load and use an IP-Adapter checkpoint and image in the [IP-Adapter](../../using-diffusers/ip_adapter) guide,.
-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
 

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

@@ -30,14 +30,14 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`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)
+- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen).
+- [`ZImageLoraLoaderMixin`] provides similar functions for [Z-Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/zimage).
+- [`Flux2LoraLoaderMixin`] provides similar functions for [Flux2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux2).
+- [`LTX2LoraLoaderMixin`] provides similar functions for [Flux2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx2).
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
-<Tip>
+> [!TIP]
-
+> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
 
 ## LoraBaseMixin
 
@@ -59,6 +59,14 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.FluxLoraLoaderMixin
 
+## Flux2LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.Flux2LoraLoaderMixin
+
+## LTX2LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.LTX2LoraLoaderMixin
+
 ## CogVideoXLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.CogVideoXLoraLoaderMixin
@@ -110,6 +118,13 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
 
+## ZImageLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.ZImageLoraLoaderMixin
+
+## KandinskyLoraLoaderMixin
+[[autodoc]] loaders.lora_pipeline.KandinskyLoraLoaderMixin
+
 ## LoraBaseMixin
 
 [[autodoc]] loaders.lora_base.LoraBaseMixin

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

@@ -12,13 +12,10 @@ 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.
+Diffusers supports loading adapters such as [LoRA](../../tutorials/using_peft_for_inference) 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>
+> [!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.
-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
 

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

@@ -16,11 +16,8 @@ Textual Inversion is a training method for personalizing models by learning new
 
 [`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>
+> [!TIP]
-
+> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/textual_inversion_inference) loading guide.
-To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
-
-</Tip>
 
 ## TextualInversionLoaderMixin
 

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

@@ -16,11 +16,8 @@ This class is useful when *only* loading weights into a [`SD3Transformer2DModel`
 
 The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
 
-<Tip>
+> [!TIP]
-
+> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
 
 ## SD3Transformer2DLoadersMixin
 

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

@@ -16,11 +16,8 @@ Some training methods - like LoRA and Custom Diffusion - typically target the UN
 
 The [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.
 
-<Tip>
+> [!TIP]
-
+> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) guide.
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
 
 ## UNet2DConditionLoadersMixin
 

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

@@ -39,7 +39,7 @@ mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images
 original_image = load_image(img_url).resize((512, 512))
 mask_image = load_image(mask_url).resize((512, 512))
 
-pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
+pipe = StableDiffusionInpaintPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting")
 pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
 pipe.to("cuda")
 

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

@@ -12,15 +12,7 @@ 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.
+[`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
 

docs/source/en/api/models/autoencoder_kl_hunyuan_video15.md

@@ -0,0 +1,36 @@
+<!-- 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. -->
+
+# AutoencoderKLHunyuanVideo15
+
+The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanVideo1.5](https://github.com/Tencent/HunyuanVideo1-1.5) by Tencent.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLHunyuanVideo15
+
+vae = AutoencoderKLHunyuanVideo15.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v", subfolder="vae", torch_dtype=torch.float32)
+
+# make sure to enable tiling to avoid OOM
+vae.enable_tiling()
+```
+
+## AutoencoderKLHunyuanVideo15
+
+[[autodoc]] AutoencoderKLHunyuanVideo15
+  - decode
+  - encode
+  - all
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -0,0 +1,32 @@
+<!-- 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. -->
+
+# AutoencoderKLHunyuanImage
+
+The 2D variational autoencoder (VAE) model with KL loss used in [HunyuanImage2.1].
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLHunyuanImage
+
+vae = AutoencoderKLHunyuanImage.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)
+```
+
+## AutoencoderKLHunyuanImage
+
+[[autodoc]] AutoencoderKLHunyuanImage
+  - decode
+  - all
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -0,0 +1,32 @@
+<!-- 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. -->
+
+# AutoencoderKLHunyuanImageRefiner
+
+The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanImage2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1) for its refiner pipeline.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLHunyuanImageRefiner
+
+vae = AutoencoderKLHunyuanImageRefiner.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)
+```
+
+## AutoencoderKLHunyuanImageRefiner
+
+[[autodoc]] AutoencoderKLHunyuanImageRefiner
+  - decode
+  - all
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/autoencoderkl_audio_ltx_2.md

@@ -0,0 +1,29 @@
+<!-- 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. -->
+
+# AutoencoderKLLTX2Audio
+
+The 3D variational autoencoder (VAE) model with KL loss used in [LTX-2](https://huggingface.co/Lightricks/LTX-2) was introduced by Lightricks. This is for encoding and decoding audio latent representations.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLLTX2Audio
+
+vae = AutoencoderKLLTX2Audio.from_pretrained("Lightricks/LTX-2", subfolder="vae", torch_dtype=torch.float32).to("cuda")
+```
+
+## AutoencoderKLLTX2Audio
+
+[[autodoc]] AutoencoderKLLTX2Audio
+    - encode
+    - decode
+    - all

docs/source/en/api/models/autoencoderkl_ltx_2.md

@@ -0,0 +1,29 @@
+<!-- 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. -->
+
+# AutoencoderKLLTX2Video
+
+The 3D variational autoencoder (VAE) model with KL loss used in [LTX-2](https://huggingface.co/Lightricks/LTX-2) was introduced by Lightricks.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLLTX2Video
+
+vae = AutoencoderKLLTX2Video.from_pretrained("Lightricks/LTX-2", subfolder="vae", torch_dtype=torch.float32).to("cuda")
+```
+
+## AutoencoderKLLTX2Video
+
+[[autodoc]] AutoencoderKLLTX2Video
+    - decode
+    - encode
+    - all

