update

* up

* unguard.

.github/workflows/benchmark.yml

@@ -11,20 +11,21 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
+  BASE_PATH: benchmark_outputs
 
 jobs:
-  torch_pipelines_cuda_benchmark_tests:
+  torch_models_cuda_benchmark_tests:
     env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_BENCHMARK }}
-    name: Torch Core Pipelines CUDA Benchmarking Tests
+    name: Torch Core Models CUDA Benchmarking Tests
     strategy:
       fail-fast: false
       max-parallel: 1
     runs-on:
-      group: aws-g6-4xlarge-plus
+      group: aws-g6e-4xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -35,27 +36,47 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
+          apt update
+          apt install -y libpq-dev postgresql-client
           python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
           python -m uv pip install -e [quality,test]
-          python -m uv pip install pandas peft
-          python -m uv pip uninstall transformers && python -m uv pip install transformers==4.48.0
+          python -m uv pip install -r benchmarks/requirements.txt
       - name: Environment
         run: |
           python utils/print_env.py
       - name: Diffusers Benchmarking
         env:
-            HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
-            BASE_PATH: benchmark_outputs
+          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         run: |
-          export TOTAL_GPU_MEMORY=$(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
+          cd benchmarks && python run_all.py
+
+      - name: Push results to the Hub
+        env: 
+          HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
+        run: |
+          cd benchmarks && python push_results.py
+          mkdir $BASE_PATH && cp *.csv $BASE_PATH
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
           name: benchmark_test_reports
-          path: benchmarks/benchmark_outputs
+          path: benchmarks/${{ env.BASE_PATH }}
+      
+      # TODO: enable this once the connection problem has been resolved.
+      - name: Update benchmarking results to DB
+        env:
+          PGDATABASE: metrics
+          PGHOST: ${{ secrets.DIFFUSERS_BENCHMARKS_PGHOST }}
+          PGUSER: transformers_benchmarks
+          PGPASSWORD: ${{ secrets.DIFFUSERS_BENCHMARKS_PGPASSWORD }}
+          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
+        run: |
+          git config --global --add safe.directory /__w/diffusers/diffusers
+          commit_id=$GITHUB_SHA
+          commit_msg=$(git show -s --format=%s "$commit_id" | cut -c1-70)
+          cd benchmarks && python populate_into_db.py "$BRANCH_NAME" "$commit_id" "$commit_msg"
 
       - name: Report success status
         if: ${{ success() }}

.github/workflows/build_docker_images.yml

@@ -72,13 +72,8 @@ jobs:
         image-name:
           - diffusers-pytorch-cpu
           - diffusers-pytorch-cuda
-          - diffusers-pytorch-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/mirror_community_pipeline.yml

@@ -79,14 +79,14 @@ jobs:
 
       # Check secret is set
       - name: whoami
-        run: huggingface-cli whoami
+        run: hf auth whoami
         env:
             HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}
 
       # Push to HF! (under subfolder based on checkout ref)
       # https://huggingface.co/datasets/diffusers/community-pipelines-mirror
       - name: Mirror community pipeline to HF
-        run: huggingface-cli upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
+        run: hf upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset
         env:
             PATH_IN_REPO: ${{ env.PATH_IN_REPO }}
             HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}

.github/workflows/nightly_tests.yml

@@ -61,7 +61,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -107,7 +107,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     defaults:
       run:
         shell: bash
@@ -178,7 +178,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
+      options: --gpus all --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -222,7 +222,7 @@ jobs:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -248,7 +248,7 @@ jobs:
           BIG_GPU_MEMORY: 40
         run: |
           python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -m "big_gpu_with_torch_cuda" \
+            -m "big_accelerator" \
             --make-reports=tests_big_gpu_torch_cuda \
             --report-log=tests_big_gpu_torch_cuda.log \
             tests/
@@ -270,7 +270,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     defaults:
       run:
         shell: bash
@@ -321,55 +321,6 @@ jobs:
           name: torch_minimum_version_cuda_test_reports
           path: reports
 
-  run_nightly_onnx_tests:
-    name: Nightly ONNXRuntime CUDA tests on Ubuntu
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-onnxruntime-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: NVIDIA-SMI
-      run: nvidia-smi
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        python -m uv pip install pytest-reportlog
-    - name: Environment
-      run: python utils/print_env.py
-
-    - name: Run Nightly ONNXRuntime CUDA tests
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "Onnx" \
-          --make-reports=tests_onnx_cuda \
-          --report-log=tests_onnx_cuda.log \
-          tests/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_onnx_cuda_stats.txt
-        cat reports/tests_onnx_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: tests_onnx_cuda_reports
-        path: reports
-
   run_nightly_quantization_tests:
     name: Torch quantization nightly tests
     strategy:
@@ -382,18 +333,21 @@ jobs:
             additional_deps: ["peft"]
           - backend: "gguf"
             test_location: "gguf"
-            additional_deps: ["peft"]
+            additional_deps: ["peft", "kernels"]
           - backend: "torchao"
             test_location: "torchao"
             additional_deps: []
           - backend: "optimum_quanto"
             test_location: "quanto"
             additional_deps: []
+          - backend: "nvidia_modelopt"
+            test_location: "modelopt"
+            additional_deps: []
     runs-on:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus 0
+      options: --shm-size "20gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -445,7 +399,7 @@ jobs:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus 0
+      options: --shm-size "20gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -485,57 +439,6 @@ jobs:
           name: torch_cuda_pipeline_level_quant_reports
           path: reports
 
-  run_flax_tpu_tests:
-    name: Nightly Flax TPU Tests
-    runs-on:
-      group: gcp-ct5lp-hightpu-8t
-    if: github.event_name == 'schedule'
-
-    container:
-      image: diffusers/diffusers-flax-tpu
-      options: --shm-size "16gb" --ipc host --privileged ${{ vars.V5_LITEPOD_8_ENV}} -v /mnt/hf_cache:/mnt/hf_cache
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        python -m uv pip install pytest-reportlog
-
-    - name: Environment
-      run: python utils/print_env.py
-
-    - name: Run nightly Flax TPU tests
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-      run: |
-        python -m pytest -n 0 \
-          -s -v -k "Flax" \
-          --make-reports=tests_flax_tpu \
-          --report-log=tests_flax_tpu.log \
-          tests/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_flax_tpu_stats.txt
-        cat reports/tests_flax_tpu_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: flax_tpu_test_reports
-        path: reports
-
   generate_consolidated_report:
     name: Generate Consolidated Test Report
     needs: [
@@ -545,9 +448,9 @@ jobs:
       run_big_gpu_torch_tests,
       run_nightly_quantization_tests,
       run_nightly_pipeline_level_quantization_tests,
-      run_nightly_onnx_tests,
+      # run_nightly_onnx_tests,
       torch_minimum_version_cuda_tests,
-      run_flax_tpu_tests
+      # run_flax_tpu_tests
     ]
     if: always()
     runs-on:

.github/workflows/pr_flax_dependency_test.yml

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

.github/workflows/pr_modular_tests.yml

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

.github/workflows/pr_tests.yml

@@ -87,11 +87,6 @@ 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
@@ -121,7 +116,7 @@ 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 transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
         pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
 
     - name: Environment
@@ -147,15 +142,6 @@ 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: |
@@ -267,9 +253,9 @@ jobs:
         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
+        pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
 
     - name: Environment
       run: |
@@ -300,4 +286,3 @@ jobs:
       with:
         name: pr_main_test_reports
         path: reports
-

.github/workflows/pr_tests_gpu.yml

@@ -13,6 +13,7 @@ on:
       - "src/diffusers/loaders/peft.py"
       - "tests/pipelines/test_pipelines_common.py"
       - "tests/models/test_modeling_common.py"
+      - "examples/**/*.py"
   workflow_dispatch:
 
 concurrency:
@@ -117,7 +118,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -132,7 +133,7 @@ jobs:
           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
+          pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
 
       - name: Environment
         run: |
@@ -182,13 +183,13 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     defaults:
       run:
         shell: bash
     strategy:
       fail-fast: false
-      max-parallel: 2
+      max-parallel: 4
       matrix:
         module: [models, schedulers, lora, others]
     steps:
@@ -203,7 +204,7 @@ jobs:
         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
+        pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
 
     - name: Environment
       run: |
@@ -252,7 +253,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
+      options: --gpus all --shm-size "16gb" --ipc host
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
@@ -265,7 +266,7 @@ jobs:
     - 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
+        pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
         python -m uv pip install -e [quality,test,training]
 
     - name: Environment

.github/workflows/push_tests.yml

@@ -64,7 +64,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -109,7 +109,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     defaults:
       run:
         shell: bash
@@ -159,102 +159,6 @@ jobs:
         name: torch_cuda_test_reports_${{ matrix.module }}
         path: reports
 
-  flax_tpu_tests:
-    name: Flax TPU Tests
-    runs-on:
-      group: gcp-ct5lp-hightpu-8t
-    container:
-      image: diffusers/diffusers-flax-tpu
-      options: --shm-size "16gb" --ipc host --privileged ${{ vars.V5_LITEPOD_8_ENV}} -v /mnt/hf_cache:/mnt/hf_cache 
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run Flax TPU tests
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_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.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 \
-          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
 
