Merge branch 'shared-variants-downloads' of https://github.com/huggingface/diffusers into shared-variants-downloads

.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

@@ -11,20 +11,19 @@ env:
   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: ${{ secrets.SLACK_WEBHOOK_URL_BENCHMARK }}
-    name: Torch Core Models CUDA Benchmarking Tests
+    name: Torch Core Pipelines CUDA Benchmarking Tests
     strategy:
       fail-fast: false
       max-parallel: 1
     runs-on:
-      group: aws-g6e-4xlarge
+      group: aws-g6-4xlarge-plus
     container:
-      image: diffusers/diffusers-pytorch-cuda
+      image: diffusers/diffusers-pytorch-compile-cuda
       options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
@@ -36,47 +35,26 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          apt update
-          apt install -y libpq-dev postgresql-client
           python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
           python -m uv pip install -e [quality,test]
-          python -m uv pip install -r benchmarks/requirements.txt
+          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

@@ -34,20 +34,13 @@ jobs:
         id: file_changes
         uses: jitterbit/get-changed-files@v1
         with:
-          format: "space-delimited"
+          format: 'space-delimited'
           token: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build Changed Docker Images
-        env: 
-          CHANGED_FILES: ${{ steps.file_changes.outputs.all }}
         run: |
-          echo "$CHANGED_FILES"
-          for FILE in $CHANGED_FILES; do 
-            # skip anything that isn't still on disk
-            if [[ ! -f "$FILE" ]]; then
-              echo "Skipping removed file $FILE"
-              continue
-            fi           
+          CHANGED_FILES="${{ steps.file_changes.outputs.all }}"
+          for FILE in $CHANGED_FILES; do
             if [[ "$FILE" == docker/*Dockerfile ]]; then
               DOCKER_PATH="${FILE%/Dockerfile}"
               DOCKER_TAG=$(basename "$DOCKER_PATH")
@@ -72,9 +65,12 @@ jobs:
         image-name:
           - diffusers-pytorch-cpu
           - diffusers-pytorch-cuda
-          - 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/nightly_tests.yml

@@ -13,9 +13,8 @@ env:
   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:
@@ -100,6 +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
@@ -170,48 +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 0 --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: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test,training]
-    - name: Environment
-      run: |
-        python utils/print_env.py
-    - name: Run torch compile tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        RUN_COMPILE: yes
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_torch_compile_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_compile_test_reports
-        path: reports
+        pip install slack_sdk tabulate
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
   run_big_gpu_torch_tests:
     name: Torch tests on big GPU
@@ -248,7 +215,7 @@ jobs:
           BIG_GPU_MEMORY: 40
         run: |
           python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -m "big_accelerator" \
+            -m "big_gpu_with_torch_cuda" \
             --make-reports=tests_big_gpu_torch_cuda \
             --report-log=tests_big_gpu_torch_cuda.log \
             tests/
@@ -263,237 +230,122 @@ 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
+
+  run_flax_tpu_tests:
+    name: Nightly Flax TPU Tests
+    runs-on: docker-tpu
+    if: github.event_name == 'schedule'
 
-  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 0
+      image: diffusers/diffusers-flax-tpu
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --privileged
     defaults:
       run:
         shell: bash
     steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
 
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$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: Environment
+      run: python utils/print_env.py
 
-      - name: Run PyTorch CUDA tests
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_torch_minimum_version_cuda \
-            tests/models/test_modeling_common.py \
-            tests/pipelines/test_pipelines_common.py \
-            tests/pipelines/test_pipeline_utils.py \
-            tests/pipelines/test_pipelines.py \
-            tests/pipelines/test_pipelines_auto.py \
-            tests/schedulers/test_schedulers.py \
-            tests/others
+    - name: 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_torch_minimum_version_cuda_stats.txt
-          cat reports/tests_torch_minimum_version_cuda_failures_short.txt
+    - 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: torch_minimum_version_cuda_test_reports
-          path: reports
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: flax_tpu_test_reports
+        path: reports
 
-  run_nightly_quantization_tests:
-    name: Torch quantization nightly tests
-    strategy:
-      fail-fast: false
-      max-parallel: 2
-      matrix:
-        config:
-          - backend: "bitsandbytes"
-            test_location: "bnb"
-            additional_deps: ["peft"]
-          - backend: "gguf"
-            test_location: "gguf"
-            additional_deps: ["peft"]
-          - backend: "torchao"
-            test_location: "torchao"
-            additional_deps: []
-          - backend: "optimum_quanto"
-            test_location: "quanto"
-            additional_deps: []
+    - name: Generate Report and Notify Channel
+      if: always()
+      run: |
+        pip install slack_sdk tabulate
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
+  run_nightly_onnx_tests:
+    name: Nightly ONNXRuntime CUDA tests on Ubuntu
     runs-on:
-      group: aws-g6e-xlarge-plus
+      group: aws-g4dn-2xlarge
     container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus 0
+      image: diffusers/diffusers-onnxruntime-cuda
+      options: --gpus 0 --shm-size "16gb" --ipc host
+
     steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: nvidia-smi
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install -U ${{ matrix.config.backend }}
-          if [ "${{ join(matrix.config.additional_deps, ' ') }}" != "" ]; then
-              python -m uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
-          fi
-          python -m uv pip install pytest-reportlog
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: ${{ matrix.config.backend }} quantization tests on GPU
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-          BIG_GPU_MEMORY: 40
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            --make-reports=tests_${{ matrix.config.backend }}_torch_cuda \
-            --report-log=tests_${{ matrix.config.backend }}_torch_cuda.log \
-            tests/quantization/${{ matrix.config.test_location }}
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_${{ matrix.config.backend }}_torch_cuda_stats.txt
-          cat reports/tests_${{ matrix.config.backend }}_torch_cuda_failures_short.txt
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: torch_cuda_${{ matrix.config.backend }}_reports
-          path: reports
-          
-  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 0
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: NVIDIA-SMI
-        run: nvidia-smi
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install -U bitsandbytes optimum_quanto
-          python -m 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: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            --make-reports=tests_pipeline_level_quant_torch_cuda \
-            --report-log=tests_pipeline_level_quant_torch_cuda.log \
-            tests/quantization/test_pipeline_level_quantization.py
-      - 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
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
 
-  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: NVIDIA-SMI
+      run: nvidia-smi
 
-      - name: Create reports directory
-        run: mkdir -p combined_reports
+    - 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: Download all test reports
-        uses: actions/download-artifact@v4
-        with:
-          path: artifacts
+    - 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: Prepare reports
-        run: |
-          # Move all report files to a single directory for processing
-          find artifacts -name "*.txt" -exec cp {} combined_reports/ \;
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_onnx_cuda_stats.txt
+        cat reports/tests_onnx_cuda_failures_short.txt
 
-      - name: Install dependencies
-        run: |
-          pip install -e .[test]
-          pip install slack_sdk tabulate
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: tests_onnx_cuda_reports
+        path: reports
 
-      - 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 }}
+    - name: Generate Report and Notify Channel
+      if: always()
+      run: |
+        pip install slack_sdk tabulate
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
 # M1 runner currently not well supported
 # TODO: (Dhruv) add these back when we setup better testing for Apple Silicon
@@ -533,7 +385,7 @@ 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} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
@@ -589,7 +441,7 @@ 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} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
@@ -609,4 +461,4 @@ jobs:
 #        if: always()
 #        run: |
 #          pip install slack_sdk tabulate
-#          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+#          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