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

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # ChromaTransformer2DModel
 
-A modified flux Transformer model from [Chroma](https://huggingface.co/lodestones/Chroma)
+A modified flux Transformer model from [Chroma](https://huggingface.co/lodestones/Chroma1-HD)
 
 ## ChromaTransformer2DModel
 

docs/source/en/api/models/chronoedit_transformer_3d.md

@@ -0,0 +1,32 @@
+<!-- Copyright 2025 The ChronoEdit Team and 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. -->
+
+# ChronoEditTransformer3DModel
+
+A Diffusion Transformer model for 3D video-like data from [ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation](https://huggingface.co/papers/2510.04290) from NVIDIA and University of Toronto, by Jay Zhangjie Wu, Xuanchi Ren, Tianchang Shen, Tianshi Cao, Kai He, Yifan Lu, Ruiyuan Gao, Enze Xie, Shiyi Lan, Jose M. Alvarez, Jun Gao, Sanja Fidler, Zian Wang, Huan Ling.
+
+> **TL;DR:** ChronoEdit reframes image editing as a video generation task, using input and edited images as start/end frames to leverage pretrained video models with temporal consistency. A temporal reasoning stage introduces reasoning tokens to ensure physically plausible edits and visualize the editing trajectory.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import ChronoEditTransformer3DModel
+
+transformer = ChronoEditTransformer3DModel.from_pretrained("nvidia/ChronoEdit-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## ChronoEditTransformer3DModel
+
+[[autodoc]] ChronoEditTransformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -16,11 +16,8 @@ Consistency decoder can be used to decode the latents from the denoising UNet in
 
 The original codebase can be found at [openai/consistencydecoder](https://github.com/openai/consistencydecoder).
 
-<Tip warning={true}>
+> [!WARNING]
-
+> Inference is only supported for 2 iterations as of now.
-Inference is only supported for 2 iterations as of now.
-
-</Tip>
 
 The pipeline could not have been contributed without the help of [madebyollin](https://github.com/madebyollin) and [mrsteyk](https://github.com/mrsteyk) from [this issue](https://github.com/openai/consistencydecoder/issues/1).
 