@@ -263,7 +167,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
+      options: --gpus all --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -306,7 +210,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
+      options: --gpus all --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -348,7 +252,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
+      options: --gpus all --shm-size "16gb" --ipc host
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3

.github/workflows/push_tests_fast.yml

@@ -33,16 +33,6 @@ 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
@@ -87,24 +77,6 @@ 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

@@ -1,12 +1,7 @@
 name: Fast mps tests on main
 
 on:
-  push:
-    branches:
-      - main
-    paths:
-      - "src/diffusers/**.py"
-      - "tests/**.py"
+  workflow_dispatch:
 
 env:
   DIFFUSERS_IS_CI: yes

.github/workflows/release_tests_fast.yml

@@ -62,7 +62,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -107,7 +107,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     defaults:
       run:
         shell: bash
@@ -163,7 +163,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus all
     defaults:
       run:
         shell: bash
@@ -213,101 +213,6 @@ jobs:
         with:
           name: torch_minimum_version_cuda_test_reports
           path: reports
-          
-  flax_tpu_tests:
-    name: Flax TPU Tests
-    runs-on: docker-tpu
-    container:
-      image: diffusers/diffusers-flax-tpu
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --privileged
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run slow Flax TPU tests
-      env:
-        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
-      run: |
-        python -m pytest -n 0 \
-          -s -v -k "Flax" \
-          --make-reports=tests_flax_tpu \
-          tests/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_flax_tpu_stats.txt
-        cat reports/tests_flax_tpu_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: flax_tpu_test_reports
-        path: reports
-
-  onnx_cuda_tests:
-    name: ONNX CUDA Tests
-    runs-on:
-      group: aws-g4dn-2xlarge
-    container:
-      image: diffusers/diffusers-onnxruntime-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --gpus 0
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run slow ONNXRuntime CUDA tests
-      env:
-        HF_TOKEN: ${{ secrets.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 \
-          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
@@ -317,7 +222,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
+      options: --gpus all --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -360,7 +265,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
+      options: --gpus all --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -402,7 +307,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host
+      options: --gpus all --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers

.github/workflows/run_tests_from_a_pr.yml

@@ -30,7 +30,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: ${{ github.event.inputs.docker_image }}
-      options: --gpus 0 --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
 
     steps:
       - name: Validate test files input

.github/workflows/ssh-runner.yml

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

README.md

@@ -37,7 +37,7 @@ limitations under the License.
 
 ## Installation
 
-We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.
+We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/), please refer to their official documentation.
 