.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_peft_backend.yml

@@ -0,0 +1,134 @@
+name: Fast tests for PRs - PEFT backend
+
+on:
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "src/diffusers/**.py"
+      - "tests/**.py"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  DIFFUSERS_IS_CI: yes
+  OMP_NUM_THREADS: 4
+  MKL_NUM_THREADS: 4
+  PYTEST_TIMEOUT: 60
+
+jobs:
+  check_code_quality:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.8"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check quality
+        run: make quality
+      - name: Check if failure
+        if: ${{ failure() }}
+        run: |
+          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
+
+  check_repository_consistency:
+    needs: check_code_quality
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.8"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check repo consistency
+        run: |
+          python utils/check_copies.py
+          python utils/check_dummies.py
+          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:
+        lib-versions: ["main", "latest"]
+
+
+    name: LoRA - ${{ matrix.lib-versions }}
+
+    runs-on:
+      group: aws-general-8-plus
+
+    container:
+      image: diffusers/diffusers-pytorch-cpu
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        # TODO (sayakpaul, DN6): revisit `--no-deps`
+        if [ "${{ matrix.lib-versions }}" == "main" ]; then
+            python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
+            python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+            pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        else
+            python -m uv pip install -U peft --no-deps
+            python -m uv pip install -U transformers accelerate --no-deps
+        fi
+
+    - name: Environment
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python utils/print_env.py
+
+    - name: Run fast PyTorch LoRA CPU tests with PEFT backend
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v \
+          --make-reports=tests_${{ matrix.lib-versions }} \
+          tests/lora/
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v \
+          --make-reports=tests_models_lora_${{ matrix.lib-versions }} \
+          tests/models/ -k "lora"
+
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_${{ matrix.lib-versions }}_failures_short.txt
+        cat reports/tests_models_lora_${{ matrix.lib-versions }}_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: pr_${{ matrix.lib-versions }}_test_reports
+        path: reports

.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-*
@@ -64,7 +64,6 @@ jobs:
         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: ${{ failure() }}
@@ -87,6 +86,11 @@ jobs:
             runner: aws-general-8-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu_models_schedulers
+          - name: Fast Flax CPU tests
+            framework: flax
+            runner: aws-general-8-plus
+            image: diffusers/diffusers-flax-cpu
+            report: flax_cpu
           - name: PyTorch Example CPU tests
             framework: pytorch_examples
             runner: aws-general-8-plus
@@ -116,8 +120,7 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        python -m uv pip install accelerate
 
     - name: Environment
       run: |
@@ -142,6 +145,15 @@ jobs:
           --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: ${{ matrix.config.framework == 'pytorch_examples' }}
       run: |
@@ -222,68 +234,3 @@ jobs:
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports
-
-  run_lora_tests:
-    needs: [check_code_quality, check_repository_consistency]
-    strategy:
-      fail-fast: false
-
-    name: LoRA tests with PEFT main
-
-    runs-on:
-      group: aws-general-8-plus
-
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-
-    defaults:
-      run:
-        shell: bash
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        # TODO (sayakpaul, DN6): revisit `--no-deps`
-        python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
-        python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-        python -m uv pip install -U tokenizers
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run fast PyTorch LoRA tests with PEFT
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -s -v \
-          --make-reports=tests_peft_main \
-          tests/lora/
-        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -s -v \
-          --make-reports=tests_models_lora_peft_main \
-          tests/models/ -k "lora"
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_peft_main_failures_short.txt
-        cat reports/tests_models_lora_peft_main_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: pr_main_test_reports
-        path: reports
-

.github/workflows/pr_tests_gpu.yml

@@ -1,296 +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"
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  HF_HUB_ENABLE_HF_TRANSFER: 1
-  PYTEST_TIMEOUT: 600
-  PIPELINE_USAGE_CUTOFF: 1000000000 # set high cutoff so that only always-test pipelines run
-
-jobs:
-  check_code_quality:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check quality
-        run: make quality
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
-
-  check_repository_consistency:
-    needs: check_code_quality
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[quality]
-      - name: Check repo consistency
-        run: |
-          python utils/check_copies.py
-          python utils/check_dummies.py
-          python utils/check_support_list.py
-          make deps_table_check_updated
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
-  
-  setup_torch_cuda_pipeline_matrix:
-    needs: [check_code_quality, check_repository_consistency]
-    name: Setup Torch Pipelines CUDA Slow Tests Matrix
-    runs-on:
-      group: aws-general-8-plus
-    container:
-      image: diffusers/diffusers-pytorch-cpu
-    outputs:
-      pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Fetch Pipeline Matrix
-        id: fetch_pipeline_matrix
-        run: |
-          matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)
-          echo $matrix
-          echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
-      - name: Pipeline Tests Artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: test-pipelines.json
-          path: reports
-
-  torch_pipelines_cuda_tests:
-    name: Torch Pipelines CUDA Tests
-    needs: setup_torch_cuda_pipeline_matrix
-    strategy:
-      fail-fast: false
-      max-parallel: 8
-      matrix:
-        module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-
-      - name: Environment
-        run: |
-          python utils/print_env.py
-      - name: Extract tests
-        id: extract_tests
-        run: |
-          pattern=$(python utils/extract_tests_from_mixin.py --type pipeline)
-          echo "$pattern" > /tmp/test_pattern.txt
-          echo "pattern_file=/tmp/test_pattern.txt" >> $GITHUB_OUTPUT
-
-      - name: PyTorch CUDA checkpoint tests on Ubuntu
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          if [ "${{ matrix.module }}" = "ip_adapters" ]; then 
-              python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -s -v -k "not Flax and not Onnx" \
-              --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-              tests/pipelines/${{ matrix.module }}
-          else 
-              pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
-              python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -s -v -k "not Flax and not Onnx and $pattern" \
-              --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
-              tests/pipelines/${{ matrix.module }}
-          fi 
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: |
-          cat reports/tests_pipeline_${{ matrix.module }}_cuda_stats.txt
-          cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: pipeline_${{ matrix.module }}_test_reports
-          path: reports
-
-  torch_cuda_tests:
-    name: Torch CUDA Tests
-    needs: [check_code_quality, check_repository_consistency]
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
-    defaults:
-      run:
-        shell: bash
-    strategy:
-      fail-fast: false
-      max-parallel: 4
-      matrix:
-        module: [models, schedulers, lora, others]
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Extract tests
-      id: extract_tests
-      run: |
-        pattern=$(python utils/extract_tests_from_mixin.py --type ${{ matrix.module }})
-        echo "$pattern" > /tmp/test_pattern.txt
-        echo "pattern_file=/tmp/test_pattern.txt" >> $GITHUB_OUTPUT
-
-    - name: Run PyTorch CUDA tests
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
-      run: |
-        pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
-        if [ -z "$pattern" ]; then
-          python -m pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx" tests/${{ matrix.module }} \
-          --make-reports=tests_torch_cuda_${{ matrix.module }}  
-        else
-          python -m pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
-          --make-reports=tests_torch_cuda_${{ matrix.module }}  
-        fi
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_torch_cuda_${{ matrix.module }}_stats.txt
-        cat reports/tests_torch_cuda_${{ matrix.module }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: torch_cuda_test_reports_${{ matrix.module }}
-        path: reports
-
-  run_examples_tests:
-    name: Examples PyTorch CUDA tests on Ubuntu
-    needs: [check_code_quality, check_repository_consistency]
-    runs-on:
-      group: aws-g4dn-2xlarge
-
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 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:$PATH"
-        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
-        python -m uv pip install -e [quality,test,training]
-
-    - name: Environment
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python utils/print_env.py
-
-    - name: Run example tests on GPU
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install timm
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/examples_torch_cuda_stats.txt
-        cat reports/examples_torch_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: examples_test_reports
-        path: reports
-