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

@@ -33,6 +33,21 @@ url = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/m
 pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)
 ```
 
+## Loading from Control LoRA
+
+Control-LoRA is introduced by Stability AI in [stabilityai/control-lora](https://huggingface.co/stabilityai/control-lora) by adding low-rank parameter efficient fine tuning to ControlNet. This approach offers a more efficient and compact method to bring model control to a wider variety of consumer GPUs.
+
+```py
+from diffusers import ControlNetModel, UNet2DConditionModel
+
+lora_id = "stabilityai/control-lora"
+lora_filename = "control-LoRAs-rank128/control-lora-canny-rank128.safetensors"
+
+unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", torch_dtype=torch.bfloat16).to("cuda")
+controlnet = ControlNetModel.from_unet(unet).to(device="cuda", dtype=torch.bfloat16)
+controlnet.load_lora_adapter(lora_id, weight_name=lora_filename, prefix=None, controlnet_config=controlnet.config)
+```
+
 ## ControlNetModel
 
 [[autodoc]] ControlNetModel

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

@@ -42,4 +42,4 @@ pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", co
 
 ## FluxControlNetOutput
 
-[[autodoc]] models.controlnet_flux.FluxControlNetOutput
+[[autodoc]] models.controlnets.controlnet_flux.FluxControlNetOutput

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

@@ -43,4 +43,4 @@ controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectr
 
 ## SparseControlNetOutput
 
-[[autodoc]] models.controlnet_sparsectrl.SparseControlNetOutput
+[[autodoc]] models.controlnets.controlnet_sparsectrl.SparseControlNetOutput

docs/source/en/api/models/flux2_transformer.md

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# Flux2Transformer2DModel
+
+A Transformer model for image-like data from [Flux2](https://hf.co/black-forest-labs/FLUX.2-dev).
+
+## Flux2Transformer2DModel
+
+[[autodoc]] Flux2Transformer2DModel

docs/source/en/api/models/glm_image_transformer2d.md

@@ -0,0 +1,18 @@
+<!--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. -->
+
+# GlmImageTransformer2DModel
+
+A Diffusion Transformer model for 2D data from [GlmImageTransformer2DModel] (TODO).
+
+## GlmImageTransformer2DModel
+
+[[autodoc]] GlmImageTransformer2DModel

docs/source/en/api/models/hunyuan_video15_transformer_3d.md

@@ -0,0 +1,30 @@
+<!-- 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. -->
+
+# HunyuanVideo15Transformer3DModel
+
+A Diffusion Transformer model for 3D video-like data used in [HunyuanVideo1.5](https://github.com/Tencent/HunyuanVideo1-1.5).
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import HunyuanVideo15Transformer3DModel
+
+transformer = HunyuanVideo15Transformer3DModel.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v" subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## HunyuanVideo15Transformer3DModel
+
+[[autodoc]] HunyuanVideo15Transformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/hunyuanimage_transformer_2d.md

@@ -0,0 +1,30 @@
+<!-- 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. -->
+
+# HunyuanImageTransformer2DModel
+
+A Diffusion Transformer model for [HunyuanImage2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1).
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import HunyuanImageTransformer2DModel
+
+transformer = HunyuanImageTransformer2DModel.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## HunyuanImageTransformer2DModel
+
+[[autodoc]] HunyuanImageTransformer2DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/longcat_image_transformer2d.md

@@ -0,0 +1,25 @@
+<!--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.
+-->
+
+# LongCatImageTransformer2DModel
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import LongCatImageTransformer2DModel
+
+transformer = LongCatImageTransformer2DModel.from_pretrained("meituan-longcat/LongCat-Image ", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## LongCatImageTransformer2DModel
+
+[[autodoc]] LongCatImageTransformer2DModel

docs/source/en/api/models/ltx2_video_transformer3d.md

@@ -0,0 +1,26 @@
+<!-- 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. -->
+
+# LTX2VideoTransformer3DModel
+
+A Diffusion Transformer model for 3D data from [LTX](https://huggingface.co/Lightricks/LTX-2) was introduced by Lightricks.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import LTX2VideoTransformer3DModel
+
+transformer = LTX2VideoTransformer3DModel.from_pretrained("Lightricks/LTX-2", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+```
+
+## LTX2VideoTransformer3DModel
+
+[[autodoc]] LTX2VideoTransformer3DModel

docs/source/en/api/models/ovisimage_transformer2d.md

@@ -0,0 +1,24 @@
+<!-- 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. -->
+
+# OvisImageTransformer2DModel
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import OvisImageTransformer2DModel
+
+transformer = OvisImageTransformer2DModel.from_pretrained("AIDC-AI/Ovis-Image-7B", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## OvisImageTransformer2DModel
+
+[[autodoc]] OvisImageTransformer2DModel

docs/source/en/api/models/sana_video_transformer3d.md

@@ -0,0 +1,36 @@
+<!-- Copyright 2025 The SANA-Video Authors and 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. -->
+
+# SanaVideoTransformer3DModel
+
+A Diffusion Transformer model for 3D data (video) from [SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer](https://huggingface.co/papers/2509.24695) from NVIDIA and MIT HAN Lab, by Junsong Chen, Yuyang Zhao, Jincheng Yu, Ruihang Chu, Junyu Chen, Shuai Yang, Xianbang Wang, Yicheng Pan, Daquan Zhou, Huan Ling, Haozhe Liu, Hongwei Yi, Hao Zhang, Muyang Li, Yukang Chen, Han Cai, Sanja Fidler, Ping Luo, Song Han, Enze Xie.
+
+The abstract from the paper is:
+
+*We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720x1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Two core designs ensure our efficient, effective and long video generation: (1) Linear DiT: We leverage linear attention as the core operation, which is more efficient than vanilla attention given the large number of tokens processed in video generation. (2) Constant-Memory KV cache for Block Linear Attention: we design block-wise autoregressive approach for long video generation by employing a constant-memory state, derived from the cumulative properties of linear attention. This KV cache provides the Linear DiT with global context at a fixed memory cost, eliminating the need for a traditional KV cache and enabling efficient, minute-long video generation. In addition, we explore effective data filters and model training strategies, narrowing the training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of MovieGen. Given its low cost, SANA-Video achieves competitive performance compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover, SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x speedup). In summary, SANA-Video enables low-cost, high-quality video generation.*
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import SanaVideoTransformer3DModel
+import torch
+
+transformer = SanaVideoTransformer3DModel.from_pretrained("Efficient-Large-Model/SANA-Video_2B_480p_diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## SanaVideoTransformer3DModel
+
+[[autodoc]] SanaVideoTransformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput
+

docs/source/en/api/models/transformer2d.md

@@ -22,11 +22,8 @@ When the input is **continuous**:
 
 When the input is **discrete**:
 
-<Tip>
+> [!TIP]
-
+> It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked.
-It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked.
-
-</Tip>
 
 1. Convert input (classes of latent pixels) to embeddings and apply positional embeddings.
 2. Apply the Transformer blocks in the standard way.

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

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# BriaFiboTransformer2DModel
+
+A modified flux Transformer model from [Bria](https://huggingface.co/briaai/FIBO)
+
+## BriaFiboTransformer2DModel
+
+[[autodoc]] BriaFiboTransformer2DModel

docs/source/en/api/models/wan_animate_transformer_3d.md

@@ -0,0 +1,30 @@
+<!-- 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. -->
+
+# WanAnimateTransformer3DModel
+
+A Diffusion Transformer model for 3D video-like data was introduced in [Wan Animate](https://github.com/Wan-Video/Wan2.2) by the Alibaba Wan Team.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import WanAnimateTransformer3DModel
+
+transformer = WanAnimateTransformer3DModel.from_pretrained("Wan-AI/Wan2.2-Animate-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## WanAnimateTransformer3DModel
+
+[[autodoc]] WanAnimateTransformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/z_image_transformer2d.md

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# ZImageTransformer2DModel
+
+A Transformer model for image-like data from [Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo).
+
+## ZImageTransformer2DModel
+
+[[autodoc]] ZImageTransformer2DModel

docs/source/en/api/outputs.md

@@ -39,11 +39,8 @@ For instance, retrieving an image by indexing into it returns the tuple `(output
 outputs[:1]
 ```
 
-<Tip>
+> [!TIP]
-
+> To check a specific pipeline or model output, refer to its corresponding API documentation.
-To check a specific pipeline or model output, refer to its corresponding API documentation.
-
-</Tip>
 