 ### PyTorch
 
@@ -53,14 +53,6 @@ With `conda` (maintained by the community):
 conda install -c conda-forge diffusers
 ```
 
-### Flax
-
-With `pip` (official package):
-
-```bash
-pip install --upgrade diffusers[flax]
-```
-
 ### Apple Silicon (M1/M2) support
 
 Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.

benchmarks/README.md

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

benchmarks/base_classes.py

@@ -1,346 +0,0 @@
-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

@@ -1,26 +0,0 @@
-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

@@ -1,33 +0,0 @@
-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

@@ -1,29 +0,0 @@
-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

@@ -1,28 +0,0 @@
-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

@@ -1,28 +0,0 @@
-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

@@ -1,23 +0,0 @@
-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

@@ -1,40 +0,0 @@
-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

@@ -0,0 +1,98 @@
+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

@@ -0,0 +1,80 @@
+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

@@ -0,0 +1,82 @@
+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

@@ -0,0 +1,244 @@
+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

@@ -0,0 +1,74 @@
+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

@@ -0,0 +1,166 @@
+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 glob
-import sys
+import os
 
 import pandas as pd
 from huggingface_hub import hf_hub_download, upload_file
 from huggingface_hub.utils import EntryNotFoundError
 
 
-sys.path.append(".")
-from utils import BASE_PATH, FINAL_CSV_FILE, GITHUB_SHA, REPO_ID, collate_csv  # noqa: E402
+REPO_ID = "diffusers/benchmarks"
 
 
 def has_previous_benchmark() -> str:
+    from run_all import FINAL_CSV_FILENAME
+
     csv_path = None
     try:
-        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILE)
+        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILENAME)
     except EntryNotFoundError:
         csv_path = None
     return csv_path
@@ -26,46 +26,50 @@ def filter_float(value):
 
 
 def push_to_hf_dataset():
-    all_csvs = sorted(glob.glob(f"{BASE_PATH}/*.csv"))
-    collate_csv(all_csvs, FINAL_CSV_FILE)
+    from run_all import FINAL_CSV_FILENAME, GITHUB_SHA
 
-    # 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_FILE)
+        current_results = pd.read_csv(FINAL_CSV_FILENAME)
         previous_results = pd.read_csv(csv_path)
 
         numeric_columns = current_results.select_dtypes(include=["float64", "int64"]).columns
-        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:
-            previous_results[column] = previous_results[column].map(lambda x: filter_float(x))
+            # get previous values as floats, aligned to current index
+            prev_vals = previous_results[column].map(filter_float).reindex(current_results.index)
 
-            # 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
+            # get current values as floats
+            curr_vals = current_results[column].astype(float)
 
-            # 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}%)"
+            # 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 ""
             )
-            # There might be newly added rows. So, filter out the NaNs.
-            current_results[column] = current_results[column].map(lambda x: x.replace(" (nan%)", ""))
 
-        # Overwrite the current result file.
-        current_results.to_csv(FINAL_CSV_FILE, index=False)
+            # combine
+            current_results[column] = curr_str + append_str
+        os.remove(FINAL_CSV_FILENAME)
+        current_results.to_csv(FINAL_CSV_FILENAME, index=False)
 
     commit_message = f"upload from sha: {GITHUB_SHA}" if GITHUB_SHA is not None else "upload benchmark results"
     upload_file(
         repo_id=REPO_ID,
-        path_in_repo=FINAL_CSV_FILE,
-        path_or_fileobj=FINAL_CSV_FILE,
+        path_in_repo=FINAL_CSV_FILENAME,
+        path_or_fileobj=FINAL_CSV_FILENAME,
         repo_type="dataset",
         commit_message=commit_message,
     )
+    upload_file(
+        repo_id="diffusers/benchmark-analyzer",
+        path_in_repo=FINAL_CSV_FILENAME,
+        path_or_fileobj=FINAL_CSV_FILENAME,
+        repo_type="space",
+        commit_message=commit_message,
+    )
 
 
 if __name__ == "__main__":

benchmarks/requirements.txt

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

benchmarks/run_all.py

@@ -1,101 +1,84 @@
 import glob
+import logging
+import os
 import subprocess
-import sys
-from typing import List
+
+import pandas as pd
 
 
-sys.path.append(".")
-from benchmark_text_to_image import ALL_T2I_CKPTS  # noqa: E402
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+logger = logging.getLogger(__name__)
 
-
-PATTERN = "benchmark_*.py"
+PATTERN = "benchmarking_*.py"
+FINAL_CSV_FILENAME = "collated_results.csv"
+GITHUB_SHA = os.getenv("GITHUB_SHA", None)
 
 
 class SubprocessCallException(Exception):
     pass
 
 
-# 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`
-    """
+def run_command(command: list[str], return_stdout=False):
     try:
         output = subprocess.check_output(command, stderr=subprocess.STDOUT)
-        if return_stdout:
-            if hasattr(output, "decode"):
-                output = output.decode("utf-8")
-            return output
+        if return_stdout and hasattr(output, "decode"):
+            return output.decode("utf-8")
     except subprocess.CalledProcessError as e:
-        raise SubprocessCallException(
-            f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
-        ) from e
+        raise SubprocessCallException(f"Command `{' '.join(command)}` failed with:\n{e.output.decode()}") from e
 
 
-def main():
-    python_files = glob.glob(PATTERN)
+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
 
-    for file in python_files:
-        print(f"****** Running file: {file} ******")
-
-        # 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 += " --run_compile"
-            run_command(command.split())
-
-    # Run variants.
-    for file in python_files:
-        # See: https://github.com/pytorch/pytorch/issues/129637
-        if file == "benchmark_ip_adapters.py":
+    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 file == "benchmark_text_to_image.py":
-            for ckpt in ALL_T2I_CKPTS:
-                command = f"python {file} --ckpt {ckpt}"
+    if not df_list:
+        logger.info("All result CSVs were empty or invalid; nothing to merge.")
+        return
 
-                if "turbo" in ckpt:
-                    command += " --num_inference_steps 1"
+    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}.")
 
-                run_command(command.split())
 
-                command += " --run_compile"
-                run_command(command.split())
+def run_scripts():
+    python_files = sorted(glob.glob(PATTERN))
+    python_files = [f for f in python_files if f != "benchmarking_utils.py"]
 
-        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}"
+    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} ******")
 
-                if ckpt == "stabilityai/sdxl-turbo":
-                    command += " --num_inference_steps 2"
+        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_command(command.split())
-                command += " --run_compile"
-                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)
 
-        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())
+    logger.info(f"\nAll scripts attempted. Final collated CSV: {FINAL_CSV_FILENAME}")
 
 
 if __name__ == "__main__":
-    main()
+    run_scripts()

benchmarks/utils.py

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

docker/diffusers-doc-builder/Dockerfile

@@ -1,52 +1,42 @@
-FROM ubuntu:20.04
+FROM python:3.10-slim
+ENV PYTHONDONTWRITEBYTECODE=1
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
+RUN apt-get -y update && apt-get install -y bash \
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libsndfile1-dev \
+    libgl1
 
-RUN apt install -y bash \
-                   build-essential \
-                   git \
-                   git-lfs \
-                   curl \
-                   ca-certificates \
-                   libsndfile1-dev \
-                   python3.10 \
-                   python3-pip \
-                   libgl1 \
-                   zip \
-                   wget \
-                   python3.10-venv && \
-    rm -rf /var/lib/apt/lists
-
-# make sure to use venv
-RUN python3.10 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
+ENV UV_PYTHON=/usr/local/bin/python
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio \
-        invisible_watermark \
-        --extra-index-url https://download.pytorch.org/whl/cpu && \
-    python3.10 -m uv pip install --no-cache-dir \
-        accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        Jinja2 \
-        librosa \
-        numpy==1.26.4 \
-        scipy \
-        tensorboard \
-        transformers \
-        matplotlib \
-        setuptools==69.5.1
+RUN pip install uv
+RUN uv pip install --no-cache-dir \
+    torch \
+    torchvision \
+    torchaudio \
+    --extra-index-url https://download.pytorch.org/whl/cpu
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
+    accelerate \
+    numpy==1.26.4 \
+    hf_transfer \
+    setuptools==69.5.1 \
+    bitsandbytes \
+    torchao \
+    gguf \
+    optimum-quanto
+
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
 
 CMD ["/bin/bash"]

docker/diffusers-flax-cpu/Dockerfile

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

docker/diffusers-flax-tpu/Dockerfile

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

docker/diffusers-pytorch-cpu/Dockerfile

@@ -1,50 +1,37 @@
-FROM ubuntu:20.04
+FROM python:3.10-slim
+ENV PYTHONDONTWRITEBYTECODE=1
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update \
-    && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
+RUN apt-get -y update && apt-get install -y bash \
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libsndfile1-dev \
+    libgl1
 
-RUN apt install -y bash \
-                   build-essential \
-                   git \
-                   git-lfs \
-                   curl \
-                   ca-certificates \
-                   libsndfile1-dev \
-                   python3.10 \
-                   python3.10-dev \
-                   python3-pip \
-                   libgl1 \
-                   python3.10-venv && \
-    rm -rf /var/lib/apt/lists
-
-# make sure to use venv
-RUN python3.10 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
+ENV UV_PYTHON=/usr/local/bin/python
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio \
-        invisible_watermark \
-        --extra-index-url https://download.pytorch.org/whl/cpu && \
-    python3.10 -m uv pip install --no-cache-dir \
-        accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        Jinja2 \
-        librosa \
-        numpy==1.26.4 \
-        scipy \
-        tensorboard \
-        transformers matplotlib  \
-        hf_transfer
+RUN pip install uv
+RUN uv pip install --no-cache-dir \
+    torch \
+    torchvision \
+    torchaudio \
+    --extra-index-url https://download.pytorch.org/whl/cpu
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
+    accelerate \
+    numpy==1.26.4 \
+    hf_transfer
+
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cuda/Dockerfile

@@ -2,11 +2,13 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
+ARG PYTHON_VERSION=3.12
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
     && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
+    && add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update
 
 RUN apt install -y bash \
     build-essential \
@@ -16,36 +18,31 @@ RUN apt install -y bash \
     ca-certificates \
     libsndfile1-dev \
     libgl1 \
-    python3.10 \
-    python3.10-dev \
+    python3 \
     python3-pip \
-    python3.10-venv && \
-    rm -rf /var/lib/apt/lists
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
 
-# make sure to use venv
-RUN python3.10 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
+RUN uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio \
-    invisible_watermark && \
-    python3.10 -m pip install --no-cache-dir \
+    torchaudio
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
     accelerate \
-    datasets \
-    hf-doc-builder \
-    huggingface-hub \
-    hf_transfer \
-    Jinja2 \
-    librosa \
     numpy==1.26.4 \
-    scipy \
-    tensorboard \
-    transformers \
-    pytorch-lightning  \
+    pytorch-lightning \
     hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-minimum-cuda/Dockerfile

@@ -2,6 +2,7 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
+ARG PYTHON_VERSION=3.10
 ENV DEBIAN_FRONTEND=noninteractive
 ENV MINIMUM_SUPPORTED_TORCH_VERSION="2.1.0"
 ENV MINIMUM_SUPPORTED_TORCHVISION_VERSION="0.16.0"
@@ -9,7 +10,8 @@ ENV MINIMUM_SUPPORTED_TORCHAUDIO_VERSION="2.1.0"
 
 RUN apt-get -y update \
     && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
+    && add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update
 
 RUN apt install -y bash \
     build-essential \
@@ -19,35 +21,31 @@ RUN apt install -y bash \
     ca-certificates \
     libsndfile1-dev \
     libgl1 \
-    python3.10 \
-    python3.10-dev \
+    python3 \
     python3-pip \
-    python3.10-venv && \
-    rm -rf /var/lib/apt/lists
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
 
-# make sure to use venv
-RUN python3.10 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m uv pip install --no-cache-dir \
+RUN uv pip install --no-cache-dir \
     torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
     torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
-    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION \
-    invisible_watermark && \
-    python3.10 -m pip install --no-cache-dir \
+    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
     accelerate \
-    datasets \
-    hf-doc-builder \
-    huggingface-hub \
-    hf_transfer \
-    Jinja2 \
-    librosa \
     numpy==1.26.4 \
-    scipy \
-    tensorboard \
-    transformers \
+    pytorch-lightning \
     hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -2,50 +2,48 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
+ARG PYTHON_VERSION=3.12
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
     && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
+    && add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update
 
 RUN apt install -y bash \
-                   build-essential \
-                   git \
-                   git-lfs \
-                   curl \
-                   ca-certificates \
-                   libsndfile1-dev \
-                   libgl1 \
-                   python3.10 \