.github/workflows/push_tests.yml

@@ -83,7 +83,7 @@ jobs:
           python utils/print_env.py
       - name: 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: |
@@ -137,7 +137,7 @@ jobs:
 
     - name: Run PyTorch CUDA tests
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
@@ -159,6 +159,101 @@ jobs:
         name: torch_cuda_test_reports_${{ matrix.module }}
         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 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
 
@@ -166,7 +261,7 @@ jobs:
       group: aws-g4dn-2xlarge
 
     container:
-      image: diffusers/diffusers-pytorch-cuda
+      image: diffusers/diffusers-pytorch-compile-cuda
       options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
@@ -187,7 +282,7 @@ jobs:
         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: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
@@ -230,7 +325,7 @@ jobs:
         python utils/print_env.py
     - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
     - name: Failure short reports
@@ -253,6 +348,7 @@ jobs:
     container:
       image: diffusers/diffusers-pytorch-cuda
       options: --gpus 0 --shm-size "16gb" --ipc host
+
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
@@ -262,6 +358,7 @@ jobs:
     - name: NVIDIA-SMI
       run: |
         nvidia-smi
+
     - name: Install dependencies
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
@@ -274,7 +371,7 @@ jobs:
 
     - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install timm

.github/workflows/push_tests_fast.yml

@@ -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
@@ -77,6 +87,24 @@ jobs:
           --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: |

.github/workflows/push_tests_mps.yml

@@ -1,7 +1,12 @@
 name: Fast mps tests on main
 
 on:
-  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "src/diffusers/**.py"
+      - "tests/**.py"
 
 env:
   DIFFUSERS_IS_CI: yes
@@ -41,7 +46,7 @@ jobs:
       shell: arch -arch arm64 bash {0}
       run: |
         ${CONDA_RUN} python -m pip install --upgrade pip uv
-        ${CONDA_RUN} python -m uv pip install -e ".[quality,test]"
+        ${CONDA_RUN} python -m uv pip install -e [quality,test]
         ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio
         ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
         ${CONDA_RUN} python -m uv pip install transformers --upgrade

.github/workflows/pypi_publish.yaml

@@ -68,7 +68,7 @@ jobs:
       - name: Test installing diffusers and importing
         run: |
           pip install diffusers && pip uninstall diffusers -y
-          pip install -i https://test.pypi.org/simple/ diffusers
+          pip install -i https://testpypi.python.org/pypi diffusers
           python -c "from diffusers import __version__; print(__version__)"
           python -c "from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('fusing/unet-ldm-dummy-update'); pipe()"
           python -c "from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=None); pipe('ah suh du')"

.github/workflows/release_tests_fast.yml

@@ -81,7 +81,7 @@ jobs:
           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: |
@@ -135,7 +135,7 @@ jobs:
 
     - name: Run PyTorch CUDA tests
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
@@ -157,62 +157,100 @@ jobs:
         name: torch_cuda_${{ matrix.module }}_test_reports
         path: reports
 
-  torch_minimum_version_cuda_tests:
-    name: Torch Minimum Version CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
+  flax_tpu_tests:
+    name: Flax TPU Tests
+    runs-on: docker-tpu
     container:
-      image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      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: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
 
-      - name: Install dependencies
-        run: |
-          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          python -m uv pip install -e [quality,test]
-          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$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: Environment
+      run: |
+        python utils/print_env.py
 
-      - name: Run PyTorch CUDA tests
-        env:
-          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-          CUBLAS_WORKSPACE_CONFIG: :16:8
-        run: |
-          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -s -v -k "not Flax and not Onnx" \
-            --make-reports=tests_torch_minimum_cuda \
-            tests/models/test_modeling_common.py \
-            tests/pipelines/test_pipelines_common.py \
-            tests/pipelines/test_pipeline_utils.py \
-            tests/pipelines/test_pipelines.py \
-            tests/pipelines/test_pipelines_auto.py \
-            tests/schedulers/test_schedulers.py \
-            tests/others
+    - name: 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_torch_minimum_version_cuda_stats.txt
-          cat reports/tests_torch_minimum_version_cuda_failures_short.txt
+    - 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: torch_minimum_version_cuda_test_reports
-          path: reports
+    - 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
@@ -221,7 +259,7 @@ jobs:
       group: aws-g4dn-2xlarge
 
     container:
-      image: diffusers/diffusers-pytorch-cuda
+      image: diffusers/diffusers-pytorch-compile-cuda
       options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
@@ -240,9 +278,9 @@ jobs:
     - 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: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
@@ -285,7 +323,7 @@ jobs:
         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: |
         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
@@ -331,7 +369,7 @@ jobs:
 
     - name: Run example tests on GPU
       env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install timm

.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`).'
@@ -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
 
@@ -53,13 +53,13 @@ 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: |

.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
-

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

@@ -112,9 +112,9 @@ Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to l
 | **Documentation**                                                   | **What can I learn?**                                                                                                                                                                           |
 |---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview)                                                            | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model.  |
-| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading)                                                             | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers.                                         |
-| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/overview_techniques)                                             | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library.               |
-| [Optimization](https://huggingface.co/docs/diffusers/optimization/fp16)                                                        | Guides for how to optimize your diffusion model to run faster and consume less memory.                                                                                                          |
+| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview)                                                             | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers.                                         |
+| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview)                                             | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library.               |
+| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview)                                                        | Guides for how to optimize your diffusion model to run faster and consume less memory.                                                                                                          |
 | [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques.                                                                                               |
 ## Contribution
 

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
-huggingface-cli 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

@@ -47,10 +47,6 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         tensorboard \
         transformers \
         matplotlib \
-        setuptools==69.5.1 \
-        bitsandbytes \
-        torchao \
-        gguf \
-        optimum-quanto
+        setuptools==69.5.1
 
 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 \

docker/diffusers-onnxruntime-cuda/Dockerfile

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

docker/diffusers-pytorch-compile-cuda/Dockerfile

@@ -3,9 +3,6 @@ LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
-ENV MINIMUM_SUPPORTED_TORCH_VERSION="2.1.0"
-ENV MINIMUM_SUPPORTED_TORCHVISION_VERSION="0.16.0"
-ENV MINIMUM_SUPPORTED_TORCHAUDIO_VERSION="2.1.0"
 