 ## BaseOutput
 

docs/source/en/api/parallel.md

@@ -0,0 +1,24 @@
+<!-- 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. -->
+
+# Parallelism
+
+Parallelism strategies help speed up diffusion transformers by distributing computations across multiple devices, allowing for faster inference/training times. Refer to the [Distributed inferece](../training/distributed_inference) guide to learn more.
+
+## ParallelConfig
+
+[[autodoc]] ParallelConfig
+
+## ContextParallelConfig
+
+[[autodoc]] ContextParallelConfig
+
+[[autodoc]] hooks.apply_context_parallel

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

@@ -17,11 +17,8 @@ The abstract from the paper is:
 
 *Significant advancements have been made in the field of video generation, with the open-source community contributing a wealth of research papers and tools for training high-quality models. However, despite these efforts, the available information and resources remain insufficient for achieving commercial-level performance. In this report, we open the black box and introduce Allegro, an advanced video generation model that excels in both quality and temporal consistency. We also highlight the current limitations in the field and present a comprehensive methodology for training high-performance, commercial-level video generation models, addressing key aspects such as data, model architecture, training pipeline, and evaluation. Our user study shows that Allegro surpasses existing open-source models and most commercial models, ranking just behind Hailuo and Kling. Code: https://github.com/rhymes-ai/Allegro , Model: https://huggingface.co/rhymes-ai/Allegro , Gallery: https://rhymes.ai/allegro_gallery .*
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## Quantization
 

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

@@ -102,11 +102,8 @@ Here are some sample outputs:
     </tr>
 </table>
 
-<Tip>
+> [!TIP]
-
+> AnimateDiff tends to work better with finetuned Stable Diffusion models. If you plan on using a scheduler that can clip samples, make sure to disable it by setting `clip_sample=False` in the scheduler as this can also have an adverse effect on generated samples. Additionally, the AnimateDiff checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to `linear`.
-AnimateDiff tends to work better with finetuned Stable Diffusion models. If you plan on using a scheduler that can clip samples, make sure to disable it by setting `clip_sample=False` in the scheduler as this can also have an adverse effect on generated samples. Additionally, the AnimateDiff checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to `linear`.
-
-</Tip>
 
 ### AnimateDiffControlNetPipeline
 
@@ -799,17 +796,11 @@ frames = output.frames[0]
 export_to_gif(frames, "animation.gif")
 ```
 
-<Tip warning={true}>
+> [!WARNING]
+> FreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the `num_iters` parameter that is set when enabling it. Setting the `use_fast_sampling` parameter to `True` can improve the overall performance (at the cost of lower quality compared to when `use_fast_sampling=False` but still better results than vanilla video generation models).
 
-FreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the `num_iters` parameter that is set when enabling it. Setting the `use_fast_sampling` parameter to `True` can improve the overall performance (at the cost of lower quality compared to when `use_fast_sampling=False` but still better results than vanilla video generation models).
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-</Tip>
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 <table>
     <tr>

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

@@ -23,11 +23,8 @@ The abstract from the paper is:
 
 You can find additional information about Attend-and-Excite on the [project page](https://attendandexcite.github.io/Attend-and-Excite/), the [original codebase](https://github.com/AttendAndExcite/Attend-and-Excite), or try it out in a [demo](https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite).
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## StableDiffusionAttendAndExcitePipeline
 

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

@@ -38,11 +38,8 @@ During inference:
 * The _quality_ of the predicted audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference.
 * The _length_ of the predicted audio sample can be controlled by varying the `audio_length_in_s` argument.
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## AudioLDMPipeline
 [[autodoc]] AudioLDMPipeline

docs/source/en/api/pipelines/audioldm2.md

@@ -58,11 +58,8 @@ See table below for details on the three checkpoints:
 
 The following example demonstrates how to construct good music and speech generation using the aforementioned tips: [example](https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2#diffusers.AudioLDM2Pipeline.__call__.example).
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## AudioLDM2Pipeline
 [[autodoc]] AudioLDM2Pipeline

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

@@ -16,11 +16,8 @@ AuraFlow is inspired by [Stable Diffusion 3](../pipelines/stable_diffusion/stabl
 
 It was developed by the Fal team and more details about it can be found in [this blog post](https://blog.fal.ai/auraflow/).
 
-<Tip>
+> [!TIP]
-
+> AuraFlow can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details.
-AuraFlow can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details.
-
-</Tip>
 
 ## Quantization
 

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

@@ -26,11 +26,8 @@ The original codebase can be found at [salesforce/LAVIS](https://github.com/sale
 
 `BlipDiffusionPipeline` and `BlipDiffusionControlNetPipeline` were contributed by [`ayushtues`](https://github.com/ayushtues/).
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 
 ## BlipDiffusionPipeline

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

@@ -0,0 +1,45 @@
+<!--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.
+-->
+
+# Bria Fibo
+
+Text-to-image models have mastered imagination - but not control. FIBO changes that.
+
+FIBO is trained on structured JSON captions up to 1,000+ words and designed to understand and control different visual parameters such as lighting, composition, color, and camera settings, enabling precise and reproducible outputs.
+
+With only 8 billion parameters, FIBO provides a new level of image quality, prompt adherence and proffesional control.
+
+FIBO is trained exclusively on a structured prompt and will not work with freeform text prompts.
+you can use the [FIBO-VLM-prompt-to-JSON](https://huggingface.co/briaai/FIBO-VLM-prompt-to-JSON) model or the [FIBO-gemini-prompt-to-JSON](https://huggingface.co/briaai/FIBO-gemini-prompt-to-JSON)  to convert your freeform text prompt to a structured JSON prompt.
+
+> [!NOTE]
+> Avoid using freeform text prompts directly with FIBO because it does not produce the best results.
+
+Refer to the Bria Fibo Hugging Face [page](https://huggingface.co/briaai/FIBO) to learn more.
+
+
+## Usage
+
+_As the model is gated, before using it with diffusers you first need to go to the [Bria Fibo Hugging Face page](https://huggingface.co/briaai/FIBO), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
+
+Use the command below to log in:
+
+```bash
+hf auth login
+```
+
+
+## BriaFiboPipeline
+
+[[autodoc]] BriaFiboPipeline
+	- all
+	- __call__

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

@@ -19,23 +19,21 @@ specific language governing permissions and limitations under the License.
 
 Chroma is a text to image generation model based on Flux.
 
-Original model checkpoints for Chroma can be found [here](https://huggingface.co/lodestones/Chroma).
+Original model checkpoints for Chroma can be found here:
+* High-resolution finetune: [lodestones/Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD)
+* Base model: [lodestones/Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base)
+* Original repo with progress checkpoints: [lodestones/Chroma](https://huggingface.co/lodestones/Chroma) (loading this repo with `from_pretrained` will load a Diffusers-compatible version of the `unlocked-v37` checkpoint)
 
-<Tip>
+> [!TIP]
-
+> Chroma can use all the same optimizations as Flux.
-Chroma can use all the same optimizations as Flux.
-
-</Tip>
 
 ## Inference
 
-The Diffusers version of Chroma is based on the [`unlocked-v37`](https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors) version of the original model, which is available in the [Chroma repository](https://huggingface.co/lodestones/Chroma).
-
 ```python
 import torch
 from diffusers import ChromaPipeline
 
-pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16)
+pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.bfloat16)
 pipe.enable_model_cpu_offload()
 
 prompt = [
@@ -66,10 +64,10 @@ Then run the following example
 import torch
 from diffusers import ChromaTransformer2DModel, ChromaPipeline
 
-model_id = "lodestones/Chroma"
+model_id = "lodestones/Chroma1-HD"
 dtype = torch.bfloat16
 
-transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors", torch_dtype=dtype)
+transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma1-HD/blob/main/Chroma1-HD.safetensors", torch_dtype=dtype)
 
 pipe = ChromaPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=dtype)
 pipe.enable_model_cpu_offload()

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

@@ -0,0 +1,211 @@
+<!-- Copyright 2025 The ChronoEdit Team and 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. -->
+
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+    <a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
+      <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+    </a>
+  </div>
+</div>
+
+# ChronoEdit
+
+[ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation](https://huggingface.co/papers/2510.04290) from NVIDIA and University of Toronto, by Jay Zhangjie Wu, Xuanchi Ren, Tianchang Shen, Tianshi Cao, Kai He, Yifan Lu, Ruiyuan Gao, Enze Xie, Shiyi Lan, Jose M. Alvarez, Jun Gao, Sanja Fidler, Zian Wang, Huan Ling.
+
+> **TL;DR:** ChronoEdit reframes image editing as a video generation task, using input and edited images as start/end frames to leverage pretrained video models with temporal consistency. A temporal reasoning stage introduces reasoning tokens to ensure physically plausible edits and visualize the editing trajectory.
+
+*Recent advances in large generative models have greatly enhanced both image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, where edited objects must remain coherent. This capability is especially vital for world simulation related tasks. In this paper, we present ChronoEdit, a framework that reframes image editing as a video generation problem. First, ChronoEdit treats the input and edited images as the first and last frames of a video, allowing it to leverage large pretrained video generative models that capture not only object appearance but also the implicit physics of motion and interaction through learned temporal consistency. Second, ChronoEdit introduces a temporal reasoning stage that explicitly performs editing at inference time. Under this setting, target frame is jointly denoised with reasoning tokens to imagine a plausible editing trajectory that constrains the solution space to physically viable transformations. The reasoning tokens are then dropped after a few steps to avoid the high computational cost of rendering a full video. To validate ChronoEdit, we introduce PBench-Edit, a new benchmark of image-prompt pairs for contexts that require physical consistency, and demonstrate that ChronoEdit surpasses state-of-the-art baselines in both visual fidelity and physical plausibility. Project page for code and models: [this https URL](https://research.nvidia.com/labs/toronto-ai/chronoedit).*
+
+The ChronoEdit pipeline is developed by the ChronoEdit Team. The original code is available on [GitHub](https://github.com/nv-tlabs/ChronoEdit), and pretrained models can be found in the [nvidia/ChronoEdit](https://huggingface.co/collections/nvidia/chronoedit) collection on Hugging Face.
+
+Available Models/LoRAs:
+- [nvidia/ChronoEdit-14B-Diffusers](https://huggingface.co/nvidia/ChronoEdit-14B-Diffusers)
+- [nvidia/ChronoEdit-14B-Diffusers-Upscaler-Lora](https://huggingface.co/nvidia/ChronoEdit-14B-Diffusers-Upscaler-Lora)
+- [nvidia/ChronoEdit-14B-Diffusers-Paint-Brush-Lora](https://huggingface.co/nvidia/ChronoEdit-14B-Diffusers-Paint-Brush-Lora)
+
+### Image Editing
+
+```py
+import torch
+import numpy as np
+from diffusers import AutoencoderKLWan, ChronoEditTransformer3DModel, ChronoEditPipeline
+from diffusers.utils import export_to_video, load_image
+from transformers import CLIPVisionModel
+from PIL import Image
+
+model_id = "nvidia/ChronoEdit-14B-Diffusers"
+image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+transformer = ChronoEditTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
+pipe = ChronoEditPipeline.from_pretrained(model_id, image_encoder=image_encoder, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+image = load_image(
+    "https://huggingface.co/spaces/nvidia/ChronoEdit/resolve/main/examples/3.png"
+)
+max_area = 720 * 1280
+aspect_ratio = image.height / image.width
+mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+print("width", width, "height", height)
+image = image.resize((width, height))
+prompt = (
+    "The user wants to transform the image by adding a small, cute mouse sitting inside the floral teacup, enjoying a spa bath. The mouse should appear relaxed and cheerful, with a tiny white bath towel draped over its head like a turban. It should be positioned comfortably in the cup’s liquid, with gentle steam rising around it to blend with the cozy atmosphere. "
+    "The mouse’s pose should be natural—perhaps sitting upright with paws resting lightly on the rim or submerged in the tea. The teacup’s floral design, gold trim, and warm lighting must remain unchanged to preserve the original aesthetic. The steam should softly swirl around the mouse, enhancing the spa-like, whimsical mood."