-                   python3.10-dev \
-                   python3-pip \
-                   python3.10-venv && \
-    rm -rf /var/lib/apt/lists
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libsndfile1-dev \
+    libgl1 \
+    python3 \
+    python3-pip \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
 
-# make sure to use venv
-RUN python3.10 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    python3.10 -m pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio \
-        invisible_watermark && \
-    python3.10 -m uv pip install --no-cache-dir \
-        accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        hf_transfer \
-        Jinja2 \
-        librosa \
-        numpy==1.26.4 \
-        scipy \
-        tensorboard \
-        transformers \
-        xformers  \
-        hf_transfer
+RUN uv pip install --no-cache-dir \
+    torch \
+    torchvision \
+    torchaudio
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
+    accelerate \
+    numpy==1.26.4 \
+    pytorch-lightning \
+    hf_transfer \
+    xformers
 
 CMD ["/bin/bash"]

docs/source/en/_toctree.yml

@@ -1,36 +1,37 @@
-- sections:
+- title: Get started
+  sections:
   - local: index
-    title: 🧨 Diffusers
-  - local: quicktour
-    title: Quicktour
-  - local: stable_diffusion
-    title: Effective and efficient diffusion
+    title: Diffusers
   - local: installation
     title: Installation
-  title: Get started
-- sections:
-  - local: tutorials/tutorial_overview
-    title: Overview
-  - local: using-diffusers/write_own_pipeline
-    title: Understanding pipelines, models and schedulers
+  - local: quicktour
+    title: Quickstart
+  - local: stable_diffusion
+    title: Basic performance
+
+- title: Pipelines
+  isExpanded: false
+  sections:
+  - local: using-diffusers/loading
+    title: DiffusionPipeline
   - local: tutorials/autopipeline
     title: AutoPipeline
-  - local: tutorials/basic_training
-    title: Train a diffusion model
-  title: Tutorials
-- sections:
-  - local: using-diffusers/loading
-    title: Load pipelines
   - local: using-diffusers/custom_pipeline_overview
-    title: Load community pipelines and components
+    title: Community pipelines and components
+  - local: using-diffusers/callback
+    title: Pipeline callbacks
+  - local: using-diffusers/reusing_seeds
+    title: Reproducibility
   - local: using-diffusers/schedulers
-    title: Load schedulers and models
+    title: Schedulers
   - local: using-diffusers/other-formats
-    title: Model files and layouts
+    title: Model formats
   - local: using-diffusers/push_to_hub
-    title: Push files to the Hub
-  title: Load pipelines and adapters
-- sections:
+    title: Sharing pipelines and models
+
+- title: Adapters
+  isExpanded: false
+  sections:
   - local: tutorials/using_peft_for_inference
     title: LoRA
   - local: using-diffusers/ip_adapter
@@ -43,45 +44,56 @@
     title: DreamBooth
   - local: using-diffusers/textual_inversion_inference
     title: Textual inversion
-  title: Adapters
+
+- title: Inference
   isExpanded: false
-- sections:
-  - local: using-diffusers/unconditional_image_generation
-    title: Unconditional image generation
-  - local: using-diffusers/conditional_image_generation
-    title: Text-to-image
-  - local: using-diffusers/img2img
-    title: Image-to-image
-  - local: using-diffusers/inpaint
-    title: Inpainting
-  - local: using-diffusers/text-img2vid
-    title: Video generation
-  - 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: training/distributed_inference
-    title: Distributed inference
-  - local: using-diffusers/scheduler_features
-    title: Scheduler features
-  - local: using-diffusers/callback
-    title: Pipeline callbacks
-  - local: using-diffusers/reusing_seeds
-    title: Reproducible pipelines
-  - local: using-diffusers/image_quality
-    title: Controlling image quality
+  sections:
   - local: using-diffusers/weighted_prompts
     title: Prompt techniques
-  title: Inference techniques
-- sections:
-  - local: advanced_inference/outpaint
-    title: Outpainting
-  title: Advanced inference
-- sections:
+  - local: using-diffusers/create_a_server
+    title: Create a server
+  - local: using-diffusers/batched_inference
+    title: Batch inference
+  - local: training/distributed_inference
+    title: Distributed inference
+
+- title: Inference optimization
+  isExpanded: false
+  sections:
+  - local: optimization/fp16
+    title: Accelerate inference
+  - local: optimization/cache
+    title: Caching
+  - local: optimization/attention_backends
+    title: Attention backends
+  - local: optimization/memory
+    title: Reduce memory usage
+  - local: optimization/speed-memory-optims
+    title: Compiling and offloading quantized models
+  - title: Community optimizations
+    sections:
+    - local: optimization/pruna
+      title: Pruna
+    - local: optimization/xformers
+      title: xFormers
+    - local: optimization/tome
+      title: Token merging
+    - local: optimization/deepcache
+      title: DeepCache
+    - local: optimization/cache_dit
+      title: CacheDiT
+    - local: optimization/tgate
+      title: TGATE
+    - local: optimization/xdit
+      title: xDiT
+    - local: optimization/para_attn
+      title: ParaAttention
+    - local: using-diffusers/image_quality
+      title: FreeU
+
+- title: Hybrid Inference
+  isExpanded: false
+  sections:
   - local: hybrid_inference/overview
     title: Overview
   - local: hybrid_inference/vae_decode
@@ -90,8 +102,112 @@
     title: VAE Encode
   - local: hybrid_inference/api_reference
     title: API Reference
-  title: Hybrid Inference
-- sections:
+
+- title: Modular Diffusers
+  isExpanded: false
+  sections:
+  - local: modular_diffusers/overview
+    title: Overview
+  - local: modular_diffusers/quickstart
+    title: Quickstart
+  - local: modular_diffusers/modular_diffusers_states
+    title: States
+  - local: modular_diffusers/pipeline_block
+    title: ModularPipelineBlocks
+  - local: modular_diffusers/sequential_pipeline_blocks
+    title: SequentialPipelineBlocks
+  - local: modular_diffusers/loop_sequential_pipeline_blocks
+    title: LoopSequentialPipelineBlocks
+  - local: modular_diffusers/auto_pipeline_blocks
+    title: AutoPipelineBlocks
+  - local: modular_diffusers/modular_pipeline
+    title: ModularPipeline
+  - local: modular_diffusers/components_manager
+    title: ComponentsManager
+  - local: modular_diffusers/guiders
+    title: Guiders
+
+- title: Training
+  isExpanded: false
+  sections:
+  - local: training/overview
+    title: Overview
+  - local: training/create_dataset
+    title: Create a dataset for training
+  - local: training/adapt_a_model
+    title: Adapt a model to a new task
+  - local: tutorials/basic_training
+    title: Train a diffusion model
+  - title: Models
+    sections:
+    - local: training/unconditional_training
+      title: Unconditional image generation
+    - local: training/text2image
+      title: Text-to-image
+    - local: training/sdxl
+      title: Stable Diffusion XL
+    - local: training/kandinsky
+      title: Kandinsky 2.2
+    - local: training/wuerstchen
+      title: Wuerstchen
+    - local: training/controlnet
+      title: ControlNet
+    - local: training/t2i_adapters
+      title: T2I-Adapters
+    - local: training/instructpix2pix
+      title: InstructPix2Pix
+    - local: training/cogvideox
+      title: CogVideoX
+  - title: Methods
+    sections:
+    - local: training/text_inversion
+      title: Textual Inversion
+    - local: training/dreambooth
+      title: DreamBooth
+    - local: training/lora
+      title: LoRA
+    - local: training/custom_diffusion
+      title: Custom Diffusion
+    - local: training/lcm_distill
+      title: Latent Consistency Distillation
+    - local: training/ddpo
+      title: Reinforcement learning training with DDPO
+
+- title: Quantization
+  isExpanded: false
+  sections:
+  - local: quantization/overview
+    title: Getting started
+  - local: quantization/bitsandbytes
+    title: bitsandbytes
+  - local: quantization/gguf
+    title: gguf
+  - local: quantization/torchao
+    title: torchao
+  - local: quantization/quanto
+    title: quanto
+  - local: quantization/modelopt
+    title: NVIDIA ModelOpt
+
+- title: Model accelerators and hardware
+  isExpanded: false
+  sections:
+  - local: optimization/onnx
+    title: ONNX
+  - local: optimization/open_vino
+    title: OpenVINO
+  - local: optimization/coreml
+    title: Core ML
+  - local: optimization/mps
+    title: Metal Performance Shaders (MPS)
+  - local: optimization/habana
+    title: Intel Gaudi
+  - local: optimization/neuron
+    title: AWS Neuron
+
+- title: Specific pipeline examples
+  isExpanded: false
+  sections:
   - local: using-diffusers/consisid
     title: ConsisID
   - local: using-diffusers/sdxl
@@ -116,104 +232,30 @@
     title: Stable Video Diffusion
   - local: using-diffusers/marigold_usage
     title: Marigold Computer Vision
-  title: Specific pipeline examples
-- sections:
-  - local: training/overview
-    title: Overview
-  - local: training/create_dataset
-    title: Create a dataset for training
-  - local: training/adapt_a_model
-    title: Adapt a model to a new task
-  - isExpanded: false
+
+- title: Resources
+  isExpanded: false
+  sections:
+  - title: Task recipes
     sections:
-    - local: training/unconditional_training
+    - local: using-diffusers/unconditional_image_generation
       title: Unconditional image generation
-    - local: training/text2image
+    - local: using-diffusers/conditional_image_generation
       title: Text-to-image
-    - local: training/sdxl
-      title: Stable Diffusion XL
-    - local: training/kandinsky
-      title: Kandinsky 2.2
-    - local: training/wuerstchen
-      title: Wuerstchen
-    - local: training/controlnet
-      title: ControlNet
-    - local: training/t2i_adapters
-      title: T2I-Adapters
-    - local: training/instructpix2pix
-      title: InstructPix2Pix
-    - local: training/cogvideox
-      title: CogVideoX
-    title: Models
-  - isExpanded: false
-    sections:
-    - local: training/text_inversion
-      title: Textual Inversion
-    - local: training/dreambooth
-      title: DreamBooth
-    - local: training/lora
-      title: LoRA
-    - local: training/custom_diffusion
-      title: Custom Diffusion
-    - local: training/lcm_distill
-      title: Latent Consistency Distillation
-    - local: training/ddpo
-      title: Reinforcement learning training with DDPO
-    title: Methods
-  title: Training
-- sections:
-  - local: quantization/overview
-    title: Getting Started
-  - local: quantization/bitsandbytes
-    title: bitsandbytes
-  - local: quantization/gguf
-    title: gguf
-  - local: quantization/torchao
-    title: torchao
-  - local: quantization/quanto
-    title: quanto
-  title: Quantization Methods
-- sections:
-  - local: optimization/fp16
-    title: Accelerate inference
-  - local: optimization/cache
-    title: Caching
-  - local: optimization/memory
-    title: Reduce memory usage
-  - local: optimization/pruna
-    title: Pruna
-  - local: optimization/xformers
-    title: xFormers
-  - local: optimization/tome
-    title: Token merging
-  - local: optimization/deepcache
-    title: DeepCache
-  - local: optimization/tgate
-    title: TGATE
-  - local: optimization/xdit
-    title: xDiT
-  - local: optimization/para_attn
-    title: ParaAttention
-  - sections:
-    - local: using-diffusers/stable_diffusion_jax_how_to
-      title: JAX/Flax
-    - local: optimization/onnx
-      title: ONNX
-    - local: optimization/open_vino
-      title: OpenVINO
-    - local: optimization/coreml
-      title: Core ML
-    title: Optimized model formats
-  - sections:
-    - local: optimization/mps
-      title: Metal Performance Shaders (MPS)
-    - local: optimization/habana
-      title: Intel Gaudi
-    - local: optimization/neuron
-      title: AWS Neuron
-    title: Optimized hardware
-  title: Accelerate inference and reduce memory
-- sections:
+    - local: using-diffusers/img2img
+      title: Image-to-image
+    - local: using-diffusers/inpaint
+      title: Inpainting
+    - local: advanced_inference/outpaint
+      title: Outpainting
+    - local: using-diffusers/text-img2vid
+      title: Video generation
+    - local: using-diffusers/depth2img
+      title: Depth-to-image
+  - local: using-diffusers/write_own_pipeline
+    title: Understanding pipelines, models and schedulers
+  - local: community_projects
+    title: Projects built with Diffusers
   - local: conceptual/philosophy
     title: Philosophy
   - local: using-diffusers/controlling_generation
@@ -224,13 +266,11 @@
     title: Diffusers' Ethical Guidelines
   - local: conceptual/evaluation
     title: Evaluating Diffusion Models
-  title: Conceptual Guides
-- sections:
-  - local: community_projects
-    title: Projects built with Diffusers
-  title: Community Projects
-- sections:
-  - isExpanded: false
+
+- title: API
+  isExpanded: false
+  sections:
+  - title: Main Classes
     sections:
     - local: api/configuration
       title: Configuration
@@ -240,8 +280,21 @@
       title: Outputs
     - local: api/quantization
       title: Quantization
-    title: Main Classes
-  - isExpanded: false
+    - local: api/parallel
+      title: Parallel inference
+  - title: Modular
+    sections:
+    - local: api/modular_diffusers/pipeline
+      title: Pipeline
+    - local: api/modular_diffusers/pipeline_blocks
+      title: Blocks
+    - local: api/modular_diffusers/pipeline_states
+      title: States
+    - local: api/modular_diffusers/pipeline_components
+      title: Components and configs
+    - local: api/modular_diffusers/guiders
+      title: Guiders
+  - title: Loaders
     sections:
     - local: api/loaders/ip_adapter
       title: IP-Adapter
@@ -257,14 +310,14 @@
       title: SD3Transformer2D
     - local: api/loaders/peft
       title: PEFT
-    title: Loaders
-  - isExpanded: false
+  - title: Models
     sections:
     - local: api/models/overview
       title: Overview
     - local: api/models/auto_model
       title: AutoModel
-    - sections:
+    - title: ControlNets
+      sections:
       - local: api/models/controlnet
         title: ControlNetModel
       - local: api/models/controlnet_union
@@ -279,12 +332,14 @@
         title: SD3ControlNetModel
       - local: api/models/controlnet_sparsectrl
         title: SparseControlNetModel
-      title: ControlNets
-    - sections:
+    - title: Transformers
+      sections:
       - local: api/models/allegro_transformer3d
         title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
+      - local: api/models/bria_transformer
+        title: BriaTransformer2DModel
       - local: api/models/chroma_transformer
         title: ChromaTransformer2DModel
       - local: api/models/cogvideox_transformer3d
@@ -325,10 +380,14 @@
         title: PixArtTransformer2DModel
       - local: api/models/prior_transformer
         title: PriorTransformer
+      - local: api/models/qwenimage_transformer2d
+        title: QwenImageTransformer2DModel
       - local: api/models/sana_transformer2d
         title: SanaTransformer2DModel
       - local: api/models/sd3_transformer2d
         title: SD3Transformer2DModel
+      - local: api/models/skyreels_v2_transformer_3d
+        title: SkyReelsV2Transformer3DModel
       - local: api/models/stable_audio_transformer
         title: StableAudioDiTModel
       - local: api/models/transformer2d
@@ -337,8 +396,8 @@
         title: TransformerTemporalModel
       - local: api/models/wan_transformer_3d
         title: WanTransformer3DModel
-      title: Transformers
-    - sections:
+    - title: UNets
+      sections:
       - local: api/models/stable_cascade_unet
         title: StableCascadeUNet
       - local: api/models/unet
@@ -353,8 +412,8 @@
         title: UNetMotionModel
       - local: api/models/uvit2d
         title: UViT2DModel
-      title: UNets
-    - sections:
+    - title: VAEs
+      sections:
       - local: api/models/asymmetricautoencoderkl
         title: AsymmetricAutoencoderKL
       - local: api/models/autoencoder_dc
@@ -375,6 +434,8 @@
         title: AutoencoderKLMagvit
       - local: api/models/autoencoderkl_mochi
         title: AutoencoderKLMochi
+      - local: api/models/autoencoderkl_qwenimage
+        title: AutoencoderKLQwenImage
       - local: api/models/autoencoder_kl_wan
         title: AutoencoderKLWan
       - local: api/models/consistency_decoder_vae
@@ -385,9 +446,7 @@
         title: Tiny AutoEncoder
       - local: api/models/vq
         title: VQModel
-      title: VAEs
-    title: Models
-  - isExpanded: false
+  - title: Pipelines
     sections:
     - local: api/pipelines/overview
       title: Overview
@@ -409,6 +468,8 @@
       title: AutoPipeline
     - local: api/pipelines/blip_diffusion
       title: BLIP-Diffusion
+    - local: api/pipelines/bria_3_2
+      title: Bria 3.2
     - local: api/pipelines/chroma
       title: Chroma
     - local: api/pipelines/cogvideox
@@ -513,6 +574,8 @@
       title: PixArt-α
     - local: api/pipelines/pixart_sigma
       title: PixArt-Σ
+    - local: api/pipelines/qwenimage
+      title: QwenImage
     - local: api/pipelines/sana
       title: Sana
     - local: api/pipelines/sana_sprint
@@ -523,11 +586,14 @@
       title: Semantic Guidance
     - local: api/pipelines/shap_e
       title: Shap-E
+    - local: api/pipelines/skyreels_v2
+      title: SkyReels-V2
     - local: api/pipelines/stable_audio
       title: Stable Audio
     - local: api/pipelines/stable_cascade
       title: Stable Cascade
-    - sections:
+    - title: Stable Diffusion
+      sections:
       - local: api/pipelines/stable_diffusion/overview
         title: Overview
       - local: api/pipelines/stable_diffusion/depth2img
@@ -564,7 +630,6 @@
         title: T2I-Adapter
       - local: api/pipelines/stable_diffusion/text2img
         title: Text-to-image
-      title: Stable Diffusion
     - local: api/pipelines/stable_unclip
       title: Stable unCLIP
     - local: api/pipelines/text_to_video
@@ -583,8 +648,7 @@
       title: Wan
     - local: api/pipelines/wuerstchen
       title: Wuerstchen
-    title: Pipelines
-  - isExpanded: false
+  - title: Schedulers
     sections:
     - local: api/schedulers/overview
       title: Overview
@@ -654,8 +718,7 @@
       title: UniPCMultistepScheduler
     - local: api/schedulers/vq_diffusion
       title: VQDiffusionScheduler
-    title: Schedulers
-  - isExpanded: false
+  - title: Internal classes
     sections:
     - local: api/internal_classes_overview
       title: Overview
@@ -673,5 +736,3 @@
       title: VAE Image Processor
     - local: api/video_processor
       title: Video Processor
-    title: Internal classes
-  title: API

docs/source/en/api/cache.md

@@ -28,3 +28,9 @@ Cache methods speedup diffusion transformers by storing and reusing intermediate
 [[autodoc]] FasterCacheConfig
 
 [[autodoc]] apply_faster_cache
+
+### FirstBlockCacheConfig
+
+[[autodoc]] FirstBlockCacheConfig
+
+[[autodoc]] apply_first_block_cache

docs/source/en/api/configuration.md

@@ -14,11 +14,8 @@ specific language governing permissions and limitations under the License.
 
 Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which stores all the parameters that are passed to their respective `__init__` methods in a JSON-configuration file.
 
-<Tip>
-
-To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
-
-</Tip>
+> [!TIP]
+> To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
 
 ## ConfigMixin
 

docs/source/en/api/image_processor.md

@@ -20,6 +20,12 @@ All pipelines with [`VaeImageProcessor`] accept PIL Image, PyTorch tensor, or Nu
 
 [[autodoc]] image_processor.VaeImageProcessor
 
+## InpaintProcessor
+
+The [`InpaintProcessor`] accepts `mask` and `image` inputs and process them together. Optionally, it can accept padding_mask_crop and apply mask overlay.
+
+[[autodoc]] image_processor.InpaintProcessor
+
 ## VaeImageProcessorLDM3D
 
 The [`VaeImageProcessorLDM3D`] accepts RGB and depth inputs and returns RGB and depth outputs.

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

@@ -14,11 +14,8 @@ specific language governing permissions and limitations under the License.
 
 [IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.
 
-<Tip>
-
-Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading](../../using-diffusers/loading_adapters#ip-adapter) guide, and you can see how to use it in the [usage](../../using-diffusers/ip_adapter) guide.
-
-</Tip>
+> [!TIP]
+> Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading](../../using-diffusers/loading_adapters#ip-adapter) guide, and you can see how to use it in the [usage](../../using-diffusers/ip_adapter) guide.
 
 ## IPAdapterMixin
 

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

@@ -26,16 +26,19 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
 - [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
 - [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
+- [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
 - [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
 - [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
+- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen)
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
-<Tip>
+> [!TIP]
+> To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
 
-To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
+## LoraBaseMixin
 
-</Tip>
+[[autodoc]] loaders.lora_base.LoraBaseMixin
 
 ## StableDiffusionLoraLoaderMixin
 
@@ -88,6 +91,10 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
 
+## SkyReelsV2LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
+
 ## AmusedLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
@@ -96,10 +103,10 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
 
+## QwenImageLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
+
 ## LoraBaseMixin
 
-[[autodoc]] loaders.lora_base.LoraBaseMixin
-
-## WanLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
+[[autodoc]] loaders.lora_base.LoraBaseMixin

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

@@ -14,11 +14,8 @@ specific language governing permissions and limitations under the License.
 
 Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
 
-<Tip>
-
-Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
-
-</Tip>
+> [!TIP]
+> Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
 
 ## PeftAdapterMixin
 

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

@@ -16,11 +16,8 @@ Textual Inversion is a training method for personalizing models by learning new
 
 [`TextualInversionLoaderMixin`] provides a function for loading Textual Inversion embeddings from Diffusers and Automatic1111 into the text encoder and loading a special token to activate the embeddings.
 
-<Tip>
-
-To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
-
-</Tip>
+> [!TIP]
+> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
 
 ## TextualInversionLoaderMixin
 

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

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

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

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

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

@@ -44,15 +44,3 @@ model = AutoencoderKL.from_single_file(url)
 ## DecoderOutput
 
 [[autodoc]] models.autoencoders.vae.DecoderOutput
-
-## FlaxAutoencoderKL
-
-[[autodoc]] FlaxAutoencoderKL
-
-## FlaxAutoencoderKLOutput
-
-[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
-
-## FlaxDecoderOutput
-
-[[autodoc]] models.vae_flax.FlaxDecoderOutput

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

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

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

@@ -10,9 +10,10 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Overview
+# BriaTransformer2DModel
 
-The inference pipeline supports and enables a wide range of techniques that are divided into two categories:
+A modified flux Transformer model from [Bria](https://huggingface.co/briaai/BRIA-3.2)
 
-* Pipeline functionality: these techniques modify the pipeline or extend it for other applications. For example, pipeline callbacks add new features to a pipeline and a pipeline can also be extended for distributed inference.
-* Improve inference quality: these techniques increase the visual quality of the generated images. For example, you can enhance your prompts with GPT2 to create better images with lower effort.
+## BriaTransformer2DModel
+
+[[autodoc]] BriaTransformer2DModel

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

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

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

@@ -40,11 +40,3 @@ pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=contro
 ## ControlNetOutput
 
 [[autodoc]] models.controlnets.controlnet.ControlNetOutput
-
-## FlaxControlNetModel
-
-[[autodoc]] FlaxControlNetModel
-
-## FlaxControlNetOutput
-
-[[autodoc]] models.controlnets.controlnet_flax.FlaxControlNetOutput

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

@@ -19,10 +19,6 @@ All models are built from the base [`ModelMixin`] class which is a [`torch.nn.Mo
 ## ModelMixin
 [[autodoc]] ModelMixin
 
-## FlaxModelMixin
-
-[[autodoc]] FlaxModelMixin
-
 ## PushToHubMixin
 
 [[autodoc]] utils.PushToHubMixin

docs/source/en/api/models/qwenimage_transformer2d.md

@@ -0,0 +1,28 @@
+<!-- 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. -->
+
+# QwenImageTransformer2DModel
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import QwenImageTransformer2DModel
+
+transformer = QwenImageTransformer2DModel.from_pretrained("Qwen/QwenImage-20B", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## QwenImageTransformer2DModel
+
+[[autodoc]] QwenImageTransformer2DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/skyreels_v2_transformer_3d.md

@@ -0,0 +1,30 @@
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# SkyReelsV2Transformer3DModel
+
+A Diffusion Transformer model for 3D video-like data was introduced in [SkyReels-V2](https://github.com/SkyworkAI/SkyReels-V2) by the Skywork AI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import SkyReelsV2Transformer3DModel
+
+transformer = SkyReelsV2Transformer3DModel.from_pretrained("Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## SkyReelsV2Transformer3DModel
+
+[[autodoc]] SkyReelsV2Transformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

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

docs/source/en/api/models/unet2d-cond.md

@@ -23,9 +23,3 @@ The abstract from the paper is:
 
 ## UNet2DConditionOutput
 [[autodoc]] models.unets.unet_2d_condition.UNet2DConditionOutput
-
-## FlaxUNet2DConditionModel
-[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionModel
-
-## FlaxUNet2DConditionOutput
-[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput

docs/source/en/api/modular_diffusers/guiders.md

@@ -0,0 +1,39 @@
+# Guiders
+
+Guiders are components in Modular Diffusers that control how the diffusion process is guided during generation. They implement various guidance techniques to improve generation quality and control.
+
+## BaseGuidance
+
+[[autodoc]] diffusers.guiders.guider_utils.BaseGuidance
+
+## ClassifierFreeGuidance
+
+[[autodoc]] diffusers.guiders.classifier_free_guidance.ClassifierFreeGuidance
+
+## ClassifierFreeZeroStarGuidance
+
+[[autodoc]] diffusers.guiders.classifier_free_zero_star_guidance.ClassifierFreeZeroStarGuidance
+
+## SkipLayerGuidance
+
+[[autodoc]] diffusers.guiders.skip_layer_guidance.SkipLayerGuidance
+
+## SmoothedEnergyGuidance
+
+[[autodoc]] diffusers.guiders.smoothed_energy_guidance.SmoothedEnergyGuidance
+
+## PerturbedAttentionGuidance
+
+[[autodoc]] diffusers.guiders.perturbed_attention_guidance.PerturbedAttentionGuidance
+
+## AdaptiveProjectedGuidance
+
+[[autodoc]] diffusers.guiders.adaptive_projected_guidance.AdaptiveProjectedGuidance
+
+## AutoGuidance
+
+[[autodoc]] diffusers.guiders.auto_guidance.AutoGuidance
+
+## TangentialClassifierFreeGuidance
+
+[[autodoc]] diffusers.guiders.tangential_classifier_free_guidance.TangentialClassifierFreeGuidance

docs/source/en/api/modular_diffusers/pipeline.md

@@ -0,0 +1,5 @@
+# Pipeline
+
+## ModularPipeline
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.ModularPipeline

docs/source/en/api/modular_diffusers/pipeline_blocks.md

@@ -0,0 +1,17 @@
+# Pipeline blocks
+
+## ModularPipelineBlocks
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.ModularPipelineBlocks
+
+## SequentialPipelineBlocks
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.SequentialPipelineBlocks
+
+## LoopSequentialPipelineBlocks
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.LoopSequentialPipelineBlocks
+
+## AutoPipelineBlocks
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.AutoPipelineBlocks

docs/source/en/api/modular_diffusers/pipeline_components.md

@@ -0,0 +1,17 @@
+# Components and configs
+
+## ComponentSpec
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.ComponentSpec
+
+## ConfigSpec
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.ConfigSpec
+
+## ComponentsManager
+
+[[autodoc]] diffusers.modular_pipelines.components_manager.ComponentsManager
+
+## InsertableDict
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline_utils.InsertableDict

docs/source/en/api/modular_diffusers/pipeline_states.md

@@ -0,0 +1,9 @@
+# Pipeline states
+
+## PipelineState
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.PipelineState
+
+## BlockState
+
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.BlockState 

docs/source/en/api/outputs.md

@@ -39,11 +39,8 @@ For instance, retrieving an image by indexing into it returns the tuple `(output
 outputs[:1]
 ```
 
-<Tip>
-
-To check a specific pipeline or model output, refer to its corresponding API documentation.
-
-</Tip>
+> [!TIP]
+> To check a specific pipeline or model output, refer to its corresponding API documentation.
 
 ## BaseOutput
 
@@ -54,10 +51,6 @@ To check a specific pipeline or model output, refer to its corresponding API doc
 
 [[autodoc]] pipelines.ImagePipelineOutput
 
-## FlaxImagePipelineOutput
-
-[[autodoc]] pipelines.pipeline_flax_utils.FlaxImagePipelineOutput
-
 ## AudioPipelineOutput
 
 [[autodoc]] pipelines.AudioPipelineOutput

docs/source/en/api/parallel.md

@@ -0,0 +1,24 @@
+<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# Parallelism
+
+Parallelism strategies help speed up diffusion transformers by distributing computations across multiple devices, allowing for faster inference/training times. Refer to the [Distributed inferece](../training/distributed_inference) guide to learn more.
+
+## ParallelConfig
+
+[[autodoc]] ParallelConfig
+
+## ContextParallelConfig
+
+[[autodoc]] ContextParallelConfig
+
+[[autodoc]] hooks.apply_context_parallel

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

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

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

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # aMUSEd
 
 aMUSEd was introduced in [aMUSEd: An Open MUSE Reproduction](https://huggingface.co/papers/2401.01808) by Suraj Patil, William Berman, Robin Rombach, and Patrick von Platen.

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

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

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

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Attend-and-Excite
 
 Attend-and-Excite for Stable Diffusion was proposed in [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://attendandexcite.github.io/Attend-and-Excite/) and provides textual attention control over image generation.
@@ -20,11 +23,8 @@ The abstract from the paper is:
 
 You can find additional information about Attend-and-Excite on the [project page](https://attendandexcite.github.io/Attend-and-Excite/), the [original codebase](https://github.com/AttendAndExcite/Attend-and-Excite), or try it out in a [demo](https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite).
 
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 ## StableDiffusionAttendAndExcitePipeline
 

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

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # AudioLDM
 
 AudioLDM was proposed in [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://huggingface.co/papers/2301.12503) by Haohe Liu et al. Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview), AudioLDM
@@ -35,11 +38,8 @@ During inference:
 * The _quality_ of the predicted audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference.
 * The _length_ of the predicted audio sample can be controlled by varying the `audio_length_in_s` argument.
 
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 ## AudioLDMPipeline
 [[autodoc]] AudioLDMPipeline

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

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

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

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

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

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # BLIP-Diffusion
 
 BLIP-Diffusion was proposed in [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://huggingface.co/papers/2305.14720). It enables zero-shot subject-driven generation and control-guided zero-shot generation.
@@ -23,11 +26,8 @@ The original codebase can be found at [salesforce/LAVIS](https://github.com/sale
 
 `BlipDiffusionPipeline` and `BlipDiffusionControlNetPipeline` were contributed by [`ayushtues`](https://github.com/ayushtues/).
 
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 
 ## BlipDiffusionPipeline

docs/source/en/api/pipelines/bria_3_2.md

@@ -0,0 +1,44 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Bria 3.2
+
+Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to provide on par results to leading open-source models, and outperforming other licensed models.
+In addition to being built entirely on licensed data, 3.2 provides several advantages for enterprise and commercial use:
+
+- Efficient Compute - the model is X3 smaller than the equivalent models in the market (4B parameters vs 12B parameters other open source models)
+- Architecture Consistency: Same architecture as 3.1—ideal for users looking to upgrade without disruption.
+- Fine-tuning Speedup: 2x faster fine-tuning on L40S and A100.
+
+Original model checkpoints for Bria 3.2 can be found [here](https://huggingface.co/briaai/BRIA-3.2).
+Github repo for Bria 3.2 can be found [here](https://github.com/Bria-AI/BRIA-3.2).
+
+If you want to learn more about the Bria platform, and get free traril access, please visit [bria.ai](https://bria.ai).
+
+
+## Usage
+
+_As the model is gated, before using it with diffusers you first need to go to the [Bria 3.2 Hugging Face page](https://huggingface.co/briaai/BRIA-3.2), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
+
+Use the command below to log in:
+
+```bash
+hf auth login
+```
+
+
+## BriaPipeline
+
+[[autodoc]] BriaPipeline
+	- all
+	- __call__
+

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

@@ -21,15 +21,39 @@ Chroma is a text to image generation model based on Flux.
 
 Original model checkpoints for Chroma can be found [here](https://huggingface.co/lodestones/Chroma).
 
-<Tip>
+> [!TIP]
+> Chroma can use all the same optimizations as Flux.
 
-Chroma can use all the same optimizations as Flux.
+## Inference
 
-</Tip>
+The Diffusers version of Chroma is based on the [`unlocked-v37`](https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors) version of the original model, which is available in the [Chroma repository](https://huggingface.co/lodestones/Chroma).
 
-## Inference (Single File)
+```python
+import torch
+from diffusers import ChromaPipeline
 
-The `ChromaTransformer2DModel` supports loading checkpoints in the original format. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.
+pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload()
+
+prompt = [
+    "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
+]
+negative_prompt =  ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]
+
+image = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    generator=torch.Generator("cpu").manual_seed(433),
+    num_inference_steps=40,
+    guidance_scale=3.0,
+    num_images_per_prompt=1,
+).images[0]
+image.save("chroma.png")
+```
+
+## Loading from a single file
+
+To use updated model checkpoints that are not in the Diffusers format, you can use the `ChromaTransformer2DModel` class to load the model from a single file in the original format. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.
 
 The following example demonstrates how to run Chroma from a single file.
 
@@ -38,30 +62,29 @@ Then run the following example
 ```python
 import torch
 from diffusers import ChromaTransformer2DModel, ChromaPipeline
-from transformers import T5EncoderModel
 
-bfl_repo = "black-forest-labs/FLUX.1-dev"
+model_id = "lodestones/Chroma"
 dtype = torch.bfloat16
 
-transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v35.safetensors", torch_dtype=dtype)
-
-text_encoder = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
-tokenizer = T5Tokenizer.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype)
-
-pipe = ChromaPipeline.from_pretrained(bfl_repo, transformer=transformer, text_encoder=text_encoder, tokenizer=tokenizer, torch_dtype=dtype)
+transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors", torch_dtype=dtype)
 
+pipe = ChromaPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=dtype)
 pipe.enable_model_cpu_offload()
 
-prompt = "A cat holding a sign that says hello world"
+prompt = [
+    "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
+]
+negative_prompt =  ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]
+
 image = pipe(
-    prompt,
-    guidance_scale=4.0,
-    output_type="pil",
-    num_inference_steps=26,
-    generator=torch.Generator("cpu").manual_seed(0)
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    generator=torch.Generator("cpu").manual_seed(433),
+    num_inference_steps=40,
+    guidance_scale=3.0,
 ).images[0]
 
-image.save("image.png")
+image.save("chroma-single-file.png")
 ```
 
 ## ChromaPipeline
@@ -69,3 +92,9 @@ image.save("image.png")
 [[autodoc]] ChromaPipeline
 	- all
 	- __call__
+
+## ChromaImg2ImgPipeline
+
+[[autodoc]] ChromaImg2ImgPipeline
+	- all
+	- __call__

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

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

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

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

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

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

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

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

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

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

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

@@ -28,11 +28,8 @@ This model was contributed by [takuma104](https://huggingface.co/takuma104). ❤
 
 The original codebase can be found at [lllyasviel/ControlNet](https://github.com/lllyasviel/ControlNet), and you can find official ControlNet checkpoints on [lllyasviel's](https://huggingface.co/lllyasviel) Hub profile.
 
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 ## StableDiffusionControlNetPipeline
 [[autodoc]] StableDiffusionControlNetPipeline
@@ -72,11 +69,3 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 
 ## StableDiffusionPipelineOutput
 [[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
-
-## FlaxStableDiffusionControlNetPipeline
-[[autodoc]] FlaxStableDiffusionControlNetPipeline
-	- all
-	- __call__
-
-## FlaxStableDiffusionControlNetPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput

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

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

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

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

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

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

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

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

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

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # ControlNet-XS
 
 <div class="flex flex-wrap space-x-1">
@@ -28,11 +31,8 @@ Here's the overview from the [project page](https://vislearn.github.io/ControlNe
 
 This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️
 
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 ## StableDiffusionControlNetXSPipeline
 [[autodoc]] StableDiffusionControlNetXSPipeline

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

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # ControlNet-XS with Stable Diffusion XL
 
 ControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produce good results.
@@ -24,17 +27,11 @@ Here's the overview from the [project page](https://vislearn.github.io/ControlNe
 
 This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️
 
-<Tip warning={true}>
+> [!WARNING]
+> 🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!
 
-🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!
-
-</Tip>
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 ## StableDiffusionXLControlNetXSPipeline
 [[autodoc]] StableDiffusionXLControlNetXSPipeline

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

@@ -18,11 +18,33 @@
 
 *Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications. Our platform covers a video curation pipeline, pre-trained world foundation models, examples of post-training of pre-trained world foundation models, and video tokenizers. To help Physical AI builders solve the most critical problems of our society, we make our platform open-source and our models open-weight with permissive licenses available via https://github.com/NVIDIA/Cosmos.*
 
-<Tip>
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+## Loading original format checkpoints
 
-</Tip>
+Original format checkpoints that have not been converted to diffusers-expected format can be loaded using the `from_single_file` method.
+
+```python
+import torch
+from diffusers import Cosmos2TextToImagePipeline, CosmosTransformer3DModel
+
+model_id = "nvidia/Cosmos-Predict2-2B-Text2Image"
+transformer = CosmosTransformer3DModel.from_single_file(
+    "https://huggingface.co/nvidia/Cosmos-Predict2-2B-Text2Image/blob/main/model.pt",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+pipe = Cosmos2TextToImagePipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+prompt = "A close-up shot captures a vibrant yellow scrubber vigorously working on a grimy plate, its bristles moving in circular motions to lift stubborn grease and food residue. The dish, once covered in remnants of a hearty meal, gradually reveals its original glossy surface. Suds form and bubble around the scrubber, creating a satisfying visual of cleanliness in progress. The sound of scrubbing fills the air, accompanied by the gentle clinking of the dish against the sink. As the scrubber continues its task, the dish transforms, gleaming under the bright kitchen lights, symbolizing the triumph of cleanliness over mess."
+negative_prompt = "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality."
+
+output = pipe(
+    prompt=prompt, negative_prompt=negative_prompt, generator=torch.Generator().manual_seed(1)
+).images[0]
+output.save("output.png")
+```
 
 ## CosmosTextToWorldPipeline
 

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

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Dance Diffusion
 
 [Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is by Zach Evans.
@@ -17,11 +20,8 @@ specific language governing permissions and limitations under the License.
 Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by [Harmonai](https://github.com/Harmonai-org).
 
 
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 ## DanceDiffusionPipeline
 [[autodoc]] DanceDiffusionPipeline

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

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

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

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # DiffEdit
 
 [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://huggingface.co/papers/2210.11427) is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord.

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

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

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

@@ -21,11 +21,10 @@ Flux is a series of text-to-image generation models based on diffusion transform
 
 Original model checkpoints for Flux can be found [here](https://huggingface.co/black-forest-labs). Original inference code can be found [here](https://github.com/black-forest-labs/flux).
 
-<Tip>
-
-Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.  For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
-
-</Tip>
+> [!TIP]
+> Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.  For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
+>
+> [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
 
 Flux comes in the following variants:
 
@@ -39,6 +38,7 @@ Flux comes in the following variants:
 | Canny Control (LoRA) | [`black-forest-labs/FLUX.1-Canny-dev-lora`](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev-lora) |
 | Depth Control (LoRA) | [`black-forest-labs/FLUX.1-Depth-dev-lora`](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev-lora) |
 | Redux (Adapter) | [`black-forest-labs/FLUX.1-Redux-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Redux-dev) |
+| Kontext | [`black-forest-labs/FLUX.1-kontext`](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev) |
 
 All checkpoints have different usage which we detail below.
 
@@ -273,6 +273,107 @@ images = pipe(
 images[0].save("flux-redux.png")
 ```
 
+### Kontext
+
+Flux Kontext is a model that allows in-context control of the image generation process, allowing for editing, refinement, relighting, style transfer, character customization, and more.
+
+```python
+import torch
+from diffusers import FluxKontextPipeline
+from diffusers.utils import load_image
+
+pipe = FluxKontextPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yarn-art-pikachu.png").convert("RGB")
+prompt = "Make Pikachu hold a sign that says 'Black Forest Labs is awesome', yarn art style, detailed, vibrant colors"
+image = pipe(
+    image=image,
+    prompt=prompt,
+    guidance_scale=2.5,
+    generator=torch.Generator().manual_seed(42),
+).images[0]
+image.save("flux-kontext.png")
+```
+
+Flux Kontext comes with an integrity safety checker, which should be run after the image generation step. To run the safety checker, install the official repository from [black-forest-labs/flux](https://github.com/black-forest-labs/flux) and add the following code:
+
+```python
+from flux.content_filters import PixtralContentFilter
+
+# ... pipeline invocation to generate images
+
+integrity_checker = PixtralContentFilter(torch.device("cuda"))
+image_ = np.array(image) / 255.0
+image_ = 2 * image_ - 1
+image_ = torch.from_numpy(image_).to("cuda", dtype=torch.float32).unsqueeze(0).permute(0, 3, 1, 2)
+if integrity_checker.test_image(image_):
+    raise ValueError("Your image has been flagged. Choose another prompt/image or try again.")
+```
+
+### Kontext Inpainting
+`FluxKontextInpaintPipeline` enables image modification within a fixed mask region. It currently supports both text-based conditioning and image-reference conditioning.
+<hfoptions id="kontext-inpaint">
+<hfoption id="text-only">
+
+
+```python
+import torch
+from diffusers import FluxKontextInpaintPipeline
+from diffusers.utils import load_image
+
+prompt = "Change the yellow dinosaur to green one"
+img_url = (
+    "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/dinosaur_input.jpeg?raw=true"
+)
+mask_url = (
+    "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/dinosaur_mask.png?raw=true"
+)
+
+source = load_image(img_url)
+mask = load_image(mask_url)
+
+pipe = FluxKontextInpaintPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+image = pipe(prompt=prompt, image=source, mask_image=mask, strength=1.0).images[0]
+image.save("kontext_inpainting_normal.png")
+```
+</hfoption>
+<hfoption id="image conditioning">
+
+```python
+import torch
+from diffusers import FluxKontextInpaintPipeline
+from diffusers.utils import load_image
+
+pipe = FluxKontextInpaintPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+prompt = "Replace this ball"
+img_url = "https://images.pexels.com/photos/39362/the-ball-stadion-football-the-pitch-39362.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500"
+mask_url = "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/ball_mask.png?raw=true"
+image_reference_url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTah3x6OL_ECMBaZ5ZlJJhNsyC-OSMLWAI-xw&s"
+
+source = load_image(img_url)
+mask = load_image(mask_url)
+image_reference = load_image(image_reference_url)
+
+mask = pipe.mask_processor.blur(mask, blur_factor=12)
+image = pipe(
+    prompt=prompt, image=source, mask_image=mask, image_reference=image_reference, strength=1.0
+).images[0]
+image.save("kontext_inpainting_ref.png")
+```
+</hfoption>
+</hfoptions>
+
 ## Combining Flux Turbo LoRAs with Flux Control, Fill, and Redux
 
 We can combine Flux Turbo LoRAs with Flux Control and other pipelines like Fill and Redux to enable few-steps' inference. The example below shows how to do that for Flux Control LoRA for depth and turbo LoRA from [`ByteDance/Hyper-SD`](https://hf.co/ByteDance/Hyper-SD).
@@ -316,11 +417,8 @@ When unloading the Control LoRA weights, call `pipe.unload_lora_weights(reset_to
 
 ## IP-Adapter
 
-<Tip>
-
-Check out [IP-Adapter](../../../using-diffusers/ip_adapter) to learn more about how IP-Adapters work.
-
-</Tip>
+> [!TIP]
+> Check out [IP-Adapter](../../../using-diffusers/ip_adapter) to learn more about how IP-Adapters work.
 
 An IP-Adapter lets you prompt Flux with images, in addition to the text prompt. This is especially useful when describing complex concepts that are difficult to articulate through text alone and you have reference images.
 
@@ -500,9 +598,8 @@ image.save("flux.png")
 
 The `FluxTransformer2DModel` supports loading checkpoints in the original format shipped by Black Forest Labs. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.
 
-<Tip>
-`FP8` inference can be brittle depending on the GPU type, CUDA version, and `torch` version that you are using. It is recommended that you use the `optimum-quanto` library in order to run FP8 inference on your machine.
-</Tip>
+> [!TIP]
+> `FP8` inference can be brittle depending on the GPU type, CUDA version, and `torch` version that you are using. It is recommended that you use the `optimum-quanto` library in order to run FP8 inference on your machine.
 
 The following example demonstrates how to run Flux with less than 16GB of VRAM.
 
@@ -603,3 +700,15 @@ image.save("flux-fp8-dev.png")
 [[autodoc]] FluxFillPipeline
 	- all
 	- __call__
+
+## FluxKontextPipeline
+
+[[autodoc]] FluxKontextPipeline
+	- all
+	- __call__
+
+## FluxKontextInpaintPipeline
+
+[[autodoc]] FluxKontextInpaintPipeline
+	- all
+	- __call__

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

@@ -22,11 +22,8 @@
 
 *We present a neural network structure, FramePack, to train next-frame (or next-frame-section) prediction models for video generation. The FramePack compresses input frames to make the transformer context length a fixed number regardless of the video length. As a result, we are able to process a large number of frames using video diffusion with computation bottleneck similar to image diffusion. This also makes the training video batch sizes significantly higher (batch sizes become comparable to image diffusion training). We also propose an anti-drifting sampling method that generates frames in inverted temporal order with early-established endpoints to avoid exposure bias (error accumulation over iterations). Finally, we show that existing video diffusion models can be finetuned with FramePack, and their visual quality may be improved because the next-frame prediction supports more balanced diffusion schedulers with less extreme flow shift timesteps.*
 
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
+> [!TIP]
+> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
 
 ## Available models
 

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

@@ -16,11 +16,8 @@
 
 [HiDream-I1](https://huggingface.co/HiDream-ai) by HiDream.ai
 
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
+> [!TIP]
+> [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
 
 ## Available models
 

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

@@ -54,7 +54,7 @@ pipeline_quant_config = PipelineQuantizationConfig(
       "bnb_4bit_quant_type": "nf4",
       "bnb_4bit_compute_dtype": torch.bfloat16
       },
-    components_to_quantize=["transformer"]
+    components_to_quantize="transformer"
 )
 
 pipeline = HunyuanVideoPipeline.from_pretrained(
@@ -91,7 +91,7 @@ pipeline_quant_config = PipelineQuantizationConfig(
       "bnb_4bit_quant_type": "nf4",
       "bnb_4bit_compute_dtype": torch.bfloat16
       },
-    components_to_quantize=["transformer"]
+    components_to_quantize="transformer"
 )
 
 pipeline = HunyuanVideoPipeline.from_pretrained(
@@ -139,7 +139,7 @@ export_to_video(video, "output.mp4", fps=15)
         "bnb_4bit_quant_type": "nf4",
         "bnb_4bit_compute_dtype": torch.bfloat16
         },
-      components_to_quantize=["transformer"]
+      components_to_quantize="transformer"
   )
 
   pipeline = HunyuanVideoPipeline.from_pretrained(