 RUN apt-get -y update \
     && apt-get install -y software-properties-common \
@@ -32,9 +29,9 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-    torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
-    torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
-    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION \
+    "torch<2.5.0" \
+    torchvision \
+    torchaudio \
     invisible_watermark && \
     python3.10 -m pip install --no-cache-dir \
     accelerate \

docker/diffusers-pytorch-cpu/Dockerfile

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

docker/diffusers-pytorch-cuda/Dockerfile

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

docker/diffusers-pytorch-xformers-cuda/Dockerfile

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

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/_toctree.yml

@@ -17,6 +17,12 @@
     title: AutoPipeline
   - local: tutorials/basic_training
     title: Train a diffusion model
+  - local: tutorials/using_peft_for_inference
+    title: Load LoRAs for inference
+  - local: tutorials/fast_diffusion
+    title: Accelerate inference of text-to-image diffusion models
+  - local: tutorials/inference_with_big_models
+    title: Working with big models
   title: Tutorials
 - sections:
   - local: using-diffusers/loading
@@ -27,24 +33,11 @@
     title: Load schedulers and models
   - local: using-diffusers/other-formats
     title: Model files and layouts
+  - local: using-diffusers/loading_adapters
+    title: Load adapters
   - local: using-diffusers/push_to_hub
     title: Push files to the Hub
   title: Load pipelines and adapters
-- sections:
-  - local: tutorials/using_peft_for_inference
-    title: LoRA
-  - local: using-diffusers/ip_adapter
-    title: IP-Adapter
-  - local: using-diffusers/controlnet
-    title: ControlNet
-  - local: using-diffusers/t2i_adapter
-    title: T2I-Adapter
-  - local: using-diffusers/dreambooth
-    title: DreamBooth
-  - local: using-diffusers/textual_inversion_inference
-    title: Textual inversion
-  title: Adapters
-  isExpanded: false
 - sections:
   - local: using-diffusers/unconditional_image_generation
     title: Unconditional image generation
@@ -55,19 +48,17 @@
   - local: using-diffusers/inpaint
     title: Inpainting
   - local: using-diffusers/text-img2vid
-    title: Video generation
+    title: Text or image-to-video
   - local: using-diffusers/depth2img
     title: Depth-to-image
   title: Generative tasks
 - sections:
   - local: using-diffusers/overview_techniques
     title: Overview
-  - local: using-diffusers/create_a_server
-    title: Create a server
-  - local: using-diffusers/batched_inference
-    title: Batch inference
   - local: training/distributed_inference
     title: Distributed inference
+  - local: using-diffusers/merge_loras
+    title: Merge LoRAs
   - local: using-diffusers/scheduler_features
     title: Scheduler features
   - local: using-diffusers/callback
@@ -84,50 +75,26 @@
     title: Outpainting
   title: Advanced inference
 - sections:
-  - local: hybrid_inference/overview
-    title: Overview
-  - local: hybrid_inference/vae_decode
-    title: VAE Decode
-  - local: hybrid_inference/vae_encode
-    title: VAE Encode
-  - local: hybrid_inference/api_reference
-    title: API Reference
-  title: Hybrid Inference
-- sections:
-  - local: modular_diffusers/overview
-    title: Overview
-  - local: modular_diffusers/modular_pipeline
-    title: Modular Pipeline
-  - local: modular_diffusers/components_manager
-    title: Components Manager
-  - local: modular_diffusers/modular_diffusers_states
-    title: Modular Diffusers States
-  - local: modular_diffusers/pipeline_block
-    title: Pipeline Block
-  - local: modular_diffusers/sequential_pipeline_blocks
-    title: Sequential Pipeline Blocks
-  - local: modular_diffusers/loop_sequential_pipeline_blocks
-    title: Loop Sequential Pipeline Blocks
-  - local: modular_diffusers/auto_pipeline_blocks
-    title: Auto Pipeline Blocks
-  - local: modular_diffusers/end_to_end_guide
-    title: End-to-End Example
-  title: Modular Diffusers
-- sections:
-  - local: using-diffusers/consisid
-    title: ConsisID
+  - local: using-diffusers/cogvideox
+    title: CogVideoX
   - local: using-diffusers/sdxl
     title: Stable Diffusion XL
   - local: using-diffusers/sdxl_turbo
     title: SDXL Turbo
   - local: using-diffusers/kandinsky
     title: Kandinsky
-  - local: using-diffusers/omnigen
-    title: OmniGen
+  - local: using-diffusers/ip_adapter
+    title: IP-Adapter
   - local: using-diffusers/pag
     title: PAG
+  - local: using-diffusers/controlnet
+    title: ControlNet
+  - local: using-diffusers/t2i_adapter
+    title: T2I-Adapter
   - local: using-diffusers/inference_with_lcm
     title: Latent Consistency Model
+  - local: using-diffusers/textual_inversion_inference
+    title: Textual inversion
   - local: using-diffusers/shap-e
     title: Shap-E
   - local: using-diffusers/diffedit
@@ -188,24 +155,14 @@
     title: Getting Started
   - local: quantization/bitsandbytes
     title: bitsandbytes
-  - local: quantization/gguf
-    title: gguf
-  - local: quantization/torchao
-    title: torchao
-  - local: quantization/quanto
-    title: quanto
   title: Quantization Methods
 - sections:
   - local: optimization/fp16
-    title: Accelerate inference
-  - local: optimization/cache
-    title: Caching
+    title: Speed up inference
   - local: optimization/memory
     title: Reduce memory usage
-  - local: optimization/speed-memory-optims
-    title: Compile and offloading quantized models
-  - local: optimization/pruna
-    title: Pruna
+  - local: optimization/torch2.0
+    title: PyTorch 2.0
   - local: optimization/xformers
     title: xFormers
   - local: optimization/tome
@@ -216,8 +173,6 @@
     title: TGATE
   - local: optimization/xdit
     title: xDiT
-  - local: optimization/para_attn
-    title: ParaAttention
   - sections:
     - local: using-diffusers/stable_diffusion_jax_how_to
       title: JAX/Flax
@@ -232,7 +187,7 @@
     - local: optimization/mps
       title: Metal Performance Shaders (MPS)
     - local: optimization/habana
-      title: Intel Gaudi
+      title: Habana Gaudi
     - local: optimization/neuron
       title: AWS Neuron
     title: Optimized hardware
@@ -277,8 +232,6 @@
       title: Textual Inversion
     - local: api/loaders/unet
       title: UNet
-    - local: api/loaders/transformer_sd3
-      title: SD3Transformer2D
     - local: api/loaders/peft
       title: PEFT
     title: Loaders
@@ -286,19 +239,13 @@
     sections:
     - local: api/models/overview
       title: Overview
-    - local: api/models/auto_model
-      title: AutoModel
     - sections:
       - local: api/models/controlnet
         title: ControlNetModel
-      - local: api/models/controlnet_union
-        title: ControlNetUnionModel
       - local: api/models/controlnet_flux
         title: FluxControlNetModel
       - local: api/models/controlnet_hunyuandit
         title: HunyuanDiT2DControlNetModel
-      - local: api/models/controlnet_sana
-        title: SanaControlNetModel
       - local: api/models/controlnet_sd3
         title: SD3ControlNetModel
       - local: api/models/controlnet_sparsectrl
@@ -309,48 +256,26 @@
         title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
-      - local: api/models/chroma_transformer
-        title: ChromaTransformer2DModel
       - local: api/models/cogvideox_transformer3d
         title: CogVideoXTransformer3DModel
       - local: api/models/cogview3plus_transformer2d
         title: CogView3PlusTransformer2DModel
-      - local: api/models/cogview4_transformer2d
-        title: CogView4Transformer2DModel
-      - local: api/models/consisid_transformer3d
-        title: ConsisIDTransformer3DModel
-      - local: api/models/cosmos_transformer3d
-        title: CosmosTransformer3DModel
       - local: api/models/dit_transformer2d
         title: DiTTransformer2DModel
-      - local: api/models/easyanimate_transformer3d
-        title: EasyAnimateTransformer3DModel
       - local: api/models/flux_transformer
         title: FluxTransformer2DModel
-      - local: api/models/hidream_image_transformer
-        title: HiDreamImageTransformer2DModel
       - local: api/models/hunyuan_transformer2d
         title: HunyuanDiT2DModel
-      - local: api/models/hunyuan_video_transformer_3d
-        title: HunyuanVideoTransformer3DModel
       - local: api/models/latte_transformer3d
         title: LatteTransformer3DModel
-      - local: api/models/ltx_video_transformer3d
-        title: LTXVideoTransformer3DModel
-      - local: api/models/lumina2_transformer2d
-        title: Lumina2Transformer2DModel
       - local: api/models/lumina_nextdit2d
         title: LuminaNextDiT2DModel
       - local: api/models/mochi_transformer3d
         title: MochiTransformer3DModel