+)
+
+output = pipe(
+    image=image,
+    prompt=prompt,
+    height=height,
+    width=width,
+    num_frames=5,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+    enable_temporal_reasoning=False,
+    num_temporal_reasoning_steps=0,
+).frames[0]
+Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output.png")
+```
+
+Optionally, enable **temporal reasoning** for improved physical consistency:
+```py
+output = pipe(
+    image=image,
+    prompt=prompt,
+    height=height,
+    width=width,
+    num_frames=29,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+    enable_temporal_reasoning=True,
+    num_temporal_reasoning_steps=50,
+).frames[0]
+export_to_video(output, "output.mp4", fps=16)
+Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output.png")
+```
+
+### Inference with 8-Step Distillation Lora
+
+```py
+import torch
+import numpy as np
+from diffusers import AutoencoderKLWan, ChronoEditTransformer3DModel, ChronoEditPipeline
+from diffusers.schedulers import UniPCMultistepScheduler
+from diffusers.utils import export_to_video, load_image
+from transformers import CLIPVisionModel
+from PIL import Image
+
+model_id = "nvidia/ChronoEdit-14B-Diffusers"
+image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+transformer = ChronoEditTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
+pipe = ChronoEditPipeline.from_pretrained(model_id, image_encoder=image_encoder, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16)
+pipe.load_lora_weights("nvidia/ChronoEdit-14B-Diffusers", weight_name="lora/chronoedit_distill_lora.safetensors", adapter_name="distill")
+pipe.fuse_lora(adapter_names=["distill"], lora_scale=1.0)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=2.0)
+pipe.to("cuda")
+
+image = load_image(
+    "https://huggingface.co/spaces/nvidia/ChronoEdit/resolve/main/examples/3.png"
+)
+max_area = 720 * 1280
+aspect_ratio = image.height / image.width
+mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+print("width", width, "height", height)
+image = image.resize((width, height))
+prompt = (
+    "The user wants to transform the image by adding a small, cute mouse sitting inside the floral teacup, enjoying a spa bath. The mouse should appear relaxed and cheerful, with a tiny white bath towel draped over its head like a turban. It should be positioned comfortably in the cup’s liquid, with gentle steam rising around it to blend with the cozy atmosphere. "
+    "The mouse’s pose should be natural—perhaps sitting upright with paws resting lightly on the rim or submerged in the tea. The teacup’s floral design, gold trim, and warm lighting must remain unchanged to preserve the original aesthetic. The steam should softly swirl around the mouse, enhancing the spa-like, whimsical mood."
+)
+
+output = pipe(
+    image=image,
+    prompt=prompt,
+    height=height,
+    width=width,
+    num_frames=5,
+    num_inference_steps=8,
+    guidance_scale=1.0,
+    enable_temporal_reasoning=False,
+    num_temporal_reasoning_steps=0,
+).frames[0]
+export_to_video(output, "output.mp4", fps=16)
+Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output.png")
+```
+
+### Inference with Multiple LoRAs
+
+```py
+import torch
+import numpy as np
+from diffusers import AutoencoderKLWan, ChronoEditTransformer3DModel, ChronoEditPipeline
+from diffusers.schedulers import UniPCMultistepScheduler
+from diffusers.utils import export_to_video, load_image
+from transformers import CLIPVisionModel
+from PIL import Image
+
+model_id = "nvidia/ChronoEdit-14B-Diffusers"
+image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+transformer = ChronoEditTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
+pipe = ChronoEditPipeline.from_pretrained(model_id, image_encoder=image_encoder, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16)
+pipe.load_lora_weights("nvidia/ChronoEdit-14B-Diffusers-Paint-Brush-Lora", weight_name="paintbrush_lora_diffusers.safetensors", adapter_name="paintbrush")
+pipe.load_lora_weights("nvidia/ChronoEdit-14B-Diffusers", weight_name="lora/chronoedit_distill_lora.safetensors", adapter_name="distill")
+pipe.fuse_lora(adapter_names=["paintbrush", "distill"], lora_scale=1.0)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=2.0)
+pipe.to("cuda")
+
+image = load_image(
+    "https://raw.githubusercontent.com/nv-tlabs/ChronoEdit/refs/heads/main/assets/images/input_paintbrush.png"
+)
+max_area = 720 * 1280
+aspect_ratio = image.height / image.width
+mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+print("width", width, "height", height)
+image = image.resize((width, height))
+prompt = (
+    "Turn the pencil sketch in the image into an actual object that is consistent with the image’s content. The user wants to change the sketch to a crown and a hat."
+)
+
+output = pipe(
+    image=image,
+    prompt=prompt,
+    height=height,
+    width=width,
+    num_frames=5,
+    num_inference_steps=8,
+    guidance_scale=1.0,
+    enable_temporal_reasoning=False,
+    num_temporal_reasoning_steps=0,
+).frames[0]
+export_to_video(output, "output.mp4", fps=16)
+Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output_1.png")
+```
+
+## ChronoEditPipeline
+
+[[autodoc]] ChronoEditPipeline
+  - all
+  - __call__
+
+## ChronoEditPipelineOutput
+
+[[autodoc]] pipelines.chronoedit.pipeline_output.ChronoEditPipelineOutput

