fix

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

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

.github/workflows/benchmark.yml

@@ -7,25 +7,24 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  BASE_PATH: benchmark_outputs
 
 jobs:
-  torch_models_cuda_benchmark_tests:
+  torch_pipelines_cuda_benchmark_tests:
     env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_BENCHMARK }}
-    name: Torch Core Models CUDA Benchmarking Tests
+    name: Torch Core Pipelines CUDA Benchmarking Tests
     strategy:
       fail-fast: false
       max-parallel: 1
     runs-on:
-      group: aws-g6e-4xlarge
+      group: aws-g6-4xlarge-plus
     container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      image: diffusers/diffusers-pytorch-compile-cuda
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -36,46 +35,26 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          apt update
-          apt install -y libpq-dev postgresql-client
-          uv pip install -e ".[quality]"
-          uv pip install -r benchmarks/requirements.txt
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install pandas peft
       - name: Environment
         run: |
           python utils/print_env.py
       - name: Diffusers Benchmarking
         env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+            HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
+            BASE_PATH: benchmark_outputs
         run: |
-          cd benchmarks && python run_all.py
-
-      - name: Push results to the Hub
-        env: 
-          HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
-        run: |
-          cd benchmarks && python push_results.py
-          mkdir $BASE_PATH && cp *.csv $BASE_PATH
+          export TOTAL_GPU_MEMORY=$(python -c "import torch; print(torch.cuda.get_device_properties(0).total_memory / (1024**3))")
+          cd benchmarks && mkdir ${BASE_PATH} && python run_all.py && python push_results.py
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
           name: benchmark_test_reports
-          path: benchmarks/${{ env.BASE_PATH }}
-      
-      # TODO: enable this once the connection problem has been resolved.
-      - name: Update benchmarking results to DB
-        env:
-          PGDATABASE: metrics
-          PGHOST: ${{ secrets.DIFFUSERS_BENCHMARKS_PGHOST }}
-          PGUSER: transformers_benchmarks
-          PGPASSWORD: ${{ secrets.DIFFUSERS_BENCHMARKS_PGPASSWORD }}
-          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
-        run: |
-          git config --global --add safe.directory /__w/diffusers/diffusers
-          commit_id=$GITHUB_SHA
-          commit_msg=$(git show -s --format=%s "$commit_id" | cut -c1-70)
-          cd benchmarks && python populate_into_db.py "$BRANCH_NAME" "$commit_id" "$commit_msg"
+          path: benchmarks/benchmark_outputs
 
       - name: Report success status
         if: ${{ success() }}

.github/workflows/build_docker_images.yml

@@ -38,43 +38,15 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build Changed Docker Images
-        env: 
-          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=()
-
+          CHANGED_FILES="${{ steps.file_changes.outputs.all }}"
           for FILE in $CHANGED_FILES; do
-            # skip anything that isn't still on disk
-            if [[ ! -e "$FILE" ]]; then
-              echo "Skipping removed file $FILE"
-              continue
+            if [[ "$FILE" == docker/*Dockerfile ]]; then
+              DOCKER_PATH="${FILE%/Dockerfile}"
+              DOCKER_TAG=$(basename "$DOCKER_PATH")
+              echo "Building Docker image for $DOCKER_TAG"
+              docker build -t "$DOCKER_TAG" "$DOCKER_PATH"
             fi
-
-            for IMAGE in "${ALLOWED_IMAGES[@]}"; do
-              if [[ "$FILE" == docker/${IMAGE}/* ]]; then
-                IMAGES_TO_BUILD["$IMAGE"]=1
-              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 != ''
 
@@ -93,8 +65,13 @@ jobs:
         image-name:
           - diffusers-pytorch-cpu
           - diffusers-pytorch-cuda
+          - diffusers-pytorch-compile-cuda
           - diffusers-pytorch-xformers-cuda
           - diffusers-pytorch-minimum-cuda
+          - diffusers-flax-cpu
+          - diffusers-flax-tpu
+          - diffusers-onnxruntime-cpu
+          - diffusers-onnxruntime-cuda
           - diffusers-doc-builder
 
     steps:

.github/workflows/build_pr_documentation.yml

@@ -12,33 +12,7 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  check-links:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        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/mirror_community_pipeline.yml

@@ -74,19 +74,19 @@ jobs:
           python-version: "3.10"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install --upgrade huggingface_hub
 
       # Check secret is set
       - name: whoami
-        run: hf auth whoami
+        run: huggingface-cli whoami
         env:
             HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}
 
       # Push to HF! (under subfolder based on checkout ref)
       # https://huggingface.co/datasets/diffusers/community-pipelines-mirror
       - name: Mirror community pipeline to HF
-        run: hf upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
+        run: huggingface-cli upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
         env:
             PATH_IN_REPO: ${{ env.PATH_IN_REPO }}
             HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}

.github/workflows/nightly_tests.yml

@@ -7,15 +7,14 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 600
   RUN_SLOW: yes
   RUN_NIGHTLY: yes
-  PIPELINE_USAGE_CUTOFF: 0
+  PIPELINE_USAGE_CUTOFF: 5000
   SLACK_API_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
-  CONSOLIDATED_REPORT_PATH: consolidated_test_report.md
 
 jobs:
   setup_torch_cuda_pipeline_matrix:
@@ -61,7 +60,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -71,11 +70,10 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip uninstall accelerate && 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 
-          uv pip install pytest-reportlog
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -85,8 +83,8 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-             -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \
             tests/pipelines/${{ matrix.module }}
@@ -101,6 +99,11 @@ jobs:
         with:
           name: pipeline_${{ matrix.module }}_test_reports
           path: reports
+      - name: Generate Report and Notify Channel
+        if: always()
+        run: |
+          pip install slack_sdk tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
   run_nightly_tests_for_other_torch_modules:
     name: Nightly Torch CUDA Tests
@@ -108,7 +111,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -125,12 +128,11 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
-        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
-        #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 
-        uv pip install pytest-reportlog
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install pytest-reportlog
     - name: Environment
       run: python utils/print_env.py
 
@@ -141,8 +143,8 @@ jobs:
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           --report-log=tests_torch_${{ matrix.module }}_cuda.log \
           tests/${{ matrix.module }}
@@ -154,8 +156,8 @@ jobs:
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          --make-reports=examples_torch_cuda \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v --make-reports=examples_torch_cuda \
           --report-log=examples_torch_cuda.log \
           examples/
 
@@ -172,49 +174,11 @@ jobs:
         name: torch_${{ matrix.module }}_cuda_test_reports
         path: reports
 
-  run_torch_compile_tests:
-    name: PyTorch Compile CUDA tests
-
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
+    - name: Generate Report and Notify Channel
+      if: always()
       run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        uv pip install -e ".[quality,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
-    - name: Run torch compile tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        RUN_COMPILE: yes
-      run: |
-        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
-      with:
-        name: torch_compile_test_reports
-        path: reports
+        pip install slack_sdk tabulate
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
   run_big_gpu_torch_tests:
     name: Torch tests on big GPU
@@ -225,7 +189,7 @@ jobs:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -235,12 +199,11 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          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
-          #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 
-          uv pip install pytest-reportlog
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -251,8 +214,8 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
           BIG_GPU_MEMORY: 40
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -m "big_accelerator" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -m "big_gpu_with_torch_cuda" \
             --make-reports=tests_big_gpu_torch_cuda \
             --report-log=tests_big_gpu_torch_cuda.log \
             tests/
@@ -267,14 +230,19 @@ jobs:
         with:
           name: torch_cuda_big_gpu_test_reports
           path: reports
-
+      - name: Generate Report and Notify Channel
+        if: always()
+        run: |
+          pip install slack_sdk tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+          
   torch_minimum_version_cuda_tests:
     name: Torch Minimum Version CUDA Tests
     runs-on:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -286,11 +254,10 @@ jobs:
 
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          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
-          #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 
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
       - name: Environment
         run: |
@@ -298,12 +265,12 @@ jobs:
 
       - name: Run PyTorch CUDA tests
         env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_torch_minimum_version_cuda \
             tests/models/test_modeling_common.py \
             tests/pipelines/test_pipelines_common.py \
@@ -325,34 +292,137 @@ jobs:
         with:
           name: torch_minimum_version_cuda_test_reports
           path: reports
+ 
+  run_flax_tpu_tests:
+    name: Nightly Flax TPU Tests
+    runs-on:
+      group: gcp-ct5lp-hightpu-8t
+    if: github.event_name == 'schedule'
+
+    container:
+      image: diffusers/diffusers-flax-tpu
+      options: --shm-size "16gb" --ipc host --privileged ${{ vars.V5_LITEPOD_8_ENV}} -v /mnt/hf_cache:/mnt/hf_cache
+    defaults:
+      run:
+        shell: bash
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install pytest-reportlog
+
+    - name: Environment
+      run: python utils/print_env.py
+
+    - name: Run nightly Flax TPU tests
+      env:
+        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+      run: |
+        python -m pytest -n 0 \
+          -s -v -k "Flax" \
+          --make-reports=tests_flax_tpu \
+          --report-log=tests_flax_tpu.log \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_flax_tpu_stats.txt
+        cat reports/tests_flax_tpu_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: flax_tpu_test_reports
+        path: reports
+
+    - name: Generate Report and Notify Channel
+      if: always()
+      run: |
+        pip install slack_sdk tabulate
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
+  run_nightly_onnx_tests:
+    name: Nightly ONNXRuntime CUDA tests on Ubuntu
+    runs-on:
+      group: aws-g4dn-2xlarge
+    container:
+      image: diffusers/diffusers-onnxruntime-cuda
+      options: --gpus 0 --shm-size "16gb" --ipc host
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: NVIDIA-SMI
+      run: nvidia-smi
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install pytest-reportlog
+    - name: Environment
+      run: python utils/print_env.py
+
+    - name: Run Nightly ONNXRuntime CUDA tests
+      env:
+        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_onnx_cuda \
+          --report-log=tests_onnx_cuda.log \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_onnx_cuda_stats.txt
+        cat reports/tests_onnx_cuda_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: tests_onnx_cuda_reports
+        path: reports
+
+    - name: Generate Report and Notify Channel
+      if: always()
+      run: |
+        pip install slack_sdk tabulate
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
   run_nightly_quantization_tests:
     name: Torch quantization nightly tests
     strategy:
       fail-fast: false
       max-parallel: 2
-      matrix:
+      matrix: 
         config:
           - backend: "bitsandbytes"
             test_location: "bnb"
-            additional_deps: ["peft"]
           - backend: "gguf"
             test_location: "gguf"
-            additional_deps: ["peft", "kernels"]
           - backend: "torchao"
             test_location: "torchao"
-            additional_deps: []
-          - backend: "optimum_quanto"
-            test_location: "quanto"
-            additional_deps: []
-          - backend: "nvidia_modelopt"
-            test_location: "modelopt"
-            additional_deps: []
     runs-on:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus all
+      options: --shm-size "20gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -362,14 +432,10 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip install -U ${{ matrix.config.backend }}
-          if [ "${{ join(matrix.config.additional_deps, ' ') }}" != "" ]; then
-              uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
-          fi
-          uv pip install pytest-reportlog
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install -U ${{ matrix.config.backend }}
+          python -m uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -380,7 +446,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
           BIG_GPU_MEMORY: 40
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
             --make-reports=tests_${{ matrix.config.backend }}_torch_cuda \
             --report-log=tests_${{ matrix.config.backend }}_torch_cuda.log \
             tests/quantization/${{ matrix.config.test_location }}
@@ -395,115 +461,11 @@ jobs:
         with:
           name: torch_cuda_${{ matrix.config.backend }}_reports
           path: reports
-          
-  run_nightly_pipeline_level_quantization_tests:
-    name: Torch quantization nightly tests
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-    runs-on:
-      group: aws-g6e-xlarge-plus
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus all
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: nvidia-smi
-      - name: Install dependencies
+      - name: Generate Report and Notify Channel
+        if: always()
         run: |
-          uv pip install -e ".[quality]"
-          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
-          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
-      - name: Pipeline-level quantization tests on GPU
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-          BIG_GPU_MEMORY: 40
-        run: |
-          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
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_pipeline_level_quant_torch_cuda_stats.txt
-          cat reports/tests_pipeline_level_quant_torch_cuda_failures_short.txt
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: torch_cuda_pipeline_level_quant_reports
-          path: reports
-
-  generate_consolidated_report:
-    name: Generate Consolidated Test Report
-    needs: [
-      run_nightly_tests_for_torch_pipelines,
-      run_nightly_tests_for_other_torch_modules,
-      run_torch_compile_tests,
-      run_big_gpu_torch_tests,
-      run_nightly_quantization_tests,
-      run_nightly_pipeline_level_quantization_tests,
-      # run_nightly_onnx_tests,
-      torch_minimum_version_cuda_tests,
-      # run_flax_tpu_tests
-    ]
-    if: always()
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: Create reports directory
-        run: mkdir -p combined_reports
-
-      - name: Download all test reports
-        uses: actions/download-artifact@v4
-        with:
-          path: artifacts
-
-      - name: Prepare reports
-        run: |
-          # Move all report files to a single directory for processing
-          find artifacts -name "*.txt" -exec cp {} combined_reports/ \;
-
-      - name: Install dependencies
-        run: |
-          pip install -e .[test]
           pip install slack_sdk tabulate
-
-      - name: Generate consolidated report
-        run: |
-          python utils/consolidated_test_report.py \
-            --reports_dir combined_reports \
-            --output_file $CONSOLIDATED_REPORT_PATH \
-            --slack_channel_name diffusers-ci-nightly
-
-      - name: Show consolidated report
-        run: |
-          cat $CONSOLIDATED_REPORT_PATH >> $GITHUB_STEP_SUMMARY
-
-      - name: Upload consolidated report
-        uses: actions/upload-artifact@v4
-        with:
-          name: consolidated_test_report
-          path: ${{ env.CONSOLIDATED_REPORT_PATH }}
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
 # M1 runner currently not well supported
 # TODO: (Dhruv) add these back when we setup better testing for Apple Silicon
@@ -530,11 +492,11 @@ jobs:
 #      - name: Install dependencies
 #        shell: arch -arch arm64 bash {0}
 #        run: |
-#          ${CONDA_RUN} pip install --upgrade pip uv
-#          ${CONDA_RUN} uv pip install -e ".[quality]"
-#          ${CONDA_RUN} uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} uv pip install pytest-reportlog
+#          ${CONDA_RUN} python -m pip install --upgrade pip uv
+#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
+#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
+#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
 #      - name: Environment
 #        shell: arch -arch arm64 bash {0}
 #        run: |
@@ -543,9 +505,9 @@ jobs:
 #        shell: arch -arch arm64 bash {0}
 #        env:
 #          HF_HOME: /System/Volumes/Data/mnt/cache
-#          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+#          HF_TOKEN: ${{ secrets.HF_TOKEN }}
 #        run: |
-#          ${CONDA_RUN} pytest -n 1  --make-reports=tests_torch_mps \
+#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
 #            tests/
 #      - name: Failure short reports
@@ -586,11 +548,11 @@ jobs:
 #      - name: Install dependencies
 #        shell: arch -arch arm64 bash {0}
 #        run: |
-#          ${CONDA_RUN} pip install --upgrade pip uv
-#          ${CONDA_RUN} uv pip install -e ".[quality]"
-#          ${CONDA_RUN} uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} uv pip install pytest-reportlog
+#          ${CONDA_RUN} python -m pip install --upgrade pip uv
+#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
+#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
+#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
 #      - name: Environment
 #        shell: arch -arch arm64 bash {0}
 #        run: |
@@ -599,9 +561,9 @@ jobs:
 #        shell: arch -arch arm64 bash {0}
 #        env:
 #          HF_HOME: /System/Volumes/Data/mnt/cache
-#          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+#          HF_TOKEN: ${{ secrets.HF_TOKEN }}
 #        run: |
-#          ${CONDA_RUN} pytest -n 1  --make-reports=tests_torch_mps \
+#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
 #            tests/
 #      - name: Failure short reports

.github/workflows/pr_dependency_test.yml

@@ -25,8 +25,11 @@ jobs:
           python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install -e .
-          pip install pytest
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m pip install --upgrade pip uv
+          python -m uv pip install -e .
+          python -m uv pip install pytest
       - name: Check for soft dependencies
         run: |
-            pytest tests/others/test_dependencies.py
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          pytest tests/others/test_dependencies.py

.github/workflows/pr_flax_dependency_test.yml

@@ -0,0 +1,38 @@
+name: Run Flax dependency tests
+
+on:
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "src/diffusers/**.py"
+  push:
+    branches:
+      - main
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  check_flax_dependencies:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.8"
+      - name: Install dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m pip install --upgrade pip uv
+          python -m uv pip install -e .
+          python -m uv pip install "jax[cpu]>=0.2.16,!=0.3.2"
+          python -m uv pip install "flax>=0.4.1"
+          python -m uv pip install "jaxlib>=0.1.65"
+          python -m uv pip install pytest
+      - name: Check for soft dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          pytest tests/others/test_dependencies.py

.github/workflows/pr_modular_tests.yml

@@ -1,139 +0,0 @@
-name: Fast PR tests for Modular
-
-on:
-  pull_request:
-    branches: [main]
-    paths:
-      - "src/diffusers/modular_pipelines/**.py"
-      - "src/diffusers/models/modeling_utils.py"
-      - "src/diffusers/models/model_loading_utils.py"
-      - "src/diffusers/pipelines/pipeline_utils.py"
-      - "src/diffusers/pipeline_loading_utils.py"
-      - "src/diffusers/loaders/lora_base.py"
-      - "src/diffusers/loaders/lora_pipeline.py"
-      - "src/diffusers/loaders/peft.py"
-      - "tests/modular_pipelines/**.py"
-      - ".github/**.yml"
-      - "utils/**.py"
-      - "setup.py"
-  push:
-    branches:
-      - ci-*
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
-  OMP_NUM_THREADS: 4
-  MKL_NUM_THREADS: 4
-  PYTEST_TIMEOUT: 60
-
-jobs:
-  check_code_quality:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          pip install --upgrade pip
-          pip install .[quality]
-      - name: Check quality
-        run: make quality
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
-
-  check_repository_consistency:
-    needs: check_code_quality
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          pip install --upgrade pip
-          pip install .[quality]
-      - name: Check repo consistency
-        run: |
-          python utils/check_copies.py
-          python utils/check_dummies.py
-          python utils/check_support_list.py
-          make deps_table_check_updated
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
-
-  run_fast_tests:
-    needs: [check_code_quality, check_repository_consistency]
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - name: Fast PyTorch Modular Pipeline CPU tests
-            framework: pytorch_pipelines
-            runner: aws-highmemory-32-plus
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_cpu_modular_pipelines
-
-    name: ${{ matrix.config.name }}
-
-    runs-on:
-      group: ${{ matrix.config.runner }}
-
-    container:
-      image: ${{ matrix.config.image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-
-    defaults:
-      run:
-        shell: bash
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        uv pip install -e ".[quality]"
-        #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
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run fast PyTorch Pipeline CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
-      run: |
-        pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/modular_pipelines
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
-        path: reports
-
-

.github/workflows/pr_style_bot.yml

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

.github/workflows/pr_test_fetcher.yml

@@ -33,7 +33,8 @@ jobs:
         fetch-depth: 0
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -89,16 +90,19 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
-        uv pip install accelerate
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pip install -e [quality,test]
+        python -m pip install accelerate
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run all selected tests on CPU
       run: |
-        pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.modules }}_tests_cpu ${{ fromJson(needs.setup_pr_tests.outputs.test_map)[matrix.modules] }}
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.modules }}_tests_cpu ${{ fromJson(needs.setup_pr_tests.outputs.test_map)[matrix.modules] }}
 
     - name: Failure short reports
       if: ${{ failure() }}
@@ -144,16 +148,19 @@ jobs:
 
     - name: Install dependencies
       run: |
-        pip install -e [quality]
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pip install -e [quality,test]
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run Hub tests for models, schedulers, and pipelines on a staging env
       if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}
       run: |
-        HUGGINGFACE_CO_STAGING=true pytest \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        HUGGINGFACE_CO_STAGING=true python -m pytest \
           -m "is_staging_test" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests

.github/workflows/pr_tests.yml

@@ -2,7 +2,8 @@ name: Fast tests for PRs
 
 on:
   pull_request:
-    branches: [main]
+    branches:
+      - main
     paths:
       - "src/diffusers/**.py"
       - "benchmarks/**.py"
@@ -11,7 +12,6 @@ on:
       - "tests/**.py"
       - ".github/**.yml"
       - "utils/**.py"
-      - "setup.py"
   push:
     branches:
       - ci-*
@@ -22,7 +22,7 @@ concurrency:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60
@@ -38,7 +38,7 @@ jobs:
           python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install .[quality]
       - name: Check quality
         run: make quality
@@ -58,13 +58,12 @@ jobs:
           python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install .[quality]
       - name: Check repo consistency
         run: |
           python utils/check_copies.py
           python utils/check_dummies.py
-          python utils/check_support_list.py
           make deps_table_check_updated
       - name: Check if failure
         if: ${{ failure() }}
@@ -87,6 +86,11 @@ jobs:
             runner: aws-general-8-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu_models_schedulers
+          - name: Fast Flax CPU tests
+            framework: flax
+            runner: aws-general-8-plus
+            image: diffusers/diffusers-flax-cpu
+            report: flax_cpu
           - name: PyTorch Example CPU tests
             framework: pytorch_examples
             runner: aws-general-8-plus
@@ -114,36 +118,48 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
-        #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
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install accelerate
 
     - 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: ${{ matrix.config.framework == 'pytorch_pipelines' }}
       run: |
-        pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/pipelines
 
     - name: Run fast PyTorch Model Scheduler CPU tests
       if: ${{ matrix.config.framework == 'pytorch_models' }}
       run: |
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx and not Dependency" \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx and not Dependency" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/models tests/schedulers tests/others
 
+    - name: Run fast Flax TPU tests
+      if: ${{ matrix.config.framework == 'flax' }}
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Flax" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests
+
     - name: Run example PyTorch CPU tests
       if: ${{ matrix.config.framework == 'pytorch_examples' }}
       run: |
-        uv pip install ".[training]"
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install peft timm
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
           examples
 
@@ -191,16 +207,19 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        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: ${{ matrix.config.framework == 'hub_tests_pytorch' }}
       run: |
-        HUGGINGFACE_CO_STAGING=true pytest \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        HUGGINGFACE_CO_STAGING=true python -m pytest \
           -m "is_staging_test" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests
@@ -242,34 +261,36 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
         # TODO (sayakpaul, DN6): revisit `--no-deps`
-        uv pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
-        uv pip install -U tokenizers
-        uv pip uninstall accelerate && 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
+        python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
+        python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        python -m uv pip install -U tokenizers
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run fast PyTorch LoRA tests with PEFT
       run: |
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v \
           --make-reports=tests_peft_main \
           tests/lora/
-        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_models_lora_peft_main \
           tests/models/ -k "lora"
 
     - name: Failure short reports
       if: ${{ failure() }}
       run: |
-        cat reports/tests_peft_main_failures_short.txt
-        cat reports/tests_models_lora_peft_main_failures_short.txt
+        cat reports/tests_lora_failures_short.txt
+        cat reports/tests_models_lora_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}

.github/workflows/pr_tests_gpu.yml

@@ -1,294 +0,0 @@
-name: Fast GPU Tests on PR
-
-on:
-  pull_request:
-    branches: main
-    paths:
-      - "src/diffusers/models/modeling_utils.py"
-      - "src/diffusers/models/model_loading_utils.py"
-      - "src/diffusers/pipelines/pipeline_utils.py"
-      - "src/diffusers/pipeline_loading_utils.py"
-      - "src/diffusers/loaders/lora_base.py"
-      - "src/diffusers/loaders/lora_pipeline.py"
-      - "src/diffusers/loaders/peft.py"
-      - "tests/pipelines/test_pipelines_common.py"
-      - "tests/models/test_modeling_common.py"
-      - "examples/**/*.py"
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
-  PYTEST_TIMEOUT: 600
-  PIPELINE_USAGE_CUTOFF: 1000000000 # set high cutoff so that only always-test pipelines run
-
-jobs:
-  check_code_quality:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-      - name: Install dependencies
-        run: |
-          pip install --upgrade pip
-          pip install .[quality]
-      - name: Check quality
-        run: make quality
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
-
-  check_repository_consistency:
-    needs: check_code_quality
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-      - name: Install dependencies
-        run: |
-          pip install --upgrade pip
-          pip install .[quality]
-      - name: Check repo consistency
-        run: |
-          python utils/check_copies.py
-          python utils/check_dummies.py
-          python utils/check_support_list.py
-          make deps_table_check_updated
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
-
-  setup_torch_cuda_pipeline_matrix:
-    needs: [check_code_quality, check_repository_consistency]
-    name: Setup Torch Pipelines CUDA Slow Tests Matrix
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-    outputs:
-      pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: Install dependencies
-        run: |
-          uv pip install -e ".[quality]"
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Fetch Pipeline Matrix
-        id: fetch_pipeline_matrix
-        run: |
-          matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)
-          echo $matrix
-          echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
-      - name: Pipeline Tests Artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: test-pipelines.json
-          path: reports
-
-  torch_pipelines_cuda_tests:
-    name: Torch Pipelines CUDA Tests
-    needs: setup_torch_cuda_pipeline_matrix
-    strategy:
-      fail-fast: false
-      max-parallel: 8
-      matrix:
-        module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-      - name: Install dependencies
-        run: |
-          uv pip install -e ".[quality]"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
-
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Extract tests
-        id: extract_tests
-        run: |
-          pattern=$(python utils/extract_tests_from_mixin.py --type pipeline)
-          echo "$pattern" > /tmp/test_pattern.txt
-          echo "pattern_file=/tmp/test_pattern.txt" >> $GITHUB_OUTPUT
-
-      - name: PyTorch CUDA checkpoint tests on Ubuntu
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          if [ "${{ matrix.module }}" = "ip_adapters" ]; then
-              pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -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 }})
-              pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -k "not Flax and not Onnx and $pattern" \
-              --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-              tests/pipelines/${{ matrix.module }}
-          fi
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_pipeline_${{ matrix.module }}_cuda_stats.txt
-          cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: pipeline_${{ matrix.module }}_test_reports
-          path: reports
-
-  torch_cuda_tests:
-    name: Torch CUDA Tests
-    needs: [check_code_quality, check_repository_consistency]
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
-    defaults:
-      run:
-        shell: bash
-    strategy:
-      fail-fast: false
-      max-parallel: 4
-      matrix:
-        module: [models, schedulers, lora, others]
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        uv pip install -e ".[quality]"
-        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
-        #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
-
-    - name: Extract tests
-      id: extract_tests
-      run: |
-        pattern=$(python utils/extract_tests_from_mixin.py --type ${{ matrix.module }})
-        echo "$pattern" > /tmp/test_pattern.txt
-        echo "pattern_file=/tmp/test_pattern.txt" >> $GITHUB_OUTPUT
-
-    - name: Run PyTorch CUDA tests
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
-      run: |
-        pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
-        if [ -z "$pattern" ]; then
-          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
-          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
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_torch_cuda_${{ matrix.module }}_stats.txt
-        cat reports/tests_torch_cuda_${{ matrix.module }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_cuda_test_reports_${{ matrix.module }}
-        path: reports
-
-  run_examples_tests:
-    name: Examples PyTorch CUDA tests on Ubuntu
-    needs: [check_code_quality, check_repository_consistency]
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: |
-        nvidia-smi
-    - name: Install dependencies
-      run: |
-        #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
-        uv pip install -e ".[quality,training]"
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run example tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-      run: |
-        uv pip install ".[training]"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/examples_torch_cuda_stats.txt
-        cat reports/examples_torch_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: examples_test_reports
-        path: reports
-

.github/workflows/pr_torch_dependency_test.yml

@@ -25,8 +25,12 @@ jobs:
           python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install -e .
-          pip install torch torchvision torchaudio pytest
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m pip install --upgrade pip uv
+          python -m uv pip install -e .
+          python -m uv pip install torch torchvision torchaudio
+          python -m uv pip install pytest
       - name: Check for soft dependencies
         run: |
-            pytest tests/others/test_dependencies.py
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$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_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   PIPELINE_USAGE_CUTOFF: 50000
 
@@ -34,7 +34,8 @@ jobs:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -63,7 +64,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -74,10 +75,9 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
       - name: Environment
         run: |
           python utils/print_env.py
@@ -87,8 +87,8 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -109,7 +109,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -126,11 +126,10 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
-        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
-        #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
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
     - name: Environment
       run: |
@@ -142,8 +141,8 @@ jobs:
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_cuda_${{ matrix.module }} \
           tests/${{ matrix.module }}
 
@@ -160,6 +159,102 @@ jobs:
         name: torch_cuda_test_reports_${{ matrix.module }}
         path: reports
 
+  flax_tpu_tests:
+    name: Flax TPU Tests
+    runs-on:
+      group: gcp-ct5lp-hightpu-8t
+    container:
+      image: diffusers/diffusers-flax-tpu
+      options: --shm-size "16gb" --ipc host --privileged ${{ vars.V5_LITEPOD_8_ENV}} -v /mnt/hf_cache:/mnt/hf_cache 
+    defaults:
+      run:
+        shell: bash
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run Flax TPU tests
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: |
+        python -m pytest -n 0 \
+          -s -v -k "Flax" \
+          --make-reports=tests_flax_tpu \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_flax_tpu_stats.txt
+        cat reports/tests_flax_tpu_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: flax_tpu_test_reports
+        path: reports
+
+  onnx_cuda_tests:
+    name: ONNX CUDA Tests
+    runs-on:
+      group: aws-g4dn-2xlarge
+    container:
+      image: diffusers/diffusers-onnxruntime-cuda
+      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --gpus 0
+    defaults:
+      run:
+        shell: bash
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run ONNXRuntime CUDA tests
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_onnx_cuda \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_onnx_cuda_stats.txt
+        cat reports/tests_onnx_cuda_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: onnx_cuda_test_reports
+        path: reports
+
   run_torch_compile_tests:
     name: PyTorch Compile CUDA tests
 
@@ -167,8 +262,8 @@ jobs:
       group: aws-g4dn-2xlarge
 
     container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      image: diffusers/diffusers-pytorch-compile-cuda
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -181,18 +276,17 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,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
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
     - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
         RUN_COMPILE: yes
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "compile" --make-reports=tests_torch_compile_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -212,7 +306,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -225,15 +319,16 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
     - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_xformers_cuda_failures_short.txt
@@ -253,7 +348,8 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
+
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
@@ -263,20 +359,24 @@ jobs:
     - name: NVIDIA-SMI
       run: |
         nvidia-smi
+
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
-        uv pip install ".[training]"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install timm
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/push_tests_fast.yml

@@ -18,7 +18,7 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
@@ -33,6 +33,16 @@ jobs:
             runner: aws-general-8-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu
+          - name: Fast Flax CPU tests on Ubuntu
+            framework: flax
+            runner: aws-general-8-plus
+            image: diffusers/diffusers-flax-cpu
+            report: flax_cpu
+          - name: Fast ONNXRuntime CPU tests on Ubuntu
+            framework: onnxruntime
+            runner: aws-general-8-plus
+            image: diffusers/diffusers-onnxruntime-cpu
+            report: onnx_cpu
           - name: PyTorch Example CPU tests on Ubuntu
             framework: pytorch_examples
             runner: aws-general-8-plus
@@ -60,25 +70,47 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        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: ${{ matrix.config.framework == 'pytorch' }}
       run: |
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run fast Flax TPU tests
+      if: ${{ matrix.config.framework == 'flax' }}
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Flax" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run fast ONNXRuntime CPU tests
+      if: ${{ matrix.config.framework == 'onnxruntime' }}
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/
 
     - name: Run example PyTorch CPU tests
       if: ${{ matrix.config.framework == 'pytorch_examples' }}
       run: |
-        uv pip install ".[training]"
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install peft timm
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
           examples
 

.github/workflows/push_tests_mps.yml

@@ -1,14 +1,19 @@
 name: Fast mps tests on main
 
 on:
-  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "src/diffusers/**.py"
+      - "tests/**.py"
 
 env:
   DIFFUSERS_IS_CI: yes
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
@@ -57,7 +62,7 @@ jobs:
         HF_HOME: /System/Volumes/Data/mnt/cache
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
-        ${CONDA_RUN} python -m pytest -n 0 --make-reports=tests_torch_mps tests/
+        ${CONDA_RUN} python -m pytest -n 0 -s -v --make-reports=tests_torch_mps tests/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/release_tests_fast.yml

@@ -32,7 +32,8 @@ jobs:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -61,7 +62,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -72,19 +73,20 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
       - name: Environment
         run: |
           python utils/print_env.py
       - name: Slow PyTorch CUDA checkpoint tests on Ubuntu
         env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -105,7 +107,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -122,9 +124,10 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
-        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
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
     - name: Environment
       run: |
@@ -132,12 +135,12 @@ jobs:
 
     - name: Run PyTorch CUDA tests
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           tests/${{ matrix.module }}
 
@@ -160,7 +163,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -172,9 +175,10 @@ jobs:
 
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          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
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
       - name: Environment
         run: |
@@ -182,12 +186,12 @@ jobs:
 
       - name: Run PyTorch CUDA tests
         env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_torch_minimum_cuda \
             tests/models/test_modeling_common.py \
             tests/pipelines/test_pipelines_common.py \
@@ -209,6 +213,101 @@ jobs:
         with:
           name: torch_minimum_version_cuda_test_reports
           path: reports
+          
+  flax_tpu_tests:
+    name: Flax TPU Tests
+    runs-on: docker-tpu
+    container:
+      image: diffusers/diffusers-flax-tpu
+      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --privileged
+    defaults:
+      run:
+        shell: bash
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run slow Flax TPU tests
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: |
+        python -m pytest -n 0 \
+          -s -v -k "Flax" \
+          --make-reports=tests_flax_tpu \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_flax_tpu_stats.txt
+        cat reports/tests_flax_tpu_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: flax_tpu_test_reports
+        path: reports
+
+  onnx_cuda_tests:
+    name: ONNX CUDA Tests
+    runs-on:
+      group: aws-g4dn-2xlarge
+    container:
+      image: diffusers/diffusers-onnxruntime-cuda
+      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --gpus 0
+    defaults:
+      run:
+        shell: bash
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run slow ONNXRuntime CUDA tests
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_onnx_cuda \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_onnx_cuda_stats.txt
+        cat reports/tests_onnx_cuda_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: onnx_cuda_test_reports
+        path: reports
 
   run_torch_compile_tests:
     name: PyTorch Compile CUDA tests
@@ -217,8 +316,8 @@ jobs:
       group: aws-g4dn-2xlarge
 
     container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      image: diffusers/diffusers-pytorch-compile-cuda
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -231,16 +330,17 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
-    - name: Run torch compile tests on GPU
+    - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
         RUN_COMPILE: yes
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "compile" --make-reports=tests_torch_compile_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -260,7 +360,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -273,15 +373,16 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
     - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_xformers_cuda_failures_short.txt
@@ -301,7 +402,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -315,18 +416,21 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
-        uv pip install ".[training]"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install timm
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/run_tests_from_a_pr.yml

@@ -7,8 +7,8 @@ on:
         default: 'diffusers/diffusers-pytorch-cuda'
         description: 'Name of the Docker image'
         required: true
-      pr_number:
-        description: 'PR number to test on'
+      branch:
+        description: 'PR Branch to test on'
         required: true
       test:
         description: 'Tests to run (e.g.: `tests/models`).'
@@ -30,7 +30,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: ${{ github.event.inputs.docker_image }}
-      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
 
     steps:
       - name: Validate test files input
@@ -43,8 +43,8 @@ jobs:
             exit 1
           fi
 
-          if [[ ! "$PY_TEST" =~ ^tests/(models|pipelines|lora) ]]; then
-            echo "Error: The input string must contain either 'models', 'pipelines', or 'lora' after 'tests/'."
+          if [[ ! "$PY_TEST" =~ ^tests/(models|pipelines) ]]; then
+            echo "Error: The input string must contain either 'models' or 'pipelines' after 'tests/'."
             exit 1
           fi
 
@@ -53,18 +53,19 @@ jobs:
             exit 1
           fi
           echo "$PY_TEST"
-        
-        shell: bash -e {0}
 
       - name: Checkout PR branch
         uses: actions/checkout@v4
         with:
-          ref: refs/pull/${{ inputs.pr_number }}/head
+          ref: ${{ github.event.inputs.branch }}
+          repository: ${{ github.event.pull_request.head.repo.full_name }}
+
 
       - name: Install pytest
         run: |
-          uv pip install -e ".[quality]"
-          uv pip install peft
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft
 
       - name: Run tests
         env:

.github/workflows/ssh-runner.yml

@@ -31,7 +31,7 @@ jobs:
       group: "${{ github.event.inputs.runner_type }}"
     container:
       image: ${{ github.event.inputs.docker_image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus all --privileged
+      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0 --privileged
 
     steps:
       - name: Checkout diffusers

.github/workflows/trufflehog.yml

@@ -13,6 +13,3 @@ jobs:
         fetch-depth: 0
     - name: Secret Scanning
       uses: trufflesecurity/trufflehog@main
-      with:
-        extra_args: --results=verified,unknown
-

.gitignore

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

CONTRIBUTING.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

PHILOSOPHY.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

README.md

@@ -37,7 +37,7 @@ limitations under the License.
 
 ## Installation
 
-We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/), please refer to their official documentation.
+We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.
 
 ### PyTorch
 
@@ -53,6 +53,14 @@ With `conda` (maintained by the community):
 conda install -c conda-forge diffusers
 ```
 
+### Flax
+
+With `pip` (official package):
+
+```bash
+pip install --upgrade diffusers[flax]
+```
+
 ### Apple Silicon (M1/M2) support
 
 Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.
@@ -171,7 +179,7 @@ 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/stable-diffusion-v1-5/stable-diffusion-inpainting"> stable-diffusion-v1-5/stable-diffusion-inpainting </a></td>
+      <td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
   </tr>
   <tr style="border-top: 2px solid black">
     <td>Image Variation</td>

benchmarks/README.md

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

benchmarks/base_classes.py

@@ -0,0 +1,346 @@
+import os
+import sys
+
+import torch
+
+from diffusers import (
+    AutoPipelineForImage2Image,
+    AutoPipelineForInpainting,
+    AutoPipelineForText2Image,
+    ControlNetModel,
+    LCMScheduler,
+    StableDiffusionAdapterPipeline,
+    StableDiffusionControlNetPipeline,
+    StableDiffusionXLAdapterPipeline,
+    StableDiffusionXLControlNetPipeline,
+    T2IAdapter,
+    WuerstchenCombinedPipeline,
+)
+from diffusers.utils import load_image
+
+
+sys.path.append(".")
+
+from utils import (  # noqa: E402
+    BASE_PATH,
+    PROMPT,
+    BenchmarkInfo,
+    benchmark_fn,
+    bytes_to_giga_bytes,
+    flush,
+    generate_csv_dict,
+    write_to_csv,
+)
+
+
+RESOLUTION_MAPPING = {
+    "Lykon/DreamShaper": (512, 512),
+    "lllyasviel/sd-controlnet-canny": (512, 512),
+    "diffusers/controlnet-canny-sdxl-1.0": (1024, 1024),
+    "TencentARC/t2iadapter_canny_sd14v1": (512, 512),
+    "TencentARC/t2i-adapter-canny-sdxl-1.0": (1024, 1024),
+    "stabilityai/stable-diffusion-2-1": (768, 768),
+    "stabilityai/stable-diffusion-xl-base-1.0": (1024, 1024),
+    "stabilityai/stable-diffusion-xl-refiner-1.0": (1024, 1024),
+    "stabilityai/sdxl-turbo": (512, 512),
+}
+
+
+class BaseBenchmak:
+    pipeline_class = None
+
+    def __init__(self, args):
+        super().__init__()
+
+    def run_inference(self, args):
+        raise NotImplementedError
+
+    def benchmark(self, args):
+        raise NotImplementedError
+
+    def get_result_filepath(self, args):
+        pipeline_class_name = str(self.pipe.__class__.__name__)
+        name = (
+            args.ckpt.replace("/", "_")
+            + "_"
+            + pipeline_class_name
+            + f"-bs@{args.batch_size}-steps@{args.num_inference_steps}-mco@{args.model_cpu_offload}-compile@{args.run_compile}.csv"
+        )
+        filepath = os.path.join(BASE_PATH, name)
+        return filepath
+
+
+class TextToImageBenchmark(BaseBenchmak):
+    pipeline_class = AutoPipelineForText2Image
+
+    def __init__(self, args):
+        pipe = self.pipeline_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)
+        pipe = pipe.to("cuda")
+
+        if args.run_compile:
+            if not isinstance(pipe, WuerstchenCombinedPipeline):
+                pipe.unet.to(memory_format=torch.channels_last)
+                print("Run torch compile")
+                pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+
+                if hasattr(pipe, "movq") and getattr(pipe, "movq", None) is not None:
+                    pipe.movq.to(memory_format=torch.channels_last)
+                    pipe.movq = torch.compile(pipe.movq, mode="reduce-overhead", fullgraph=True)
+            else:
+                print("Run torch compile")
+                pipe.decoder = torch.compile(pipe.decoder, mode="reduce-overhead", fullgraph=True)
+                pipe.vqgan = torch.compile(pipe.vqgan, mode="reduce-overhead", fullgraph=True)
+
+        pipe.set_progress_bar_config(disable=True)
+        self.pipe = pipe
+
+    def run_inference(self, pipe, args):
+        _ = pipe(
+            prompt=PROMPT,
+            num_inference_steps=args.num_inference_steps,
+            num_images_per_prompt=args.batch_size,
+        )
+
+    def benchmark(self, args):
+        flush()
+
+        print(f"[INFO] {self.pipe.__class__.__name__}: Running benchmark with: {vars(args)}\n")
+
+        time = benchmark_fn(self.run_inference, self.pipe, args)  # in seconds.
+        memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated())  # in GBs.
+        benchmark_info = BenchmarkInfo(time=time, memory=memory)
+
+        pipeline_class_name = str(self.pipe.__class__.__name__)
+        flush()
+        csv_dict = generate_csv_dict(
+            pipeline_cls=pipeline_class_name, ckpt=args.ckpt, args=args, benchmark_info=benchmark_info
+        )
+        filepath = self.get_result_filepath(args)
+        write_to_csv(filepath, csv_dict)
+        print(f"Logs written to: {filepath}")
+        flush()
+
+
+class TurboTextToImageBenchmark(TextToImageBenchmark):
+    def __init__(self, args):
+        super().__init__(args)
+
+    def run_inference(self, pipe, args):
+        _ = pipe(
+            prompt=PROMPT,
+            num_inference_steps=args.num_inference_steps,
+            num_images_per_prompt=args.batch_size,
+            guidance_scale=0.0,
+        )
+
+
+class LCMLoRATextToImageBenchmark(TextToImageBenchmark):
+    lora_id = "latent-consistency/lcm-lora-sdxl"
+
+    def __init__(self, args):
+        super().__init__(args)
+        self.pipe.load_lora_weights(self.lora_id)
+        self.pipe.fuse_lora()
+        self.pipe.unload_lora_weights()
+        self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)
+
+    def get_result_filepath(self, args):
+        pipeline_class_name = str(self.pipe.__class__.__name__)
+        name = (
+            self.lora_id.replace("/", "_")
+            + "_"
+            + pipeline_class_name
+            + f"-bs@{args.batch_size}-steps@{args.num_inference_steps}-mco@{args.model_cpu_offload}-compile@{args.run_compile}.csv"
+        )
+        filepath = os.path.join(BASE_PATH, name)
+        return filepath
+
+    def run_inference(self, pipe, args):
+        _ = pipe(
+            prompt=PROMPT,
+            num_inference_steps=args.num_inference_steps,
+            num_images_per_prompt=args.batch_size,
+            guidance_scale=1.0,
+        )
+
+    def benchmark(self, args):
+        flush()
+
+        print(f"[INFO] {self.pipe.__class__.__name__}: Running benchmark with: {vars(args)}\n")
+
+        time = benchmark_fn(self.run_inference, self.pipe, args)  # in seconds.
+        memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated())  # in GBs.
+        benchmark_info = BenchmarkInfo(time=time, memory=memory)
+
+        pipeline_class_name = str(self.pipe.__class__.__name__)
+        flush()
+        csv_dict = generate_csv_dict(
+            pipeline_cls=pipeline_class_name, ckpt=self.lora_id, args=args, benchmark_info=benchmark_info
+        )
+        filepath = self.get_result_filepath(args)
+        write_to_csv(filepath, csv_dict)
+        print(f"Logs written to: {filepath}")
+        flush()
+
+
+class ImageToImageBenchmark(TextToImageBenchmark):
+    pipeline_class = AutoPipelineForImage2Image
+    url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/1665_Girl_with_a_Pearl_Earring.jpg"
+    image = load_image(url).convert("RGB")
+
+    def __init__(self, args):
+        super().__init__(args)
+        self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
+
+    def run_inference(self, pipe, args):
+        _ = pipe(
+            prompt=PROMPT,
+            image=self.image,
+            num_inference_steps=args.num_inference_steps,
+            num_images_per_prompt=args.batch_size,
+        )
+
+
+class TurboImageToImageBenchmark(ImageToImageBenchmark):
+    def __init__(self, args):
+        super().__init__(args)
+
+    def run_inference(self, pipe, args):
+        _ = pipe(
+            prompt=PROMPT,
+            image=self.image,
+            num_inference_steps=args.num_inference_steps,
+            num_images_per_prompt=args.batch_size,
+            guidance_scale=0.0,
+            strength=0.5,
+        )
+
+
+class InpaintingBenchmark(ImageToImageBenchmark):
+    pipeline_class = AutoPipelineForInpainting
+    mask_url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/overture-creations-5sI6fQgYIuo_mask.png"
+    mask = load_image(mask_url).convert("RGB")
+
+    def __init__(self, args):
+        super().__init__(args)
+        self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
+        self.mask = self.mask.resize(RESOLUTION_MAPPING[args.ckpt])
+
+    def run_inference(self, pipe, args):
+        _ = pipe(
+            prompt=PROMPT,
+            image=self.image,
+            mask_image=self.mask,
+            num_inference_steps=args.num_inference_steps,
+            num_images_per_prompt=args.batch_size,
+        )
+
+
+class IPAdapterTextToImageBenchmark(TextToImageBenchmark):
+    url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png"
+    image = load_image(url)
+
+    def __init__(self, args):
+        pipe = self.pipeline_class.from_pretrained(args.ckpt, torch_dtype=torch.float16).to("cuda")
+        pipe.load_ip_adapter(
+            args.ip_adapter_id[0],
+            subfolder="models" if "sdxl" not in args.ip_adapter_id[1] else "sdxl_models",
+            weight_name=args.ip_adapter_id[1],
+        )
+
+        if args.run_compile:
+            pipe.unet.to(memory_format=torch.channels_last)
+            print("Run torch compile")
+            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+
+        pipe.set_progress_bar_config(disable=True)
+        self.pipe = pipe
+
+    def run_inference(self, pipe, args):
+        _ = pipe(
+            prompt=PROMPT,
+            ip_adapter_image=self.image,
+            num_inference_steps=args.num_inference_steps,
+            num_images_per_prompt=args.batch_size,
+        )
+
+
+class ControlNetBenchmark(TextToImageBenchmark):
+    pipeline_class = StableDiffusionControlNetPipeline
+    aux_network_class = ControlNetModel
+    root_ckpt = "Lykon/DreamShaper"
+
+    url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_image_condition.png"
+    image = load_image(url).convert("RGB")
+
+    def __init__(self, args):
+        aux_network = self.aux_network_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)
+        pipe = self.pipeline_class.from_pretrained(self.root_ckpt, controlnet=aux_network, torch_dtype=torch.float16)
+        pipe = pipe.to("cuda")
+
+        pipe.set_progress_bar_config(disable=True)
+        self.pipe = pipe
+
+        if args.run_compile:
+            pipe.unet.to(memory_format=torch.channels_last)
+            pipe.controlnet.to(memory_format=torch.channels_last)
+
+            print("Run torch compile")
+            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+            pipe.controlnet = torch.compile(pipe.controlnet, mode="reduce-overhead", fullgraph=True)
+
+        self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
+
+    def run_inference(self, pipe, args):
+        _ = pipe(
+            prompt=PROMPT,
+            image=self.image,
+            num_inference_steps=args.num_inference_steps,
+            num_images_per_prompt=args.batch_size,
+        )
+
+
+class ControlNetSDXLBenchmark(ControlNetBenchmark):
+    pipeline_class = StableDiffusionXLControlNetPipeline
+    root_ckpt = "stabilityai/stable-diffusion-xl-base-1.0"
+
+    def __init__(self, args):
+        super().__init__(args)
+
+
+class T2IAdapterBenchmark(ControlNetBenchmark):
+    pipeline_class = StableDiffusionAdapterPipeline
+    aux_network_class = T2IAdapter
+    root_ckpt = "Lykon/DreamShaper"
+
+    url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_for_adapter.png"
+    image = load_image(url).convert("L")
+
+    def __init__(self, args):
+        aux_network = self.aux_network_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)
+        pipe = self.pipeline_class.from_pretrained(self.root_ckpt, adapter=aux_network, torch_dtype=torch.float16)
+        pipe = pipe.to("cuda")
+
+        pipe.set_progress_bar_config(disable=True)
+        self.pipe = pipe
+
+        if args.run_compile:
+            pipe.unet.to(memory_format=torch.channels_last)
+            pipe.adapter.to(memory_format=torch.channels_last)
+
+            print("Run torch compile")
+            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+            pipe.adapter = torch.compile(pipe.adapter, mode="reduce-overhead", fullgraph=True)
+
+        self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
+
+
+class T2IAdapterSDXLBenchmark(T2IAdapterBenchmark):
+    pipeline_class = StableDiffusionXLAdapterPipeline
+    root_ckpt = "stabilityai/stable-diffusion-xl-base-1.0"
+
+    url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_for_adapter_sdxl.png"
+    image = load_image(url)
+
+    def __init__(self, args):
+        super().__init__(args)

benchmarks/benchmark_controlnet.py

@@ -0,0 +1,26 @@
+import argparse
+import sys
+
+
+sys.path.append(".")
+from base_classes import ControlNetBenchmark, ControlNetSDXLBenchmark  # noqa: E402
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="lllyasviel/sd-controlnet-canny",
+        choices=["lllyasviel/sd-controlnet-canny", "diffusers/controlnet-canny-sdxl-1.0"],
+    )
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_inference_steps", type=int, default=50)
+    parser.add_argument("--model_cpu_offload", action="store_true")
+    parser.add_argument("--run_compile", action="store_true")
+    args = parser.parse_args()
+
+    benchmark_pipe = (
+        ControlNetBenchmark(args) if args.ckpt == "lllyasviel/sd-controlnet-canny" else ControlNetSDXLBenchmark(args)
+    )
+    benchmark_pipe.benchmark(args)

benchmarks/benchmark_ip_adapters.py

@@ -0,0 +1,33 @@
+import argparse
+import sys
+
+
+sys.path.append(".")
+from base_classes import IPAdapterTextToImageBenchmark  # noqa: E402
+
+
+IP_ADAPTER_CKPTS = {
+    # because original SD v1.5 has been taken down.
+    "Lykon/DreamShaper": ("h94/IP-Adapter", "ip-adapter_sd15.bin"),
+    "stabilityai/stable-diffusion-xl-base-1.0": ("h94/IP-Adapter", "ip-adapter_sdxl.bin"),
+}
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="rstabilityai/stable-diffusion-xl-base-1.0",
+        choices=list(IP_ADAPTER_CKPTS.keys()),
+    )
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_inference_steps", type=int, default=50)
+    parser.add_argument("--model_cpu_offload", action="store_true")
+    parser.add_argument("--run_compile", action="store_true")
+    args = parser.parse_args()
+
+    args.ip_adapter_id = IP_ADAPTER_CKPTS[args.ckpt]
+    benchmark_pipe = IPAdapterTextToImageBenchmark(args)
+    args.ckpt = f"{args.ckpt} (IP-Adapter)"
+    benchmark_pipe.benchmark(args)

benchmarks/benchmark_sd_img.py

@@ -0,0 +1,29 @@
+import argparse
+import sys
+
+
+sys.path.append(".")
+from base_classes import ImageToImageBenchmark, TurboImageToImageBenchmark  # noqa: E402
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="Lykon/DreamShaper",
+        choices=[
+            "Lykon/DreamShaper",
+            "stabilityai/stable-diffusion-2-1",
+            "stabilityai/stable-diffusion-xl-refiner-1.0",
+            "stabilityai/sdxl-turbo",
+        ],
+    )
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_inference_steps", type=int, default=50)
+    parser.add_argument("--model_cpu_offload", action="store_true")
+    parser.add_argument("--run_compile", action="store_true")
+    args = parser.parse_args()
+
+    benchmark_pipe = ImageToImageBenchmark(args) if "turbo" not in args.ckpt else TurboImageToImageBenchmark(args)
+    benchmark_pipe.benchmark(args)

benchmarks/benchmark_sd_inpainting.py

@@ -0,0 +1,28 @@
+import argparse
+import sys
+
+
+sys.path.append(".")
+from base_classes import InpaintingBenchmark  # noqa: E402
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="Lykon/DreamShaper",
+        choices=[
+            "Lykon/DreamShaper",
+            "stabilityai/stable-diffusion-2-1",
+            "stabilityai/stable-diffusion-xl-base-1.0",
+        ],
+    )
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_inference_steps", type=int, default=50)
+    parser.add_argument("--model_cpu_offload", action="store_true")
+    parser.add_argument("--run_compile", action="store_true")
+    args = parser.parse_args()
+
+    benchmark_pipe = InpaintingBenchmark(args)
+    benchmark_pipe.benchmark(args)

benchmarks/benchmark_t2i_adapter.py

@@ -0,0 +1,28 @@
+import argparse
+import sys
+
+
+sys.path.append(".")
+from base_classes import T2IAdapterBenchmark, T2IAdapterSDXLBenchmark  # noqa: E402
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="TencentARC/t2iadapter_canny_sd14v1",
+        choices=["TencentARC/t2iadapter_canny_sd14v1", "TencentARC/t2i-adapter-canny-sdxl-1.0"],
+    )
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_inference_steps", type=int, default=50)
+    parser.add_argument("--model_cpu_offload", action="store_true")
+    parser.add_argument("--run_compile", action="store_true")
+    args = parser.parse_args()
+
+    benchmark_pipe = (
+        T2IAdapterBenchmark(args)
+        if args.ckpt == "TencentARC/t2iadapter_canny_sd14v1"
+        else T2IAdapterSDXLBenchmark(args)
+    )
+    benchmark_pipe.benchmark(args)

benchmarks/benchmark_t2i_lcm_lora.py

@@ -0,0 +1,23 @@
+import argparse
+import sys
+
+
+sys.path.append(".")
+from base_classes import LCMLoRATextToImageBenchmark  # noqa: E402
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="stabilityai/stable-diffusion-xl-base-1.0",
+    )
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_inference_steps", type=int, default=4)
+    parser.add_argument("--model_cpu_offload", action="store_true")
+    parser.add_argument("--run_compile", action="store_true")
+    args = parser.parse_args()
+
+    benchmark_pipe = LCMLoRATextToImageBenchmark(args)
+    benchmark_pipe.benchmark(args)

benchmarks/benchmark_text_to_image.py

@@ -0,0 +1,40 @@
+import argparse
+import sys
+
+
+sys.path.append(".")
+from base_classes import TextToImageBenchmark, TurboTextToImageBenchmark  # noqa: E402
+
+
+ALL_T2I_CKPTS = [
+    "Lykon/DreamShaper",
+    "segmind/SSD-1B",
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    "kandinsky-community/kandinsky-2-2-decoder",
+    "warp-ai/wuerstchen",
+    "stabilityai/sdxl-turbo",
+]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="Lykon/DreamShaper",
+        choices=ALL_T2I_CKPTS,
+    )
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--num_inference_steps", type=int, default=50)
+    parser.add_argument("--model_cpu_offload", action="store_true")
+    parser.add_argument("--run_compile", action="store_true")
+    args = parser.parse_args()
+
+    benchmark_cls = None
+    if "turbo" in args.ckpt:
+        benchmark_cls = TurboTextToImageBenchmark
+    else:
+        benchmark_cls = TextToImageBenchmark
+
+    benchmark_pipe = benchmark_cls(args)
+    benchmark_pipe.benchmark(args)

benchmarks/benchmarking_flux.py

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

benchmarks/benchmarking_ltx.py

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

benchmarks/benchmarking_sdxl.py

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

benchmarks/benchmarking_utils.py

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

benchmarks/benchmarking_wan.py

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

benchmarks/populate_into_db.py

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

benchmarks/push_results.py

@@ -1,19 +1,19 @@
-import os
+import glob
+import sys
 
 import pandas as pd
 from huggingface_hub import hf_hub_download, upload_file
 from huggingface_hub.utils import EntryNotFoundError
 
 
-REPO_ID = "diffusers/benchmarks"
+sys.path.append(".")
+from utils import BASE_PATH, FINAL_CSV_FILE, GITHUB_SHA, REPO_ID, collate_csv  # noqa: E402
 
 
 def has_previous_benchmark() -> str:
-    from run_all import FINAL_CSV_FILENAME
-
     csv_path = None
     try:
-        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILENAME)
+        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILE)
     except EntryNotFoundError:
         csv_path = None
     return csv_path
@@ -26,50 +26,46 @@ def filter_float(value):
 
 
 def push_to_hf_dataset():
-    from run_all import FINAL_CSV_FILENAME, GITHUB_SHA
+    all_csvs = sorted(glob.glob(f"{BASE_PATH}/*.csv"))
+    collate_csv(all_csvs, FINAL_CSV_FILE)
 
+    # If there's an existing benchmark file, we should report the changes.
     csv_path = has_previous_benchmark()
     if csv_path is not None:
-        current_results = pd.read_csv(FINAL_CSV_FILENAME)
+        current_results = pd.read_csv(FINAL_CSV_FILE)
         previous_results = pd.read_csv(csv_path)
 
         numeric_columns = current_results.select_dtypes(include=["float64", "int64"]).columns
+        numeric_columns = [
+            c for c in numeric_columns if c not in ["batch_size", "num_inference_steps", "actual_gpu_memory (gbs)"]
+        ]
 
         for column in numeric_columns:
-            # get previous values as floats, aligned to current index
-            prev_vals = previous_results[column].map(filter_float).reindex(current_results.index)
+            previous_results[column] = previous_results[column].map(lambda x: filter_float(x))
 
-            # get current values as floats
-            curr_vals = current_results[column].astype(float)
+            # Calculate the percentage change
+            current_results[column] = current_results[column].astype(float)
+            previous_results[column] = previous_results[column].astype(float)
+            percent_change = ((current_results[column] - previous_results[column]) / previous_results[column]) * 100
 
-            # stringify the current values
-            curr_str = curr_vals.map(str)
-
-            # build an appendage only when prev exists and differs
-            append_str = prev_vals.where(prev_vals.notnull() & (prev_vals != curr_vals), other=pd.NA).map(
-                lambda x: f" ({x})" if pd.notnull(x) else ""
+            # Format the values with '+' or '-' sign and append to original values
+            current_results[column] = current_results[column].map(str) + percent_change.map(
+                lambda x: f" ({'+' if x > 0 else ''}{x:.2f}%)"
             )
+            # There might be newly added rows. So, filter out the NaNs.
+            current_results[column] = current_results[column].map(lambda x: x.replace(" (nan%)", ""))
 
-            # combine
-            current_results[column] = curr_str + append_str
-        os.remove(FINAL_CSV_FILENAME)
-        current_results.to_csv(FINAL_CSV_FILENAME, index=False)
+        # Overwrite the current result file.
+        current_results.to_csv(FINAL_CSV_FILE, index=False)
 
     commit_message = f"upload from sha: {GITHUB_SHA}" if GITHUB_SHA is not None else "upload benchmark results"
     upload_file(
         repo_id=REPO_ID,
-        path_in_repo=FINAL_CSV_FILENAME,
-        path_or_fileobj=FINAL_CSV_FILENAME,
+        path_in_repo=FINAL_CSV_FILE,
+        path_or_fileobj=FINAL_CSV_FILE,
         repo_type="dataset",
         commit_message=commit_message,
     )
-    upload_file(
-        repo_id="diffusers/benchmark-analyzer",
-        path_in_repo=FINAL_CSV_FILENAME,
-        path_or_fileobj=FINAL_CSV_FILENAME,
-        repo_type="space",
-        commit_message=commit_message,
-    )
 
 
 if __name__ == "__main__":

benchmarks/requirements.txt

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

benchmarks/run_all.py

@@ -1,84 +1,101 @@
 import glob
-import logging
-import os
 import subprocess
-
-import pandas as pd
+import sys
+from typing import List
 
 
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
-logger = logging.getLogger(__name__)
+sys.path.append(".")
+from benchmark_text_to_image import ALL_T2I_CKPTS  # noqa: E402
 
-PATTERN = "benchmarking_*.py"
-FINAL_CSV_FILENAME = "collated_results.csv"
-GITHUB_SHA = os.getenv("GITHUB_SHA", None)
+
+PATTERN = "benchmark_*.py"
 
 
 class SubprocessCallException(Exception):
     pass
 
 
-def run_command(command: list[str], return_stdout=False):
+# Taken from `test_examples_utils.py`
+def run_command(command: List[str], return_stdout=False):
+    """
+    Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture
+    if an error occurred while running `command`
+    """
     try:
         output = subprocess.check_output(command, stderr=subprocess.STDOUT)
-        if return_stdout and hasattr(output, "decode"):
-            return output.decode("utf-8")
+        if return_stdout:
+            if hasattr(output, "decode"):
+                output = output.decode("utf-8")
+            return output
     except subprocess.CalledProcessError as e:
-        raise SubprocessCallException(f"Command `{' '.join(command)}` failed with:\n{e.output.decode()}") from e
+        raise SubprocessCallException(
+            f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
+        ) from e
 
 
-def merge_csvs(final_csv: str = "collated_results.csv"):
-    all_csvs = glob.glob("*.csv")
-    all_csvs = [f for f in all_csvs if f != final_csv]
-    if not all_csvs:
-        logger.info("No result CSVs found to merge.")
-        return
-
-    df_list = []
-    for f in all_csvs:
-        try:
-            d = pd.read_csv(f)
-        except pd.errors.EmptyDataError:
-            # If a file existed but was zero‐bytes or corrupted, skip it
-            continue
-        df_list.append(d)
-
-    if not df_list:
-        logger.info("All result CSVs were empty or invalid; nothing to merge.")
-        return
-
-    final_df = pd.concat(df_list, ignore_index=True)
-    if GITHUB_SHA is not None:
-        final_df["github_sha"] = GITHUB_SHA
-    final_df.to_csv(final_csv, index=False)
-    logger.info(f"Merged {len(all_csvs)} partial CSVs → {final_csv}.")
-
-
-def run_scripts():
-    python_files = sorted(glob.glob(PATTERN))
-    python_files = [f for f in python_files if f != "benchmarking_utils.py"]
+def main():
+    python_files = glob.glob(PATTERN)
 
     for file in python_files:
-        script_name = file.split(".py")[0].split("_")[-1]  # example: benchmarking_foo.py -> foo
-        logger.info(f"\n****** Running file: {file} ******")
+        print(f"****** Running file: {file} ******")
 
-        partial_csv = f"{script_name}.csv"
-        if os.path.exists(partial_csv):
-            logger.info(f"Found {partial_csv}. Removing for safer numbers and duplication.")
-            os.remove(partial_csv)
+        # Run with canonical settings.
+        if file != "benchmark_text_to_image.py" and file != "benchmark_ip_adapters.py":
+            command = f"python {file}"
+            run_command(command.split())
 
-        command = ["python", file]
-        try:
-            run_command(command)
-            logger.info(f"→ {file} finished normally.")
-        except SubprocessCallException as e:
-            logger.info(f"Error running {file}:\n{e}")
-        finally:
-            logger.info(f"→ Merging partial CSVs after {file} …")
-            merge_csvs(final_csv=FINAL_CSV_FILENAME)
+            command += " --run_compile"
+            run_command(command.split())
 
-    logger.info(f"\nAll scripts attempted. Final collated CSV: {FINAL_CSV_FILENAME}")
+    # Run variants.
+    for file in python_files:
+        # See: https://github.com/pytorch/pytorch/issues/129637
+        if file == "benchmark_ip_adapters.py":
+            continue
+
+        if file == "benchmark_text_to_image.py":
+            for ckpt in ALL_T2I_CKPTS:
+                command = f"python {file} --ckpt {ckpt}"
+
+                if "turbo" in ckpt:
+                    command += " --num_inference_steps 1"
+
+                run_command(command.split())
+
+                command += " --run_compile"
+                run_command(command.split())
+
+        elif file == "benchmark_sd_img.py":
+            for ckpt in ["stabilityai/stable-diffusion-xl-refiner-1.0", "stabilityai/sdxl-turbo"]:
+                command = f"python {file} --ckpt {ckpt}"
+
+                if ckpt == "stabilityai/sdxl-turbo":
+                    command += " --num_inference_steps 2"
+
+                run_command(command.split())
+                command += " --run_compile"
+                run_command(command.split())
+
+        elif file in ["benchmark_sd_inpainting.py", "benchmark_ip_adapters.py"]:
+            sdxl_ckpt = "stabilityai/stable-diffusion-xl-base-1.0"
+            command = f"python {file} --ckpt {sdxl_ckpt}"
+            run_command(command.split())
+
+            command += " --run_compile"
+            run_command(command.split())
+
+        elif file in ["benchmark_controlnet.py", "benchmark_t2i_adapter.py"]:
+            sdxl_ckpt = (
+                "diffusers/controlnet-canny-sdxl-1.0"
+                if "controlnet" in file
+                else "TencentARC/t2i-adapter-canny-sdxl-1.0"
+            )
+            command = f"python {file} --ckpt {sdxl_ckpt}"
+            run_command(command.split())
+
+            command += " --run_compile"
+            run_command(command.split())
 
 
 if __name__ == "__main__":
-    run_scripts()
+    main()

benchmarks/utils.py

@@ -0,0 +1,98 @@
+import argparse
+import csv
+import gc
+import os
+from dataclasses import dataclass
+from typing import Dict, List, Union
+
+import torch
+import torch.utils.benchmark as benchmark
+
+
+GITHUB_SHA = os.getenv("GITHUB_SHA", None)
+BENCHMARK_FIELDS = [
+    "pipeline_cls",
+    "ckpt_id",
+    "batch_size",
+    "num_inference_steps",
+    "model_cpu_offload",
+    "run_compile",
+    "time (secs)",
+    "memory (gbs)",
+    "actual_gpu_memory (gbs)",
+    "github_sha",
+]
+
+PROMPT = "ghibli style, a fantasy landscape with castles"
+BASE_PATH = os.getenv("BASE_PATH", ".")
+TOTAL_GPU_MEMORY = float(os.getenv("TOTAL_GPU_MEMORY", torch.cuda.get_device_properties(0).total_memory / (1024**3)))
+
+REPO_ID = "diffusers/benchmarks"
+FINAL_CSV_FILE = "collated_results.csv"
+
+
+@dataclass
+class BenchmarkInfo:
+    time: float
+    memory: float
+
+
+def flush():
+    """Wipes off memory."""
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.reset_max_memory_allocated()
+    torch.cuda.reset_peak_memory_stats()
+
+
+def bytes_to_giga_bytes(bytes):
+    return f"{(bytes / 1024 / 1024 / 1024):.3f}"
+
+
+def benchmark_fn(f, *args, **kwargs):
+    t0 = benchmark.Timer(
+        stmt="f(*args, **kwargs)",
+        globals={"args": args, "kwargs": kwargs, "f": f},
+        num_threads=torch.get_num_threads(),
+    )
+    return f"{(t0.blocked_autorange().mean):.3f}"
+
+
+def generate_csv_dict(
+    pipeline_cls: str, ckpt: str, args: argparse.Namespace, benchmark_info: BenchmarkInfo
+) -> Dict[str, Union[str, bool, float]]:
+    """Packs benchmarking data into a dictionary for latter serialization."""
+    data_dict = {
+        "pipeline_cls": pipeline_cls,
+        "ckpt_id": ckpt,
+        "batch_size": args.batch_size,
+        "num_inference_steps": args.num_inference_steps,
+        "model_cpu_offload": args.model_cpu_offload,
+        "run_compile": args.run_compile,
+        "time (secs)": benchmark_info.time,
+        "memory (gbs)": benchmark_info.memory,
+        "actual_gpu_memory (gbs)": f"{(TOTAL_GPU_MEMORY):.3f}",
+        "github_sha": GITHUB_SHA,
+    }
+    return data_dict
+
+
+def write_to_csv(file_name: str, data_dict: Dict[str, Union[str, bool, float]]):
+    """Serializes a dictionary into a CSV file."""
+    with open(file_name, mode="w", newline="") as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=BENCHMARK_FIELDS)
+        writer.writeheader()
+        writer.writerow(data_dict)
+
+
+def collate_csv(input_files: List[str], output_file: str):
+    """Collates multiple identically structured CSVs into a single CSV file."""
+    with open(output_file, mode="w", newline="") as outfile:
+        writer = csv.DictWriter(outfile, fieldnames=BENCHMARK_FIELDS)
+        writer.writeheader()
+
+        for file in input_files:
+            with open(file, mode="r") as infile:
+                reader = csv.DictReader(infile)
+                for row in reader:
+                    writer.writerow(row)

docker/diffusers-doc-builder/Dockerfile

@@ -1,45 +1,52 @@
-FROM python:3.10-slim
-ENV PYTHONDONTWRITEBYTECODE=1
+FROM ubuntu:20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update && apt-get install -y bash \
-    build-essential \
-    git \
-    git-lfs \
-    curl \
-    ca-certificates \
-    libglib2.0-0 \
-    libsndfile1-dev \
-    libgl1 \
-    zip \
-    wget
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa
 
-ENV UV_PYTHON=/usr/local/bin/python
+RUN apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.10 \
+                   python3-pip \
+                   libgl1 \
+                   zip \
+                   wget \
+                   python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN pip install uv
-RUN uv pip install --no-cache-dir \
-    torch \
-    torchvision \
-    torchaudio \
-    --extra-index-url https://download.pytorch.org/whl/cpu
-
-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 \
-    numpy==1.26.4 \
-    hf_xet \
-    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
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        invisible_watermark \
+        --extra-index-url https://download.pytorch.org/whl/cpu && \
+    python3.10 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers \
+        matplotlib \
+        setuptools==69.5.1
 
 CMD ["/bin/bash"]

docker/diffusers-flax-cpu/Dockerfile

@@ -0,0 +1,49 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa
+
+RUN apt install -y bash \
+        build-essential \
+        git \
+        git-lfs \
+        curl \
+        ca-certificates \
+        libsndfile1-dev \
+        libgl1 \
+        python3.10 \
+        python3-pip \
+        python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3 -m uv pip install --upgrade --no-cache-dir \
+        clu \
+        "jax[cpu]>=0.2.16,!=0.3.2" \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers \
+        hf_transfer
+
+CMD ["/bin/bash"]

docker/diffusers-flax-tpu/Dockerfile

@@ -0,0 +1,51 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa
+
+RUN apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   libgl1 \
+                   python3.10 \
+                   python3-pip \
+                   python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3 -m pip install --no-cache-dir \
+        "jax[tpu]>=0.2.16,!=0.3.2" \
+        -f https://storage.googleapis.com/jax-releases/libtpu_releases.html && \
+    python3 -m uv pip install --upgrade --no-cache-dir \
+        clu \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers \
+        hf_transfer
+
+CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -28,9 +28,9 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3 -m uv pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio\
+        torch==2.1.2 \
+        torchvision==0.16.2 \
+        torchaudio==2.1.2 \
         onnxruntime \
         --extra-index-url https://download.pytorch.org/whl/cpu && \
     python3 -m uv pip install --no-cache-dir \
@@ -44,6 +44,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         scipy \
         tensorboard \
         transformers \
-        hf_xet
+        hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cuda/Dockerfile

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

docker/diffusers-pytorch-compile-cuda/Dockerfile

@@ -0,0 +1,50 @@
+FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa
+
+RUN apt install -y bash \
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libsndfile1-dev \
+    libgl1 \
+    python3.10 \
+    python3.10-dev \
+    python3-pip \
+    python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
+    torch \
+    torchvision \
+    torchaudio \
+    invisible_watermark && \
+    python3.10 -m pip install --no-cache-dir \
+    accelerate \
+    datasets \
+    hf-doc-builder \
+    huggingface-hub \
+    hf_transfer \
+    Jinja2 \
+    librosa \
+    numpy==1.26.4 \
+    scipy \
+    tensorboard \
+    transformers \
+    hf_transfer
+
+CMD ["/bin/bash"]

docker/diffusers-pytorch-cpu/Dockerfile

@@ -1,38 +1,50 @@
-FROM python:3.10-slim
-ENV PYTHONDONTWRITEBYTECODE=1
+FROM ubuntu:20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update && apt-get install -y bash \
-    build-essential \
-    git \
-    git-lfs \
-    curl \
-    ca-certificates \
-    libglib2.0-0 \
-    libsndfile1-dev \
-    libgl1
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa
 
-ENV UV_PYTHON=/usr/local/bin/python
+RUN apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.10 \
+                   python3.10-dev \
+                   python3-pip \
+                   libgl1 \
+                   python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN pip install uv
-RUN uv pip install --no-cache-dir \
-    torch \
-    torchvision \
-    torchaudio \
-    --extra-index-url https://download.pytorch.org/whl/cpu
-
-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 \
-    numpy==1.26.4 \
-    hf_xet
-
-RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        invisible_watermark \
+        --extra-index-url https://download.pytorch.org/whl/cpu && \
+    python3.10 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers matplotlib  \
+        hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cuda/Dockerfile

@@ -2,13 +2,11 @@ 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 && \
-    apt-get update
+    && add-apt-repository ppa:deadsnakes/ppa
 
 RUN apt install -y bash \
     build-essential \
@@ -16,34 +14,38 @@ RUN apt install -y bash \
     git-lfs \
     curl \
     ca-certificates \
-    libglib2.0-0 \
     libsndfile1-dev \
     libgl1 \
-    python3 \
+    python3.10 \
+    python3.10-dev \
     python3-pip \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
+    python3.10-venv && \
+    rm -rf /var/lib/apt/lists
 
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-ENV PATH="/root/.local/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:$PATH"
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN uv pip install --no-cache-dir \
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio
-
-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 \
+    torchaudio \
+    invisible_watermark && \
+    python3.10 -m pip install --no-cache-dir \
     accelerate \
+    datasets \
+    hf-doc-builder \
+    huggingface-hub \
+    hf_transfer \
+    Jinja2 \
+    librosa \
     numpy==1.26.4 \
-    pytorch-lightning \
-    hf_xet
+    scipy \
+    tensorboard \
+    transformers \
+    pytorch-lightning  \
+    hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-minimum-cuda/Dockerfile

@@ -2,7 +2,6 @@ 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"
@@ -10,8 +9,7 @@ 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 && \
-    apt-get update
+    && add-apt-repository ppa:deadsnakes/ppa
 
 RUN apt install -y bash \
     build-essential \
@@ -19,34 +17,37 @@ RUN apt install -y bash \
     git-lfs \
     curl \
     ca-certificates \
-    libglib2.0-0 \
     libsndfile1-dev \
     libgl1 \
-    python3 \
+    python3.10 \
+    python3.10-dev \
     python3-pip \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
+    python3.10-venv && \
+    rm -rf /var/lib/apt/lists
 
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-ENV PATH="/root/.local/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:$PATH"
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN uv pip install --no-cache-dir \
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
     torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
     torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
-    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION
-
-RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
-
-# Extra dependencies
-RUN uv pip install --no-cache-dir \
+    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION \
+    invisible_watermark && \
+    python3.10 -m pip install --no-cache-dir \
     accelerate \
+    datasets \
+    hf-doc-builder \
+    huggingface-hub \
+    hf_transfer \
+    Jinja2 \
+    librosa \
     numpy==1.26.4 \
-    pytorch-lightning \
-    hf_xet
+    scipy \
+    tensorboard \
+    transformers \
+    hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -2,49 +2,50 @@ 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 && \
-    apt-get update
+    && add-apt-repository ppa:deadsnakes/ppa
 
 RUN apt install -y bash \
-    build-essential \
-    git \
-    git-lfs \
-    curl \
-    ca-certificates \
-    libglib2.0-0 \
-    libsndfile1-dev \
-    libgl1 \
-    python3 \
-    python3-pip \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   libgl1 \
+                   python3.10 \
+                   python3.10-dev \
+                   python3-pip \
+                   python3.10-venv && \
+    rm -rf /var/lib/apt/lists
 
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-ENV PATH="/root/.local/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:$PATH"
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN uv pip install --no-cache-dir \
-    torch \
-    torchvision \
-    torchaudio
-
-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 \
-    numpy==1.26.4 \
-    pytorch-lightning \
-    hf_xet \
-    xformers
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        invisible_watermark && \
+    python3.10 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        hf_transfer \
+        Jinja2 \
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers \
+        xformers  \
+        hf_transfer
 
 CMD ["/bin/bash"]

docs/TRANSLATING.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

docs/source/en/advanced_inference/outpaint.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

docs/source/en/api/activations.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -25,16 +25,3 @@ Customized activation functions for supporting various models in 🤗 Diffusers.
 ## ApproximateGELU
 
 [[autodoc]] models.activations.ApproximateGELU
-
-
-## SwiGLU
-
-[[autodoc]] models.activations.SwiGLU
-
-## FP32SiLU
-
-[[autodoc]] models.activations.FP32SiLU
-
-## LinearActivation
-
-[[autodoc]] models.activations.LinearActivation

docs/source/en/api/attnprocessor.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -147,20 +147,3 @@ An attention processor is a class for applying different types of attention mech
 ## XLAFlashAttnProcessor2_0
 
 [[autodoc]] models.attention_processor.XLAFlashAttnProcessor2_0
-
-## XFormersJointAttnProcessor
-
-[[autodoc]] models.attention_processor.XFormersJointAttnProcessor
-
-## IPAdapterXFormersAttnProcessor
-
-[[autodoc]] models.attention_processor.IPAdapterXFormersAttnProcessor
-
-## FluxIPAdapterJointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FluxIPAdapterJointAttnProcessor2_0
-
-
-## XLAFluxFlashAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.XLAFluxFlashAttnProcessor2_0

docs/source/en/api/cache.md

@@ -1,36 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# Caching methods
-
-Cache methods speedup diffusion transformers by storing and reusing intermediate outputs of specific layers, such as attention and feedforward layers, instead of recalculating them at each inference step.
-
-## CacheMixin
-
-[[autodoc]] CacheMixin
-
-## PyramidAttentionBroadcastConfig
-
-[[autodoc]] PyramidAttentionBroadcastConfig
-
-[[autodoc]] apply_pyramid_attention_broadcast
-
-## FasterCacheConfig
-
-[[autodoc]] FasterCacheConfig
-
-[[autodoc]] apply_faster_cache
-
-### FirstBlockCacheConfig
-
-[[autodoc]] FirstBlockCacheConfig
-
-[[autodoc]] apply_first_block_cache

docs/source/en/api/configuration.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -14,8 +14,11 @@ 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]
-> To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
+<Tip>
+
+To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
+
+</Tip>
 
 ## ConfigMixin
 

docs/source/en/api/image_processor.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -20,12 +20,6 @@ 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/internal_classes_overview.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -14,8 +14,11 @@ 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]
-> Learn how to load and use an IP-Adapter checkpoint and image in the [IP-Adapter](../../using-diffusers/ip_adapter) guide,.
+<Tip>
+
+Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading](../../using-diffusers/loading_adapters#ip-adapter) guide, and you can see how to use it in the [usage](../../using-diffusers/ip_adapter) guide.
+
+</Tip>
 
 ## IPAdapterMixin
 

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -20,27 +20,14 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`FluxLoraLoaderMixin`] provides similar functions for [Flux](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux).
 - [`CogVideoXLoraLoaderMixin`] provides similar functions for [CogVideoX](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox).
 - [`Mochi1LoraLoaderMixin`] provides similar functions for [Mochi](https://huggingface.co/docs/diffusers/main/en/api/pipelines/mochi).
-- [`AuraFlowLoraLoaderMixin`] provides similar functions for [AuraFlow](https://huggingface.co/fal/AuraFlow).
-- [`LTXVideoLoraLoaderMixin`] provides similar functions for [LTX-Video](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
-- [`SanaLoraLoaderMixin`] provides similar functions for [Sana](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana).
-- [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
-- [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
-- [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
-- [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
-- [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
-- [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
-- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen).
-- [`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).
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
-> [!TIP]
-> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
+<Tip>
 
-## LoraBaseMixin
+To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
 
-[[autodoc]] loaders.lora_base.LoraBaseMixin
+</Tip>
 
 ## StableDiffusionLoraLoaderMixin
 
@@ -58,10 +45,6 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 
 [[autodoc]] loaders.lora_pipeline.FluxLoraLoaderMixin
 
-## Flux2LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.Flux2LoraLoaderMixin
-
 ## CogVideoXLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.CogVideoXLoraLoaderMixin
@@ -69,57 +52,11 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 ## Mochi1LoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.Mochi1LoraLoaderMixin
-## AuraFlowLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.AuraFlowLoraLoaderMixin
-
-## LTXVideoLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.LTXVideoLoraLoaderMixin
-
-## SanaLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.SanaLoraLoaderMixin
-
-## HunyuanVideoLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.HunyuanVideoLoraLoaderMixin
-
-## Lumina2LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.Lumina2LoraLoaderMixin
-
-## CogView4LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.CogView4LoraLoaderMixin
-
-## WanLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
-
-## SkyReelsV2LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
 
 ## AmusedLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
 
-## HiDreamImageLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
-
-## QwenImageLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
-
-## 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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -12,10 +12,13 @@ specific language governing permissions and limitations under the License.
 
 # PEFT
 
-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.
+Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
 
-> [!TIP]
-> Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
+<Tip>
+
+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/single_file.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -16,8 +16,11 @@ 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]
-> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/textual_inversion_inference) loading guide.
+<Tip>
+
+To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
+
+</Tip>
 
 ## TextualInversionLoaderMixin
 

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -16,8 +16,11 @@ 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]
-> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
+<Tip>
+
+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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -16,8 +16,11 @@ 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]
-> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) guide.
+<Tip>
+
+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/logging.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,4 +1,4 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # AsymmetricAutoencoderKL
 
-Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://huggingface.co/papers/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
+Improved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://arxiv.org/abs/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.
 
 The abstract from the paper is:
 
@@ -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("stable-diffusion-v1-5/stable-diffusion-inpainting")
+pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
 pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
 pipe.to("cuda")
 

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,21 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# AutoModel
-
-[`AutoModel`] automatically retrieves the correct model class from the checkpoint `config.json` file.
-
-## AutoModel
-
-[[autodoc]] AutoModel
-	- all
-	- from_pretrained

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

@@ -1,4 +1,4 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,4 +1,4 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,36 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# 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

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# 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

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# 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/autoencoder_kl_wan.md

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLWan
-
-The 3D variational autoencoder (VAE) model with KL loss used in [Wan 2.1](https://github.com/Wan-Video/Wan2.1) by the Alibaba Wan Team.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLWan
-
-vae = AutoencoderKLWan.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32)
-```
-
-## AutoencoderKLWan
-
-[[autodoc]] AutoencoderKLWan
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # AutoencoderKL
 
-The variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://huggingface.co/papers/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
+The variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
 
 The abstract from the paper is:
 
@@ -44,3 +44,15 @@ model = AutoencoderKL.from_single_file(url)
 ## DecoderOutput
 
 [[autodoc]] models.autoencoders.vae.DecoderOutput
+
+## FlaxAutoencoderKL
+
+[[autodoc]] FlaxAutoencoderKL
+
+## FlaxAutoencoderKLOutput
+
+[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
+
+## FlaxDecoderOutput
+
+[[autodoc]] models.vae_flax.FlaxDecoderOutput

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

@@ -1,4 +1,4 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import AutoencoderKLAllegro
 
-vae = AutoencoderKLAllegro.from_pretrained("rhymes-ai/Allegro", subfolder="vae", torch_dtype=torch.float32).to("cuda")
+vae = AutoencoderKLCogVideoX.from_pretrained("rhymes-ai/Allegro", subfolder="vae", torch_dtype=torch.float32).to("cuda")
 ```
 
 ## AutoencoderKLAllegro

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,40 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLCosmos
-
-[Cosmos Tokenizers](https://github.com/NVIDIA/Cosmos-Tokenizer).
-
-Supported models:
-- [nvidia/Cosmos-1.0-Tokenizer-CV8x8x8](https://huggingface.co/nvidia/Cosmos-1.0-Tokenizer-CV8x8x8)
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLCosmos
-
-vae = AutoencoderKLCosmos.from_pretrained("nvidia/Cosmos-1.0-Tokenizer-CV8x8x8", subfolder="vae")
-```
-
-## AutoencoderKLCosmos
-
-[[autodoc]] AutoencoderKLCosmos
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,4 +1,4 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,37 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLMagvit
-
-The 3D variational autoencoder (VAE) model with KL loss used in [EasyAnimate](https://github.com/aigc-apps/EasyAnimate) was introduced by Alibaba PAI.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLMagvit
-
-vae = AutoencoderKLMagvit.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="vae", torch_dtype=torch.float16).to("cuda")
-```
-
-## AutoencoderKLMagvit
-
-[[autodoc]] AutoencoderKLMagvit
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,4 +1,4 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

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

@@ -1,35 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLQwenImage
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLQwenImage
-
-vae = AutoencoderKLQwenImage.from_pretrained("Qwen/QwenImage-20B", subfolder="vae")
-```
-
-## AutoencoderKLQwenImage
-
-[[autodoc]] AutoencoderKLQwenImage
-    - decode
-    - encode
-    - all
-
-## AutoencoderKLOutput
-
-[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,19 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# BriaTransformer2DModel
-
-A modified flux Transformer model from [Bria](https://huggingface.co/briaai/BRIA-3.2)
-
-## BriaTransformer2DModel
-
-[[autodoc]] BriaTransformer2DModel

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

@@ -1,19 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# ChromaTransformer2DModel
-
-A modified flux Transformer model from [Chroma](https://huggingface.co/lodestones/Chroma1-HD)
-
-## ChromaTransformer2DModel
-
-[[autodoc]] ChromaTransformer2DModel

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

@@ -1,32 +0,0 @@
-<!-- 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/cogvideox_transformer3d.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

docs/source/en/api/models/cogview3plus_transformer2d.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at

docs/source/en/api/models/cogview4_transformer2d.md

@@ -1,30 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# CogView4Transformer2DModel
-
-A Diffusion Transformer model for 2D data from [CogView4]()
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import CogView4Transformer2DModel
-
-transformer = CogView4Transformer2DModel.from_pretrained("THUDM/CogView4-6B", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
-```
-
-## CogView4Transformer2DModel
-
-[[autodoc]] CogView4Transformer2DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/consisid_transformer3d.md

@@ -1,30 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# ConsisIDTransformer3DModel
-
-A Diffusion Transformer model for 3D data from [ConsisID](https://github.com/PKU-YuanGroup/ConsisID) was introduced in [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://huggingface.co/papers/2411.17440) by Peking University & University of Rochester & etc.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import ConsisIDTransformer3DModel
-
-transformer = ConsisIDTransformer3DModel.from_pretrained("BestWishYsh/ConsisID-preview", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
-```
-
-## ConsisIDTransformer3DModel
-
-[[autodoc]] ConsisIDTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -16,8 +16,11 @@ 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).
 
-> [!WARNING]
-> Inference is only supported for 2 iterations as of now.
+<Tip warning={true}>
+
+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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -40,3 +40,11 @@ pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=contro
 ## ControlNetOutput
 
 [[autodoc]] models.controlnets.controlnet.ControlNetOutput
+
+## FlaxControlNetModel
+
+[[autodoc]] FlaxControlNetModel
+
+## FlaxControlNetOutput
+
+[[autodoc]] models.controlnets.controlnet_flax.FlaxControlNetOutput