-      - local: api/models/omnigen_transformer
-        title: OmniGenTransformer2DModel
       - local: api/models/pixart_transformer2d
         title: PixArtTransformer2DModel
       - local: api/models/prior_transformer
         title: PriorTransformer
-      - local: api/models/sana_transformer2d
-        title: SanaTransformer2DModel
       - local: api/models/sd3_transformer2d
         title: SD3Transformer2DModel
       - local: api/models/stable_audio_transformer
@@ -359,18 +284,16 @@
         title: Transformer2DModel
       - local: api/models/transformer_temporal
         title: TransformerTemporalModel
-      - local: api/models/wan_transformer_3d
-        title: WanTransformer3DModel
       title: Transformers
     - sections:
       - local: api/models/stable_cascade_unet
         title: StableCascadeUNet
       - local: api/models/unet
         title: UNet1DModel
-      - local: api/models/unet2d-cond
-        title: UNet2DConditionModel
       - local: api/models/unet2d
         title: UNet2DModel
+      - local: api/models/unet2d-cond
+        title: UNet2DConditionModel
       - local: api/models/unet3d-cond
         title: UNet3DConditionModel
       - local: api/models/unet-motion
@@ -379,28 +302,16 @@
         title: UViT2DModel
       title: UNets
     - sections:
-      - local: api/models/asymmetricautoencoderkl
-        title: AsymmetricAutoencoderKL
-      - local: api/models/autoencoder_dc
-        title: AutoencoderDC
       - local: api/models/autoencoderkl
         title: AutoencoderKL
       - local: api/models/autoencoderkl_allegro
         title: AutoencoderKLAllegro
       - local: api/models/autoencoderkl_cogvideox
         title: AutoencoderKLCogVideoX
-      - local: api/models/autoencoderkl_cosmos
-        title: AutoencoderKLCosmos
-      - local: api/models/autoencoder_kl_hunyuan_video
-        title: AutoencoderKLHunyuanVideo
-      - local: api/models/autoencoderkl_ltx_video
-        title: AutoencoderKLLTXVideo
-      - local: api/models/autoencoderkl_magvit
-        title: AutoencoderKLMagvit
       - local: api/models/autoencoderkl_mochi
         title: AutoencoderKLMochi
-      - local: api/models/autoencoder_kl_wan
-        title: AutoencoderKLWan
+      - local: api/models/asymmetricautoencoderkl
+        title: AsymmetricAutoencoderKL
       - local: api/models/consistency_decoder_vae
         title: ConsistencyDecoderVAE
       - local: api/models/autoencoder_oobleck
@@ -433,16 +344,10 @@
       title: AutoPipeline
     - local: api/pipelines/blip_diffusion
       title: BLIP-Diffusion
-    - local: api/pipelines/chroma
-      title: Chroma
     - local: api/pipelines/cogvideox
       title: CogVideoX
     - local: api/pipelines/cogview3
       title: CogView3
-    - local: api/pipelines/cogview4
-      title: CogView4
-    - local: api/pipelines/consisid
-      title: ConsisID
     - local: api/pipelines/consistency_models
       title: Consistency Models
     - local: api/pipelines/controlnet
@@ -455,16 +360,10 @@
       title: ControlNet with Stable Diffusion 3
     - local: api/pipelines/controlnet_sdxl
       title: ControlNet with Stable Diffusion XL
-    - local: api/pipelines/controlnet_sana
-      title: ControlNet-Sana
     - local: api/pipelines/controlnetxs
       title: ControlNet-XS
     - local: api/pipelines/controlnetxs_sdxl
       title: ControlNet-XS with Stable Diffusion XL
-    - local: api/pipelines/controlnet_union
-      title: ControlNetUnion
-    - local: api/pipelines/cosmos
-      title: Cosmos
     - local: api/pipelines/dance_diffusion
       title: Dance Diffusion
     - local: api/pipelines/ddim
@@ -477,20 +376,10 @@
       title: DiffEdit
     - local: api/pipelines/dit
       title: DiT
-    - local: api/pipelines/easyanimate
-      title: EasyAnimate
     - local: api/pipelines/flux
       title: Flux
-    - local: api/pipelines/control_flux_inpaint
-      title: FluxControlInpaint
-    - local: api/pipelines/framepack
-      title: Framepack
-    - local: api/pipelines/hidream
-      title: HiDream-I1
     - local: api/pipelines/hunyuandit
       title: Hunyuan-DiT
-    - local: api/pipelines/hunyuan_video
-      title: HunyuanVideo
     - local: api/pipelines/i2vgenxl
       title: I2VGen-XL
     - local: api/pipelines/pix2pix
@@ -511,10 +400,6 @@
       title: Latte
     - local: api/pipelines/ledits_pp
       title: LEDITS++
-    - local: api/pipelines/ltx_video
-      title: LTXVideo
-    - local: api/pipelines/lumina2
-      title: Lumina 2.0
     - local: api/pipelines/lumina
       title: Lumina-T2X
     - local: api/pipelines/marigold
@@ -525,8 +410,6 @@
       title: MultiDiffusion
     - local: api/pipelines/musicldm
       title: MusicLDM
-    - local: api/pipelines/omnigen
-      title: OmniGen
     - local: api/pipelines/pag
       title: PAG
     - local: api/pipelines/paint_by_example
@@ -537,10 +420,6 @@
       title: PixArt-α
     - local: api/pipelines/pixart_sigma
       title: PixArt-Σ
-    - local: api/pipelines/sana
-      title: Sana
-    - local: api/pipelines/sana_sprint
-      title: Sana Sprint
     - local: api/pipelines/self_attention_guidance
       title: Self-Attention Guidance
     - local: api/pipelines/semantic_stable_diffusion
@@ -554,40 +433,40 @@
     - sections:
       - local: api/pipelines/stable_diffusion/overview
         title: Overview
-      - local: api/pipelines/stable_diffusion/depth2img
-        title: Depth-to-image
-      - local: api/pipelines/stable_diffusion/gligen
-        title: GLIGEN (Grounded Language-to-Image Generation)
-      - local: api/pipelines/stable_diffusion/image_variation
-        title: Image variation
+      - local: api/pipelines/stable_diffusion/text2img
+        title: Text-to-image
       - local: api/pipelines/stable_diffusion/img2img
         title: Image-to-image
       - local: api/pipelines/stable_diffusion/svd
         title: Image-to-video
       - local: api/pipelines/stable_diffusion/inpaint
         title: Inpainting
-      - local: api/pipelines/stable_diffusion/k_diffusion
-        title: K-Diffusion
-      - local: api/pipelines/stable_diffusion/latent_upscale
-        title: Latent upscaler
-      - local: api/pipelines/stable_diffusion/ldm3d_diffusion
-        title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
+      - local: api/pipelines/stable_diffusion/depth2img
+        title: Depth-to-image
+      - local: api/pipelines/stable_diffusion/image_variation
+        title: Image variation
       - local: api/pipelines/stable_diffusion/stable_diffusion_safe
         title: Safe Stable Diffusion
-      - local: api/pipelines/stable_diffusion/sdxl_turbo
-        title: SDXL Turbo
       - local: api/pipelines/stable_diffusion/stable_diffusion_2
         title: Stable Diffusion 2
       - local: api/pipelines/stable_diffusion/stable_diffusion_3
         title: Stable Diffusion 3
       - local: api/pipelines/stable_diffusion/stable_diffusion_xl
         title: Stable Diffusion XL
+      - local: api/pipelines/stable_diffusion/sdxl_turbo
+        title: SDXL Turbo
+      - local: api/pipelines/stable_diffusion/latent_upscale
+        title: Latent upscaler
       - local: api/pipelines/stable_diffusion/upscale
         title: Super-resolution
+      - local: api/pipelines/stable_diffusion/k_diffusion
+        title: K-Diffusion
+      - local: api/pipelines/stable_diffusion/ldm3d_diffusion
+        title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
       - local: api/pipelines/stable_diffusion/adapter
         title: T2I-Adapter
-      - local: api/pipelines/stable_diffusion/text2img
-        title: Text-to-image
+      - local: api/pipelines/stable_diffusion/gligen
+        title: GLIGEN (Grounded Language-to-Image Generation)
       title: Stable Diffusion
     - local: api/pipelines/stable_unclip
       title: Stable unCLIP
@@ -601,10 +480,6 @@
       title: UniDiffuser
     - local: api/pipelines/value_guided_sampling
       title: Value-guided sampling
-    - local: api/pipelines/visualcloze
-      title: VisualCloze
-    - local: api/pipelines/wan
-      title: Wan
     - local: api/pipelines/wuerstchen
       title: Wuerstchen
     title: Pipelines
@@ -614,10 +489,6 @@
       title: Overview
     - local: api/schedulers/cm_stochastic_iterative
       title: CMStochasticIterativeScheduler
-    - local: api/schedulers/ddim_cogvideox
-      title: CogVideoXDDIMScheduler
-    - local: api/schedulers/multistep_dpm_solver_cogvideox
-      title: CogVideoXDPMScheduler
     - local: api/schedulers/consistency_decoder
       title: ConsistencyDecoderScheduler
     - local: api/schedulers/cosine_dpm
@@ -687,8 +558,6 @@
       title: Attention Processor
     - local: api/activations
       title: Custom activation functions
-    - local: api/cache
-      title: Caching methods
     - local: api/normalization
       title: Custom normalization layers
     - local: api/utilities