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

@@ -50,7 +50,7 @@ from diffusers.utils import export_to_video
 pipeline_quant_config = PipelineQuantizationConfig(
   quant_backend="torchao",
   quant_kwargs={"quant_type": "int8wo"},
-  components_to_quantize=["transformer"]
+  components_to_quantize="transformer"
 )
 
 # fp8 layerwise weight-casting

docs/source/en/api/pipelines/cogview3.md

@@ -21,11 +21,8 @@ The abstract from the paper is:
 
 *Recent advancements in text-to-image generative systems have been largely driven by diffusion models. However, single-stage text-to-image diffusion models still face challenges, in terms of computational efficiency and the refinement of image details. To tackle the issue, we propose CogView3, an innovative cascaded framework that enhances the performance of text-to-image diffusion. CogView3 is the first model implementing relay diffusion in the realm of text-to-image generation, executing the task by first creating low-resolution images and subsequently applying relay-based super-resolution. This methodology not only results in competitive text-to-image outputs but also greatly reduces both training and inference costs. Our experimental results demonstrate that CogView3 outperforms SDXL, the current state-of-the-art open-source text-to-image diffusion model, by 77.0% in human evaluations, all while requiring only about 1/2 of the inference time. The distilled variant of CogView3 achieves comparable performance while only utilizing 1/10 of the inference time by SDXL.*
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
 

docs/source/en/api/pipelines/cogview4.md

@@ -15,11 +15,8 @@
 
 # CogView4
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
 

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

@@ -25,11 +25,8 @@ The abstract from the paper is:
 
 *Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity. It is an important task in video generation but remains an open problem for generative models. This paper pushes the technical frontier of IPT2V in two directions that have not been resolved in the literature: (1) A tuning-free pipeline without tedious case-by-case finetuning, and (2) A frequency-aware heuristic identity-preserving Diffusion Transformer (DiT)-based control scheme. To achieve these goals, we propose **ConsisID**, a tuning-free DiT-based controllable IPT2V model to keep human-**id**entity **consis**tent in the generated video. Inspired by prior findings in frequency analysis of vision/diffusion transformers, it employs identity-control signals in the frequency domain, where facial features can be decomposed into low-frequency global features (e.g., profile, proportions) and high-frequency intrinsic features (e.g., identity markers that remain unaffected by pose changes). First, from a low-frequency perspective, we introduce a global facial extractor, which encodes the reference image and facial key points into a latent space, generating features enriched with low-frequency information. These features are then integrated into the shallow layers of the network to alleviate training challenges associated with DiT. Second, from a high-frequency perspective, we design a local facial extractor to capture high-frequency details and inject them into the transformer blocks, enhancing the model's ability to preserve fine-grained features. To leverage the frequency information for identity preservation, we propose a hierarchical training strategy, transforming a vanilla pre-trained video generation model into an IPT2V model. Extensive experiments demonstrate that our frequency-aware heuristic scheme provides an optimal control solution for DiT-based models. Thanks to this scheme, our **ConsisID** achieves excellent results in generating high-quality, identity-preserving videos, making strides towards more effective IPT2V. The model weight of ConsID is publicly available at https://github.com/PKU-YuanGroup/ConsisID.*
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 This pipeline was contributed by [SHYuanBest](https://github.com/SHYuanBest). The original codebase can be found [here](https://github.com/PKU-YuanGroup/ConsisID). The original weights can be found under [hf.co/BestWishYsh](https://huggingface.co/BestWishYsh).
 

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

@@ -26,11 +26,8 @@ FLUX.1 Depth and Canny [dev] is a 12 billion parameter rectified flow transforme
 | Canny | [Black Forest Labs](https://huggingface.co/black-forest-labs) | [Link](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev) |
 
 
-<Tip>
+> [!TIP]
-
+> Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more. For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
-Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more. For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
-
-</Tip>
 
 ```python
 import torch

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

@@ -28,11 +28,8 @@ This model was contributed by [takuma104](https://huggingface.co/takuma104). ❤
 
 The original codebase can be found at [lllyasviel/ControlNet](https://github.com/lllyasviel/ControlNet), and you can find official ControlNet checkpoints on [lllyasviel's](https://huggingface.co/lllyasviel) Hub profile.
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## StableDiffusionControlNetPipeline
 [[autodoc]] StableDiffusionControlNetPipeline

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

@@ -44,11 +44,8 @@ XLabs ControlNets are also supported, which was contributed by the [XLabs team](
 | HED | [The XLabs Team](https://huggingface.co/XLabs-AI) | [Link](https://huggingface.co/XLabs-AI/flux-controlnet-hed-diffusers) |
 
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## FluxControlNetPipeline
 [[autodoc]] FluxControlNetPipeline

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

@@ -24,11 +24,8 @@ The abstract from the paper is:
 
 This code is implemented by Tencent Hunyuan Team. You can find pre-trained checkpoints for Hunyuan-DiT ControlNets on [Tencent Hunyuan](https://huggingface.co/Tencent-Hunyuan).
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## HunyuanDiTControlNetPipeline
 [[autodoc]] HunyuanDiTControlNetPipeline

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

@@ -38,11 +38,8 @@ This controlnet code is mainly implemented by [The InstantX Team](https://huggin
 | Inpainting | [The AlimamaCreative Team](https://huggingface.co/alimama-creative) | [link](https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting) |
 
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## StableDiffusion3ControlNetPipeline
 [[autodoc]] StableDiffusion3ControlNetPipeline

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

@@ -26,19 +26,13 @@ The abstract from the paper is:
 
 You can find additional smaller Stable Diffusion XL (SDXL) ControlNet checkpoints from the 🤗 [Diffusers](https://huggingface.co/diffusers) Hub organization, and browse [community-trained](https://huggingface.co/models?other=stable-diffusion-xl&other=controlnet) checkpoints on the Hub.
 
-<Tip warning={true}>
+> [!WARNING]
-
+> 🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!
-🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!
-
-</Tip>
 
 If you don't see a checkpoint you're interested in, you can train your own SDXL ControlNet with our [training script](../../../../../examples/controlnet/README_sdxl).
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## StableDiffusionXLControlNetPipeline
 [[autodoc]] StableDiffusionXLControlNetPipeline

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

@@ -31,11 +31,8 @@ Here's the overview from the [project page](https://vislearn.github.io/ControlNe
 
 This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## StableDiffusionControlNetXSPipeline
 [[autodoc]] StableDiffusionControlNetXSPipeline

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

@@ -27,17 +27,11 @@ Here's the overview from the [project page](https://vislearn.github.io/ControlNe
 
 This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️
 
-<Tip warning={true}>
+> [!WARNING]
+> 🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!
 
-🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-</Tip>
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## StableDiffusionXLControlNetXSPipeline
 [[autodoc]] StableDiffusionXLControlNetXSPipeline

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

@@ -18,11 +18,8 @@
 
 *Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications. Our platform covers a video curation pipeline, pre-trained world foundation models, examples of post-training of pre-trained world foundation models, and video tokenizers. To help Physical AI builders solve the most critical problems of our society, we make our platform open-source and our models open-weight with permissive licenses available via https://github.com/NVIDIA/Cosmos.*
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## Loading original format checkpoints
 
@@ -73,6 +70,12 @@ output.save("output.png")
   - all
   - __call__
 
+## Cosmos2_5_PredictBasePipeline
+
+[[autodoc]] Cosmos2_5_PredictBasePipeline
+  - all
+  - __call__
+
 ## CosmosPipelineOutput
 
 [[autodoc]] pipelines.cosmos.pipeline_output.CosmosPipelineOutput

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

@@ -20,11 +20,8 @@ specific language governing permissions and limitations under the License.
 Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by [Harmonai](https://github.com/Harmonai-org).
 
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## DanceDiffusionPipeline
 [[autodoc]] DanceDiffusionPipeline

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

@@ -20,11 +20,8 @@ The abstract from the paper is:
 
 The original codebase can be found at [hohonathanho/diffusion](https://github.com/hojonathanho/diffusion).
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 # DDPMPipeline
 [[autodoc]] DDPMPipeline

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

@@ -21,7 +21,7 @@ The abstract from the paper is:
 
 *Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to edit an image based on a text query. Semantic image editing is an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image. Current editing methods based on diffusion models usually require to provide a mask, making the task much easier by treating it as a conditional inpainting task. In contrast, our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited, by contrasting predictions of a diffusion model conditioned on different text prompts. Moreover, we rely on latent inference to preserve content in those regions of interest and show excellent synergies with mask-based diffusion. DiffEdit achieves state-of-the-art editing performance on ImageNet. In addition, we evaluate semantic image editing in more challenging settings, using images from the COCO dataset as well as text-based generated images.*
 
-The original codebase can be found at [Xiang-cd/DiffEdit-stable-diffusion](https://github.com/Xiang-cd/DiffEdit-stable-diffusion), and you can try it out in this [demo](https://blog.problemsolversguild.com/technical/research/2022/11/02/DiffEdit-Implementation.html).
+The original codebase can be found at [Xiang-cd/DiffEdit-stable-diffusion](https://github.com/Xiang-cd/DiffEdit-stable-diffusion), and you can try it out in this [demo](https://blog.problemsolversguild.com/posts/2022-11-02-diffedit-implementation.html).
 
 This pipeline was contributed by [clarencechen](https://github.com/clarencechen). ❤️
 

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

@@ -20,11 +20,8 @@ The abstract from the paper is:
 
 The original codebase can be found at [facebookresearch/dit](https://github.com/facebookresearch/dit).
 
-<Tip>
+> [!TIP]
-
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
 
 ## DiTPipeline
 [[autodoc]] DiTPipeline