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
@@ -15,152 +15,40 @@ specific language governing permissions and limitations under the License.
 An attention processor is a class for applying different types of attention mechanisms.
 
 ## AttnProcessor
-
 [[autodoc]] models.attention_processor.AttnProcessor
 
+## AttnProcessor2_0
 [[autodoc]] models.attention_processor.AttnProcessor2_0
 
+## AttnAddedKVProcessor
 [[autodoc]] models.attention_processor.AttnAddedKVProcessor
 
+## AttnAddedKVProcessor2_0
 [[autodoc]] models.attention_processor.AttnAddedKVProcessor2_0
 
-[[autodoc]] models.attention_processor.AttnProcessorNPU
-
-[[autodoc]] models.attention_processor.FusedAttnProcessor2_0
-
-## Allegro
-
-[[autodoc]] models.attention_processor.AllegroAttnProcessor2_0
-
-## AuraFlow
-
-[[autodoc]] models.attention_processor.AuraFlowAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedAuraFlowAttnProcessor2_0
-
-## CogVideoX
-
-[[autodoc]] models.attention_processor.CogVideoXAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedCogVideoXAttnProcessor2_0
-
 ## CrossFrameAttnProcessor
-
 [[autodoc]] pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor
 
-## Custom Diffusion
-
+## CustomDiffusionAttnProcessor
 [[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor
 
+## CustomDiffusionAttnProcessor2_0
 [[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor2_0
 
+## CustomDiffusionXFormersAttnProcessor
 [[autodoc]] models.attention_processor.CustomDiffusionXFormersAttnProcessor
 
-## Flux
-
-[[autodoc]] models.attention_processor.FluxAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedFluxAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FluxSingleAttnProcessor2_0
-
-## Hunyuan
-
-[[autodoc]] models.attention_processor.HunyuanAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedHunyuanAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGHunyuanAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGCFGHunyuanAttnProcessor2_0
-
-## IdentitySelfAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGIdentitySelfAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0
-
-## IP-Adapter
-
-[[autodoc]] models.attention_processor.IPAdapterAttnProcessor
-
-[[autodoc]] models.attention_processor.IPAdapterAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.SD3IPAdapterJointAttnProcessor2_0
-
-## JointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.JointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGJointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGCFGJointAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.FusedJointAttnProcessor2_0
-
-## LoRA
-
-[[autodoc]] models.attention_processor.LoRAAttnProcessor
-
-[[autodoc]] models.attention_processor.LoRAAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.LoRAAttnAddedKVProcessor
-
-[[autodoc]] models.attention_processor.LoRAXFormersAttnProcessor
-
-## Lumina-T2X
-
-[[autodoc]] models.attention_processor.LuminaAttnProcessor2_0
-
-## Mochi
-
-[[autodoc]] models.attention_processor.MochiAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.MochiVaeAttnProcessor2_0
-
-## Sana
-
-[[autodoc]] models.attention_processor.SanaLinearAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.SanaMultiscaleAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGCFGSanaLinearAttnProcessor2_0
-
-[[autodoc]] models.attention_processor.PAGIdentitySanaLinearAttnProcessor2_0
-
-## Stable Audio
-
-[[autodoc]] models.attention_processor.StableAudioAttnProcessor2_0
+## FusedAttnProcessor2_0
+[[autodoc]] models.attention_processor.FusedAttnProcessor2_0
 
 ## SlicedAttnProcessor
-
 [[autodoc]] models.attention_processor.SlicedAttnProcessor
 
+## SlicedAttnAddedKVProcessor
 [[autodoc]] models.attention_processor.SlicedAttnAddedKVProcessor
 
 ## XFormersAttnProcessor
-
 [[autodoc]] models.attention_processor.XFormersAttnProcessor
 
-[[autodoc]] models.attention_processor.XFormersAttnAddedKVProcessor
-
-## 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
+## AttnProcessorNPU
+[[autodoc]] models.attention_processor.AttnProcessorNPU

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

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

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
@@ -24,12 +24,6 @@ Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading]
 
 [[autodoc]] loaders.ip_adapter.IPAdapterMixin
 
-## SD3IPAdapterMixin
-
-[[autodoc]] loaders.ip_adapter.SD3IPAdapterMixin
-    - all
-    - is_ip_adapter_active
-
 ## IPAdapterMaskProcessor
 
 [[autodoc]] image_processor.IPAdapterMaskProcessor

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

@@ -1,4 +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
@@ -17,18 +17,7 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`StableDiffusionLoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
 - [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`StableDiffusionLoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.
 - [`SD3LoraLoaderMixin`] provides similar functions for [Stable Diffusion 3](https://huggingface.co/blog/sd3).
-- [`FluxLoraLoaderMixin`] provides similar functions for [Flux](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux).
-- [`CogVideoXLoraLoaderMixin`] provides similar functions for [CogVideoX](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox).
-- [`Mochi1LoraLoaderMixin`] provides similar functions for [Mochi](https://huggingface.co/docs/diffusers/main/en/api/pipelines/mochi).
-- [`AuraFlowLoraLoaderMixin`] provides similar functions for [AuraFlow](https://huggingface.co/fal/AuraFlow).
-- [`LTXVideoLoraLoaderMixin`] provides similar functions for [LTX-Video](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
-- [`SanaLoraLoaderMixin`] provides similar functions for [Sana](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana).
-- [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
-- [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
-- [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
-- [`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)
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
 <Tip>
@@ -37,10 +26,6 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 </Tip>
 
-## LoraBaseMixin
-
-[[autodoc]] loaders.lora_base.LoraBaseMixin
-
 ## StableDiffusionLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.StableDiffusionLoraLoaderMixin
@@ -53,53 +38,10 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.SD3LoraLoaderMixin
 
-## FluxLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.FluxLoraLoaderMixin
-
-## CogVideoXLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.CogVideoXLoraLoaderMixin
-
-## Mochi1LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.Mochi1LoraLoaderMixin
-## AuraFlowLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.AuraFlowLoraLoaderMixin
-
-## LTXVideoLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.LTXVideoLoraLoaderMixin
-
-## SanaLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.SanaLoraLoaderMixin
-
-## HunyuanVideoLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.HunyuanVideoLoraLoaderMixin
-
-## Lumina2LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.Lumina2LoraLoaderMixin
-
-## CogView4LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.CogView4LoraLoaderMixin
-
-## WanLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
-
 ## AmusedLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
 
-## HiDreamImageLoraLoaderMixin
+## LoraBaseMixin
 
-[[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
-
-## WanLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
+[[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

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

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

@@ -1,29 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# SD3Transformer2D
-
-This class is useful when *only* loading weights into a [`SD3Transformer2DModel`]. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check [`SD3LoraLoaderMixin`](lora#diffusers.loaders.SD3LoraLoaderMixin) class instead.
-
-The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
-
-<Tip>
-
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
-
-</Tip>
-
-## SD3Transformer2DLoadersMixin
-
-[[autodoc]] loaders.transformer_sd3.SD3Transformer2DLoadersMixin
-    - all
-    - _load_ip_adapter_weights

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

@@ -1,4 +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/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
@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import AllegroTransformer3DModel
 
-transformer = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+vae = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
 ```
 
 ## AllegroTransformer3DModel

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:
 

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

@@ -1,29 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# AutoModel
-
-The `AutoModel` is designed to make it easy to load a checkpoint without needing to know the specific model class. `AutoModel` automatically retrieves the correct model class from the checkpoint `config.json` file.
-
-```python
-from diffusers import AutoModel, AutoPipelineForText2Image
-
-unet = AutoModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
-pipe = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", unet=unet)
-```
-
-
-## AutoModel
-
-[[autodoc]] AutoModel
-	- all
-	- from_pretrained

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

@@ -1,72 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderDC
-
-The 2D Autoencoder model used in [SANA](https://huggingface.co/papers/2410.10629) and introduced in [DCAE](https://huggingface.co/papers/2410.10733) by authors Junyu Chen\*, Han Cai\*, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, Song Han from MIT HAN Lab.
-
-The abstract from the paper is:
-
-*We present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated impressive results at a moderate spatial compression ratio (e.g., 8x), but fail to maintain satisfactory reconstruction accuracy for high spatial compression ratios (e.g., 64x). We address this challenge by introducing two key techniques: (1) Residual Autoencoding, where we design our models to learn residuals based on the space-to-channel transformed features to alleviate the optimization difficulty of high spatial-compression autoencoders; (2) Decoupled High-Resolution Adaptation, an efficient decoupled three-phases training strategy for mitigating the generalization penalty of high spatial-compression autoencoders. With these designs, we improve the autoencoder's spatial compression ratio up to 128 while maintaining the reconstruction quality. Applying our DC-AE to latent diffusion models, we achieve significant speedup without accuracy drop. For example, on ImageNet 512x512, our DC-AE provides 19.1x inference speedup and 17.9x training speedup on H100 GPU for UViT-H while achieving a better FID, compared with the widely used SD-VAE-f8 autoencoder. Our code is available at [this https URL](https://github.com/mit-han-lab/efficientvit).*
-
-The following DCAE models are released and supported in Diffusers.
-
-| Diffusers format | Original format |
-|:----------------:|:---------------:|
-| [`mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers) | [`mit-han-lab/dc-ae-f32c32-sana-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0)
-| [`mit-han-lab/dc-ae-f32c32-in-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0-diffusers) | [`mit-han-lab/dc-ae-f32c32-in-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0)
-| [`mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers) | [`mit-han-lab/dc-ae-f32c32-mix-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-mix-1.0)
-| [`mit-han-lab/dc-ae-f64c128-in-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-in-1.0-diffusers) | [`mit-han-lab/dc-ae-f64c128-in-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-in-1.0)
-| [`mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers) | [`mit-han-lab/dc-ae-f64c128-mix-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-mix-1.0)
-| [`mit-han-lab/dc-ae-f128c512-in-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0-diffusers) | [`mit-han-lab/dc-ae-f128c512-in-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0)
-| [`mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers) | [`mit-han-lab/dc-ae-f128c512-mix-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-mix-1.0)
-
-This model was contributed by [lawrence-cj](https://github.com/lawrence-cj).
-
-Load a model in Diffusers format with [`~ModelMixin.from_pretrained`].
-
-```python
-from diffusers import AutoencoderDC
-
-ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers", torch_dtype=torch.float32).to("cuda")
-```
-
-## Load a model in Diffusers via `from_single_file`
-
-```python
-from difusers import AutoencoderDC
-
-ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0/blob/main/model.safetensors"
-model = AutoencoderDC.from_single_file(ckpt_path) 
-
-```
-
-The `AutoencoderDC` model has `in` and `mix` single file checkpoint variants that have matching checkpoint keys, but use different scaling factors. It is not possible for Diffusers to automatically infer the correct config file to use with the model based on just the checkpoint and will default to configuring the model using the `mix` variant config file. To override the automatically determined config, please use the `config` argument when using single file loading with `in` variant checkpoints. 
-
-```python
-from diffusers import AutoencoderDC
-
-ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0/blob/main/model.safetensors"
-model = AutoencoderDC.from_single_file(ckpt_path, config="mit-han-lab/dc-ae-f128c512-in-1.0-diffusers")
-```
-
-
-## AutoencoderDC
-
-[[autodoc]] AutoencoderDC
-  - encode
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput
-

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

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLHunyuanVideo
-
-The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanVideo](https://github.com/Tencent/HunyuanVideo/), which was introduced in [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://huggingface.co/papers/2412.03603) by Tencent.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLHunyuanVideo
-
-vae = AutoencoderKLHunyuanVideo.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder="vae", torch_dtype=torch.float16)
-```
-
-## AutoencoderKLHunyuanVideo
-
-[[autodoc]] AutoencoderKLHunyuanVideo
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

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

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

@@ -1,4 +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:
 

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

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

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

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

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

@@ -1,4 +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/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/Chroma)
-
-## ChromaTransformer2DModel
-
-[[autodoc]] ChromaTransformer2DModel

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
@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import CogVideoXTransformer3DModel
 
-transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
+vae = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
 ```
 
 ## CogVideoXTransformer3DModel

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
@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import CogView3PlusTransformer2DModel
 
-transformer = CogView3PlusTransformer2DModel.from_pretrained("THUDM/CogView3Plus-3b", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+vae = CogView3PlusTransformer2DModel.from_pretrained("THUDM/CogView3Plus-3b", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
 ```
 
 ## CogView3PlusTransformer2DModel

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

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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team and The InstantX Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team and The InstantX 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/controlnet_hunyuandit.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team and Tencent Hunyuan Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team and Tencent Hunyuan 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.
 
 # HunyuanDiT2DControlNetModel
 
-HunyuanDiT2DControlNetModel is an implementation of ControlNet for [Hunyuan-DiT](https://huggingface.co/papers/2405.08748).
+HunyuanDiT2DControlNetModel is an implementation of ControlNet for [Hunyuan-DiT](https://arxiv.org/abs/2405.08748).
 
 ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
 

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

@@ -1,29 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# SanaControlNetModel
-
-The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
-
-The abstract from the paper is:
-
-*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
-
-This model was contributed by [ishan24](https://huggingface.co/ishan24). ❤️
-The original codebase can be found at [NVlabs/Sana](https://github.com/NVlabs/Sana), and you can find official ControlNet checkpoints on [Efficient-Large-Model's](https://huggingface.co/Efficient-Large-Model) Hub profile.
-
-## SanaControlNetModel
-[[autodoc]] SanaControlNetModel
-
-## SanaControlNetOutput
-[[autodoc]] models.controlnets.controlnet_sana.SanaControlNetOutput
-

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

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team and The InstantX Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team and The InstantX 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
@@ -38,5 +38,5 @@ pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-di
 
 ## SD3ControlNetOutput
 
-[[autodoc]] models.controlnets.controlnet_sd3.SD3ControlNetOutput
+[[autodoc]] models.controlnet_sd3.SD3ControlNetOutput
 

docs/source/en/api/models/controlnet_sparsectrl.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
@@ -11,11 +11,11 @@ specific language governing permissions and limitations under the License. -->
 
 # SparseControlNetModel
 
-SparseControlNetModel is an implementation of ControlNet for [AnimateDiff](https://huggingface.co/papers/2307.04725).
+SparseControlNetModel is an implementation of ControlNet for [AnimateDiff](https://arxiv.org/abs/2307.04725).
 
 ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
 
-The SparseCtrl version of ControlNet was introduced in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://huggingface.co/papers/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
+The SparseCtrl version of ControlNet was introduced in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://arxiv.org/abs/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
 
 The abstract from the paper is:
 

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

@@ -1,35 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team and The InstantX 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.
--->
-
-# ControlNetUnionModel
-
-ControlNetUnionModel is an implementation of ControlNet for Stable Diffusion XL.
-
-The ControlNet model was introduced in [ControlNetPlus](https://github.com/xinsir6/ControlNetPlus) by xinsir6. It supports multiple conditioning inputs without increasing computation.
-
-*We design a new architecture that can support 10+ control types in condition text-to-image generation and can generate high resolution images visually comparable with midjourney. The network is based on the original ControlNet architecture, we propose two new modules to: 1 Extend the original ControlNet to support different image conditions using the same network parameter. 2 Support multiple conditions input without increasing computation offload, which is especially important for designers who want to edit image in detail, different conditions use the same condition encoder, without adding extra computations or parameters.*
-
-## Loading
-
-By default the [`ControlNetUnionModel`] should be loaded with [`~ModelMixin.from_pretrained`].
-
-```py
-from diffusers import StableDiffusionXLControlNetUnionPipeline, ControlNetUnionModel
-
-controlnet = ControlNetUnionModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0")
-pipe = StableDiffusionXLControlNetUnionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet)
-```
-
-## ControlNetUnionModel
-
-[[autodoc]] ControlNetUnionModel
-

docs/source/en/api/models/cosmos_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. -->
-
-# CosmosTransformer3DModel
-
-A Diffusion Transformer model for 3D video-like data was introduced in [Cosmos World Foundation Model Platform for Physical AI](https://huggingface.co/papers/2501.03575) by NVIDIA.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import CosmosTransformer3DModel
-
-transformer = CosmosTransformer3DModel.from_pretrained("nvidia/Cosmos-1.0-Diffusion-7B-Text2World", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## CosmosTransformer3DModel
-
-[[autodoc]] CosmosTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/dit_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/easyanimate_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. -->
-
-# EasyAnimateTransformer3DModel
-
-A Diffusion Transformer model for 3D data from [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 EasyAnimateTransformer3DModel
-
-transformer = EasyAnimateTransformer3DModel.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
-```
-
-## EasyAnimateTransformer3DModel
-
-[[autodoc]] EasyAnimateTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/flux_transformer.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/hidream_image_transformer.md

@@ -1,46 +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. -->
-
-# HiDreamImageTransformer2DModel
-
-A Transformer model for image-like data from [HiDream-I1](https://huggingface.co/HiDream-ai).
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import HiDreamImageTransformer2DModel
-
-transformer = HiDreamImageTransformer2DModel.from_pretrained("HiDream-ai/HiDream-I1-Full", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## Loading GGUF quantized checkpoints for HiDream-I1
-
-GGUF checkpoints for the `HiDreamImageTransformer2DModel` can  be loaded using `~FromOriginalModelMixin.from_single_file`
-
-```python
-import torch
-from diffusers import GGUFQuantizationConfig, HiDreamImageTransformer2DModel
-
-ckpt_path = "https://huggingface.co/city96/HiDream-I1-Dev-gguf/blob/main/hidream-i1-dev-Q2_K.gguf"
-transformer = HiDreamImageTransformer2DModel.from_single_file(
-    ckpt_path,
-    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
-    torch_dtype=torch.bfloat16
-)
-```
-
-## HiDreamImageTransformer2DModel
-
-[[autodoc]] HiDreamImageTransformer2DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/hunyuan_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/hunyuan_video_transformer_3d.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. -->
-
-# HunyuanVideoTransformer3DModel
-
-A Diffusion Transformer model for 3D video-like data was introduced in [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://huggingface.co/papers/2412.03603) by Tencent.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import HunyuanVideoTransformer3DModel
-
-transformer = HunyuanVideoTransformer3DModel.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## HunyuanVideoTransformer3DModel
-
-[[autodoc]] HunyuanVideoTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/latte_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/ltx_video_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. -->
-
-# LTXVideoTransformer3DModel
-
-A Diffusion Transformer model for 3D data from [LTX](https://huggingface.co/Lightricks/LTX-Video) was introduced by Lightricks.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import LTXVideoTransformer3DModel
-
-transformer = LTXVideoTransformer3DModel.from_pretrained("Lightricks/LTX-Video", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
-```
-
-## LTXVideoTransformer3DModel
-
-[[autodoc]] LTXVideoTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/lumina2_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. -->
-
-# Lumina2Transformer2DModel
-
-A Diffusion Transformer model for 3D video-like data was introduced in [Lumina Image 2.0](https://huggingface.co/Alpha-VLLM/Lumina-Image-2.0) by Alpha-VLLM.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import Lumina2Transformer2DModel
-
-transformer = Lumina2Transformer2DModel.from_pretrained("Alpha-VLLM/Lumina-Image-2.0", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## Lumina2Transformer2DModel
-
-[[autodoc]] Lumina2Transformer2DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput