Merge branch 'main' into version-checks-cache

I recently noticed that we are spending a non-negligible amount of time in `version.parse` when running pipelines (approx. ~50ms per step for the QwenImageEdit pipeline on a ZeroGPU Space for instance, which in this case represents almost 10% of the actual compute). The calls to those version checks originate from:
- 4588bbeb42/src/diffusers/hooks/hooks.py (L277)

Maybe that the issue can otherwise be solved from root (why do we need to unwrap the modules at each call?) or maybe that my particular setup triggered this? (I patched the forward method at the blocks level but I don't feel like it has an incidence over _set_context)

.github/workflows/benchmark.yml

@@ -7,7 +7,7 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
@@ -38,8 +38,9 @@ jobs:
         run: |
           apt update
           apt install -y libpq-dev postgresql-client
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip install -r benchmarks/requirements.txt
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install -r benchmarks/requirements.txt
       - name: Environment
         run: |
           python utils/print_env.py

.github/workflows/build_docker_images.yml

@@ -42,39 +42,18 @@ jobs:
           CHANGED_FILES: ${{ steps.file_changes.outputs.all }}
         run: |
           echo "$CHANGED_FILES"
-          ALLOWED_IMAGES=(
+          for FILE in $CHANGED_FILES; do 
-            diffusers-pytorch-cpu
-            diffusers-pytorch-cuda
-            diffusers-pytorch-xformers-cuda
-            diffusers-pytorch-minimum-cuda
-            diffusers-doc-builder
-          )
-
-          declare -A IMAGES_TO_BUILD=()
-
-          for FILE in $CHANGED_FILES; do
             # skip anything that isn't still on disk
-            if [[ ! -e "$FILE" ]]; then
+            if [[ ! -f "$FILE" ]]; then
               echo "Skipping removed file $FILE"
               continue
+            fi           
+            if [[ "$FILE" == docker/*Dockerfile ]]; then
+              DOCKER_PATH="${FILE%/Dockerfile}"
+              DOCKER_TAG=$(basename "$DOCKER_PATH")
+              echo "Building Docker image for $DOCKER_TAG"
+              docker build -t "$DOCKER_TAG" "$DOCKER_PATH"
             fi
-
-            for IMAGE in "${ALLOWED_IMAGES[@]}"; do
-              if [[ "$FILE" == docker/${IMAGE}/* ]]; then
-                IMAGES_TO_BUILD["$IMAGE"]=1
-              fi
-            done
-          done
-
-          if [[ ${#IMAGES_TO_BUILD[@]} -eq 0 ]]; then
-            echo "No relevant Docker changes detected."
-            exit 0
-          fi
-
-          for IMAGE in "${!IMAGES_TO_BUILD[@]}"; do
-            DOCKER_PATH="docker/${IMAGE}"
-            echo "Building Docker image for $IMAGE"
-            docker build -t "$IMAGE" "$DOCKER_PATH"
           done
         if: steps.file_changes.outputs.all != ''
 
@@ -93,6 +72,7 @@ jobs:
         image-name:
           - diffusers-pytorch-cpu
           - diffusers-pytorch-cuda
+          - diffusers-pytorch-cuda
           - diffusers-pytorch-xformers-cuda
           - diffusers-pytorch-minimum-cuda
           - diffusers-doc-builder

.github/workflows/build_pr_documentation.yml

@@ -12,33 +12,7 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  check-links:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-
-      - name: Install uv
-        run: |
-          curl -LsSf https://astral.sh/uv/install.sh | sh
-          echo "$HOME/.cargo/bin" >> $GITHUB_PATH
-
-      - name: Install doc-builder
-        run: |
-          uv pip install --system git+https://github.com/huggingface/doc-builder.git@main
-
-      - name: Check documentation links
-        run: |
-          uv run doc-builder check-links docs/source/en
-
   build:
-    needs: check-links
     uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
     with:
       commit_sha: ${{ github.event.pull_request.head.sha }}

.github/workflows/mirror_community_pipeline.yml

@@ -74,7 +74,7 @@ jobs:
           python-version: "3.10"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install --upgrade huggingface_hub
 
       # Check secret is set

.github/workflows/nightly_tests.yml

@@ -7,7 +7,7 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 600
@@ -71,11 +71,10 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install -e [quality,test]
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+          python -m uv pip install pytest-reportlog
-          uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -85,8 +84,8 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-             -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \
             tests/pipelines/${{ matrix.module }}
@@ -125,12 +124,11 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        uv pip install peft@git+https://github.com/huggingface/peft.git
+        python -m uv pip install -e [quality,test]
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        #uv pip uninstall transformers huggingface_hub && 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
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+        python -m uv pip install pytest-reportlog
-        uv pip install pytest-reportlog
     - name: Environment
       run: python utils/print_env.py
 
@@ -141,8 +139,8 @@ jobs:
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           --report-log=tests_torch_${{ matrix.module }}_cuda.log \
           tests/${{ matrix.module }}
@@ -154,8 +152,8 @@ jobs:
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          --make-reports=examples_torch_cuda \
+          -s -v --make-reports=examples_torch_cuda \
           --report-log=examples_torch_cuda.log \
           examples/
 
@@ -193,9 +191,8 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        python -m uv pip install -e [quality,test,training]
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
     - name: Environment
       run: |
         python utils/print_env.py
@@ -204,7 +201,7 @@ jobs:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         RUN_COMPILE: yes
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "compile" --make-reports=tests_torch_compile_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -235,12 +232,11 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip install peft@git+https://github.com/huggingface/peft.git
+          python -m uv pip install -e [quality,test]
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-          #uv pip uninstall transformers huggingface_hub && 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
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+          python -m uv pip install pytest-reportlog
-          uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -251,7 +247,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
           BIG_GPU_MEMORY: 40
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
             -m "big_accelerator" \
             --make-reports=tests_big_gpu_torch_cuda \
             --report-log=tests_big_gpu_torch_cuda.log \
@@ -286,11 +282,10 @@ jobs:
 
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip install peft@git+https://github.com/huggingface/peft.git
+          python -m uv pip install -e [quality,test]
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-          #uv pip uninstall transformers huggingface_hub && 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
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
 
       - name: Environment
         run: |
@@ -302,8 +297,8 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_torch_minimum_version_cuda \
             tests/models/test_modeling_common.py \
             tests/pipelines/test_pipelines_common.py \
@@ -362,14 +357,13 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip install -U ${{ matrix.config.backend }}
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install -U ${{ matrix.config.backend }}
           if [ "${{ join(matrix.config.additional_deps, ' ') }}" != "" ]; then
-              uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
+              python -m uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
           fi
-          uv pip install pytest-reportlog
+          python -m uv pip install pytest-reportlog
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
       - name: Environment
         run: |
           python utils/print_env.py
@@ -380,7 +374,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
           BIG_GPU_MEMORY: 40
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
             --make-reports=tests_${{ matrix.config.backend }}_torch_cuda \
             --report-log=tests_${{ matrix.config.backend }}_torch_cuda.log \
             tests/quantization/${{ matrix.config.test_location }}
@@ -415,11 +409,10 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip install -U bitsandbytes optimum_quanto
+          python -m uv pip install -e [quality,test]
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+          python -m uv pip install -U bitsandbytes optimum_quanto
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+          python -m uv pip install pytest-reportlog
-          uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -430,7 +423,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
           BIG_GPU_MEMORY: 40
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
             --make-reports=tests_pipeline_level_quant_torch_cuda \
             --report-log=tests_pipeline_level_quant_torch_cuda.log \
             tests/quantization/test_pipeline_level_quantization.py
@@ -530,11 +523,11 @@ jobs:
 #      - name: Install dependencies
 #        shell: arch -arch arm64 bash {0}
 #        run: |
-#          ${CONDA_RUN} pip install --upgrade pip uv
+#          ${CONDA_RUN} python -m pip install --upgrade pip uv
-#          ${CONDA_RUN} uv pip install -e ".[quality]"
+#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
-#          ${CONDA_RUN} uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} uv pip install accelerate@git+https://github.com/huggingface/accelerate
+#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} uv pip install pytest-reportlog
+#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
 #      - name: Environment
 #        shell: arch -arch arm64 bash {0}
 #        run: |
@@ -545,7 +538,7 @@ jobs:
 #          HF_HOME: /System/Volumes/Data/mnt/cache
 #          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
 #        run: |
-#          ${CONDA_RUN} pytest -n 1  --make-reports=tests_torch_mps \
+#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
 #            tests/
 #      - name: Failure short reports
@@ -586,11 +579,11 @@ jobs:
 #      - name: Install dependencies
 #        shell: arch -arch arm64 bash {0}
 #        run: |
-#          ${CONDA_RUN} pip install --upgrade pip uv
+#          ${CONDA_RUN} python -m pip install --upgrade pip uv
-#          ${CONDA_RUN} uv pip install -e ".[quality]"
+#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
-#          ${CONDA_RUN} uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} uv pip install accelerate@git+https://github.com/huggingface/accelerate
+#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} uv pip install pytest-reportlog
+#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
 #      - name: Environment
 #        shell: arch -arch arm64 bash {0}
 #        run: |
@@ -601,7 +594,7 @@ jobs:
 #          HF_HOME: /System/Volumes/Data/mnt/cache
 #          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
 #        run: |
-#          ${CONDA_RUN} pytest -n 1  --make-reports=tests_torch_mps \
+#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
 #            --report-log=tests_torch_mps.log \
 #            tests/
 #      - name: Failure short reports

.github/workflows/pr_dependency_test.yml

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

.github/workflows/pr_modular_tests.yml

@@ -26,7 +26,7 @@ concurrency:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60
@@ -42,7 +42,7 @@ jobs:
           python-version: "3.10"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install .[quality]
       - name: Check quality
         run: make quality
@@ -62,7 +62,7 @@ jobs:
           python-version: "3.10"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install .[quality]
       - name: Check repo consistency
         run: |
@@ -108,20 +108,22 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        python -m uv pip install -e [quality,test]
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+        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
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run fast PyTorch Pipeline CPU tests
       if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
       run: |
-        pytest -n 8 --max-worker-restart=0 --dist=loadfile \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          -k "not Flax and not Onnx" \
+        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
 

.github/workflows/pr_test_fetcher.yml

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

.github/workflows/pr_tests.yml

@@ -22,7 +22,7 @@ concurrency:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60
@@ -38,7 +38,7 @@ jobs:
           python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install .[quality]
       - name: Check quality
         run: make quality
@@ -58,7 +58,7 @@ jobs:
           python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install .[quality]
       - name: Check repo consistency
         run: |
@@ -114,36 +114,40 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        python -m uv pip install -e [quality,test]
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+        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
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run fast PyTorch Pipeline CPU tests
       if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
       run: |
-        pytest -n 8 --max-worker-restart=0 --dist=loadfile \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          -k "not Flax and not Onnx" \
+        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/pipelines
 
     - name: Run fast PyTorch Model Scheduler CPU tests
       if: ${{ matrix.config.framework == 'pytorch_models' }}
       run: |
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          -k "not Flax and not Onnx and not Dependency" \
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx and not Dependency" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/models tests/schedulers tests/others
 
     - name: Run example PyTorch CPU tests
       if: ${{ matrix.config.framework == 'pytorch_examples' }}
       run: |
-        uv pip install ".[training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m uv pip install peft timm
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
           examples
 
@@ -191,16 +195,19 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run Hub tests for models, schedulers, and pipelines on a staging env
       if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}
       run: |
-        HUGGINGFACE_CO_STAGING=true pytest \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        HUGGINGFACE_CO_STAGING=true python -m pytest \
           -m "is_staging_test" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests
@@ -242,26 +249,28 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
         # TODO (sayakpaul, DN6): revisit `--no-deps`
-        uv pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
+        python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
-        uv pip install -U tokenizers
+        python -m uv pip install -U tokenizers
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        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
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run fast PyTorch LoRA tests with PEFT
       run: |
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          \
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v \
           --make-reports=tests_peft_main \
           tests/lora/
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          \
+          -s -v \
           --make-reports=tests_models_lora_peft_main \
           tests/models/ -k "lora"
 

.github/workflows/pr_tests_gpu.yml

@@ -1,4 +1,4 @@
-name: Fast GPU Tests on PR
+name: Fast GPU Tests on PR 
 
 on:
   pull_request:
@@ -24,7 +24,7 @@ env:
   DIFFUSERS_IS_CI: yes
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   PIPELINE_USAGE_CUTOFF: 1000000000 # set high cutoff so that only always-test pipelines run
 
@@ -39,7 +39,7 @@ jobs:
           python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install .[quality]
       - name: Check quality
         run: make quality
@@ -59,7 +59,7 @@ jobs:
           python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install --upgrade pip
+          python -m pip install --upgrade pip
           pip install .[quality]
       - name: Check repo consistency
         run: |
@@ -71,7 +71,7 @@ jobs:
         if: ${{ failure() }}
         run: |
           echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
-
+  
   setup_torch_cuda_pipeline_matrix:
     needs: [check_code_quality, check_repository_consistency]
     name: Setup Torch Pipelines CUDA Slow Tests Matrix
@@ -88,7 +88,8 @@ jobs:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -129,10 +130,10 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install -e [quality,test]
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+          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: |
@@ -150,18 +151,18 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          if [ "${{ matrix.module }}" = "ip_adapters" ]; then
+          if [ "${{ matrix.module }}" = "ip_adapters" ]; then 
-              pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+              python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -k "not Flax and not Onnx" \
+              -s -v -k "not Flax and not Onnx" \
               --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
               tests/pipelines/${{ matrix.module }}
-          else
+          else 
               pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
-              pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+              python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -k "not Flax and not Onnx and $pattern" \
+              -s -v -k "not Flax and not Onnx and $pattern" \
               --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
               tests/pipelines/${{ matrix.module }}
-          fi
+          fi 
 
       - name: Failure short reports
         if: ${{ failure() }}
@@ -199,11 +200,11 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        uv pip install peft@git+https://github.com/huggingface/peft.git
+        python -m uv pip install -e [quality,test]
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        #uv pip uninstall transformers huggingface_hub && 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
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+        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: |
@@ -224,11 +225,11 @@ jobs:
       run: |
         pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
         if [ -z "$pattern" ]; then
-          pytest -n 1  --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx" tests/${{ matrix.module }} \
+          python -m pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx" tests/${{ matrix.module }} \
-          --make-reports=tests_torch_cuda_${{ matrix.module }}
+          --make-reports=tests_torch_cuda_${{ matrix.module }}  
         else
-          pytest -n 1  --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
+          python -m pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
-          --make-reports=tests_torch_cuda_${{ matrix.module }}
+          --make-reports=tests_torch_cuda_${{ matrix.module }}  
         fi
 
     - name: Failure short reports
@@ -264,20 +265,22 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+        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
-        uv pip install -e ".[quality,training]"
+        python -m uv pip install -e [quality,test,training]
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run example tests on GPU
       env:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        uv pip install ".[training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        python -m uv pip install timm
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/pr_torch_dependency_test.yml

@@ -25,8 +25,12 @@ jobs:
           python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install -e .
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          pip install torch torchvision torchaudio pytest
+          python -m pip install --upgrade pip uv
+          python -m uv pip install -e .
+          python -m uv pip install torch torchvision torchaudio
+          python -m uv pip install pytest
       - name: Check for soft dependencies
         run: |
-            pytest tests/others/test_dependencies.py
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          pytest tests/others/test_dependencies.py

.github/workflows/push_tests.yml

@@ -14,7 +14,7 @@ env:
   DIFFUSERS_IS_CI: yes
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   PIPELINE_USAGE_CUTOFF: 50000
 
@@ -34,7 +34,8 @@ jobs:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -74,10 +75,9 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install -e [quality,test]
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
       - name: Environment
         run: |
           python utils/print_env.py
@@ -87,8 +87,8 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -126,11 +126,10 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        uv pip install peft@git+https://github.com/huggingface/peft.git
+        python -m uv pip install -e [quality,test]
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        #uv pip uninstall transformers huggingface_hub && 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
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
 
     - name: Environment
       run: |
@@ -142,8 +141,8 @@ jobs:
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_cuda_${{ matrix.module }} \
           tests/${{ matrix.module }}
 
@@ -181,9 +180,8 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        python -m uv pip install -e [quality,test,training]
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
     - name: Environment
       run: |
         python utils/print_env.py
@@ -192,7 +190,7 @@ jobs:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         RUN_COMPILE: yes
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "compile" --make-reports=tests_torch_compile_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -225,7 +223,8 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -233,7 +232,7 @@ jobs:
       env:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_xformers_cuda_failures_short.txt
@@ -265,18 +264,21 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run example tests on GPU
       env:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        uv pip install ".[training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        python -m uv pip install timm
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/push_tests_fast.yml

@@ -18,7 +18,7 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
@@ -60,25 +60,29 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run fast PyTorch CPU tests
       if: ${{ matrix.config.framework == 'pytorch' }}
       run: |
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          -k "not Flax and not Onnx" \
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/
 
     - name: Run example PyTorch CPU tests
       if: ${{ matrix.config.framework == 'pytorch_examples' }}
       run: |
-        uv pip install ".[training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m uv pip install peft timm
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
           examples
 

.github/workflows/push_tests_mps.yml

@@ -8,7 +8,7 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   RUN_SLOW: no
 
@@ -57,7 +57,7 @@ jobs:
         HF_HOME: /System/Volumes/Data/mnt/cache
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
-        ${CONDA_RUN} python -m pytest -n 0 --make-reports=tests_torch_mps tests/
+        ${CONDA_RUN} python -m pytest -n 0 -s -v --make-reports=tests_torch_mps tests/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/release_tests_fast.yml

@@ -32,7 +32,8 @@ jobs:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -72,8 +73,9 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          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
@@ -83,8 +85,8 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -122,9 +124,10 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        uv pip install peft@git+https://github.com/huggingface/peft.git
+        python -m uv pip install -e [quality,test]
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
     - name: Environment
       run: |
@@ -136,8 +139,8 @@ jobs:
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           tests/${{ matrix.module }}
 
@@ -172,9 +175,10 @@ jobs:
 
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip install peft@git+https://github.com/huggingface/peft.git
+          python -m uv pip install -e [quality,test]
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
       - name: Environment
         run: |
@@ -186,8 +190,8 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_torch_minimum_cuda \
             tests/models/test_modeling_common.py \
             tests/pipelines/test_pipelines_common.py \
@@ -231,7 +235,8 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -240,7 +245,7 @@ jobs:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         RUN_COMPILE: yes
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "compile" --make-reports=tests_torch_compile_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -273,7 +278,8 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -281,7 +287,7 @@ jobs:
       env:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_torch_xformers_cuda_failures_short.txt
@@ -315,18 +321,21 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run example tests on GPU
       env:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        uv pip install ".[training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        python -m uv pip install timm
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/run_tests_from_a_pr.yml

@@ -63,8 +63,9 @@ jobs:
 
       - name: Install pytest
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-          uv pip install peft
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft
 
       - name: Run tests
         env:

.gitignore

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

README.md

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

docker/diffusers-doc-builder/Dockerfile

@@ -1,45 +1,56 @@
-FROM python:3.10-slim
+FROM ubuntu:20.04
-ENV PYTHONDONTWRITEBYTECODE=1
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get -y update && apt-get install -y bash \
+RUN apt-get -y update \
-    build-essential \
+    && apt-get install -y software-properties-common \
-    git \
+    && add-apt-repository ppa:deadsnakes/ppa
-    git-lfs \
-    curl \
-    ca-certificates \
-    libglib2.0-0 \
-    libsndfile1-dev \
-    libgl1 \
-    zip \
-    wget
 
-ENV UV_PYTHON=/usr/local/bin/python
+RUN apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.10 \
+                   python3-pip \
+                   libgl1 \
+                   zip \
+                   wget \
+                   python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN pip install uv
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-RUN uv pip install --no-cache-dir \
+    python3.10 -m uv pip install --no-cache-dir \
-    torch \
+        torch \
-    torchvision \
+        torchvision \
-    torchaudio \
+        torchaudio \
-    --extra-index-url https://download.pytorch.org/whl/cpu
+        invisible_watermark \
-
+        --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]"
+    python3.10 -m uv pip install --no-cache-dir \
-
+        accelerate \
-# Extra dependencies
+        datasets \
-RUN uv pip install --no-cache-dir \
+        hf-doc-builder \
-    accelerate \
+        huggingface-hub \
-    numpy==1.26.4 \
+        Jinja2 \
-    hf_xet \
+        librosa \
-    setuptools==69.5.1 \
+        numpy==1.26.4 \
-    bitsandbytes \
+        scipy \
-    torchao \
+        tensorboard \
-    gguf \
+        transformers \
-    optimum-quanto
+        matplotlib \
-
+        setuptools==69.5.1 \
-RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
+        bitsandbytes \
+        torchao \
+        gguf \
+        optimum-quanto
 
 CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cpu/Dockerfile

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

docker/diffusers-onnxruntime-cuda/Dockerfile

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

docker/diffusers-pytorch-cpu/Dockerfile

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

docker/diffusers-pytorch-cuda/Dockerfile

@@ -2,13 +2,11 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
-ARG PYTHON_VERSION=3.12
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
     && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa && \
+    && add-apt-repository ppa:deadsnakes/ppa
-    apt-get update
 
 RUN apt install -y bash \
     build-essential \
@@ -16,34 +14,38 @@ RUN apt install -y bash \
     git-lfs \
     curl \
     ca-certificates \
-    libglib2.0-0 \
     libsndfile1-dev \
     libgl1 \
-    python3 \
+    python3.10 \
+    python3.10-dev \
     python3-pip \
-    && apt-get clean \
+    python3.10-venv && \
-    && rm -rf /var/lib/apt/lists/*
+    rm -rf /var/lib/apt/lists
 
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+# make sure to use venv
-ENV PATH="/root/.local/bin:$PATH"
+RUN python3.10 -m venv /opt/venv
-ENV VIRTUAL_ENV="/opt/venv"
+ENV PATH="/opt/venv/bin:$PATH"
-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 uv pip install --no-cache-dir \
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio
+    torchaudio \
-
+    invisible_watermark && \
-RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+    python3.10 -m pip install --no-cache-dir \
-
-# Extra dependencies
-RUN uv pip install --no-cache-dir \
     accelerate \
+    datasets \
+    hf-doc-builder \
+    huggingface-hub \
+    hf_transfer \
+    Jinja2 \
+    librosa \
     numpy==1.26.4 \
-    pytorch-lightning \
+    scipy \
-    hf_xet
+    tensorboard \
+    transformers \
+    pytorch-lightning  \
+    hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-minimum-cuda/Dockerfile

@@ -2,7 +2,6 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
-ARG PYTHON_VERSION=3.10
 ENV DEBIAN_FRONTEND=noninteractive
 ENV MINIMUM_SUPPORTED_TORCH_VERSION="2.1.0"
 ENV MINIMUM_SUPPORTED_TORCHVISION_VERSION="0.16.0"
@@ -10,8 +9,7 @@ ENV MINIMUM_SUPPORTED_TORCHAUDIO_VERSION="2.1.0"
 
 RUN apt-get -y update \
     && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa && \
+    && add-apt-repository ppa:deadsnakes/ppa
-    apt-get update
 
 RUN apt install -y bash \
     build-essential \
@@ -19,34 +17,37 @@ RUN apt install -y bash \
     git-lfs \
     curl \
     ca-certificates \
-    libglib2.0-0 \
     libsndfile1-dev \
     libgl1 \
-    python3 \
+    python3.10 \
+    python3.10-dev \
     python3-pip \
-    && apt-get clean \
+    python3.10-venv && \
-    && rm -rf /var/lib/apt/lists/*
+    rm -rf /var/lib/apt/lists
 
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+# make sure to use venv
-ENV PATH="/root/.local/bin:$PATH"
+RUN python3.10 -m venv /opt/venv
-ENV VIRTUAL_ENV="/opt/venv"
+ENV PATH="/opt/venv/bin:$PATH"
-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 uv pip install --no-cache-dir \
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
     torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
     torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
-    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION
+    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION \
-
+    invisible_watermark && \
-RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+    python3.10 -m pip install --no-cache-dir \
-
-# Extra dependencies
-RUN uv pip install --no-cache-dir \
     accelerate \
+    datasets \
+    hf-doc-builder \
+    huggingface-hub \
+    hf_transfer \
+    Jinja2 \
+    librosa \
     numpy==1.26.4 \
-    pytorch-lightning \
+    scipy \
-    hf_xet
+    tensorboard \
+    transformers \
+    hf_transfer
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -2,49 +2,50 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
-ARG PYTHON_VERSION=3.12
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
     && apt-get install -y software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa && \
+    && add-apt-repository ppa:deadsnakes/ppa
-    apt-get update
 
 RUN apt install -y bash \
-    build-essential \
+                   build-essential \
-    git \
+                   git \
-    git-lfs \
+                   git-lfs \
-    curl \
+                   curl \
-    ca-certificates \
+                   ca-certificates \
-    libglib2.0-0 \
+                   libsndfile1-dev \
-    libsndfile1-dev \
+                   libgl1 \
-    libgl1 \
+                   python3.10 \
-    python3 \
+                   python3.10-dev \
-    python3-pip \
+                   python3-pip \
-    && apt-get clean \
+                   python3.10-venv && \
-    && rm -rf /var/lib/apt/lists/*
+    rm -rf /var/lib/apt/lists
 
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+# make sure to use venv
-ENV PATH="/root/.local/bin:$PATH"
+RUN python3.10 -m venv /opt/venv
-ENV VIRTUAL_ENV="/opt/venv"
+ENV PATH="/opt/venv/bin:$PATH"
-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 uv pip install --no-cache-dir \
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
-    torch \
+    python3.10 -m pip install --no-cache-dir \
-    torchvision \
+        torch \
-    torchaudio
+        torchvision \
-
+        torchaudio \
-RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+        invisible_watermark && \
-
+    python3.10 -m uv pip install --no-cache-dir \
-# Extra dependencies
+        accelerate \
-RUN uv pip install --no-cache-dir \
+        datasets \
-    accelerate \
+        hf-doc-builder \
-    numpy==1.26.4 \
+        huggingface-hub \
-    pytorch-lightning \
+        hf_transfer \
-    hf_xet \
+        Jinja2 \
-    xformers
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers \
+        xformers  \
+        hf_transfer
 
 CMD ["/bin/bash"]

docs/source/en/_toctree.yml

@@ -1,4 +1,5 @@
-- sections:
+- title: Get started
+  sections:
   - local: index
     title: Diffusers
   - local: installation
@@ -7,8 +8,9 @@
     title: Quickstart
   - local: stable_diffusion
     title: Basic performance
-  title: Get started
+
-- isExpanded: false
+- title: Pipelines
+  isExpanded: false
   sections:
   - local: using-diffusers/loading
     title: DiffusionPipeline
@@ -22,14 +24,13 @@
     title: Reproducibility
   - local: using-diffusers/schedulers
     title: Schedulers
-  - local: using-diffusers/automodel
-    title: AutoModel
   - local: using-diffusers/other-formats
     title: Model formats
   - local: using-diffusers/push_to_hub
     title: Sharing pipelines and models
-  title: Pipelines
+
-- isExpanded: false
+- title: Adapters
+  isExpanded: false
   sections:
   - local: tutorials/using_peft_for_inference
     title: LoRA
@@ -43,19 +44,21 @@
     title: DreamBooth
   - local: using-diffusers/textual_inversion_inference
     title: Textual inversion
-  title: Adapters
+
-- isExpanded: false
+- title: Inference
+  isExpanded: false
   sections:
   - local: using-diffusers/weighted_prompts
-    title: Prompting
+    title: Prompt techniques
   - local: using-diffusers/create_a_server
     title: Create a server
   - local: using-diffusers/batched_inference
     title: Batch inference
   - local: training/distributed_inference
     title: Distributed inference
-  title: Inference
+
-- isExpanded: false
+- title: Inference optimization
+  isExpanded: false
   sections:
   - local: optimization/fp16
     title: Accelerate inference
@@ -67,7 +70,8 @@
     title: Reduce memory usage
   - local: optimization/speed-memory-optims
     title: Compiling and offloading quantized models
-  - sections:
+  - title: Community optimizations
+    sections:
     - local: optimization/pruna
       title: Pruna
     - local: optimization/xformers
@@ -86,9 +90,9 @@
       title: ParaAttention
     - local: using-diffusers/image_quality
       title: FreeU
-    title: Community optimizations
+
-  title: Inference optimization
+- title: Hybrid Inference
-- isExpanded: false
+  isExpanded: false
   sections:
   - local: hybrid_inference/overview
     title: Overview
@@ -98,8 +102,9 @@
     title: VAE Encode
   - local: hybrid_inference/api_reference
     title: API Reference
-  title: Hybrid Inference
+
-- isExpanded: false
+- title: Modular Diffusers
+  isExpanded: false
   sections:
   - local: modular_diffusers/overview
     title: Overview
@@ -121,10 +126,9 @@
     title: ComponentsManager
   - local: modular_diffusers/guiders
     title: Guiders
-  - local: modular_diffusers/custom_blocks
+
-    title: Building Custom Blocks
+- title: Training
-  title: Modular Diffusers
+  isExpanded: false
-- isExpanded: false
   sections:
   - local: training/overview
     title: Overview
@@ -134,7 +138,8 @@
     title: Adapt a model to a new task
   - local: tutorials/basic_training
     title: Train a diffusion model
-  - sections:
+  - title: Models
+    sections:
     - local: training/unconditional_training
       title: Unconditional image generation
     - local: training/text2image
@@ -153,8 +158,8 @@
       title: InstructPix2Pix
     - local: training/cogvideox
       title: CogVideoX
-    title: Models
+  - title: Methods
-  - sections:
+    sections:
     - local: training/text_inversion
       title: Textual Inversion
     - local: training/dreambooth
@@ -167,9 +172,9 @@
       title: Latent Consistency Distillation
     - local: training/ddpo
       title: Reinforcement learning training with DDPO
-    title: Methods
+
-  title: Training
+- title: Quantization
-- isExpanded: false
+  isExpanded: false
   sections:
   - local: quantization/overview
     title: Getting started
@@ -183,8 +188,9 @@
     title: quanto
   - local: quantization/modelopt
     title: NVIDIA ModelOpt
-  title: Quantization
+
-- isExpanded: false
+- title: Model accelerators and hardware
+  isExpanded: false
   sections:
   - local: optimization/onnx
     title: ONNX
@@ -198,8 +204,9 @@
     title: Intel Gaudi
   - local: optimization/neuron
     title: AWS Neuron
-  title: Model accelerators and hardware
+
-- isExpanded: false
+- title: Specific pipeline examples
+  isExpanded: false
   sections:
   - local: using-diffusers/consisid
     title: ConsisID
@@ -225,10 +232,12 @@
     title: Stable Video Diffusion
   - local: using-diffusers/marigold_usage
     title: Marigold Computer Vision
-  title: Specific pipeline examples
+
-- isExpanded: false
+- title: Resources
+  isExpanded: false
   sections:
-  - sections:
+  - title: Task recipes
+    sections:
     - local: using-diffusers/unconditional_image_generation
       title: Unconditional image generation
     - local: using-diffusers/conditional_image_generation
@@ -243,7 +252,6 @@
       title: Video generation
     - local: using-diffusers/depth2img
       title: Depth-to-image
-    title: Task recipes
   - local: using-diffusers/write_own_pipeline
     title: Understanding pipelines, models and schedulers
   - local: community_projects
@@ -258,10 +266,12 @@
     title: Diffusers' Ethical Guidelines
   - local: conceptual/evaluation
     title: Evaluating Diffusion Models
-  title: Resources
+
-- isExpanded: false
+- title: API
+  isExpanded: false
   sections:
-  - sections:
+  - title: Main Classes
+    sections:
     - local: api/configuration
       title: Configuration
     - local: api/logging
@@ -272,8 +282,8 @@
       title: Quantization
     - local: api/parallel
       title: Parallel inference
-    title: Main Classes
+  - title: Modular
-  - sections:
+    sections:
     - local: api/modular_diffusers/pipeline
       title: Pipeline
     - local: api/modular_diffusers/pipeline_blocks
@@ -284,8 +294,8 @@
       title: Components and configs
     - local: api/modular_diffusers/guiders
       title: Guiders
-    title: Modular
+  - title: Loaders
-  - sections:
+    sections:
     - local: api/loaders/ip_adapter
       title: IP-Adapter
     - local: api/loaders/lora
@@ -300,13 +310,14 @@
       title: SD3Transformer2D
     - local: api/loaders/peft
       title: PEFT
-    title: Loaders
+  - title: Models
-  - sections:
+    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
@@ -321,20 +332,16 @@
         title: SD3ControlNetModel
       - local: api/models/controlnet_sparsectrl
         title: SparseControlNetModel
-      title: ControlNets
+    - title: Transformers
-    - sections:
+      sections:
       - local: api/models/allegro_transformer3d
         title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
-      - local: api/models/transformer_bria_fibo
-        title: BriaFiboTransformer2DModel
       - local: api/models/bria_transformer
         title: BriaTransformer2DModel
       - local: api/models/chroma_transformer
         title: ChromaTransformer2DModel
-      - local: api/models/chronoedit_transformer_3d
-        title: ChronoEditTransformer3DModel
       - local: api/models/cogvideox_transformer3d
         title: CogVideoXTransformer3DModel
       - local: api/models/cogview3plus_transformer2d
@@ -349,18 +356,12 @@
         title: DiTTransformer2DModel
       - local: api/models/easyanimate_transformer3d
         title: EasyAnimateTransformer3DModel
-      - local: api/models/flux2_transformer
-        title: Flux2Transformer2DModel
       - local: api/models/flux_transformer
         title: FluxTransformer2DModel
       - local: api/models/hidream_image_transformer
         title: HiDreamImageTransformer2DModel
       - local: api/models/hunyuan_transformer2d
         title: HunyuanDiT2DModel
-      - local: api/models/hunyuanimage_transformer_2d
-        title: HunyuanImageTransformer2DModel
-      - local: api/models/hunyuan_video15_transformer_3d
-        title: HunyuanVideo15Transformer3DModel
       - local: api/models/hunyuan_video_transformer_3d
         title: HunyuanVideoTransformer3DModel
       - local: api/models/latte_transformer3d
@@ -375,8 +376,6 @@
         title: MochiTransformer3DModel
       - local: api/models/omnigen_transformer
         title: OmniGenTransformer2DModel
-      - local: api/models/ovisimage_transformer2d
-        title: OvisImageTransformer2DModel
       - local: api/models/pixart_transformer2d
         title: PixArtTransformer2DModel
       - local: api/models/prior_transformer
@@ -385,8 +384,6 @@
         title: QwenImageTransformer2DModel
       - local: api/models/sana_transformer2d
         title: SanaTransformer2DModel
-      - local: api/models/sana_video_transformer3d
-        title: SanaVideoTransformer3DModel
       - local: api/models/sd3_transformer2d
         title: SD3Transformer2DModel
       - local: api/models/skyreels_v2_transformer_3d
@@ -397,14 +394,10 @@
         title: Transformer2DModel
       - local: api/models/transformer_temporal
         title: TransformerTemporalModel
-      - local: api/models/wan_animate_transformer_3d
-        title: WanAnimateTransformer3DModel
       - local: api/models/wan_transformer_3d
         title: WanTransformer3DModel
-      - local: api/models/z_image_transformer2d
+    - title: UNets
-        title: ZImageTransformer2DModel        
+      sections:
-      title: Transformers
-    - sections:
       - local: api/models/stable_cascade_unet
         title: StableCascadeUNet
       - local: api/models/unet
@@ -419,8 +412,8 @@
         title: UNetMotionModel
       - local: api/models/uvit2d
         title: UViT2DModel
-      title: UNets
+    - title: VAEs
-    - sections:
+      sections:
       - local: api/models/asymmetricautoencoderkl
         title: AsymmetricAutoencoderKL
       - local: api/models/autoencoder_dc
@@ -433,14 +426,8 @@
         title: AutoencoderKLCogVideoX
       - local: api/models/autoencoderkl_cosmos
         title: AutoencoderKLCosmos
-      - local: api/models/autoencoder_kl_hunyuanimage
-        title: AutoencoderKLHunyuanImage
-      - local: api/models/autoencoder_kl_hunyuanimage_refiner
-        title: AutoencoderKLHunyuanImageRefiner
       - local: api/models/autoencoder_kl_hunyuan_video
         title: AutoencoderKLHunyuanVideo
-      - local: api/models/autoencoder_kl_hunyuan_video15
-        title: AutoencoderKLHunyuanVideo15
       - local: api/models/autoencoderkl_ltx_video
         title: AutoencoderKLLTXVideo
       - local: api/models/autoencoderkl_magvit
@@ -459,240 +446,210 @@
         title: Tiny AutoEncoder
       - local: api/models/vq
         title: VQModel
-      title: VAEs
+  - title: Pipelines
-    title: Models
+    sections:
-  - sections:
     - local: api/pipelines/overview
       title: Overview
+    - local: api/pipelines/allegro
+      title: Allegro
+    - local: api/pipelines/amused
+      title: aMUSEd
+    - local: api/pipelines/animatediff
+      title: AnimateDiff
+    - local: api/pipelines/attend_and_excite
+      title: Attend-and-Excite
+    - local: api/pipelines/audioldm
+      title: AudioLDM
+    - local: api/pipelines/audioldm2
+      title: AudioLDM 2
+    - local: api/pipelines/aura_flow
+      title: AuraFlow
     - local: api/pipelines/auto_pipeline
       title: AutoPipeline
-    - sections:
+    - local: api/pipelines/blip_diffusion
-      - local: api/pipelines/audioldm
+      title: BLIP-Diffusion
-        title: AudioLDM
+    - local: api/pipelines/bria_3_2
-      - local: api/pipelines/audioldm2
+      title: Bria 3.2
-        title: AudioLDM 2
+    - local: api/pipelines/chroma
-      - local: api/pipelines/dance_diffusion
+      title: Chroma
-        title: Dance Diffusion
+    - local: api/pipelines/cogvideox
-      - local: api/pipelines/musicldm
+      title: CogVideoX
-        title: MusicLDM
+    - local: api/pipelines/cogview3
-      - local: api/pipelines/stable_audio
+      title: CogView3
-        title: Stable Audio
+    - local: api/pipelines/cogview4
-      title: Audio
+      title: CogView4
-    - sections:
+    - local: api/pipelines/consisid
-      - local: api/pipelines/amused
+      title: ConsisID
-        title: aMUSEd
+    - local: api/pipelines/consistency_models
-      - local: api/pipelines/animatediff
+      title: Consistency Models
-        title: AnimateDiff
+    - local: api/pipelines/controlnet
-      - local: api/pipelines/attend_and_excite
+      title: ControlNet
-        title: Attend-and-Excite
+    - local: api/pipelines/controlnet_flux
-      - local: api/pipelines/aura_flow
+      title: ControlNet with Flux.1
-        title: AuraFlow
+    - local: api/pipelines/controlnet_hunyuandit
-      - local: api/pipelines/blip_diffusion
+      title: ControlNet with Hunyuan-DiT
-        title: BLIP-Diffusion
+    - local: api/pipelines/controlnet_sd3
-      - local: api/pipelines/bria_3_2
+      title: ControlNet with Stable Diffusion 3
-        title: Bria 3.2
+    - local: api/pipelines/controlnet_sdxl
-      - local: api/pipelines/bria_fibo
+      title: ControlNet with Stable Diffusion XL
-        title: Bria Fibo
+    - local: api/pipelines/controlnet_sana
-      - local: api/pipelines/chroma
+      title: ControlNet-Sana
-        title: Chroma
+    - local: api/pipelines/controlnetxs
-      - local: api/pipelines/cogview3
+      title: ControlNet-XS
-        title: CogView3
+    - local: api/pipelines/controlnetxs_sdxl
-      - local: api/pipelines/cogview4
+      title: ControlNet-XS with Stable Diffusion XL
-        title: CogView4
+    - local: api/pipelines/controlnet_union
-      - local: api/pipelines/consistency_models
+      title: ControlNetUnion
-        title: Consistency Models
+    - local: api/pipelines/cosmos
-      - local: api/pipelines/controlnet
+      title: Cosmos
-        title: ControlNet
+    - local: api/pipelines/dance_diffusion
-      - local: api/pipelines/controlnet_flux
+      title: Dance Diffusion
-        title: ControlNet with Flux.1
+    - local: api/pipelines/ddim
-      - local: api/pipelines/controlnet_hunyuandit
+      title: DDIM
-        title: ControlNet with Hunyuan-DiT
+    - local: api/pipelines/ddpm
-      - local: api/pipelines/controlnet_sd3
+      title: DDPM
-        title: ControlNet with Stable Diffusion 3
+    - local: api/pipelines/deepfloyd_if
-      - local: api/pipelines/controlnet_sdxl
+      title: DeepFloyd IF
-        title: ControlNet with Stable Diffusion XL
+    - local: api/pipelines/diffedit
-      - local: api/pipelines/controlnet_sana
+      title: DiffEdit
-        title: ControlNet-Sana
+    - local: api/pipelines/dit
-      - local: api/pipelines/controlnetxs
+      title: DiT
-        title: ControlNet-XS
+    - local: api/pipelines/easyanimate
-      - local: api/pipelines/controlnetxs_sdxl
+      title: EasyAnimate
-        title: ControlNet-XS with Stable Diffusion XL
+    - local: api/pipelines/flux
-      - local: api/pipelines/controlnet_union
+      title: Flux
-        title: ControlNetUnion
+    - local: api/pipelines/control_flux_inpaint
-      - local: api/pipelines/cosmos
+      title: FluxControlInpaint
-        title: Cosmos
+    - local: api/pipelines/framepack
-      - local: api/pipelines/ddim
+      title: Framepack
-        title: DDIM
+    - local: api/pipelines/hidream
-      - local: api/pipelines/ddpm
+      title: HiDream-I1
-        title: DDPM
+    - local: api/pipelines/hunyuandit
-      - local: api/pipelines/deepfloyd_if
+      title: Hunyuan-DiT
-        title: DeepFloyd IF
+    - local: api/pipelines/hunyuan_video
-      - local: api/pipelines/diffedit
+      title: HunyuanVideo
-        title: DiffEdit
+    - local: api/pipelines/i2vgenxl
-      - local: api/pipelines/dit
+      title: I2VGen-XL
-        title: DiT
+    - local: api/pipelines/pix2pix
-      - local: api/pipelines/easyanimate
+      title: InstructPix2Pix
-        title: EasyAnimate
+    - local: api/pipelines/kandinsky
-      - local: api/pipelines/flux
+      title: Kandinsky 2.1
-        title: Flux
+    - local: api/pipelines/kandinsky_v22
-      - local: api/pipelines/flux2
+      title: Kandinsky 2.2
-        title: Flux2
+    - local: api/pipelines/kandinsky3
-      - local: api/pipelines/control_flux_inpaint
+      title: Kandinsky 3
-        title: FluxControlInpaint
+    - local: api/pipelines/kolors
-      - local: api/pipelines/hidream
+      title: Kolors
-        title: HiDream-I1
+    - local: api/pipelines/latent_consistency_models
-      - local: api/pipelines/hunyuandit
+      title: Latent Consistency Models
-        title: Hunyuan-DiT
+    - local: api/pipelines/latent_diffusion
-      - local: api/pipelines/hunyuanimage21
+      title: Latent Diffusion
-        title: HunyuanImage2.1
+    - local: api/pipelines/latte
-      - local: api/pipelines/pix2pix
+      title: Latte
-        title: InstructPix2Pix
+    - local: api/pipelines/ledits_pp
-      - local: api/pipelines/kandinsky
+      title: LEDITS++
-        title: Kandinsky 2.1
+    - local: api/pipelines/ltx_video
-      - local: api/pipelines/kandinsky_v22
+      title: LTXVideo
-        title: Kandinsky 2.2
+    - local: api/pipelines/lumina2
-      - local: api/pipelines/kandinsky3
+      title: Lumina 2.0
-        title: Kandinsky 3
+    - local: api/pipelines/lumina
-      - local: api/pipelines/kandinsky5_image
+      title: Lumina-T2X
-        title: Kandinsky 5.0 Image
+    - local: api/pipelines/marigold
-      - local: api/pipelines/kolors
+      title: Marigold
-        title: Kolors
+    - local: api/pipelines/mochi
-      - local: api/pipelines/latent_consistency_models
+      title: Mochi
-        title: Latent Consistency Models
+    - local: api/pipelines/panorama
-      - local: api/pipelines/latent_diffusion
+      title: MultiDiffusion
-        title: Latent Diffusion
+    - local: api/pipelines/musicldm
-      - local: api/pipelines/ledits_pp
+      title: MusicLDM
-        title: LEDITS++
+    - local: api/pipelines/omnigen
-      - local: api/pipelines/lumina2
+      title: OmniGen
-        title: Lumina 2.0
+    - local: api/pipelines/pag
-      - local: api/pipelines/lumina
+      title: PAG
-        title: Lumina-T2X
+    - local: api/pipelines/paint_by_example
-      - local: api/pipelines/marigold
+      title: Paint by Example
-        title: Marigold
+    - local: api/pipelines/pia
-      - local: api/pipelines/panorama
+      title: Personalized Image Animator (PIA)
-        title: MultiDiffusion
+    - local: api/pipelines/pixart
-      - local: api/pipelines/omnigen
+      title: PixArt-α
-        title: OmniGen
+    - local: api/pipelines/pixart_sigma
-      - local: api/pipelines/ovis_image
+      title: PixArt-Σ
-        title: Ovis-Image
+    - local: api/pipelines/qwenimage
-      - local: api/pipelines/pag
+      title: QwenImage
-        title: PAG
+    - local: api/pipelines/sana
-      - local: api/pipelines/paint_by_example
+      title: Sana
-        title: Paint by Example
+    - local: api/pipelines/sana_sprint
-      - local: api/pipelines/pixart
+      title: Sana Sprint
-        title: PixArt-α
+    - local: api/pipelines/self_attention_guidance
-      - local: api/pipelines/pixart_sigma
+      title: Self-Attention Guidance
-        title: PixArt-Σ
+    - local: api/pipelines/semantic_stable_diffusion
-      - local: api/pipelines/prx
+      title: Semantic Guidance
-        title: PRX
+    - local: api/pipelines/shap_e
-      - local: api/pipelines/qwenimage
+      title: Shap-E
-        title: QwenImage
+    - local: api/pipelines/skyreels_v2
-      - local: api/pipelines/sana
+      title: SkyReels-V2
-        title: Sana
+    - local: api/pipelines/stable_audio
-      - local: api/pipelines/sana_sprint
+      title: Stable Audio
-        title: Sana Sprint
+    - local: api/pipelines/stable_cascade
-      - local: api/pipelines/sana_video
+      title: Stable Cascade
-        title: Sana Video
+    - title: Stable Diffusion
-      - local: api/pipelines/self_attention_guidance
+      sections:
-        title: Self-Attention Guidance
+      - local: api/pipelines/stable_diffusion/overview
-      - local: api/pipelines/semantic_stable_diffusion
+        title: Overview
-        title: Semantic Guidance
+      - local: api/pipelines/stable_diffusion/depth2img
-      - local: api/pipelines/shap_e
+        title: Depth-to-image
-        title: Shap-E
+      - local: api/pipelines/stable_diffusion/gligen
-      - local: api/pipelines/stable_cascade
+        title: GLIGEN (Grounded Language-to-Image Generation)
-        title: Stable Cascade
+      - local: api/pipelines/stable_diffusion/image_variation
-      - sections:
+        title: Image variation
-        - local: api/pipelines/stable_diffusion/overview
+      - local: api/pipelines/stable_diffusion/img2img
-          title: Overview
+        title: Image-to-image
-        - local: api/pipelines/stable_diffusion/depth2img
-          title: Depth-to-image
-        - local: api/pipelines/stable_diffusion/gligen
-          title: GLIGEN (Grounded Language-to-Image Generation)
-        - local: api/pipelines/stable_diffusion/image_variation
-          title: Image variation
-        - local: api/pipelines/stable_diffusion/img2img
-          title: Image-to-image
-        - local: api/pipelines/stable_diffusion/inpaint
-          title: Inpainting
-        - local: api/pipelines/stable_diffusion/k_diffusion
-          title: K-Diffusion
-        - local: api/pipelines/stable_diffusion/latent_upscale
-          title: Latent upscaler
-        - local: api/pipelines/stable_diffusion/ldm3d_diffusion
-          title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D
-            Upscaler
-        - local: api/pipelines/stable_diffusion/stable_diffusion_safe
-          title: Safe Stable Diffusion
-        - local: api/pipelines/stable_diffusion/sdxl_turbo
-          title: SDXL Turbo
-        - local: api/pipelines/stable_diffusion/stable_diffusion_2
-          title: Stable Diffusion 2
-        - local: api/pipelines/stable_diffusion/stable_diffusion_3
-          title: Stable Diffusion 3
-        - local: api/pipelines/stable_diffusion/stable_diffusion_xl
-          title: Stable Diffusion XL
-        - local: api/pipelines/stable_diffusion/upscale
-          title: Super-resolution
-        - local: api/pipelines/stable_diffusion/adapter
-          title: T2I-Adapter
-        - local: api/pipelines/stable_diffusion/text2img
-          title: Text-to-image
-        title: Stable Diffusion
-      - local: api/pipelines/stable_unclip
-        title: Stable unCLIP
-      - local: api/pipelines/unclip
-        title: unCLIP
-      - local: api/pipelines/unidiffuser
-        title: UniDiffuser
-      - local: api/pipelines/value_guided_sampling
-        title: Value-guided sampling
-      - local: api/pipelines/visualcloze
-        title: VisualCloze
-      - local: api/pipelines/wuerstchen
-        title: Wuerstchen
-      - local: api/pipelines/z_image
-        title: Z-Image
-      title: Image
-    - sections:
-      - local: api/pipelines/allegro
-        title: Allegro
-      - local: api/pipelines/chronoedit
-        title: ChronoEdit
-      - local: api/pipelines/cogvideox
-        title: CogVideoX
-      - local: api/pipelines/consisid
-        title: ConsisID
-      - local: api/pipelines/framepack
-        title: Framepack
-      - local: api/pipelines/hunyuan_video
-        title: HunyuanVideo
-      - local: api/pipelines/hunyuan_video15
-        title: HunyuanVideo1.5
-      - local: api/pipelines/i2vgenxl
-        title: I2VGen-XL
-      - local: api/pipelines/kandinsky5_video
-        title: Kandinsky 5.0 Video
-      - local: api/pipelines/latte
-        title: Latte
-      - local: api/pipelines/ltx_video
-        title: LTXVideo
-      - local: api/pipelines/mochi
-        title: Mochi
-      - local: api/pipelines/pia
-        title: Personalized Image Animator (PIA)
-      - local: api/pipelines/skyreels_v2
-        title: SkyReels-V2
       - local: api/pipelines/stable_diffusion/svd
-        title: Stable Video Diffusion
+        title: Image-to-video
-      - local: api/pipelines/text_to_video
+      - local: api/pipelines/stable_diffusion/inpaint
-        title: Text-to-video
+        title: Inpainting
-      - local: api/pipelines/text_to_video_zero
+      - local: api/pipelines/stable_diffusion/k_diffusion
-        title: Text2Video-Zero
+        title: K-Diffusion
-      - local: api/pipelines/wan
+      - local: api/pipelines/stable_diffusion/latent_upscale
-        title: Wan
+        title: Latent upscaler
-      title: Video
+      - local: api/pipelines/stable_diffusion/ldm3d_diffusion
-    title: Pipelines
+        title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
-  - sections:
+      - local: api/pipelines/stable_diffusion/stable_diffusion_safe
+        title: Safe Stable Diffusion
+      - local: api/pipelines/stable_diffusion/sdxl_turbo
+        title: SDXL Turbo
+      - local: api/pipelines/stable_diffusion/stable_diffusion_2
+        title: Stable Diffusion 2
+      - local: api/pipelines/stable_diffusion/stable_diffusion_3
+        title: Stable Diffusion 3
+      - local: api/pipelines/stable_diffusion/stable_diffusion_xl
+        title: Stable Diffusion XL
+      - local: api/pipelines/stable_diffusion/upscale
+        title: Super-resolution
+      - local: api/pipelines/stable_diffusion/adapter
+        title: T2I-Adapter
+      - local: api/pipelines/stable_diffusion/text2img
+        title: Text-to-image
+    - local: api/pipelines/stable_unclip
+      title: Stable unCLIP
+    - local: api/pipelines/text_to_video
+      title: Text-to-video
+    - local: api/pipelines/text_to_video_zero
+      title: Text2Video-Zero
+    - local: api/pipelines/unclip
+      title: unCLIP
+    - local: api/pipelines/unidiffuser
+      title: UniDiffuser
+    - local: api/pipelines/value_guided_sampling
+      title: Value-guided sampling
+    - local: api/pipelines/visualcloze
+      title: VisualCloze
+    - local: api/pipelines/wan
+      title: Wan
+    - local: api/pipelines/wuerstchen
+      title: Wuerstchen
+  - title: Schedulers
+    sections:
     - local: api/schedulers/overview
       title: Overview
     - local: api/schedulers/cm_stochastic_iterative
@@ -761,8 +718,8 @@
       title: UniPCMultistepScheduler
     - local: api/schedulers/vq_diffusion
       title: VQDiffusionScheduler
-    title: Schedulers
+  - title: Internal classes
-  - sections:
+    sections:
     - local: api/internal_classes_overview
       title: Overview
     - local: api/attnprocessor
@@ -779,5 +736,3 @@
       title: VAE Image Processor
     - local: api/video_processor
       title: Video Processor
-    title: Internal classes
-  title: API

docs/source/en/api/cache.md

@@ -34,9 +34,3 @@ Cache methods speedup diffusion transformers by storing and reusing intermediate
 [[autodoc]] FirstBlockCacheConfig
 
 [[autodoc]] apply_first_block_cache
-
-### TaylorSeerCacheConfig
-
-[[autodoc]] TaylorSeerCacheConfig
-
-[[autodoc]] apply_taylorseer_cache

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

@@ -15,7 +15,7 @@ specific language governing permissions and limitations under the License.
 [IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.
 
 > [!TIP]
-> Learn how to load and use an IP-Adapter checkpoint and image in the [IP-Adapter](../../using-diffusers/ip_adapter) guide,.
+> 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

@@ -30,13 +30,11 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
 - [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
-- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen).
+- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen)
-- [`ZImageLoraLoaderMixin`] provides similar functions for [Z-Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/zimage).
-- [`Flux2LoraLoaderMixin`] provides similar functions for [Flux2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux2).
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
 > [!TIP]
-> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
+> To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
 
 ## LoraBaseMixin
 
@@ -58,10 +56,6 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 
 [[autodoc]] loaders.lora_pipeline.FluxLoraLoaderMixin
 
-## Flux2LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.Flux2LoraLoaderMixin
-
 ## CogVideoXLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.CogVideoXLoraLoaderMixin
@@ -113,13 +107,6 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 
 [[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
 
-## ZImageLoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.ZImageLoraLoaderMixin
-
-## KandinskyLoraLoaderMixin
-[[autodoc]] loaders.lora_pipeline.KandinskyLoraLoaderMixin
-
 ## LoraBaseMixin
 
 [[autodoc]] loaders.lora_base.LoraBaseMixin

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

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # PEFT
 
-Diffusers supports loading adapters such as [LoRA](../../tutorials/using_peft_for_inference) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
+Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
 
 > [!TIP]
 > Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.

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

@@ -17,7 +17,7 @@ Textual Inversion is a training method for personalizing models by learning new
 [`TextualInversionLoaderMixin`] provides a function for loading Textual Inversion embeddings from Diffusers and Automatic1111 into the text encoder and loading a special token to activate the embeddings.
 
 > [!TIP]
-> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/textual_inversion_inference) loading guide.
+> 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

@@ -17,7 +17,7 @@ This class is useful when *only* loading weights into a [`SD3Transformer2DModel`
 The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
 
 > [!TIP]
-> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
+> 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

@@ -17,7 +17,7 @@ Some training methods - like LoRA and Custom Diffusion - typically target the UN
 The [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.
 
 > [!TIP]
-> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) guide.
+> 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/asymmetricautoencoderkl.md

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

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

@@ -12,7 +12,15 @@ specific language governing permissions and limitations under the License.
 
 # AutoModel
 
-[`AutoModel`] automatically retrieves the correct model class from the checkpoint `config.json` file.
+The `AutoModel` is designed to make it easy to load a checkpoint without needing to know the specific model class. `AutoModel` automatically retrieves the correct model class from the checkpoint `config.json` file.
+
+```python
+from diffusers import AutoModel, AutoPipelineForText2Image
+
+unet = AutoModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
+pipe = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", unet=unet)
+```
+
 
 ## AutoModel
 

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

@@ -1,36 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLHunyuanVideo15
-
-The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanVideo1.5](https://github.com/Tencent/HunyuanVideo1-1.5) by Tencent.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLHunyuanVideo15
-
-vae = AutoencoderKLHunyuanVideo15.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v", subfolder="vae", torch_dtype=torch.float32)
-
-# make sure to enable tiling to avoid OOM
-vae.enable_tiling()
-```
-
-## AutoencoderKLHunyuanVideo15
-
-[[autodoc]] AutoencoderKLHunyuanVideo15
-  - decode
-  - encode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLHunyuanImage
-
-The 2D variational autoencoder (VAE) model with KL loss used in [HunyuanImage2.1].
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLHunyuanImage
-
-vae = AutoencoderKLHunyuanImage.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)
-```
-
-## AutoencoderKLHunyuanImage
-
-[[autodoc]] AutoencoderKLHunyuanImage
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# AutoencoderKLHunyuanImageRefiner
-
-The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanImage2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1) for its refiner pipeline.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import AutoencoderKLHunyuanImageRefiner
-
-vae = AutoencoderKLHunyuanImageRefiner.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)
-```
-
-## AutoencoderKLHunyuanImageRefiner
-
-[[autodoc]] AutoencoderKLHunyuanImageRefiner
-  - decode
-  - all
-
-## DecoderOutput
-
-[[autodoc]] models.autoencoders.vae.DecoderOutput

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

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

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

@@ -1,32 +0,0 @@
-<!-- Copyright 2025 The ChronoEdit Team and HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# ChronoEditTransformer3DModel
-
-A Diffusion Transformer model for 3D video-like data from [ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation](https://huggingface.co/papers/2510.04290) from NVIDIA and University of Toronto, by Jay Zhangjie Wu, Xuanchi Ren, Tianchang Shen, Tianshi Cao, Kai He, Yifan Lu, Ruiyuan Gao, Enze Xie, Shiyi Lan, Jose M. Alvarez, Jun Gao, Sanja Fidler, Zian Wang, Huan Ling.
-
-> **TL;DR:** ChronoEdit reframes image editing as a video generation task, using input and edited images as start/end frames to leverage pretrained video models with temporal consistency. A temporal reasoning stage introduces reasoning tokens to ensure physically plausible edits and visualize the editing trajectory.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import ChronoEditTransformer3DModel
-
-transformer = ChronoEditTransformer3DModel.from_pretrained("nvidia/ChronoEdit-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## ChronoEditTransformer3DModel
-
-[[autodoc]] ChronoEditTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -1,19 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Flux2Transformer2DModel
-
-A Transformer model for image-like data from [Flux2](https://hf.co/black-forest-labs/FLUX.2-dev).
-
-## Flux2Transformer2DModel
-
-[[autodoc]] Flux2Transformer2DModel

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

@@ -1,30 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# HunyuanVideo15Transformer3DModel
-
-A Diffusion Transformer model for 3D video-like data used in [HunyuanVideo1.5](https://github.com/Tencent/HunyuanVideo1-1.5).
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import HunyuanVideo15Transformer3DModel
-
-transformer = HunyuanVideo15Transformer3DModel.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v" subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## HunyuanVideo15Transformer3DModel
-
-[[autodoc]] HunyuanVideo15Transformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -1,30 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# HunyuanImageTransformer2DModel
-
-A Diffusion Transformer model for [HunyuanImage2.1](https://github.com/Tencent-Hunyuan/HunyuanImage-2.1).
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import HunyuanImageTransformer2DModel
-
-transformer = HunyuanImageTransformer2DModel.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## HunyuanImageTransformer2DModel
-
-[[autodoc]] HunyuanImageTransformer2DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -1,24 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# OvisImageTransformer2DModel
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import OvisImageTransformer2DModel
-
-transformer = OvisImageTransformer2DModel.from_pretrained("AIDC-AI/Ovis-Image-7B", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## OvisImageTransformer2DModel
-
-[[autodoc]] OvisImageTransformer2DModel

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

@@ -1,36 +0,0 @@
-<!-- Copyright 2025 The SANA-Video Authors and HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# SanaVideoTransformer3DModel
-
-A Diffusion Transformer model for 3D data (video) from [SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer](https://huggingface.co/papers/2509.24695) from NVIDIA and MIT HAN Lab, by Junsong Chen, Yuyang Zhao, Jincheng Yu, Ruihang Chu, Junyu Chen, Shuai Yang, Xianbang Wang, Yicheng Pan, Daquan Zhou, Huan Ling, Haozhe Liu, Hongwei Yi, Hao Zhang, Muyang Li, Yukang Chen, Han Cai, Sanja Fidler, Ping Luo, Song Han, Enze Xie.
-
-The abstract from the paper is:
-
-*We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720x1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Two core designs ensure our efficient, effective and long video generation: (1) Linear DiT: We leverage linear attention as the core operation, which is more efficient than vanilla attention given the large number of tokens processed in video generation. (2) Constant-Memory KV cache for Block Linear Attention: we design block-wise autoregressive approach for long video generation by employing a constant-memory state, derived from the cumulative properties of linear attention. This KV cache provides the Linear DiT with global context at a fixed memory cost, eliminating the need for a traditional KV cache and enabling efficient, minute-long video generation. In addition, we explore effective data filters and model training strategies, narrowing the training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of MovieGen. Given its low cost, SANA-Video achieves competitive performance compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover, SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x speedup). In summary, SANA-Video enables low-cost, high-quality video generation.*
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import SanaVideoTransformer3DModel
-import torch
-
-transformer = SanaVideoTransformer3DModel.from_pretrained("Efficient-Large-Model/SANA-Video_2B_480p_diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## SanaVideoTransformer3DModel
-
-[[autodoc]] SanaVideoTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput
-

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

@@ -1,19 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# BriaFiboTransformer2DModel
-
-A modified flux Transformer model from [Bria](https://huggingface.co/briaai/FIBO)
-
-## BriaFiboTransformer2DModel
-
-[[autodoc]] BriaFiboTransformer2DModel

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

@@ -1,30 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License. -->
-
-# WanAnimateTransformer3DModel
-
-A Diffusion Transformer model for 3D video-like data was introduced in [Wan Animate](https://github.com/Wan-Video/Wan2.2) by the Alibaba Wan Team.
-
-The model can be loaded with the following code snippet.
-
-```python
-from diffusers import WanAnimateTransformer3DModel
-
-transformer = WanAnimateTransformer3DModel.from_pretrained("Wan-AI/Wan2.2-Animate-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
-```
-
-## WanAnimateTransformer3DModel
-
-[[autodoc]] WanAnimateTransformer3DModel
-
-## Transformer2DModelOutput
-
-[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -1,19 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# ZImageTransformer2DModel
-
-A Transformer model for image-like data from [Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo).
-
-## ZImageTransformer2DModel
-
-[[autodoc]] ZImageTransformer2DModel

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

@@ -1,45 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Bria Fibo
-
-Text-to-image models have mastered imagination - but not control. FIBO changes that.
-
-FIBO is trained on structured JSON captions up to 1,000+ words and designed to understand and control different visual parameters such as lighting, composition, color, and camera settings, enabling precise and reproducible outputs.
-
-With only 8 billion parameters, FIBO provides a new level of image quality, prompt adherence and proffesional control.
-
-FIBO is trained exclusively on a structured prompt and will not work with freeform text prompts.
-you can use the [FIBO-VLM-prompt-to-JSON](https://huggingface.co/briaai/FIBO-VLM-prompt-to-JSON) model or the [FIBO-gemini-prompt-to-JSON](https://huggingface.co/briaai/FIBO-gemini-prompt-to-JSON)  to convert your freeform text prompt to a structured JSON prompt.
-
-> [!NOTE]
-> Avoid using freeform text prompts directly with FIBO because it does not produce the best results.
-
-Refer to the Bria Fibo Hugging Face [page](https://huggingface.co/briaai/FIBO) to learn more.
-
-
-## Usage
-
-_As the model is gated, before using it with diffusers you first need to go to the [Bria Fibo Hugging Face page](https://huggingface.co/briaai/FIBO), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
-
-Use the command below to log in:
-
-```bash
-hf auth login
-```
-
-
-## BriaFiboPipeline
-
-[[autodoc]] BriaFiboPipeline
-	- all
-	- __call__

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

@@ -19,21 +19,20 @@ specific language governing permissions and limitations under the License.
 
 Chroma is a text to image generation model based on Flux.
 
-Original model checkpoints for Chroma can be found here:
+Original model checkpoints for Chroma can be found [here](https://huggingface.co/lodestones/Chroma).
-* High-resolution finetune: [lodestones/Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD)
-* Base model: [lodestones/Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base)
-* Original repo with progress checkpoints: [lodestones/Chroma](https://huggingface.co/lodestones/Chroma) (loading this repo with `from_pretrained` will load a Diffusers-compatible version of the `unlocked-v37` checkpoint)
 
 > [!TIP]
 > Chroma can use all the same optimizations as Flux.
 
 ## Inference
 
+The Diffusers version of Chroma is based on the [`unlocked-v37`](https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors) version of the original model, which is available in the [Chroma repository](https://huggingface.co/lodestones/Chroma).
+
 ```python
 import torch
 from diffusers import ChromaPipeline
 
-pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.bfloat16)
+pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16)
 pipe.enable_model_cpu_offload()
 
 prompt = [
@@ -64,10 +63,10 @@ Then run the following example
 import torch
 from diffusers import ChromaTransformer2DModel, ChromaPipeline
 
-model_id = "lodestones/Chroma1-HD"
+model_id = "lodestones/Chroma"
 dtype = torch.bfloat16
 
-transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma1-HD/blob/main/Chroma1-HD.safetensors", 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()

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

@@ -1,156 +0,0 @@
-<!-- Copyright 2025 The ChronoEdit Team and HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License. -->
-
-<div style="float: right;">
-  <div class="flex flex-wrap space-x-1">
-    <a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
-      <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
-    </a>
-  </div>
-</div>
-
-# ChronoEdit
-
-[ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation](https://huggingface.co/papers/2510.04290) from NVIDIA and University of Toronto, by Jay Zhangjie Wu, Xuanchi Ren, Tianchang Shen, Tianshi Cao, Kai He, Yifan Lu, Ruiyuan Gao, Enze Xie, Shiyi Lan, Jose M. Alvarez, Jun Gao, Sanja Fidler, Zian Wang, Huan Ling.
-
-> **TL;DR:** ChronoEdit reframes image editing as a video generation task, using input and edited images as start/end frames to leverage pretrained video models with temporal consistency. A temporal reasoning stage introduces reasoning tokens to ensure physically plausible edits and visualize the editing trajectory.
-
-*Recent advances in large generative models have greatly enhanced both image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, where edited objects must remain coherent. This capability is especially vital for world simulation related tasks. In this paper, we present ChronoEdit, a framework that reframes image editing as a video generation problem. First, ChronoEdit treats the input and edited images as the first and last frames of a video, allowing it to leverage large pretrained video generative models that capture not only object appearance but also the implicit physics of motion and interaction through learned temporal consistency. Second, ChronoEdit introduces a temporal reasoning stage that explicitly performs editing at inference time. Under this setting, target frame is jointly denoised with reasoning tokens to imagine a plausible editing trajectory that constrains the solution space to physically viable transformations. The reasoning tokens are then dropped after a few steps to avoid the high computational cost of rendering a full video. To validate ChronoEdit, we introduce PBench-Edit, a new benchmark of image-prompt pairs for contexts that require physical consistency, and demonstrate that ChronoEdit surpasses state-of-the-art baselines in both visual fidelity and physical plausibility. Project page for code and models: [this https URL](https://research.nvidia.com/labs/toronto-ai/chronoedit).*
-
-The ChronoEdit pipeline is developed by the ChronoEdit Team. The original code is available on [GitHub](https://github.com/nv-tlabs/ChronoEdit), and pretrained models can be found in the [nvidia/ChronoEdit](https://huggingface.co/collections/nvidia/chronoedit) collection on Hugging Face.
-
-
-### Image Editing
-
-```py
-import torch
-import numpy as np
-from diffusers import AutoencoderKLWan, ChronoEditTransformer3DModel, ChronoEditPipeline
-from diffusers.utils import export_to_video, load_image
-from transformers import CLIPVisionModel
-from PIL import Image
-
-model_id = "nvidia/ChronoEdit-14B-Diffusers"
-image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-transformer = ChronoEditTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
-pipe = ChronoEditPipeline.from_pretrained(model_id, image_encoder=image_encoder, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-image = load_image(
-    "https://huggingface.co/spaces/nvidia/ChronoEdit/resolve/main/examples/3.png"
-)
-max_area = 720 * 1280
-aspect_ratio = image.height / image.width
-mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-print("width", width, "height", height)
-image = image.resize((width, height))
-prompt = (
-    "The user wants to transform the image by adding a small, cute mouse sitting inside the floral teacup, enjoying a spa bath. The mouse should appear relaxed and cheerful, with a tiny white bath towel draped over its head like a turban. It should be positioned comfortably in the cup’s liquid, with gentle steam rising around it to blend with the cozy atmosphere. "
-    "The mouse’s pose should be natural—perhaps sitting upright with paws resting lightly on the rim or submerged in the tea. The teacup’s floral design, gold trim, and warm lighting must remain unchanged to preserve the original aesthetic. The steam should softly swirl around the mouse, enhancing the spa-like, whimsical mood."
-)
-
-output = pipe(
-    image=image,
-    prompt=prompt,
-    height=height,
-    width=width,
-    num_frames=5,
-    num_inference_steps=50,
-    guidance_scale=5.0,
-    enable_temporal_reasoning=False,
-    num_temporal_reasoning_steps=0,
-).frames[0]
-Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output.png")
-```
-
-Optionally, enable **temporal reasoning** for improved physical consistency:
-```py
-output = pipe(
-    image=image,
-    prompt=prompt,
-    height=height,
-    width=width,
-    num_frames=29,
-    num_inference_steps=50,
-    guidance_scale=5.0,
-    enable_temporal_reasoning=True,
-    num_temporal_reasoning_steps=50,
-).frames[0]
-export_to_video(output, "output.mp4", fps=16)
-Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output.png")
-```
-
-### Inference with 8-Step Distillation Lora
-
-```py
-import torch
-import numpy as np
-from diffusers import AutoencoderKLWan, ChronoEditTransformer3DModel, ChronoEditPipeline
-from diffusers.utils import export_to_video, load_image
-from transformers import CLIPVisionModel
-from PIL import Image
-
-model_id = "nvidia/ChronoEdit-14B-Diffusers"
-image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-transformer = ChronoEditTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
-pipe = ChronoEditPipeline.from_pretrained(model_id, image_encoder=image_encoder, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16)
-lora_path = hf_hub_download(repo_id=model_id, filename="lora/chronoedit_distill_lora.safetensors")
-pipe.load_lora_weights(lora_path)
-pipe.fuse_lora(lora_scale=1.0)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=2.0)
-pipe.to("cuda")
-
-image = load_image(
-    "https://huggingface.co/spaces/nvidia/ChronoEdit/resolve/main/examples/3.png"
-)
-max_area = 720 * 1280
-aspect_ratio = image.height / image.width
-mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-print("width", width, "height", height)
-image = image.resize((width, height))
-prompt = (
-    "The user wants to transform the image by adding a small, cute mouse sitting inside the floral teacup, enjoying a spa bath. The mouse should appear relaxed and cheerful, with a tiny white bath towel draped over its head like a turban. It should be positioned comfortably in the cup’s liquid, with gentle steam rising around it to blend with the cozy atmosphere. "
-    "The mouse’s pose should be natural—perhaps sitting upright with paws resting lightly on the rim or submerged in the tea. The teacup’s floral design, gold trim, and warm lighting must remain unchanged to preserve the original aesthetic. The steam should softly swirl around the mouse, enhancing the spa-like, whimsical mood."
-)
-
-output = pipe(
-    image=image,
-    prompt=prompt,
-    height=height,
-    width=width,
-    num_frames=5,
-    num_inference_steps=8,
-    guidance_scale=1.0,
-    enable_temporal_reasoning=False,
-    num_temporal_reasoning_steps=0,
-).frames[0]
-export_to_video(output, "output.mp4", fps=16)
-Image.fromarray((output[-1] * 255).clip(0, 255).astype("uint8")).save("output.png")
-```
-
-## ChronoEditPipeline
-
-[[autodoc]] ChronoEditPipeline
-  - all
-  - __call__
-
-## ChronoEditPipelineOutput
-
-[[autodoc]] pipelines.chronoedit.pipeline_output.ChronoEditPipelineOutput

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

@@ -418,7 +418,7 @@ 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.
+> 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.
 

docs/source/en/api/pipelines/flux2.md

@@ -1,39 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Flux2
-
-<div class="flex flex-wrap space-x-1">
-  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
-  <img alt="MPS" src="https://img.shields.io/badge/MPS-000000?style=flat&logo=apple&logoColor=white%22">
-</div>
-
-Flux.2 is the recent series of image generation models from Black Forest Labs, preceded by the [Flux.1](./flux.md) series. It is an entirely new model with a new architecture and pre-training done from scratch!
-
-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/flux2).
-
-> [!TIP]
-> Flux2 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.
->
-> [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
-
-## Caption upsampling
-
-Flux.2 can potentially generate better better outputs with better prompts. We can "upsample"
-an input prompt by setting the `caption_upsample_temperature` argument in the pipeline call arguments.
-The [official implementation](https://github.com/black-forest-labs/flux2/blob/5a5d316b1b42f6b59a8c9194b77c8256be848432/src/flux2/text_encoder.py#L140) recommends this value to be 0.15.
-
-## Flux2Pipeline
-
-[[autodoc]] Flux2Pipeline
-	- all
-	- __call__

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

@@ -21,7 +21,7 @@
 
 ## Available models
 
-The following models are available for the [`HiDreamImagePipeline`] pipeline:
+The following models are available for the [`HiDreamImagePipeline`](text-to-image) pipeline:
 
 | Model name | Description |
 |:---|:---|

docs/source/en/api/pipelines/hunyuan_video15.md

@@ -1,120 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License. -->
-
-
-# HunyuanVideo-1.5
-
-HunyuanVideo-1.5 is a lightweight yet powerful video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs. This achievement is built upon several key components, including meticulous data curation, an advanced DiT architecture with selective and sliding tile attention (SSTA), enhanced bilingual understanding through glyph-aware text encoding, progressive pre-training and post-training, and an efficient video super-resolution network. Leveraging these designs, we developed a unified framework capable of high-quality text-to-video and image-to-video generation across multiple durations and resolutions. Extensive experiments demonstrate that this compact and proficient model establishes a new state-of-the-art among open-source models.
-
-You can find all the original HunyuanVideo checkpoints under the [Tencent](https://huggingface.co/tencent) organization.
-
-> [!TIP]
-> Click on the HunyuanVideo models in the right sidebar for more examples of video generation tasks.
->
-> The examples below use a checkpoint from [hunyuanvideo-community](https://huggingface.co/hunyuanvideo-community) because the weights are stored in a layout compatible with Diffusers.
-
-The example below demonstrates how to generate a video optimized for memory or inference speed.
-
-<hfoptions id="usage">
-<hfoption id="memory">
-
-Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.
-
-
-```py
-import torch
-from diffusers import AutoModel, HunyuanVideo15Pipeline
-from diffusers.utils import export_to_video
-
-
-pipeline = HunyuanVideo15Pipeline.from_pretrained(
-    "HunyuanVideo-1.5-Diffusers-480p_t2v",
-    torch_dtype=torch.bfloat16,
-)
-
-# model-offloading and tiling
-pipeline.enable_model_cpu_offload()
-pipeline.vae.enable_tiling()
-
-prompt = "A fluffy teddy bear sits on a bed of soft pillows surrounded by children's toys."
-video = pipeline(prompt=prompt, num_frames=61, num_inference_steps=30).frames[0]
-export_to_video(video, "output.mp4", fps=15)
-```
-
-## Notes
-
-- HunyuanVideo1.5 use attention masks with variable-length sequences. For best performance, we recommend using an attention backend that handles padding efficiently.
-
-    - **H100/H800:** `_flash_3_hub` or `_flash_3_varlen_hub`
-    - **A100/A800/RTX 4090:** `flash_hub` or `flash_varlen_hub`
-    - **Other GPUs:** `sage_hub`
-
-Refer to the [Attention backends](../../optimization/attention_backends) guide for more details about using a different backend.
-
-
-```py
-pipe.transformer.set_attention_backend("flash_hub")  # or your preferred backend
-```
-
-- [`HunyuanVideo15Pipeline`] use guider and does not take `guidance_scale` parameter at runtime. 
-
-You can check the default guider configuration using `pipe.guider`:
-
-```py
->>> pipe.guider 
-ClassifierFreeGuidance {
-  "_class_name": "ClassifierFreeGuidance",
-  "_diffusers_version": "0.36.0.dev0",
-  "enabled": true,
-  "guidance_rescale": 0.0,
-  "guidance_scale": 6.0,
-  "start": 0.0,
-  "stop": 1.0,
-  "use_original_formulation": false
-}
-
-State:
-  step: None
-  num_inference_steps: None
-  timestep: None
-  count_prepared: 0
-  enabled: True
-  num_conditions: 2
-```
-
-To update guider configuration, you can run `pipe.guider = pipe.guider.new(...)`
-
-```py
-pipe.guider = pipe.guider.new(guidance_scale=5.0)
-```
-
-Read more on Guider [here](../../modular_diffusers/guiders).
-
-
-
-## HunyuanVideo15Pipeline
-
-[[autodoc]] HunyuanVideo15Pipeline
-  - all
-  - __call__
-
-## HunyuanVideo15ImageToVideoPipeline
-
-[[autodoc]] HunyuanVideo15ImageToVideoPipeline
-  - all
-  - __call__
-
-## HunyuanVideo15PipelineOutput
-
-[[autodoc]] pipelines.hunyuan_video1_5.pipeline_output.HunyuanVideo15PipelineOutput

docs/source/en/api/pipelines/hunyuanimage21.md

@@ -1,152 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License. -->
-
-# HunyuanImage2.1
-
-
-HunyuanImage-2.1 is a 17B text-to-image model that is capable of generating 2K (2048 x 2048) resolution images
-
-HunyuanImage-2.1 comes in the following variants:
-
-| model type | model id |
-|:----------:|:--------:|
-| HunyuanImage-2.1 | [hunyuanvideo-community/HunyuanImage-2.1-Diffusers](https://huggingface.co/hunyuanvideo-community/HunyuanImage-2.1-Diffusers) |
-| HunyuanImage-2.1-Distilled | [hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers](https://huggingface.co/hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers) |
-| HunyuanImage-2.1-Refiner | [hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers](https://huggingface.co/hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers) |
-
-> [!TIP]
-> [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
-
-## HunyuanImage-2.1
-
-HunyuanImage-2.1 applies [Adaptive Projected Guidance (APG)](https://huggingface.co/papers/2410.02416) combined with Classifier-Free Guidance (CFG) in the denoising loop. `HunyuanImagePipeline` has a `guider` component (read more about [Guider](../modular_diffusers/guiders.md)) and does not take a `guidance_scale` parameter at runtime. To change guider-related parameters, e.g., `guidance_scale`, you can update the `guider` configuration instead.
-
-```python
-import torch
-from diffusers import HunyuanImagePipeline
-
-pipe = HunyuanImagePipeline.from_pretrained(
-    "hunyuanvideo-community/HunyuanImage-2.1-Diffusers", 
-    torch_dtype=torch.bfloat16
-)
-pipe = pipe.to("cuda")
-``` 
-
-You can inspect the `guider` object:
-
-```py
->>> pipe.guider
-AdaptiveProjectedMixGuidance {
-  "_class_name": "AdaptiveProjectedMixGuidance",
-  "_diffusers_version": "0.36.0.dev0",
-  "adaptive_projected_guidance_momentum": -0.5,
-  "adaptive_projected_guidance_rescale": 10.0,
-  "adaptive_projected_guidance_scale": 10.0,
-  "adaptive_projected_guidance_start_step": 5,
-  "enabled": true,
-  "eta": 0.0,
-  "guidance_rescale": 0.0,
-  "guidance_scale": 3.5,
-  "start": 0.0,
-  "stop": 1.0,
-  "use_original_formulation": false
-}
-
-State:
-  step: None
-  num_inference_steps: None
-  timestep: None
-  count_prepared: 0
-  enabled: True
-  num_conditions: 2
-  momentum_buffer: None
-  is_apg_enabled: False
-  is_cfg_enabled: True
-```
-
-To update the guider with a different configuration, use the `new()` method. For example, to generate an image with `guidance_scale=5.0` while keeping all other default guidance parameters:
-
-```py
-import torch
-from diffusers import HunyuanImagePipeline
-
-pipe = HunyuanImagePipeline.from_pretrained(
-    "hunyuanvideo-community/HunyuanImage-2.1-Diffusers", 
-    torch_dtype=torch.bfloat16
-)
-pipe = pipe.to("cuda")
-
-# Update the guider configuration
-pipe.guider = pipe.guider.new(guidance_scale=5.0)
-
-prompt = (
-    "A cute, cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, "
-    "wearing a red knitted scarf and a red beret with the word 'Tencent' on it, holding a paintbrush with a "
-    "focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
-)
-
-image = pipe(
-    prompt=prompt, 
-    num_inference_steps=50, 
-    height=2048, 
-    width=2048,
-).images[0]
-image.save("image.png")
-```
-
-
-## HunyuanImage-2.1-Distilled
-
-use `distilled_guidance_scale` with the guidance-distilled checkpoint, 
-
-```py
-import torch
-from diffusers import HunyuanImagePipeline
-pipe = HunyuanImagePipeline.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers", torch_dtype=torch.bfloat16)
-pipe = pipe.to("cuda")
-
-prompt = (
-    "A cute, cartoon-style anthropomorphic penguin plush toy with fluffy fur, standing in a painting studio, "
-    "wearing a red knitted scarf and a red beret with the word 'Tencent' on it, holding a paintbrush with a "
-    "focused expression as it paints an oil painting of the Mona Lisa, rendered in a photorealistic photographic style."
-)
-
-out = pipe(
-    prompt,
-    num_inference_steps=8,
-    distilled_guidance_scale=3.25,
-    height=2048,
-    width=2048,
-    generator=generator,
-).images[0]
-
-```
-
-
-## HunyuanImagePipeline
-
-[[autodoc]] HunyuanImagePipeline
-  - all
-  - __call__
-
-## HunyuanImageRefinerPipeline
-
-[[autodoc]] HunyuanImageRefinerPipeline
-  - all
-  - __call__
-
-
-## HunyuanImagePipelineOutput
-
-[[autodoc]] pipelines.hunyuan_image.pipeline_output.HunyuanImagePipelineOutput

docs/source/en/api/pipelines/kandinsky5_image.md

@@ -1,116 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team and Kandinsky Lab 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.
--->
-
-# Kandinsky 5.0 Image
-
-[Kandinsky 5.0](https://arxiv.org/abs/2511.14993) is a family of diffusion models for Video & Image generation. 
-
-Kandinsky 5.0 Image Lite is a lightweight image generation model (6B parameters).
-
-The model introduces several key innovations:
-- **Latent diffusion pipeline** with **Flow Matching** for improved training stability
-- **Diffusion Transformer (DiT)** as the main generative backbone with cross-attention to text embeddings
-- Dual text encoding using **Qwen2.5-VL** and **CLIP** for comprehensive text understanding
-- **Flux VAE** for efficient image encoding and decoding
-
-The original codebase can be found at [kandinskylab/Kandinsky-5](https://github.com/kandinskylab/Kandinsky-5).
-
-> [!TIP]
-> Check out the [Kandinsky Lab](https://huggingface.co/kandinskylab) organization on the Hub for the official model checkpoints for text-to-video generation, including pretrained, SFT, no-CFG, and distilled variants.
-
-
-## Available Models
-
-Kandinsky 5.0 Image Lite:
-
-| model_id | Description | Use Cases |
-|------------|-------------|-----------|
-| [**kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers**](https://huggingface.co/kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers) | 6B image Supervised Fine-Tuned model | Highest generation quality |
-| [**kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers**](https://huggingface.co/kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers) | 6B image editing Supervised Fine-Tuned model | Highest generation quality |
-| [**kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-Diffusers**](https://huggingface.co/kandinskylab/Kandinsky-5.0-T2I-Lite-pretrain-Diffusers) | 6B image Base pretrained model | Research and fine-tuning |
-| [**kandinskylab/Kandinsky-5.0-I2I-Lite-pretrain-Diffusers**](https://huggingface.co/kandinskylab/Kandinsky-5.0-I2I-Lite-pretrain-Diffusers) | 6B image editing Base pretrained model | Research and fine-tuning |
-
-## Usage Examples
-
-### Basic Text-to-Image Generation
-
-```python
-import torch
-from diffusers import Kandinsky5T2IPipeline
-
-# Load the pipeline
-model_id = "kandinskylab/Kandinsky-5.0-T2I-Lite-sft-Diffusers"
-pipe = Kandinsky5T2IPipeline.from_pretrained(model_id)
-_ = pipe.to(device='cuda',dtype=torch.bfloat16)
-
-# Generate image
-prompt = "A fluffy, expressive cat wearing a bright red hat with a soft, slightly textured fabric. The hat should look cozy and well-fitted on the cat’s head. On the front of the hat, add clean, bold white text that reads “SWEET”, clearly visible and neatly centered. Ensure the overall lighting highlights the hat’s color and the cat’s fur details."
-
-output = pipe(
-    prompt=prompt,
-    negative_prompt="",
-    height=1024,
-    width=1024,
-    num_inference_steps=50,
-    guidance_scale=3.5,
-).image[0]
-```
-
-### Basic Image-to-Image Generation
-
-```python
-import torch
-from diffusers import Kandinsky5I2IPipeline
-from diffusers.utils import load_image 
-# Load the pipeline
-model_id = "kandinskylab/Kandinsky-5.0-I2I-Lite-sft-Diffusers"
-pipe = Kandinsky5I2IPipeline.from_pretrained(model_id)
-
-_ = pipe.to(device='cuda',dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload()                                               # <--- Enable CPU offloading for single GPU inference
-
-# Edit the input image
-image = load_image(
-    "https://huggingface.co/kandinsky-community/kandinsky-3/resolve/main/assets/title.jpg?download=true"
-)
-
-prompt = "Change the background from a winter night scene to a bright summer day. Place the character on a sandy beach with clear blue sky, soft sunlight, and gentle waves in the distance. Replace the winter clothing with a light short-sleeved T-shirt (in soft pastel colors) and casual shorts. Ensure the character’s fur reflects warm daylight instead of cold winter tones. Add small beach details such as seashells, footprints in the sand, and a few scattered beach toys nearby. Keep the oranges in the scene, but place them naturally on the sand."
-negative_prompt = ""
-
-output = pipe(
-    image=image,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    guidance_scale=3.5,
-).image[0]
-```
-
-
-## Kandinsky5T2IPipeline
-
-[[autodoc]] Kandinsky5T2IPipeline
-    - all
-    - __call__
-
-## Kandinsky5I2IPipeline
-
-[[autodoc]] Kandinsky5I2IPipeline
-    - all
-    - __call__
-
-
-## Citation
-```bibtex
-@misc{kandinsky2025,
-    author = {Alexander Belykh and Alexander Varlamov and Alexey Letunovskiy and Anastasia Aliaskina and Anastasia Maltseva and Anastasiia Kargapoltseva and Andrey Shutkin and Anna Averchenkova and Anna Dmitrienko and Bulat Akhmatov and Denis Dimitrov and Denis Koposov and Denis Parkhomenko and Dmitrii and Ilya Vasiliev and Ivan Kirillov and Julia Agafonova and Kirill Chernyshev and Kormilitsyn Semen and Lev Novitskiy and Maria Kovaleva and Mikhail Mamaev and Mikhailov and Nikita Kiselev and Nikita Osterov and Nikolai Gerasimenko and Nikolai Vaulin and Olga Kim and Olga Vdovchenko and Polina Gavrilova and Polina Mikhailova and Tatiana Nikulina and Viacheslav Vasilev and Vladimir Arkhipkin and Vladimir Korviakov and Vladimir Polovnikov and Yury Kolabushin},
-    title = {Kandinsky 5.0: A family of diffusion models for Video & Image generation},
-    howpublished = {\url{https://github.com/kandinskylab/Kandinsky-5}},
-    year = 2025
-}
-```

docs/source/en/api/pipelines/kandinsky5_video.md

@@ -1,310 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team Kandinsky Lab 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.
--->
-
-# Kandinsky 5.0 Video
-
-[Kandinsky 5.0](https://arxiv.org/abs/2511.14993) is a family of diffusion models for Video & Image generation.
-
-Kandinsky 5.0 Lite line-up of lightweight video generation models (2B parameters) that ranks #1 among open-source models in its class. It outperforms larger models and offers the best understanding of Russian concepts in the open-source ecosystem.
-
-Kandinsky 5.0 Pro line-up of large high quality video generation models (19B parameters). It offers high qualty generation in HD and more generation formats like I2V.
-
-The model introduces several key innovations:
-- **Latent diffusion pipeline** with **Flow Matching** for improved training stability
-- **Diffusion Transformer (DiT)** as the main generative backbone with cross-attention to text embeddings
-- Dual text encoding using **Qwen2.5-VL** and **CLIP** for comprehensive text understanding
-- **HunyuanVideo 3D VAE** for efficient video encoding and decoding
-- **Sparse attention mechanisms** (NABLA) for efficient long-sequence processing
-
-The original codebase can be found at [kandinskylab/Kandinsky-5](https://github.com/kandinskylab/Kandinsky-5).
-
-> [!TIP]
-> Check out the [Kandinsky Lab](https://huggingface.co/kandinskylab) organization on the Hub for the official model checkpoints for text-to-video generation, including pretrained, SFT, no-CFG, and distilled variants.
-
-## Available Models
-
-Kandinsky 5.0 T2V Pro:
-
-| model_id | Description | Use Cases |
-|------------|-------------|-----------|
-| **kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers** | 5 second Text-to-Video Pro model | High-quality text-to-video generation |
-| **kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers** | 5 second Image-to-Video Pro model | High-quality image-to-video generation |
-
-Kandinsky 5.0 T2V Lite:
-| model_id | Description | Use Cases |
-|------------|-------------|-----------|
-| **kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers** | 5 second Supervised Fine-Tuned model | Highest generation quality |
-| **kandinskylab/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers** | 10 second Supervised Fine-Tuned model | Highest generation quality |
-| **kandinskylab/Kandinsky-5.0-T2V-Lite-nocfg-5s-Diffusers** | 5 second Classifier-Free Guidance distilled | 2× faster inference |
-| **kandinskylab/Kandinsky-5.0-T2V-Lite-nocfg-10s-Diffusers** | 10 second Classifier-Free Guidance distilled | 2× faster inference |
-| **kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers** | 5 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss |
-| **kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-10s-Diffusers** | 10 second Diffusion distilled to 16 steps | 6× faster inference, minimal quality loss |
-| **kandinskylab/Kandinsky-5.0-T2V-Lite-pretrain-5s-Diffusers** | 5 second Base pretrained model | Research and fine-tuning |
-| **kandinskylab/Kandinsky-5.0-T2V-Lite-pretrain-10s-Diffusers** | 10 second Base pretrained model | Research and fine-tuning |
-
-
-## Usage Examples
-
-### Basic Text-to-Video Generation
-
-#### Pro
-**⚠️ Warning!** all Pro models should be infered with pipeline.enable_model_cpu_offload()  
-```python
-import torch
-from diffusers import Kandinsky5T2VPipeline
-from diffusers.utils import export_to_video
-
-# Load the pipeline
-model_id = "kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers"
-pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-
-pipe = pipe.to("cuda")
-pipeline.transformer.set_attention_backend("flex")                            # <--- Set attention bakend to Flex
-pipeline.enable_model_cpu_offload()                                           # <--- Enable cpu offloading for single GPU inference
-pipeline.transformer.compile(mode="max-autotune-no-cudagraphs", dynamic=True) # <--- Compile with max-autotune-no-cudagraphs
-
-# Generate video
-prompt = "A cat and a dog baking a cake together in a kitchen."
-negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
-
-output = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=768,
-    width=1024,
-    num_frames=121,  # ~5 seconds at 24fps
-    num_inference_steps=50,
-    guidance_scale=5.0,
-).frames[0]
-
-export_to_video(output, "output.mp4", fps=24, quality=9)
-```
-
-#### Lite
-```python
-import torch
-from diffusers import Kandinsky5T2VPipeline
-from diffusers.utils import export_to_video
-
-# Load the pipeline
-model_id = "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers"
-pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-pipe = pipe.to("cuda")
-
-# Generate video
-prompt = "A cat and a dog baking a cake together in a kitchen."
-negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
-
-output = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=512,
-    width=768,
-    num_frames=121,  # ~5 seconds at 24fps
-    num_inference_steps=50,
-    guidance_scale=5.0,
-).frames[0]
-
-export_to_video(output, "output.mp4", fps=24, quality=9)
-```
-
-### 10 second Models
-**⚠️ Warning!** all 10 second models should be used with Flex attention and max-autotune-no-cudagraphs compilation:
-
-```python
-pipe = Kandinsky5T2VPipeline.from_pretrained(
-    "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-10s-Diffusers", 
-    torch_dtype=torch.bfloat16
-)
-pipe = pipe.to("cuda")
-
-pipe.transformer.set_attention_backend(
-    "flex"
-)                                       # <--- Set attention bakend to Flex
-pipe.transformer.compile(
-    mode="max-autotune-no-cudagraphs", 
-    dynamic=True
-)                                       # <--- Compile with max-autotune-no-cudagraphs
-
-prompt = "A cat and a dog baking a cake together in a kitchen."
-negative_prompt = "Static, 2D cartoon, cartoon, 2d animation, paintings, images, worst quality, low quality, ugly, deformed, walking backwards"
-
-output = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=512,
-    width=768,
-    num_frames=241,
-    num_inference_steps=50,
-    guidance_scale=5.0,
-).frames[0]
-
-export_to_video(output, "output.mp4", fps=24, quality=9)
-```
-
-### Diffusion Distilled model
-**⚠️ Warning!** all nocfg and diffusion distilled models should be infered wothout CFG (```guidance_scale=1.0```):
-
-```python
-model_id = "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
-pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-pipe = pipe.to("cuda")
-
-output = pipe(
-    prompt="A beautiful sunset over mountains",
-    num_inference_steps=16,  # <--- Model is distilled in 16 steps
-    guidance_scale=1.0,      # <--- no CFG
-).frames[0]
-
-export_to_video(output, "output.mp4", fps=24, quality=9)
-```
-
-
-### Basic Image-to-Video Generation
-**⚠️ Warning!** all Pro models should be infered with pipeline.enable_model_cpu_offload()  
-```python
-import torch
-from diffusers import Kandinsky5T2VPipeline
-from diffusers.utils import export_to_video
-
-# Load the pipeline
-model_id = "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
-pipe = Kandinsky5T2VPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-
-pipe = pipe.to("cuda")
-pipeline.transformer.set_attention_backend("flex")                            # <--- Set attention bakend to Flex
-pipeline.enable_model_cpu_offload()                                           # <--- Enable cpu offloading for single GPU inference
-pipeline.transformer.compile(mode="max-autotune-no-cudagraphs", dynamic=True) # <--- Compile with max-autotune-no-cudagraphs
-
-# Generate video
-image = load_image(
-    "https://huggingface.co/kandinsky-community/kandinsky-3/resolve/main/assets/title.jpg?download=true"
-)
-height = 896
-width = 896
-image = image.resize((width, height))
-
-prompt = "An funny furry creture smiles happily and holds a sign that says 'Kandinsky'"
-negative_prompt = ""
-
-output = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=height,
-    width=width,
-    num_frames=121,  # ~5 seconds at 24fps
-    num_inference_steps=50,
-    guidance_scale=5.0,
-).frames[0]
-
-export_to_video(output, "output.mp4", fps=24, quality=9)
-```
-
-
-
-## Kandinsky 5.0 Pro Side-by-Side evaluation
-
-<table border="0" style="width: 200; text-align: left; margin-top: 20px;">
-  <tr>
-      <td>
-          <img width="200" alt="image" src="https://github.com/user-attachments/assets/73e5ff00-2735-40fd-8f01-767de9181918" />
-      </td>
-      <td>
-         <img width="200" alt="image" src="https://github.com/user-attachments/assets/f449a9e7-74b7-481d-82da-02723e396acd" />
-      </td>
-
-  <tr>
-      <td>
-          Comparison with Veo 3 
-      </td>
-      <td>
-          Comparison with Veo 3 fast
-      </td>
-  <tr>
-      <td>
-          <img width="200" alt="image" src="https://github.com/user-attachments/assets/a6902fb6-b5e8-4093-adad-aa4caab79c6d" />
-      </td>
-      <td>
-          <img width="200" alt="image" src="https://github.com/user-attachments/assets/09986015-3d07-4de8-b942-c145039b9b2d" />
-      </td>
-  <tr>
-      <td>
-          Comparison with Wan 2.2 A14B Text-to-Video mode
-      </td>
-      <td>
-          Comparison with Wan 2.2 A14B Image-to-Video mode
-      </td>
-
-</table>
-
-
-## Kandinsky 5.0 Lite Side-by-Side evaluation
-
-The evaluation is based on the expanded prompts from the [Movie Gen benchmark](https://github.com/facebookresearch/MovieGenBench), which are available in the expanded_prompt column of the benchmark/moviegen_bench.csv file.
-
-<table border="0" style="width: 400; text-align: left; margin-top: 20px;">
-  <tr>
-      <td>
-          <img src="https://github.com/kandinskylab/kandinsky-5/raw/main/assets/sbs/kandinsky_5_video_lite_vs_sora.jpg" width=400 >
-      </td>
-      <td>
-          <img src="https://github.com/kandinskylab/kandinsky-5/raw/main/assets/sbs/kandinsky_5_video_lite_vs_wan_2.1_14B.jpg" width=400 >
-      </td>
-  <tr>
-      <td>
-          <img src="https://github.com/kandinskylab/kandinsky-5/raw/main/assets/sbs/kandinsky_5_video_lite_vs_wan_2.2_5B.jpg" width=400 >
-      </td>
-      <td>
-          <img src="https://github.com/kandinskylab/kandinsky-5/raw/main/assets/sbs/kandinsky_5_video_lite_vs_wan_2.2_A14B.jpg" width=400 >
-      </td>
-  <tr>
-      <td>
-          <img src="https://github.com/kandinskylab/kandinsky-5/raw/main/assets/sbs/kandinsky_5_video_lite_vs_wan_2.1_1.3B.jpg" width=400 >
-      </td>
-
-</table>
-
-
-
-
-## Kandinsky 5.0 Lite Distill Side-by-Side evaluation
-
-<table border="0" style="width: 400; text-align: left; margin-top: 20px;">
-  <tr>
-      <td>
-          <img src="https://github.com/kandinskylab/kandinsky-5/raw/main/assets/sbs/kandinsky_5_video_lite_5s_vs_kandinsky_5_video_lite_distill_5s.jpg" width=400 >
-      </td>
-      <td>
-          <img src="https://github.com/kandinskylab/kandinsky-5/raw/main/assets/sbs/kandinsky_5_video_lite_10s_vs_kandinsky_5_video_lite_distill_10s.jpg" width=400 >
-      </td>
-
-</table>
-
-## Kandinsky5T2VPipeline
-
-[[autodoc]] Kandinsky5T2VPipeline
-    - all
-    - __call__
-
-## Kandinsky5I2VPipeline
-
-[[autodoc]] Kandinsky5I2VPipeline
-    - all
-    - __call__
-
-
-## Citation
-```bibtex
-@misc{kandinsky2025,
-    author = {Alexander Belykh and Alexander Varlamov and Alexey Letunovskiy and Anastasia Aliaskina and Anastasia Maltseva and Anastasiia Kargapoltseva and Andrey Shutkin and Anna Averchenkova and Anna Dmitrienko and Bulat Akhmatov and Denis Dimitrov and Denis Koposov and Denis Parkhomenko and Dmitrii and Ilya Vasiliev and Ivan Kirillov and Julia Agafonova and Kirill Chernyshev and Kormilitsyn Semen and Lev Novitskiy and Maria Kovaleva and Mikhail Mamaev and Mikhailov and Nikita Kiselev and Nikita Osterov and Nikolai Gerasimenko and Nikolai Vaulin and Olga Kim and Olga Vdovchenko and Polina Gavrilova and Polina Mikhailova and Tatiana Nikulina and Viacheslav Vasilev and Vladimir Arkhipkin and Vladimir Korviakov and Vladimir Polovnikov and Yury Kolabushin},
-    title = {Kandinsky 5.0: A family of diffusion models for Video & Image generation},
-    howpublished = {\url{https://github.com/kandinskylab/Kandinsky-5}},
-    year = 2025
-}
-```

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

@@ -254,8 +254,8 @@ export_to_video(video, "output.mp4", fps=24)
   pipeline.vae.enable_tiling()
 
   def round_to_nearest_resolution_acceptable_by_vae(height, width):
-      height = height - (height % pipeline.vae_spatial_compression_ratio)
+      height = height - (height % pipeline.vae_temporal_compression_ratio)
-      width = width - (width % pipeline.vae_spatial_compression_ratio)
+      width = width - (width % pipeline.vae_temporal_compression_ratio)
       return height, width
 
   prompt = """
@@ -325,95 +325,6 @@ export_to_video(video, "output.mp4", fps=24)
 
   </details>
 
-- LTX-Video 0.9.8 distilled model is similar to the 0.9.7 variant. It is guidance and timestep-distilled, and similar inference code can be used as above. An improvement of this version is that it supports generating very long videos. Additionally, it supports using tone mapping to improve the quality of the generated video using the `tone_map_compression_ratio` parameter. The default value of `0.6` is recommended.
-
-  <details>
-  <summary>Show example code</summary>
-  
-  ```python
-  import torch
-  from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline
-  from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition
-  from diffusers.pipelines.ltx.modeling_latent_upsampler import LTXLatentUpsamplerModel
-  from diffusers.utils import export_to_video, load_video
-
-  pipeline = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.8-13B-distilled", torch_dtype=torch.bfloat16)
-  # TODO: Update the checkpoint here once updated in LTX org
-  upsampler = LTXLatentUpsamplerModel.from_pretrained("a-r-r-o-w/LTX-0.9.8-Latent-Upsampler", torch_dtype=torch.bfloat16)
-  pipe_upsample = LTXLatentUpsamplePipeline(vae=pipeline.vae, latent_upsampler=upsampler).to(torch.bfloat16)
-  pipeline.to("cuda")
-  pipe_upsample.to("cuda")
-  pipeline.vae.enable_tiling()
-
-  def round_to_nearest_resolution_acceptable_by_vae(height, width):
-      height = height - (height % pipeline.vae_spatial_compression_ratio)
-      width = width - (width % pipeline.vae_spatial_compression_ratio)
-      return height, width
-
-  prompt = """The camera pans over a snow-covered mountain range, revealing a vast expanse of snow-capped peaks and valleys.The mountains are covered in a thick layer of snow, with some areas appearing almost white while others have a slightly darker, almost grayish hue. The peaks are jagged and irregular, with some rising sharply into the sky while others are more rounded. The valleys are deep and narrow, with steep slopes that are also covered in snow. The trees in the foreground are mostly bare, with only a few leaves remaining on their branches. The sky is overcast, with thick clouds obscuring the sun. The overall impression is one of peace and tranquility, with the snow-covered mountains standing as a testament to the power and beauty of nature."""
-  # prompt = """A woman walks away from a white Jeep parked on a city street at night, then ascends a staircase and knocks on a door. The woman, wearing a dark jacket and jeans, walks away from the Jeep parked on the left side of the street, her back to the camera; she walks at a steady pace, her arms swinging slightly by her sides; the street is dimly lit, with streetlights casting pools of light on the wet pavement; a man in a dark jacket and jeans walks past the Jeep in the opposite direction; the camera follows the woman from behind as she walks up a set of stairs towards a building with a green door; she reaches the top of the stairs and turns left, continuing to walk towards the building; she reaches the door and knocks on it with her right hand; the camera remains stationary, focused on the doorway; the scene is captured in real-life footage."""
-  negative_prompt = "bright colors, symbols, graffiti, watermarks, worst quality, inconsistent motion, blurry, jittery, distorted"
-  expected_height, expected_width = 480, 832
-  downscale_factor = 2 / 3
-  # num_frames = 161
-  num_frames = 361
-
-  # 1. Generate video at smaller resolution
-  downscaled_height, downscaled_width = int(expected_height * downscale_factor), int(expected_width * downscale_factor)
-  downscaled_height, downscaled_width = round_to_nearest_resolution_acceptable_by_vae(downscaled_height, downscaled_width)
-  latents = pipeline(
-      prompt=prompt,
-      negative_prompt=negative_prompt,
-      width=downscaled_width,
-      height=downscaled_height,
-      num_frames=num_frames,
-      timesteps=[1000, 993, 987, 981, 975, 909, 725, 0.03],
-      decode_timestep=0.05,
-      decode_noise_scale=0.025,
-      image_cond_noise_scale=0.0,
-      guidance_scale=1.0,
-      guidance_rescale=0.7,
-      generator=torch.Generator().manual_seed(0),
-      output_type="latent",
-  ).frames
-
-  # 2. Upscale generated video using latent upsampler with fewer inference steps
-  # The available latent upsampler upscales the height/width by 2x
-  upscaled_height, upscaled_width = downscaled_height * 2, downscaled_width * 2
-  upscaled_latents = pipe_upsample(
-      latents=latents,
-      adain_factor=1.0,
-      tone_map_compression_ratio=0.6,
-      output_type="latent"
-  ).frames
-
-  # 3. Denoise the upscaled video with few steps to improve texture (optional, but recommended)
-  video = pipeline(
-      prompt=prompt,
-      negative_prompt=negative_prompt,
-      width=upscaled_width,
-      height=upscaled_height,
-      num_frames=num_frames,
-      denoise_strength=0.999,  # Effectively, 4 inference steps out of 5
-      timesteps=[1000, 909, 725, 421, 0],
-      latents=upscaled_latents,
-      decode_timestep=0.05,
-      decode_noise_scale=0.025,
-      image_cond_noise_scale=0.0,
-      guidance_scale=1.0,
-      guidance_rescale=0.7,
-      generator=torch.Generator().manual_seed(0),
-      output_type="pil",
-  ).frames[0]
-
-  # 4. Downscale the video to the expected resolution
-  video = [frame.resize((expected_width, expected_height)) for frame in video]
-
-  export_to_video(video, "output.mp4", fps=24)
-  ```
-
-  </details>
-
 - LTX-Video supports LoRAs with [`~loaders.LTXVideoLoraLoaderMixin.load_lora_weights`].
 
   <details>

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

@@ -75,7 +75,7 @@ The following is a summary of the recommended checkpoints, all of which produce
 | [prs-eth/marigold-depth-v1-1](https://huggingface.co/prs-eth/marigold-depth-v1-1)                   | Depth        | Affine-invariant depth prediction assigns each pixel a value between 0 (near plane) and 1 (far plane), with both planes determined by the model during inference.                    |
 | [prs-eth/marigold-normals-v0-1](https://huggingface.co/prs-eth/marigold-normals-v0-1)               | Normals      | The surface normals predictions are unit-length 3D vectors in the screen space camera, with values in the range from -1 to 1.                                                        |
 | [prs-eth/marigold-iid-appearance-v1-1](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1) | Intrinsics   | InteriorVerse decomposition is comprised of Albedo and two BRDF material properties: Roughness and Metallicity.                                                                      | 
-| [prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1)     | Intrinsics   | HyperSim decomposition of an image $I$ is comprised of Albedo $A$, Diffuse shading $S$, and Non-diffuse residual $R$: $I = A*S+R$. |
+| [prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1)     | Intrinsics   | HyperSim decomposition of an image &nbsp\\(I\\)&nbsp is comprised of Albedo &nbsp\\(A\\), Diffuse shading &nbsp\\(S\\), and Non-diffuse residual &nbsp\\(R\\): &nbsp\\(I = A*S+R\\). |
 
 > [!TIP]
 > Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff 

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

@@ -32,7 +32,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 | [Attend-and-Excite](attend_and_excite) | text2image |
 | [AudioLDM](audioldm) | text2audio |
 | [AudioLDM2](audioldm2) | text2audio |
-| [AuraFlow](aura_flow) | text2image |
+| [AuraFlow](auraflow) | text2image |
 | [BLIP Diffusion](blip_diffusion) | text2image |
 | [Bria 3.2](bria_3_2) | text2image |
 | [CogVideoX](cogvideox) | text2video |

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

@@ -1,50 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Ovis-Image
-
-![concepts](https://github.com/AIDC-AI/Ovis-Image/blob/main/docs/imgs/ovis_image_case.png)
-
-Ovis-Image is a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational constraints.
-
-[Ovis-Image Technical Report](https://arxiv.org/abs/2511.22982) from Alibaba Group, by Guo-Hua Wang, Liangfu Cao, Tianyu Cui, Minghao Fu, Xiaohao Chen, Pengxin Zhan, Jianshan Zhao, Lan Li, Bowen Fu, Jiaqi Liu, Qing-Guo Chen.
-
-The abstract from the paper is:
-
-*We introduce Ovis-Image, a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational constraints. Built upon our previous Ovis-U1 framework, Ovis-Image integrates a diffusion-based visual decoder with the stronger Ovis 2.5 multimodal backbone, leveraging a text-centric training pipeline that combines large-scale pre-training with carefully tailored post-training refinements. Despite its compact architecture, Ovis-Image achieves text rendering performance on par with significantly larger open models such as Qwen-Image and approaches closed-source systems like Seedream and GPT4o. Crucially, the model remains deployable on a single high-end GPU with moderate memory, narrowing the gap between frontier-level text rendering and practical deployment. Our results indicate that combining a strong multimodal backbone with a carefully designed, text-focused training recipe is sufficient to achieve reliable bilingual text rendering without resorting to oversized or proprietary models.*
-
-**Highlights**: 
-
-*   **Strong text rendering at a compact 7B scale**: Ovis-Image is a 7B text-to-image model that delivers text rendering quality comparable to much larger 20B-class systems such as Qwen-Image and competitive with leading closed-source models like GPT4o in text-centric scenarios, while remaining small enough to run on widely accessible hardware.
-*   **High fidelity on text-heavy, layout-sensitive prompts**: The model excels on prompts that demand tight alignment between linguistic content and rendered typography (e.g., posters, banners, logos, UI mockups, infographics), producing legible, correctly spelled, and semantically consistent text across diverse fonts, sizes, and aspect ratios without compromising overall visual quality.
-*   **Efficiency and deployability**: With its 7B parameter budget and streamlined architecture, Ovis-Image fits on a single high-end GPU with moderate memory, supports low-latency interactive use, and scales to batch production serving, bringing near–frontier text rendering to applications where tens-of-billions–parameter models are impractical.
-
-
-This pipeline was contributed by Ovis-Image Team. The original codebase can be found [here](https://github.com/AIDC-AI/Ovis-Image).
-
-Available models:
-
-| Model | Recommended dtype |
-|:-----:|:-----------------:|
-| [`AIDC-AI/Ovis-Image-7B`](https://huggingface.co/AIDC-AI/Ovis-Image-7B) | `torch.bfloat16` |
-
-Refer to [this](https://huggingface.co/collections/AIDC-AI/ovis-image) collection for more information.
-
-## OvisImagePipeline
-
-[[autodoc]] OvisImagePipeline
-	- all
-	- __call__
-
-## OvisImagePipelineOutput
-
-[[autodoc]] pipelines.ovis_image.pipeline_output.OvisImagePipelineOutput

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

@@ -1,131 +0,0 @@
-<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License. -->
-
-# PRX
-
-
-PRX generates high-quality images from text using a simplified MMDIT architecture where text tokens don't update through transformer blocks. It employs flow matching with discrete scheduling for efficient sampling and uses Google's T5Gemma-2B-2B-UL2 model for multi-language text encoding. The ~1.3B parameter transformer delivers fast inference without sacrificing quality. You can choose between Flux VAE (8x compression, 16 latent channels) for balanced quality and speed or DC-AE (32x compression, 32 latent channels) for latent compression and faster processing.
-
-## Available models
-
-PRX offers multiple variants with different VAE configurations, each optimized for specific resolutions. Base models excel with detailed prompts, capturing complex compositions and subtle details. Fine-tuned models trained on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) improve aesthetic quality, especially with simpler prompts.
-
-
-| Model | Resolution | Fine-tuned | Distilled | Description | Suggested prompts | Suggested parameters | Recommended dtype |
-|:-----:|:-----------------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|
-| [`Photoroom/prx-256-t2i`](https://huggingface.co/Photoroom/prx-256-t2i)| 256 | No | No | Base model pre-trained at 256 with Flux VAE|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-256-t2i-sft`](https://huggingface.co/Photoroom/prx-256-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i`](https://huggingface.co/Photoroom/prx-512-t2i)| 512 | No | No | Base model pre-trained at 512 with Flux VAE |Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with Flux VAE | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-sft`](https://huggingface.co/Photoroom/prx-512-t2i-sft) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-dc-ae`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae)| 512 | No | No | Base model pre-trained at 512 with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae)|Works best with detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-dc-ae-sft`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft)| 512 | Yes | No | Fine-tuned on the [Alchemist dataset](https://huggingface.co/datasets/yandex/alchemist) dataset with [Deep Compression Autoencoder (DC-AE)](https://hanlab.mit.edu/projects/dc-ae) | Can handle less detailed prompts in natural language|28 steps, cfg=5.0| `torch.bfloat16` |
-| [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled)| 512 | Yes | Yes | 8-step distilled model from [`Photoroom/prx-512-t2i-dc-ae-sft-distilled`](https://huggingface.co/Photoroom/prx-512-t2i-dc-ae-sft-distilled) | Can handle less detailed prompts in natural language|8 steps, cfg=1.0| `torch.bfloat16` |s
-
-Refer to [this](https://huggingface.co/collections/Photoroom/prx-models-68e66254c202ebfab99ad38e) collection for more information.
-
-## Loading the pipeline
-
-Load the pipeline with [`~DiffusionPipeline.from_pretrained`].
-
-```py
-from diffusers.pipelines.prx import PRXPipeline
-
-# Load pipeline - VAE and text encoder will be loaded from HuggingFace
-pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-prompt = "A front-facing portrait of a lion the golden savanna at sunset."
-image = pipe(prompt, num_inference_steps=28, guidance_scale=5.0).images[0]
-image.save("prx_output.png")
-```
-
-### Manual Component Loading
-
-Load components individually to customize the pipeline for instance to use quantized models.
-
-```py
-import torch
-from diffusers.pipelines.prx import PRXPipeline
-from diffusers.models import AutoencoderKL, AutoencoderDC
-from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-from transformers import T5GemmaModel, GemmaTokenizerFast
-from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
-from transformers import BitsAndBytesConfig as BitsAndBytesConfig
-
-quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
-# Load transformer
-transformer = PRXTransformer2DModel.from_pretrained(
-    "checkpoints/prx-512-t2i-sft",
-    subfolder="transformer",
-    quantization_config=quant_config,
-    torch_dtype=torch.bfloat16,
-)
-
-# Load scheduler
-scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
-    "checkpoints/prx-512-t2i-sft", subfolder="scheduler"
-)
-
-# Load T5Gemma text encoder
-t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2",
-                                            quantization_config=quant_config,
-                                            torch_dtype=torch.bfloat16)
-text_encoder = t5gemma_model.encoder.to(dtype=torch.bfloat16)
-tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2")
-tokenizer.model_max_length = 256
-
-# Load VAE - choose either Flux VAE or DC-AE
-# Flux VAE
-vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev",
-                                    subfolder="vae",
-                                    quantization_config=quant_config,
-                                    torch_dtype=torch.bfloat16)
-
-pipe = PRXPipeline(
-    transformer=transformer,
-    scheduler=scheduler,
-    text_encoder=text_encoder,
-    tokenizer=tokenizer,
-    vae=vae
-)
-pipe.to("cuda")
-```
-
-
-## Memory Optimization
-
-For memory-constrained environments:
-
-```py
-import torch
-from diffusers.pipelines.prx import PRXPipeline
-
-pipe = PRXPipeline.from_pretrained("Photoroom/prx-512-t2i-sft", torch_dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload()  # Offload components to CPU when not in use
-
-# Or use sequential CPU offload for even lower memory
-pipe.enable_sequential_cpu_offload()
-```
-
-## PRXPipeline
-
-[[autodoc]] PRXPipeline
-  - all
-  - __call__
-
-## PRXPipelineOutput
-
-[[autodoc]] pipelines.prx.pipeline_output.PRXPipelineOutput

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

@@ -109,7 +109,7 @@ image_1 = load_image("https://huggingface.co/datasets/huggingface/documentation-
 image_2 = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peng.png")
 image = pipe(
     image=[image_1, image_2], 
-    prompt='''put the penguin and the cat at a game show called "Qwen Edit Plus Games"''', 
+    prompt="put the penguin and the cat at a game show called "Qwen Edit Plus Games"", 
     num_inference_steps=50
 ).images[0]
 ```

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

@@ -24,6 +24,9 @@ The abstract from the paper is:
 
 *This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4. We introduce three key innovations: (1) We propose a training-free approach that transforms a pre-trained flow-matching model for continuous-time consistency distillation (sCM), eliminating costly training from scratch and achieving high training efficiency. Our hybrid distillation strategy combines sCM with latent adversarial distillation (LADD): sCM ensures alignment with the teacher model, while LADD enhances single-step generation fidelity. (2) SANA-Sprint is a unified step-adaptive model that achieves high-quality generation in 1-4 steps, eliminating step-specific training and improving efficiency. (3) We integrate ControlNet with SANA-Sprint for real-time interactive image generation, enabling instant visual feedback for user interaction. SANA-Sprint establishes a new Pareto frontier in speed-quality tradeoffs, achieving state-of-the-art performance with 7.59 FID and 0.74 GenEval in only 1 step — outperforming FLUX-schnell (7.94 FID / 0.71 GenEval) while being 10× faster (0.1s vs 1.1s on H100). It also achieves 0.1s (T2I) and 0.25s (ControlNet) latency for 1024×1024 images on H100, and 0.31s (T2I) on an RTX 4090, showcasing its exceptional efficiency and potential for AI-powered consumer applications (AIPC). Code and pre-trained models will be open-sourced.*
 
+> [!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 [lawrence-cj](https://github.com/lawrence-cj), [shuchen Xue](https://github.com/scxue) and [Enze Xie](https://github.com/xieenze). The original codebase can be found [here](https://github.com/NVlabs/Sana). The original weights can be found under [hf.co/Efficient-Large-Model](https://huggingface.co/Efficient-Large-Model/).
 
 Available models:

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

@@ -1,189 +0,0 @@
-<!-- Copyright 2025 The SANA-Video Authors and HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License. -->
-
-# Sana-Video
-
-<div class="flex flex-wrap space-x-1">
-  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
-  <img alt="MPS" src="https://img.shields.io/badge/MPS-000000?style=flat&logo=apple&logoColor=white%22">
-</div>
-
-[SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer](https://huggingface.co/papers/2509.24695) from NVIDIA and MIT HAN Lab, by Junsong Chen, Yuyang Zhao, Jincheng Yu, Ruihang Chu, Junyu Chen, Shuai Yang, Xianbang Wang, Yicheng Pan, Daquan Zhou, Huan Ling, Haozhe Liu, Hongwei Yi, Hao Zhang, Muyang Li, Yukang Chen, Han Cai, Sanja Fidler, Ping Luo, Song Han, Enze Xie.
-
-The abstract from the paper is:
-
-*We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720x1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Two core designs ensure our efficient, effective and long video generation: (1) Linear DiT: We leverage linear attention as the core operation, which is more efficient than vanilla attention given the large number of tokens processed in video generation. (2) Constant-Memory KV cache for Block Linear Attention: we design block-wise autoregressive approach for long video generation by employing a constant-memory state, derived from the cumulative properties of linear attention. This KV cache provides the Linear DiT with global context at a fixed memory cost, eliminating the need for a traditional KV cache and enabling efficient, minute-long video generation. In addition, we explore effective data filters and model training strategies, narrowing the training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of MovieGen. Given its low cost, SANA-Video achieves competitive performance compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover, SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x speedup). In summary, SANA-Video enables low-cost, high-quality video generation. [this https URL](https://github.com/NVlabs/SANA).*
-
-This pipeline was contributed by SANA Team. The original codebase can be found [here](https://github.com/NVlabs/Sana). The original weights can be found under [hf.co/Efficient-Large-Model](https://hf.co/collections/Efficient-Large-Model/sana-video).
-
-Available models:
-
-| Model | Recommended dtype |
-|:-----:|:-----------------:|
-| [`Efficient-Large-Model/SANA-Video_2B_480p_diffusers`](https://huggingface.co/Efficient-Large-Model/ANA-Video_2B_480p_diffusers) | `torch.bfloat16` |
-
-Refer to [this](https://huggingface.co/collections/Efficient-Large-Model/sana-video) collection for more information.
-
-Note: The recommended dtype mentioned is for the transformer weights. The text encoder and VAE weights must stay in `torch.bfloat16` or `torch.float32` for the model to work correctly. Please refer to the inference example below to see how to load the model with the recommended dtype. 
-
-
-## Generation Pipelines
-
-<hfoptions id="generation pipelines">`
-<hfoption id="Text-to-Video">
-
-The example below demonstrates how to use the text-to-video pipeline to generate a video using a text description.
-
-```python
-pipe = SanaVideoPipeline.from_pretrained(
-    "Efficient-Large-Model/SANA-Video_2B_480p_diffusers", 
-    torch_dtype=torch.bfloat16,
-)
-pipe.text_encoder.to(torch.bfloat16)
-pipe.vae.to(torch.float32)
-pipe.to("cuda")
-
-prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
-negative_prompt = "A chaotic sequence with misshapen, deformed limbs in heavy motion blur, sudden disappearance, jump cuts, jerky movements, rapid shot changes, frames out of sync, inconsistent character shapes, temporal artifacts, jitter, and ghosting effects, creating a disorienting visual experience."
-motion_scale = 30
-motion_prompt = f" motion score: {motion_scale}."
-prompt = prompt + motion_prompt
-
-video = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=480,
-    width=832,
-    frames=81,
-    guidance_scale=6,
-    num_inference_steps=50,
-    generator=torch.Generator(device="cuda").manual_seed(0),
-).frames[0]
-
-export_to_video(video, "sana_video.mp4", fps=16)
-```
-
-</hfoption>
-<hfoption id="Image-to-Video">
-
-The example below demonstrates how to use the image-to-video pipeline to generate a video using a text description and a starting frame.
-
-```python
-pipe = SanaImageToVideoPipeline.from_pretrained(
-    "Efficient-Large-Model/SANA-Video_2B_480p_diffusers",
-    torch_dtype=torch.bfloat16,
-)
-pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config, flow_shift=8.0)
-pipe.vae.to(torch.float32)
-pipe.text_encoder.to(torch.bfloat16)
-pipe.to("cuda")
-
-image = load_image("https://raw.githubusercontent.com/NVlabs/Sana/refs/heads/main/asset/samples/i2v-1.png")
-prompt = "A woman stands against a stunning sunset backdrop, her long, wavy brown hair gently blowing in the breeze. She wears a sleeveless, light-colored blouse with a deep V-neckline, which accentuates her graceful posture. The warm hues of the setting sun cast a golden glow across her face and hair, creating a serene and ethereal atmosphere. The background features a blurred landscape with soft, rolling hills and scattered clouds, adding depth to the scene. The camera remains steady, capturing the tranquil moment from a medium close-up angle."
-negative_prompt = "A chaotic sequence with misshapen, deformed limbs in heavy motion blur, sudden disappearance, jump cuts, jerky movements, rapid shot changes, frames out of sync, inconsistent character shapes, temporal artifacts, jitter, and ghosting effects, creating a disorienting visual experience."
-motion_scale = 30
-motion_prompt = f" motion score: {motion_scale}."
-prompt = prompt + motion_prompt
-
-motion_scale = 30.0
-
-video = pipe(
-    image=image,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=480,
-    width=832,
-    frames=81,
-    guidance_scale=6,
-    num_inference_steps=50,
-    generator=torch.Generator(device="cuda").manual_seed(0),
-).frames[0]
-
-export_to_video(video, "sana-i2v.mp4", fps=16)
-```
-
-</hfoption>
-</hfoptions>
-
-
-## Quantization
-
-Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
-
-Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`SanaVideoPipeline`] for inference with bitsandbytes.
-
-```py
-import torch
-from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, SanaVideoTransformer3DModel, SanaVideoPipeline
-from transformers import BitsAndBytesConfig as BitsAndBytesConfig, AutoModel
-
-quant_config = BitsAndBytesConfig(load_in_8bit=True)
-text_encoder_8bit = AutoModel.from_pretrained(
-    "Efficient-Large-Model/SANA-Video_2B_480p_diffusers",
-    subfolder="text_encoder",
-    quantization_config=quant_config,
-    torch_dtype=torch.float16,
-)
-
-quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
-transformer_8bit = SanaVideoTransformer3DModel.from_pretrained(
-    "Efficient-Large-Model/SANA-Video_2B_480p_diffusers",
-    subfolder="transformer",
-    quantization_config=quant_config,
-    torch_dtype=torch.float16,
-)
-
-pipeline = SanaVideoPipeline.from_pretrained(
-    "Efficient-Large-Model/SANA-Video_2B_480p_diffusers",
-    text_encoder=text_encoder_8bit,
-    transformer=transformer_8bit,
-    torch_dtype=torch.float16,
-    device_map="balanced",
-)
-
-model_score = 30
-prompt = "Evening, backlight, side lighting, soft light, high contrast, mid-shot, centered composition, clean solo shot, warm color. A young Caucasian man stands in a forest, golden light glimmers on his hair as sunlight filters through the leaves. He wears a light shirt, wind gently blowing his hair and collar, light dances across his face with his movements. The background is blurred, with dappled light and soft tree shadows in the distance. The camera focuses on his lifted gaze, clear and emotional."
-negative_prompt = "A chaotic sequence with misshapen, deformed limbs in heavy motion blur, sudden disappearance, jump cuts, jerky movements, rapid shot changes, frames out of sync, inconsistent character shapes, temporal artifacts, jitter, and ghosting effects, creating a disorienting visual experience."
-motion_prompt = f" motion score: {model_score}."
-prompt = prompt + motion_prompt
-
-output = pipeline(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=480,
-    width=832,
-    num_frames=81,
-    guidance_scale=6.0,
-    num_inference_steps=50
-).frames[0]
-export_to_video(output, "sana-video-output.mp4", fps=16)
-```
-
-## SanaVideoPipeline
-
-[[autodoc]] SanaVideoPipeline
-  - all
-  - __call__
-
-
-## SanaImageToVideoPipeline
-
-[[autodoc]] SanaImageToVideoPipeline
-  - all
-  - __call__
-
-
-## SanaVideoPipelineOutput
-
-[[autodoc]] pipelines.sana_video.pipeline_sana_video.SanaVideoPipelineOutput

docs/source/en/api/pipelines/stable_diffusion/depth2img.md

@@ -21,7 +21,7 @@ The Stable Diffusion model can also infer depth based on an image using [MiDaS](
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionDepth2ImgPipeline
 

docs/source/en/api/pipelines/stable_diffusion/inpaint.md

@@ -21,14 +21,14 @@ The Stable Diffusion model can also be applied to inpainting which lets you edit
 ## Tips
 
 It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
-as [stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting). Default
+as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default
 text-to-image Stable Diffusion checkpoints, such as
 [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) are also compatible but they might be less performant.
 
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionInpaintPipeline
 

docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md

@@ -17,7 +17,7 @@ The Stable Diffusion latent upscaler model was created by [Katherine Crowson](ht
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionLatentUpscalePipeline
 

docs/source/en/api/pipelines/stable_diffusion/overview.md

@@ -22,7 +22,7 @@ Stable Diffusion is trained on 512x512 images from a subset of the LAION-5B data
 
 For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI [announcement](https://stability.ai/blog/stable-diffusion-announcement) and our own [blog post](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) for more technical details.
 
-You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case!
+You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case!
 
 The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo:
 
@@ -64,7 +64,7 @@ The table below summarizes the available Stable Diffusion pipelines, their suppo
             <a href="./inpaint">StableDiffusionInpaint</a>
             </td>
             <td class="px-4 py-2 text-gray-700">inpainting</td>
-            <td class="px-4 py-2"><a href="https://huggingface.co/spaces/stable-diffusion-v1-5/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
+            <td class="px-4 py-2"><a href="https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
             </td>
         </tr>
         <tr>

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md

@@ -36,7 +36,7 @@ Here are some examples for how to use Stable Diffusion 2 for each task:
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## Text-to-image
 

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md

@@ -271,7 +271,7 @@ Check out the full script [here](https://gist.github.com/sayakpaul/508d89d7aad4f
 
 Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
 
-Refer to the [Quantization](../../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`StableDiffusion3Pipeline`] for inference with bitsandbytes.
+Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`StableDiffusion3Pipeline`] for inference with bitsandbytes.
 
 ```py
 import torch

docs/source/en/api/pipelines/stable_diffusion/svd.md

@@ -29,7 +29,7 @@ The abstract from the paper is:
 
 Video generation is memory-intensive and one way to reduce your memory usage is to set `enable_forward_chunking` on the pipeline's UNet so you don't run the entire feedforward layer at once. Breaking it up into chunks in a loop is more efficient.
 
-Check out the [Text or image-to-video](../../../using-diffusers/text-img2vid) guide for more details about how certain parameters can affect video generation and how to optimize inference by reducing memory usage.
+Check out the [Text or image-to-video](text-img2vid) guide for more details about how certain parameters can affect video generation and how to optimize inference by reducing memory usage.
 
 ## StableVideoDiffusionPipeline
 

docs/source/en/api/pipelines/stable_diffusion/text2img.md

@@ -25,7 +25,7 @@ The abstract from the paper is:
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionPipeline
 

docs/source/en/api/pipelines/stable_diffusion/upscale.md

@@ -21,7 +21,7 @@ The Stable Diffusion upscaler diffusion model was created by the researchers and
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionUpscalePipeline
 

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

@@ -172,7 +172,7 @@ Here are some sample outputs:
 
 Video generation is memory-intensive and one way to reduce your memory usage is to set `enable_forward_chunking` on the pipeline's UNet so you don't run the entire feedforward layer at once. Breaking it up into chunks in a loop is more efficient.
 
-Check out the [Text or image-to-video](../../using-diffusers/text-img2vid) guide for more details about how certain parameters can affect video generation and how to optimize inference by reducing memory usage.
+Check out the [Text or image-to-video](text-img2vid) guide for more details about how certain parameters can affect video generation and how to optimize inference by reducing memory usage.
 
 > [!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.

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

@@ -40,7 +40,6 @@ The following Wan models are supported in Diffusers:
 - [Wan 2.2 T2V 14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers)
 - [Wan 2.2 I2V 14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers)
 - [Wan 2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers)
-- [Wan 2.2 Animate 14B](https://huggingface.co/Wan-AI/Wan2.2-Animate-14B-Diffusers)
 
 > [!TIP]
 > Click on the Wan models in the right sidebar for more examples of video generation.
@@ -96,15 +95,15 @@ pipeline = WanPipeline.from_pretrained(
 pipeline.to("cuda")
 
 prompt = """
-The camera rushes from far to near in a low-angle shot,
+The camera rushes from far to near in a low-angle shot, 
-revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
+revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in 
-for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
+for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. 
-Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
+Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic 
 shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
 """
 negative_prompt = """
-Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
+Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, 
-low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
+low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, 
 misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
 """
 
@@ -151,15 +150,15 @@ pipeline.transformer = torch.compile(
 )
 
 prompt = """
-The camera rushes from far to near in a low-angle shot,
+The camera rushes from far to near in a low-angle shot, 
-revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
+revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in 
-for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
+for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. 
-Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
+Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic 
 shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
 """
 negative_prompt = """
-Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
+Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, 
-low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
+low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, 
 misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
 """
 
@@ -250,208 +249,6 @@ The code snippets available in [this](https://github.com/huggingface/diffusers/p
 
 The general rule of thumb to keep in mind when preparing inputs for the VACE pipeline is that the input images, or frames of a video that you want to use for conditioning, should have a corresponding mask that is black in color. The black mask signifies that the model will not generate new content for that area, and only use those parts for conditioning the generation process. For parts/frames that should be generated by the model, the mask should be white in color.
 
-</hfoption>
-</hfoptions>
-
-### Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
-
-[Wan-Animate](https://huggingface.co/papers/2509.14055) by the Wan Team.
-
-*We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental integration. Wan-Animate is built upon the Wan model. To adapt it for character animation tasks, we employ a modified input paradigm to differentiate between reference conditions and regions for generation. This design unifies multiple tasks into a common symbolic representation. We use spatially-aligned skeleton signals to replicate body motion and implicit facial features extracted from source images to reenact expressions, enabling the generation of character videos with high controllability and expressiveness. Furthermore, to enhance environmental integration during character replacement, we develop an auxiliary Relighting LoRA. This module preserves the character's appearance consistency while applying the appropriate environmental lighting and color tone. Experimental results demonstrate that Wan-Animate achieves state-of-the-art performance. We are committed to open-sourcing the model weights and its source code.*
-
-The project page: https://humanaigc.github.io/wan-animate
-
-This model was mostly contributed by [M. Tolga Cangöz](https://github.com/tolgacangoz).
-
-#### Usage
-
-The Wan-Animate pipeline supports two modes of operation:
-
-1. **Animation Mode** (default): Animates a character image based on motion and expression from reference videos
-2. **Replacement Mode**: Replaces a character in a background video with a new character while preserving the scene
-
-##### Prerequisites
-
-Before using the pipeline, you need to preprocess your reference video to extract:
-- **Pose video**: Contains skeletal keypoints representing body motion
-- **Face video**: Contains facial feature representations for expression control
-
-For replacement mode, you additionally need:
-- **Background video**: The original video containing the scene
-- **Mask video**: A mask indicating where to generate content (white) vs. preserve original (black)
-
-> [!NOTE]
-> Raw videos should not be used for inputs such as `pose_video`, which the pipeline expects to be preprocessed to extract the proper information. Preprocessing scripts to prepare these inputs are available in the [original Wan-Animate repository](https://github.com/Wan-Video/Wan2.2?tab=readme-ov-file#1-preprocessing). Integration of these preprocessing steps into Diffusers is planned for a future release.
-
-The example below demonstrates how to use the Wan-Animate pipeline:
-
-<hfoptions id="Animate usage">
-<hfoption id="Animation mode">
-
-```python
-import numpy as np
-import torch
-from diffusers import AutoencoderKLWan, WanAnimatePipeline
-from diffusers.utils import export_to_video, load_image, load_video
-
-model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-pipe = WanAnimatePipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-# Load character image and preprocessed videos
-image = load_image("path/to/character.jpg")
-pose_video = load_video("path/to/pose_video.mp4")  # Preprocessed skeletal keypoints
-face_video = load_video("path/to/face_video.mp4")  # Preprocessed facial features
-
-# Resize image to match VAE constraints
-def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
-    aspect_ratio = image.height / image.width
-    mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-    image = image.resize((width, height))
-    return image, height, width
-
-image, height, width = aspect_ratio_resize(image, pipe)
-
-prompt = "A person dancing energetically in a studio with dynamic lighting and professional camera work"
-negative_prompt = "blurry, low quality, distorted, deformed, static, poorly drawn"
-
-# Generate animated video
-output = pipe(
-    image=image,
-    pose_video=pose_video,
-    face_video=face_video,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=height,
-    width=width,
-    segment_frame_length=77,
-    guidance_scale=1.0,
-    mode="animate",  # Animation mode (default)
-).frames[0]
-export_to_video(output, "animated_character.mp4", fps=30)
-```
-
-</hfoption>
-<hfoption id="Replacement mode">
-
-```python
-import numpy as np
-import torch
-from diffusers import AutoencoderKLWan, WanAnimatePipeline
-from diffusers.utils import export_to_video, load_image, load_video
-
-model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-pipe = WanAnimatePipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-# Load all required inputs for replacement mode
-image = load_image("path/to/new_character.jpg")
-pose_video = load_video("path/to/pose_video.mp4")  # Preprocessed skeletal keypoints
-face_video = load_video("path/to/face_video.mp4")  # Preprocessed facial features
-background_video = load_video("path/to/background_video.mp4")  # Original scene
-mask_video = load_video("path/to/mask_video.mp4")  # Black: preserve, White: generate
-
-# Resize image to match video dimensions
-def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
-    aspect_ratio = image.height / image.width
-    mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-    image = image.resize((width, height))
-    return image, height, width
-
-image, height, width = aspect_ratio_resize(image, pipe)
-
-prompt = "A person seamlessly integrated into the scene with consistent lighting and environment"
-negative_prompt = "blurry, low quality, inconsistent lighting, floating, disconnected from scene"
-
-# Replace character in background video
-output = pipe(
-    image=image,
-    pose_video=pose_video,
-    face_video=face_video,
-    background_video=background_video,
-    mask_video=mask_video,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=height,
-    width=width,
-    segment_frame_lengths=77,
-    guidance_scale=1.0,
-    mode="replace",  # Replacement mode
-).frames[0]
-export_to_video(output, "character_replaced.mp4", fps=30)
-```
-
-</hfoption>
-<hfoption id="Advanced options">
-
-```python
-import numpy as np
-import torch
-from diffusers import AutoencoderKLWan, WanAnimatePipeline
-from diffusers.utils import export_to_video, load_image, load_video
-
-model_id = "Wan-AI/Wan2.2-Animate-14B-Diffusers"
-vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
-pipe = WanAnimatePipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-image = load_image("path/to/character.jpg")
-pose_video = load_video("path/to/pose_video.mp4")
-face_video = load_video("path/to/face_video.mp4")
-
-def aspect_ratio_resize(image, pipe, max_area=720 * 1280):
-    aspect_ratio = image.height / image.width
-    mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
-    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
-    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
-    image = image.resize((width, height))
-    return image, height, width
-
-image, height, width = aspect_ratio_resize(image, pipe)
-
-prompt = "A person dancing energetically in a studio"
-negative_prompt = "blurry, low quality"
-
-# Advanced: Use temporal guidance and custom callback
-def callback_fn(pipe, step_index, timestep, callback_kwargs):
-    # You can modify latents or other tensors here
-    print(f"Step {step_index}, Timestep {timestep}")
-    return callback_kwargs
-
-output = pipe(
-    image=image,
-    pose_video=pose_video,
-    face_video=face_video,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    height=height,
-    width=width,
-    segment_frame_length=77,
-    num_inference_steps=50,
-    guidance_scale=5.0,
-    prev_segment_conditioning_frames=5,  # Use 5 frames for temporal guidance (1 or 5 recommended)
-    callback_on_step_end=callback_fn,
-    callback_on_step_end_tensor_inputs=["latents"],
-).frames[0]
-export_to_video(output, "animated_advanced.mp4", fps=30)
-```
-
-</hfoption>
-</hfoptions>
-
-#### Key Parameters
-
-- **mode**: Choose between `"animate"` (default) or `"replace"`
-- **prev_segment_conditioning_frames**: Number of frames for temporal guidance (1 or 5 recommended). Using 5 provides better temporal consistency but requires more memory
-- **guidance_scale**: Controls how closely the output follows the text prompt. Higher values (5-7) produce results more aligned with the prompt. For Wan-Animate, CFG is disabled by default (`guidance_scale=1.0`) but can be enabled to support negative prompts and finer control over facial expressions. (Note that CFG will only target the text prompt and face conditioning.)
-
-
 ## Notes
 
 - Wan2.1 supports LoRAs with [`~loaders.WanLoraLoaderMixin.load_lora_weights`].
@@ -484,10 +281,10 @@ export_to_video(output, "animated_advanced.mp4", fps=30)
 
   # use "steamboat willie style" to trigger the LoRA
   prompt = """
-  steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot,
+  steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot, 
-  revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
+  revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in 
-  for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
+  for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground. 
-  Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
+  Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic 
   shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
   """
 
@@ -562,12 +359,6 @@ export_to_video(output, "animated_advanced.mp4", fps=30)
   - all
   - __call__
 
-## WanAnimatePipeline
-
-[[autodoc]] WanAnimatePipeline
-  - all
-  - __call__
-
 ## WanPipelineOutput
 
-[[autodoc]] pipelines.wan.pipeline_output.WanPipelineOutput
+[[autodoc]] pipelines.wan.pipeline_output.WanPipelineOutput

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

@@ -1,66 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Z-Image
-
-<div class="flex flex-wrap space-x-1">
-  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
-</div>
-
-[Z-Image](https://huggingface.co/papers/2511.22699) is a powerful and highly efficient image generation model with 6B parameters. Currently there's only one model with two more to be released:
-
-|Model|Hugging Face|
-|---|---|
-|Z-Image-Turbo|https://huggingface.co/Tongyi-MAI/Z-Image-Turbo|
-
-## Z-Image-Turbo
-
-Z-Image-Turbo is a distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It offers sub-second inference latency on enterprise-grade H800 GPUs and fits comfortably within 16G VRAM consumer devices. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.
-
-## Image-to-image
-
-Use [`ZImageImg2ImgPipeline`] to transform an existing image based on a text prompt.
-
-```python
-import torch
-from diffusers import ZImageImg2ImgPipeline
-from diffusers.utils import load_image
-
-pipe = ZImageImg2ImgPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-init_image = load_image(url).resize((1024, 1024))
-
-prompt = "A fantasy landscape with mountains and a river, detailed, vibrant colors"
-image = pipe(
-    prompt,
-    image=init_image,
-    strength=0.6,
-    num_inference_steps=9,
-    guidance_scale=0.0,
-    generator=torch.Generator("cuda").manual_seed(42),
-).images[0]
-image.save("zimage_img2img.png")
-```
-
-## ZImagePipeline
-
-[[autodoc]] ZImagePipeline
-	- all
-	- __call__
-
-## ZImageImg2ImgPipeline
-
-[[autodoc]] ZImageImg2ImgPipeline
-	- all
-	- __call__

docs/source/en/api/utilities.md

@@ -26,10 +26,6 @@ Utility and helper functions for working with 🤗 Diffusers.
 
 [[autodoc]] utils.load_image
 
-## load_video
-
-[[autodoc]] utils.load_video
-
 ## export_to_gif
 
 [[autodoc]] utils.export_to_gif

docs/source/en/modular_diffusers/custom_blocks.md

@@ -1,492 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-
-# Building Custom Blocks
-
-[ModularPipelineBlocks](./pipeline_block) are the fundamental building blocks of a [`ModularPipeline`]. You can create custom blocks by defining their inputs, outputs, and computation logic. This guide demonstrates how to create and use a custom block.
-
-> [!TIP]
-> Explore the [Modular Diffusers Custom Blocks](https://huggingface.co/collections/diffusers/modular-diffusers-custom-blocks) collection for official custom modular blocks like Nano Banana.
-
-## Project Structure
-
-Your custom block project should use the following structure:
-
-```shell
-.
-├── block.py
-└── modular_config.json
-```
-
-- `block.py` contains the custom block implementation
-- `modular_config.json` contains the metadata needed to load the block
-
-## Example: Florence 2 Inpainting Block
-
-In this example we will create a custom block that uses the [Florence 2](https://huggingface.co/docs/transformers/model_doc/florence2) model to process an input image and generate a mask for inpainting.
-
-The first step is to define the components that the block will use. In this case, we will need to use the `Florence2ForConditionalGeneration` model and its corresponding processor `AutoProcessor`. When defining components, we must specify the name of the component within our pipeline, model class via `type_hint`, and provide a `pretrained_model_name_or_path` for the component if we intend to load the model weights from a specific repository on the Hub.
-
-```py
-# Inside block.py
-from diffusers.modular_pipelines import (
-    ModularPipelineBlocks,
-    ComponentSpec,
-)
-from transformers import AutoProcessor, Florence2ForConditionalGeneration
-
-
-class Florence2ImageAnnotatorBlock(ModularPipelineBlocks):
-
-    @property
-    def expected_components(self):
-        return [
-            ComponentSpec(
-                name="image_annotator",
-                type_hint=Florence2ForConditionalGeneration,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-            ComponentSpec(
-                name="image_annotator_processor",
-                type_hint=AutoProcessor,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-        ]
-```
-
-Next, we define the inputs and outputs of the block. The inputs include the image to be annotated, the annotation task, and the annotation prompt. The outputs include the generated mask image and annotations.
-
-```py
-from typing import List, Union
-from PIL import Image, ImageDraw
-import torch
-import numpy as np
-
-from diffusers.modular_pipelines import (
-    PipelineState,
-    ModularPipelineBlocks,
-    InputParam,
-    ComponentSpec,
-    OutputParam,
-)
-from transformers import AutoProcessor, Florence2ForConditionalGeneration
-
-
-class Florence2ImageAnnotatorBlock(ModularPipelineBlocks):
-
-    @property
-    def expected_components(self):
-        return [
-            ComponentSpec(
-                name="image_annotator",
-                type_hint=Florence2ForConditionalGeneration,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-            ComponentSpec(
-                name="image_annotator_processor",
-                type_hint=AutoProcessor,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-        ]
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [
-            InputParam(
-                "image",
-                type_hint=Union[Image.Image, List[Image.Image]],
-                required=True,
-                description="Image(s) to annotate",
-            ),
-            InputParam(
-                "annotation_task",
-                type_hint=Union[str, List[str]],
-                required=True,
-                default="<REFERRING_EXPRESSION_SEGMENTATION>",
-                description="""Annotation Task to perform on the image.
-                Supported Tasks:
-
-                <OD>
-                <REFERRING_EXPRESSION_SEGMENTATION>
-                <CAPTION>
-                <DETAILED_CAPTION>
-                <MORE_DETAILED_CAPTION>
-                <DENSE_REGION_CAPTION>
-                <CAPTION_TO_PHRASE_GROUNDING>
-                <OPEN_VOCABULARY_DETECTION>
-
-                """,
-            ),
-            InputParam(
-                "annotation_prompt",
-                type_hint=Union[str, List[str]],
-                required=True,
-                description="""Annotation Prompt to provide more context to the task.
-                Can be used to detect or segment out specific elements in the image
-                """,
-            ),
-            InputParam(
-                "annotation_output_type",
-                type_hint=str,
-                required=True,
-                default="mask_image",
-                description="""Output type from annotation predictions. Availabe options are
-                mask_image:
-                    -black and white mask image for the given image based on the task type
-                mask_overlay:
-                    - mask overlayed on the original image
-                bounding_box:
-                    - bounding boxes drawn on the original image
-                """,
-            ),
-            InputParam(
-                "annotation_overlay",
-                type_hint=bool,
-                required=True,
-                default=False,
-                description="",
-            ),
-        ]
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [
-            OutputParam(
-                "mask_image",
-                type_hint=Image,
-                description="Inpainting Mask for input Image(s)",
-            ),
-            OutputParam(
-                "annotations",
-                type_hint=dict,
-                description="Annotations Predictions for input Image(s)",
-            ),
-            OutputParam(
-                "image",
-                type_hint=Image,
-                description="Annotated input Image(s)",
-            ),
-        ]
-
-```
-
-Now we implement the `__call__` method, which contains the logic for processing the input image and generating the mask.
-
-```py
-from typing import List, Union
-from PIL import Image, ImageDraw
-import torch
-import numpy as np
-
-from diffusers.modular_pipelines import (
-    PipelineState,
-    ModularPipelineBlocks,
-    InputParam,
-    ComponentSpec,
-    OutputParam,
-)
-from transformers import AutoProcessor, Florence2ForConditionalGeneration
-
-
-class Florence2ImageAnnotatorBlock(ModularPipelineBlocks):
-
-    @property
-    def expected_components(self):
-        return [
-            ComponentSpec(
-                name="image_annotator",
-                type_hint=Florence2ForConditionalGeneration,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-            ComponentSpec(
-                name="image_annotator_processor",
-                type_hint=AutoProcessor,
-                pretrained_model_name_or_path="florence-community/Florence-2-base-ft",
-            ),
-        ]
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [
-            InputParam(
-                "image",
-                type_hint=Union[Image.Image, List[Image.Image]],
-                required=True,
-                description="Image(s) to annotate",
-            ),
-            InputParam(
-                "annotation_task",
-                type_hint=Union[str, List[str]],
-                required=True,
-                default="<REFERRING_EXPRESSION_SEGMENTATION>",
-                description="""Annotation Task to perform on the image.
-                Supported Tasks:
-
-                <OD>
-                <REFERRING_EXPRESSION_SEGMENTATION>
-                <CAPTION>
-                <DETAILED_CAPTION>
-                <MORE_DETAILED_CAPTION>
-                <DENSE_REGION_CAPTION>
-                <CAPTION_TO_PHRASE_GROUNDING>
-                <OPEN_VOCABULARY_DETECTION>
-
-                """,
-            ),
-            InputParam(
-                "annotation_prompt",
-                type_hint=Union[str, List[str]],
-                required=True,
-                description="""Annotation Prompt to provide more context to the task.
-                Can be used to detect or segment out specific elements in the image
-                """,
-            ),
-            InputParam(
-                "annotation_output_type",
-                type_hint=str,
-                required=True,
-                default="mask_image",
-                description="""Output type from annotation predictions. Availabe options are
-                mask_image:
-                    -black and white mask image for the given image based on the task type
-                mask_overlay:
-                    - mask overlayed on the original image
-                bounding_box:
-                    - bounding boxes drawn on the original image
-                """,
-            ),
-            InputParam(
-                "annotation_overlay",
-                type_hint=bool,
-                required=True,
-                default=False,
-                description="",
-            ),
-        ]
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [
-            OutputParam(
-                "mask_image",
-                type_hint=Image,
-                description="Inpainting Mask for input Image(s)",
-            ),
-            OutputParam(
-                "annotations",
-                type_hint=dict,
-                description="Annotations Predictions for input Image(s)",
-            ),
-            OutputParam(
-                "image",
-                type_hint=Image,
-                description="Annotated input Image(s)",
-            ),
-        ]
-
-    def get_annotations(self, components, images, prompts, task):
-        task_prompts = [task + prompt for prompt in prompts]
-
-        inputs = components.image_annotator_processor(
-            text=task_prompts, images=images, return_tensors="pt"
-        ).to(components.image_annotator.device, components.image_annotator.dtype)
-
-        generated_ids = components.image_annotator.generate(
-            input_ids=inputs["input_ids"],
-            pixel_values=inputs["pixel_values"],
-            max_new_tokens=1024,
-            early_stopping=False,
-            do_sample=False,
-            num_beams=3,
-        )
-        annotations = components.image_annotator_processor.batch_decode(
-            generated_ids, skip_special_tokens=False
-        )
-        outputs = []
-        for image, annotation in zip(images, annotations):
-            outputs.append(
-                components.image_annotator_processor.post_process_generation(
-                    annotation, task=task, image_size=(image.width, image.height)
-                )
-            )
-        return outputs
-
-    def prepare_mask(self, images, annotations, overlay=False, fill="white"):
-        masks = []
-        for image, annotation in zip(images, annotations):
-            mask_image = image.copy() if overlay else Image.new("L", image.size, 0)
-            draw = ImageDraw.Draw(mask_image)
-
-            for _, _annotation in annotation.items():
-                if "polygons" in _annotation:
-                    for polygon in _annotation["polygons"]:
-                        polygon = np.array(polygon).reshape(-1, 2)
-                        if len(polygon) < 3:
-                            continue
-                        polygon = polygon.reshape(-1).tolist()
-                        draw.polygon(polygon, fill=fill)
-
-                elif "bbox" in _annotation:
-                    bbox = _annotation["bbox"]
-                    draw.rectangle(bbox, fill="white")
-
-            masks.append(mask_image)
-
-        return masks
-
-    def prepare_bounding_boxes(self, images, annotations):
-        outputs = []
-        for image, annotation in zip(images, annotations):
-            image_copy = image.copy()
-            draw = ImageDraw.Draw(image_copy)
-            for _, _annotation in annotation.items():
-                bbox = _annotation["bbox"]
-                label = _annotation["label"]
-
-                draw.rectangle(bbox, outline="red", width=3)
-                draw.text((bbox[0], bbox[1] - 20), label, fill="red")
-
-            outputs.append(image_copy)
-
-        return outputs
-
-    def prepare_inputs(self, images, prompts):
-        prompts = prompts or ""
-
-        if isinstance(images, Image.Image):
-            images = [images]
-        if isinstance(prompts, str):
-            prompts = [prompts]
-
-        if len(images) != len(prompts):
-            raise ValueError("Number of images and annotation prompts must match.")
-
-        return images, prompts
-
-    @torch.no_grad()
-    def __call__(self, components, state: PipelineState) -> PipelineState:
-        block_state = self.get_block_state(state)
-        images, annotation_task_prompt = self.prepare_inputs(
-            block_state.image, block_state.annotation_prompt
-        )
-        task = block_state.annotation_task
-        fill = block_state.fill
-
-        annotations = self.get_annotations(
-            components, images, annotation_task_prompt, task
-        )
-        block_state.annotations = annotations
-        if block_state.annotation_output_type == "mask_image":
-            block_state.mask_image = self.prepare_mask(images, annotations)
-        else:
-            block_state.mask_image = None
-
-        if block_state.annotation_output_type == "mask_overlay":
-            block_state.image = self.prepare_mask(images, annotations, overlay=True, fill=fill)
-
-        elif block_state.annotation_output_type == "bounding_box":
-            block_state.image = self.prepare_bounding_boxes(images, annotations)
-
-        self.set_block_state(state, block_state)
-
-        return components, state
-
-```
-
-Once we have defined our custom block, we can save it to the Hub, using either the CLI or the [`push_to_hub`] method. This will make it easy to share and reuse our custom block with other pipelines.
-
-<hfoptions id="share">
-<hfoption id="hf CLI">
-
-```shell
-# In the folder with the `block.py` file, run:
-diffusers-cli custom_block
-```
-
-Then upload the block to the Hub:
-
-```shell
-hf upload <your repo id> . .
-```
-</hfoption>
-<hfoption id="push_to_hub">
-
-```py
-from block import Florence2ImageAnnotatorBlock
-block = Florence2ImageAnnotatorBlock()
-block.push_to_hub("<your repo id>")
-```
-
-</hfoption>
-</hfoptions>
-
-## Using Custom Blocks
-
-Load the custom block with [`~ModularPipelineBlocks.from_pretrained`] and set `trust_remote_code=True`.
-
-```py
-import torch
-from diffusers.modular_pipelines import ModularPipelineBlocks, SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import INPAINT_BLOCKS
-from diffusers.utils import load_image
-
-# Fetch the Florence2 image annotator block that will create our mask
-image_annotator_block = ModularPipelineBlocks.from_pretrained("diffusers/florence-2-custom-block", trust_remote_code=True)
-
-my_blocks = INPAINT_BLOCKS.copy()
-# insert the annotation block before the image encoding step
-my_blocks.insert("image_annotator", image_annotator_block, 1)
-
-# Create our initial set of inpainting blocks
-blocks = SequentialPipelineBlocks.from_blocks_dict(my_blocks)
-
-repo_id = "diffusers/modular-stable-diffusion-xl-base-1.0"
-pipe = blocks.init_pipeline(repo_id)
-pipe.load_components(torch_dtype=torch.float16, device_map="cuda", trust_remote_code=True)
-
-image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true")
-image = image.resize((1024, 1024))
-
-prompt = ["A red car"]
-annotation_task = "<REFERRING_EXPRESSION_SEGMENTATION>"
-annotation_prompt = ["the car"]
-
-output = pipe(
-    prompt=prompt,
-    image=image,
-    annotation_task=annotation_task,
-    annotation_prompt=annotation_prompt,
-    annotation_output_type="mask_image",
-    num_inference_steps=35,
-    guidance_scale=7.5,
-    strength=0.95,
-    output="images"
-)
-output[0].save("florence-inpainting.png")
-```
-
-## Editing Custom Blocks
-
-By default, custom blocks are saved in your cache directory. Use the `local_dir` argument to download and edit a custom block in a specific folder.
-
-```py
-import torch
-from diffusers.modular_pipelines import ModularPipelineBlocks, SequentialPipelineBlocks
-from diffusers.modular_pipelines.stable_diffusion_xl import INPAINT_BLOCKS
-from diffusers.utils import load_image
-
-# Fetch the Florence2 image annotator block that will create our mask
-image_annotator_block = ModularPipelineBlocks.from_pretrained("diffusers/florence-2-custom-block", trust_remote_code=True, local_dir="/my-local-folder")
-```
-
-Any changes made to the block files in this folder will be reflected when you load the block again.

docs/source/en/modular_diffusers/guiders.md

@@ -159,7 +159,7 @@ Change the [`~ComponentSpec.default_creation_method`] to `from_pretrained` and u
 ```py
 guider_spec = t2i_pipeline.get_component_spec("guider")
 guider_spec.default_creation_method="from_pretrained"
-guider_spec.pretrained_model_name_or_path="YiYiXu/modular-loader-t2i-guider"
+guider_spec.repo="YiYiXu/modular-loader-t2i-guider"
 guider_spec.subfolder="pag_guider"
 pag_guider = guider_spec.load()
 t2i_pipeline.update_components(guider=pag_guider)

docs/source/en/modular_diffusers/loop_sequential_pipeline_blocks.md

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # LoopSequentialPipelineBlocks
 
-[`~modular_pipelines.LoopSequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a loop. Data flows circularly, using `inputs` and `intermediate_outputs`, and each block is run iteratively. This is typically used to create a denoising loop which is iterative by default.
+[`~modular_pipelines.LoopSequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a loop. Data flows circularly, using `intermediate_inputs` and `intermediate_outputs`, and each block is run iteratively. This is typically used to create a denoising loop which is iterative by default.
 
 This guide shows you how to create [`~modular_pipelines.LoopSequentialPipelineBlocks`].
 
@@ -21,6 +21,7 @@ This guide shows you how to create [`~modular_pipelines.LoopSequentialPipelineBl
 [`~modular_pipelines.LoopSequentialPipelineBlocks`], is also known as the *loop wrapper* because it defines the loop structure, iteration variables, and configuration. Within the loop wrapper, you need the following variables.
 
 - `loop_inputs` are user provided values and equivalent to [`~modular_pipelines.ModularPipelineBlocks.inputs`].
+- `loop_intermediate_inputs` are intermediate variables from the [`~modular_pipelines.PipelineState`] and equivalent to [`~modular_pipelines.ModularPipelineBlocks.intermediate_inputs`].
 - `loop_intermediate_outputs` are new intermediate variables created by the block and added to the [`~modular_pipelines.PipelineState`]. It is equivalent to [`~modular_pipelines.ModularPipelineBlocks.intermediate_outputs`].
 - `__call__` method defines the loop structure and iteration logic.
 
@@ -89,4 +90,4 @@ Add more loop blocks to run within each iteration with [`~modular_pipelines.Loop
 
 ```py
 loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock(), "block2": LoopBlock})
-```
+```

docs/source/en/modular_diffusers/modular_pipeline.md

@@ -313,14 +313,14 @@ unet_spec
 ComponentSpec(
     name='unet',
     type_hint=<class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>,
-    pretrained_model_name_or_path='RunDiffusion/Juggernaut-XL-v9',
+    repo='RunDiffusion/Juggernaut-XL-v9',
     subfolder='unet',
     variant='fp16',
     default_creation_method='from_pretrained'
 )
 
 # modify to load from a different repository
-unet_spec.pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
+unet_spec.repo = "stabilityai/stable-diffusion-xl-base-1.0"
 
 # load component with modified spec
 unet = unet_spec.load(torch_dtype=torch.float16)

docs/source/en/modular_diffusers/pipeline_block.md

@@ -37,7 +37,17 @@ A [`~modular_pipelines.ModularPipelineBlocks`] requires `inputs`, and `intermedi
     ]
     ```
 
-- `intermediate_outputs` are new values created by a block and added to the [`~modular_pipelines.PipelineState`]. The `intermediate_outputs` are available as `inputs` for subsequent blocks or available as the final output from running the pipeline.
+- `intermediate_inputs` are values typically created from a previous block but it can also be directly provided if no preceding block generates them. Unlike `inputs`, `intermediate_inputs` can be modified.
+
+    Use `InputParam` to define `intermediate_inputs`.
+
+    ```py
+    user_intermediate_inputs = [
+        InputParam(name="processed_image", type_hint="torch.Tensor", description="image that has been preprocessed and normalized"),
+    ]
+    ```
+
+- `intermediate_outputs` are new values created by a block and added to the [`~modular_pipelines.PipelineState`]. The `intermediate_outputs` are available as `intermediate_inputs` for subsequent blocks or available as the final output from running the pipeline.
 
     Use `OutputParam` to define `intermediate_outputs`.
 
@@ -55,8 +65,8 @@ The intermediate inputs and outputs share data to connect blocks. They are acces
 
 The computation a block performs is defined in the `__call__` method and it follows a specific structure.
 
-1. Retrieve the [`~modular_pipelines.BlockState`] to get a local view of the `inputs`
+1. Retrieve the [`~modular_pipelines.BlockState`] to get a local view of the `inputs` and `intermediate_inputs`.
-2. Implement the computation logic on the `inputs`.
+2. Implement the computation logic on the `inputs` and `intermediate_inputs`.
 3. Update [`~modular_pipelines.PipelineState`] to push changes from the local [`~modular_pipelines.BlockState`] back to the global [`~modular_pipelines.PipelineState`].
 4. Return the components and state which becomes available to the next block.
 
@@ -66,7 +76,7 @@ def __call__(self, components, state):
     block_state = self.get_block_state(state)
 
     # Your computation logic here
-    # block_state contains all your inputs
+    # block_state contains all your inputs and intermediate_inputs
     # Access them like: block_state.image, block_state.processed_image
 
     # Update the pipeline state with your updated block_states
@@ -102,4 +112,4 @@ def __call__(self, components, state):
     unet = components.unet
     vae = components.vae
     scheduler = components.scheduler
-```
+```

docs/source/en/modular_diffusers/quickstart.md

@@ -183,7 +183,7 @@ from diffusers.modular_pipelines import ComponentsManager
 components = ComponentManager()
 
 dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", components_manager=components, collection="diffdiff")
-dd_pipeline.load_componenets(torch_dtype=torch.float16)
+dd_pipeline.load_default_componenets(torch_dtype=torch.float16)
 dd_pipeline.to("cuda")
 ```
 

docs/source/en/modular_diffusers/sequential_pipeline_blocks.md

@@ -12,11 +12,11 @@ specific language governing permissions and limitations under the License.
 
 # SequentialPipelineBlocks
 
-[`~modular_pipelines.SequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a sequence. Data flows linearly from one block to the next using `inputs` and `intermediate_outputs`. Each block in [`~modular_pipelines.SequentialPipelineBlocks`] usually represents a step in the pipeline, and by combining them, you gradually build a pipeline.
+[`~modular_pipelines.SequentialPipelineBlocks`] are a multi-block type that composes other [`~modular_pipelines.ModularPipelineBlocks`] together in a sequence. Data flows linearly from one block to the next using `intermediate_inputs` and `intermediate_outputs`. Each block in [`~modular_pipelines.SequentialPipelineBlocks`] usually represents a step in the pipeline, and by combining them, you gradually build a pipeline.
 
 This guide shows you how to connect two blocks into a [`~modular_pipelines.SequentialPipelineBlocks`].
 
-Create two [`~modular_pipelines.ModularPipelineBlocks`]. The first block, `InputBlock`, outputs a `batch_size` value and the second block, `ImageEncoderBlock` uses `batch_size` as `inputs`.
+Create two [`~modular_pipelines.ModularPipelineBlocks`]. The first block, `InputBlock`, outputs a `batch_size` value and the second block, `ImageEncoderBlock` uses `batch_size` as `intermediate_inputs`.
 
 <hfoptions id="sequential">
 <hfoption id="InputBlock">
@@ -110,4 +110,4 @@ Inspect the sub-blocks in [`~modular_pipelines.SequentialPipelineBlocks`] by cal
 ```py
 print(blocks)
 print(blocks.doc)
-```
+```

docs/source/en/optimization/attention_backends.md

@@ -21,7 +21,6 @@ Refer to the table below for an overview of the available attention families and
 | attention family | main feature |
 |---|---|
 | FlashAttention | minimizes memory reads/writes through tiling and recomputation |
-| AI Tensor Engine for ROCm | FlashAttention implementation optimized for AMD ROCm accelerators |
 | SageAttention | quantizes attention to int8 |
 | PyTorch native | built-in PyTorch implementation using [scaled_dot_product_attention](./fp16#scaled-dot-product-attention) |
 | xFormers | memory-efficient attention with support for various attention kernels |
@@ -32,7 +31,7 @@ This guide will show you how to set and use the different attention backends.
 
 The [`~ModelMixin.set_attention_backend`] method iterates through all the modules in the model and sets the appropriate attention backend to use. The attention backend setting persists until [`~ModelMixin.reset_attention_backend`] is called.
 
-The example below demonstrates how to enable the `_flash_3_hub` implementation for FlashAttention-3 from the [`kernels`](https://github.com/huggingface/kernels) library, which allows you to instantly use optimized compute kernels from the Hub without requiring any setup.
+The example below demonstrates how to enable the `_flash_3_hub` implementation for FlashAttention-3 from the [kernel](https://github.com/huggingface/kernels) library, which allows you to instantly use optimized compute kernels from the Hub without requiring any setup.
 
 > [!NOTE]
 > FlashAttention-3 is not supported for non-Hopper architectures, in which case, use FlashAttention with `set_attention_backend("flash")`.
@@ -82,45 +81,6 @@ with attention_backend("_flash_3_hub"):
 > [!TIP]
 > Most attention backends support `torch.compile` without graph breaks and can be used to further speed up inference.
 
-## Checks
-
-The attention dispatcher includes debugging checks that catch common errors before they cause problems.
-
-1. Device checks verify that query, key, and value tensors live on the same device.
-2. Data type checks confirm tensors have matching dtypes and use either bfloat16 or float16.
-3. Shape checks validate tensor dimensions and prevent mixing attention masks with causal flags.
-
-Enable these checks by setting the `DIFFUSERS_ATTN_CHECKS` environment variable. Checks add overhead to every attention operation, so they're disabled by default. 
-
-```bash
-export DIFFUSERS_ATTN_CHECKS=yes
-```
-
-The checks are run now before every attention operation.
-
-```py
-import torch
-
-query = torch.randn(1, 10, 8, 64, dtype=torch.bfloat16, device="cuda")
-key = torch.randn(1, 10, 8, 64, dtype=torch.bfloat16, device="cuda")
-value = torch.randn(1, 10, 8, 64, dtype=torch.bfloat16, device="cuda")
-
-try:
-    with attention_backend("flash"):
-        output = dispatch_attention_fn(query, key, value)
-        print("✓ Flash Attention works with checks enabled")
-except Exception as e:
-    print(f"✗ Flash Attention failed: {e}")
-```
-
-You can also configure the registry directly.
-
-```py
-from diffusers.models.attention_dispatch import _AttentionBackendRegistry
-
-_AttentionBackendRegistry._checks_enabled = True
-```
-
 ## Available backends
 
 Refer to the table below for a complete list of available attention backends and their variants.
@@ -139,16 +99,11 @@ Refer to the table below for a complete list of available attention backends and
 | `_native_npu` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | NPU-optimized attention |
 | `_native_xla` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | XLA-optimized attention |
 | `flash` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-2 |
-| `flash_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-2 from kernels |
 | `flash_varlen` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention |
-| `flash_varlen_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention from kernels |
-| `aiter` | [AI Tensor Engine for ROCm](https://github.com/ROCm/aiter) | FlashAttention for AMD ROCm |
 | `_flash_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 |
 | `_flash_varlen_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention-3 |
 | `_flash_3_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 from kernels |
-| `_flash_3_varlen_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention-3 from kernels |
 | `sage` | [SageAttention](https://github.com/thu-ml/SageAttention) | Quantized attention (INT8 QK) |
-| `sage_hub` | [SageAttention](https://github.com/thu-ml/SageAttention) | Quantized attention (INT8 QK) from kernels |
 | `sage_varlen` | [SageAttention](https://github.com/thu-ml/SageAttention) | Variable length SageAttention |
 | `_sage_qk_int8_pv_fp8_cuda` | [SageAttention](https://github.com/thu-ml/SageAttention) | INT8 QK + FP8 PV (CUDA) |
 | `_sage_qk_int8_pv_fp8_cuda_sm90` | [SageAttention](https://github.com/thu-ml/SageAttention) | INT8 QK + FP8 PV (SM90) |
@@ -156,4 +111,4 @@ Refer to the table below for a complete list of available attention backends and
 | `_sage_qk_int8_pv_fp16_triton` | [SageAttention](https://github.com/thu-ml/SageAttention) | INT8 QK + FP16 PV (Triton) |
 | `xformers` | [xFormers](https://github.com/facebookresearch/xformers) | Memory-efficient attention |
 
-</details>
+</details>

docs/source/en/optimization/cache.md

@@ -66,35 +66,4 @@ config = FasterCacheConfig(
     tensor_format="BFCHW",
 )
 pipeline.transformer.enable_cache(config)
-```
-
-## TaylorSeer Cache
-
-[TaylorSeer Cache](https://huggingface.co/papers/2403.06923) accelerates diffusion inference by using Taylor series expansions to approximate and cache intermediate activations across denoising steps. The method predicts future outputs based on past computations, reusing them at specified intervals to reduce redundant calculations.
-
-This caching mechanism delivers strong results with minimal additional memory overhead. For detailed performance analysis, see [our findings here](https://github.com/huggingface/diffusers/pull/12648#issuecomment-3610615080).
-
-To enable TaylorSeer Cache, create a [`TaylorSeerCacheConfig`] and pass it to your pipeline's transformer:
-
-- `cache_interval`: Number of steps to reuse cached outputs before performing a full forward pass
-- `disable_cache_before_step`: Initial steps that use full computations to gather data for approximations
-- `max_order`: Approximation accuracy (in theory, higher values improve quality but increase memory usage but we recommend it should be set to `1`)
-
-```python
-import torch
-from diffusers import FluxPipeline, TaylorSeerCacheConfig
-
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch.bfloat16,
-)
-pipe.to("cuda")
-
-config = TaylorSeerCacheConfig(
-    cache_interval=5,
-    max_order=1,
-    disable_cache_before_step=10,
-    taylor_factors_dtype=torch.bfloat16,
-)
-pipe.transformer.enable_cache(config)
 ```

docs/source/en/quantization/modelopt.md

@@ -11,7 +11,7 @@ specific language governing permissions and limitations under the License. -->
 
 # NVIDIA ModelOpt
 
-[NVIDIA-ModelOpt](https://github.com/NVIDIA/Model-Optimizer) is a unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed.
+[NVIDIA-ModelOpt](https://github.com/NVIDIA/TensorRT-Model-Optimizer) is a unified library of state-of-the-art model optimization techniques like quantization, pruning, distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed.
 
 Before you begin, make sure you have nvidia_modelopt installed.
 
@@ -57,7 +57,7 @@ image.save("output.png")
 >
 > The quantization methods in NVIDIA-ModelOpt are designed to reduce the memory footprint of model weights using various QAT (Quantization-Aware Training) and PTQ (Post-Training Quantization) techniques while maintaining model performance. However, the actual performance gain during inference depends on the deployment framework (e.g., TRT-LLM, TensorRT) and the specific hardware configuration.  
 > 
-> More details can be found [here](https://github.com/NVIDIA/Model-Optimizer/tree/main/examples).
+> More details can be found [here](https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples).
 
 ## NVIDIAModelOptConfig
 
@@ -86,7 +86,7 @@ The quantization methods supported are as follows:
 | **NVFP4** | `nvfp4 weight only`, `nvfp4 block quantization` | `quant_type`, `quant_type + channel_quantize + block_quantize` | `channel_quantize = -1 is only supported for now`|
 
 
-Refer to the [official modelopt documentation](https://nvidia.github.io/Model-Optimizer/) for a better understanding of the available quantization methods and the exhaustive list of configuration options available.
+Refer to the [official modelopt documentation](https://nvidia.github.io/TensorRT-Model-Optimizer/) for a better understanding of the available quantization methods and the exhaustive list of configuration options available.
 
 ## Serializing and Deserializing quantized models
 

docs/source/en/training/adapt_a_model.md

@@ -16,12 +16,12 @@ pipeline.unet.config["in_channels"]
 4
 ```
 
-Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting):
+Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting):
 
 ```py
 from diffusers import StableDiffusionPipeline
 
-pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting", use_safetensors=True)
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True)
 pipeline.unet.config["in_channels"]
 9
 ```

docs/source/en/training/distributed_inference.md

@@ -237,8 +237,6 @@ By selectively loading and unloading the models you need at a given stage and sh
 
 Use [`~ModelMixin.set_attention_backend`] to switch to a more optimized attention backend. Refer to this [table](../optimization/attention_backends#available-backends) for a complete list of available backends.
 
-Most attention backends are compatible with context parallelism. Open an [issue](https://github.com/huggingface/diffusers/issues/new) if a backend is not compatible.
-
 ### Ring Attention
 
 Key (K) and value (V) representations communicate between devices using [Ring Attention](https://huggingface.co/papers/2310.01889). This ensures each split sees every other token's K/V. Each GPU computes attention for its local K/V and passes it to the next GPU in the ring. No single GPU holds the full sequence, which reduces communication latency.
@@ -247,60 +245,40 @@ Pass a [`ContextParallelConfig`] to the `parallel_config` argument of the transf
 
 ```py
 import torch
-from torch import distributed as dist
+from diffusers import AutoModel, QwenImagePipeline, ContextParallelConfig
-from diffusers import DiffusionPipeline, ContextParallelConfig
 
-def setup_distributed():
+try:
-    if not dist.is_initialized():
+    torch.distributed.init_process_group("nccl")
-        dist.init_process_group(backend="nccl")
+    rank = torch.distributed.get_rank()
-    rank = dist.get_rank()
+    device = torch.device("cuda", rank % torch.cuda.device_count())
-    device = torch.device(f"cuda:{rank}")
     torch.cuda.set_device(device)
-    return device
+    
-
+    transformer = AutoModel.from_pretrained("Qwen/Qwen-Image", subfolder="transformer", torch_dtype=torch.bfloat16, parallel_config=ContextParallelConfig(ring_degree=2))
-def main():
+    pipeline = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", transformer=transformer, torch_dtype=torch.bfloat16, device_map="cuda")
-    device = setup_distributed()
+    pipeline.transformer.set_attention_backend("flash")
-    world_size = dist.get_world_size()
-
-    pipeline = DiffusionPipeline.from_pretrained(
-        "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, device_map=device
-    )
-    pipeline.transformer.set_attention_backend("_native_cudnn")
-
-    cp_config = ContextParallelConfig(ring_degree=world_size)
-    pipeline.transformer.enable_parallelism(config=cp_config)
 
     prompt = """
     cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
     highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
     """
-
+    
     # Must specify generator so all ranks start with same latents (or pass your own)
     generator = torch.Generator().manual_seed(42)
-    image = pipeline(
+    image = pipeline(prompt, num_inference_steps=50, generator=generator).images[0]
-        prompt,
+    
-        guidance_scale=3.5,
+    if rank == 0:
-        num_inference_steps=50,
+        image.save("output.png")
-        generator=generator,
-    ).images[0]
 
-    if dist.get_rank() == 0:
+except Exception as e:
-        image.save(f"output.png")
+    print(f"An error occurred: {e}")
+    torch.distributed.breakpoint()
+    raise
 
-    if dist.is_initialized():
+finally:
-        dist.destroy_process_group()
+    if torch.distributed.is_initialized():
-
+        torch.distributed.destroy_process_group()
-
-if __name__ == "__main__":
-    main()
 ```
 
-The script above needs to be run with a distributed launcher, such as [torchrun](https://docs.pytorch.org/docs/stable/elastic/run.html), that is compatible with PyTorch. `--nproc-per-node` is set to the number of GPUs available.
-
-/```shell
-`torchrun --nproc-per-node 2 above_script.py`. 
-/```
-
 ### Ulysses Attention
 
 [Ulysses Attention](https://huggingface.co/papers/2309.14509) splits a sequence across GPUs and performs an *all-to-all* communication (every device sends/receives data to every other device). Each GPU ends up with all tokens for only a subset of attention heads. Each GPU computes attention locally on all tokens for its head, then performs another all-to-all to regroup results by tokens for the next layer.
@@ -310,26 +288,5 @@ The script above needs to be run with a distributed launcher, such as [torchrun]
 Pass the [`ContextParallelConfig`] to [`~ModelMixin.enable_parallelism`].
 
 ```py
-# Depending on the number of GPUs available.
 pipeline.transformer.enable_parallelism(config=ContextParallelConfig(ulysses_degree=2))
-```
-
-### parallel_config
-
-Pass `parallel_config` during model initialization to enable context parallelism.
-
-```py
-CKPT_ID = "black-forest-labs/FLUX.1-dev"
-
-cp_config = ContextParallelConfig(ring_degree=2)
-transformer = AutoModel.from_pretrained(
-    CKPT_ID, 
-    subfolder="transformer", 
-    torch_dtype=torch.bfloat16, 
-    parallel_config=cp_config
-)
-
-pipeline = DiffusionPipeline.from_pretrained(
-    CKPT_ID, transformer=transformer, torch_dtype=torch.bfloat16,
-).to(device)
 ```

docs/source/en/training/dreambooth.md

@@ -548,4 +548,4 @@ Training the DeepFloyd IF model can be challenging, but here are some tips that
 
 Congratulations on training your DreamBooth model! To learn more about how to use your new model, the following guide may be helpful:
 
-- Learn how to [load a DreamBooth](../using-diffusers/dreambooth) model for inference if you trained your model with LoRA.
+- Learn how to [load a DreamBooth](../using-diffusers/loading_adapters) model for inference if you trained your model with LoRA.

docs/source/en/training/lcm_distill.md

@@ -75,7 +75,7 @@ accelerate launch train_lcm_distill_sd_wds.py \
 Most of the parameters are identical to the parameters in the [Text-to-image](text2image#script-parameters) training guide, so you'll focus on the parameters that are relevant to latent consistency distillation in this guide.
 
 - `--pretrained_teacher_model`: the path to a pretrained latent diffusion model to use as the teacher model
-- `--pretrained_vae_model_name_or_path`: path to a pretrained VAE; the SDXL VAE is known to suffer from numerical instability, so this parameter allows you to specify an alternative VAE (like this [VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)) by madebyollin which works in fp16)
+- `--pretrained_vae_model_name_or_path`: path to a pretrained VAE; the SDXL VAE is known to suffer from numerical instability, so this parameter allows you to specify an alternative VAE (like this [VAE]((https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)) by madebyollin which works in fp16)
 - `--w_min` and `--w_max`: the minimum and maximum guidance scale values for guidance scale sampling
 - `--num_ddim_timesteps`: the number of timesteps for DDIM sampling
 - `--loss_type`: the type of loss (L2 or Huber) to calculate for latent consistency distillation; Huber loss is generally preferred because it's more robust to outliers
@@ -245,5 +245,5 @@ The SDXL training script is discussed in more detail in the [SDXL training](sdxl
 
 Congratulations on distilling a LCM model! To learn more about LCM, the following may be helpful:
 
-- Learn how to use [LCMs for inference](../using-diffusers/inference_with_lcm) for text-to-image, image-to-image, and with LoRA checkpoints.
+- Learn how to use [LCMs for inference](../using-diffusers/lcm) for text-to-image, image-to-image, and with LoRA checkpoints.
 - Read the [SDXL in 4 steps with Latent Consistency LoRAs](https://huggingface.co/blog/lcm_lora) blog post to learn more about SDXL LCM-LoRA's for super fast inference, quality comparisons, benchmarks, and more.

docs/source/en/training/lora.md

@@ -198,5 +198,5 @@ image = pipeline("A naruto with blue eyes").images[0]
 
 Congratulations on training a new model with LoRA! To learn more about how to use your new model, the following guides may be helpful:
 
-- Learn how to [load different LoRA formats](../tutorials/using_peft_for_inference) trained using community trainers like Kohya and TheLastBen.
+- Learn how to [load different LoRA formats](../using-diffusers/loading_adapters#LoRA) trained using community trainers like Kohya and TheLastBen.
 - Learn how to use and [combine multiple LoRA's](../tutorials/using_peft_for_inference) with PEFT for inference.

docs/source/en/training/text2image.md

@@ -178,5 +178,5 @@ image.save("yoda-naruto.png")
 
 Congratulations on training your own text-to-image model! To learn more about how to use your new model, the following guides may be helpful:
 
-- Learn how to [load LoRA weights](../tutorials/using_peft_for_inference) for inference if you trained your model with LoRA.
+- Learn how to [load LoRA weights](../using-diffusers/loading_adapters#LoRA) for inference if you trained your model with LoRA.
 - Learn more about how certain parameters like guidance scale or techniques such as prompt weighting can help you control inference in the [Text-to-image](../using-diffusers/conditional_image_generation) task guide.

docs/source/en/training/text_inversion.md

@@ -203,4 +203,5 @@ image.save("cat-train.png")
 
 Congratulations on training your own Textual Inversion model! 🎉 To learn more about how to use your new model, the following guides may be helpful:
 
-- Learn how to [load Textual Inversion embeddings](../using-diffusers/textual_inversion_inference) and also use them as negative embeddings.
+- Learn how to [load Textual Inversion embeddings](../using-diffusers/loading_adapters) and also use them as negative embeddings.
+- Learn how to use [Textual Inversion](textual_inversion_inference) for inference with Stable Diffusion 1/2 and Stable Diffusion XL.

docs/source/en/using-diffusers/automodel.md

@@ -1,46 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# AutoModel
-
-The [`AutoModel`] class automatically detects and loads the correct model class (UNet, transformer, VAE) from a `config.json` file. You don't need to know the specific model class name ahead of time. It supports data types and device placement, and works across model types and libraries.
-
-The example below loads a transformer from Diffusers and a text encoder from Transformers. Use the `subfolder` parameter to specify where to load the `config.json` file from.
-
-```py
-import torch
-from diffusers import AutoModel, DiffusionPipeline
-
-transformer = AutoModel.from_pretrained(
-    "Qwen/Qwen-Image", subfolder="transformer", torch_dtype=torch.bfloat16, device_map="cuda"
-)
-
-text_encoder = AutoModel.from_pretrained(
-    "Qwen/Qwen-Image", subfolder="text_encoder", torch_dtype=torch.bfloat16, device_map="cuda"
-)
-```
-
-[`AutoModel`] also loads models from the [Hub](https://huggingface.co/models) that aren't included in Diffusers. Set `trust_remote_code=True` in [`AutoModel.from_pretrained`] to load custom models.
-
-```py
-import torch
-from diffusers import AutoModel
-
-transformer = AutoModel.from_pretrained(
-    "custom/custom-transformer-model", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda"
-)
-```
-
-If the custom model inherits from the [`ModelMixin`] class, it gets access to the same features as Diffusers model classes, like [regional compilation](../optimization/fp16#regional-compilation) and [group offloading](../optimization/memory#group-offloading).
-
-> [!NOTE]
-> Learn more about implementing custom models in the [Community components](../using-diffusers/custom_pipeline_overview#community-components) guide.

docs/source/en/using-diffusers/batched_inference.md

@@ -16,24 +16,24 @@ Batch inference processes multiple prompts at a time to increase throughput. It
 
 The downside is increased latency because you must wait for the entire batch to complete, and more GPU memory is required for large batches.
 
-For text-to-image, pass a list of prompts to the pipeline and for image-to-image, pass a list of images and prompts to the pipeline. The example below demonstrates batched text-to-image inference.
+<hfoptions id="usage">
+<hfoption id="text-to-image">
+
+For text-to-image, pass a list of prompts to the pipeline.
 
 ```py
 import torch
-import matplotlib.pyplot as plt
 from diffusers import DiffusionPipeline
 
 pipeline = DiffusionPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
-    torch_dtype=torch.float16,
+    torch_dtype=torch.float16
-    device_map="cuda"
+).to("cuda")
-)
 
 prompts = [
-    "Cinematic shot of a cozy coffee shop interior, warm pastel light streaming through a window where a cat rests. Shallow depth of field, glowing cups in soft focus, dreamy lofi-inspired mood, nostalgic tones, framed like a quiet film scene.",
+    "cinematic photo of A beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed",
-    "Polaroid-style photograph of a cozy coffee shop interior, bathed in warm pastel light. A cat sits on the windowsill near steaming mugs. Soft, slightly faded tones and dreamy blur evoke nostalgia, a lofi mood, and the intimate, imperfect charm of instant film.",
+    "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
-    "Soft watercolor illustration of a cozy coffee shop interior, pastel washes of color filling the space. A cat rests peacefully on the windowsill as warm light glows through. Gentle brushstrokes create a dreamy, lofi-inspired atmosphere with whimsical textures and nostalgic calm.",
+    "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
-    "Isometric pixel-art illustration of a cozy coffee shop interior in detailed 8-bit style. Warm pastel light fills the space as a cat rests on the windowsill. Blocky furniture and tiny mugs add charm, low-res retro graphics enhance the nostalgic, lofi-inspired game aesthetic."
 ]
 
 images = pipeline(
@@ -52,10 +52,6 @@ plt.tight_layout()
 plt.show()
 ```
 
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/batch-inference.png"/>
-</div>
-
 To generate multiple variations of one prompt, use the `num_images_per_prompt` argument.
 
 ```py
@@ -65,18 +61,11 @@ from diffusers import DiffusionPipeline
 
 pipeline = DiffusionPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
-    torch_dtype=torch.float16,
+    torch_dtype=torch.float16
-    device_map="cuda"
+).to("cuda")
-)
-
-prompt="""
-Isometric pixel-art illustration of a cozy coffee shop interior in detailed 8-bit style. Warm pastel light fills the
-space as a cat rests on the windowsill. Blocky furniture and tiny mugs add charm, low-res retro graphics enhance the
-nostalgic, lofi-inspired game aesthetic.
-"""
 
 images = pipeline(
-    prompt=prompt,
+    prompt="pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics",
     num_images_per_prompt=4
 ).images
 
@@ -92,10 +81,6 @@ plt.tight_layout()
 plt.show()
 ```
 
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/batch-inference-2.png"/>
-</div>
-
 Combine both approaches to generate different variations of different prompts.
 
 ```py
@@ -104,7 +89,7 @@ images = pipeline(
     num_images_per_prompt=2,
 ).images
 
-fig, axes = plt.subplots(2, 4, figsize=(12, 12))
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
 axes = axes.flatten()
 
 for i, image in enumerate(images):
@@ -116,18 +101,126 @@ plt.tight_layout()
 plt.show()
 ```
 
-<div class="flex justify-center">
+</hfoption>
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/batch-inference-3.png"/>
+<hfoption id="image-to-image">
-</div>
+
+For image-to-image, pass a list of input images and prompts to the pipeline.
+
+```py
+import torch
+from diffusers.utils import load_image
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16
+).to("cuda")
+
+input_images = [
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png"),
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"),
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png")
+]
+
+prompts = [
+    "cinematic photo of a beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed",
+    "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+    "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+images = pipeline(
+    prompt=prompts,
+    image=input_images,
+    guidance_scale=8.0,
+    strength=0.5
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+To generate multiple variations of one prompt, use the `num_images_per_prompt` argument.
+
+```py
+import torch
+import matplotlib.pyplot as plt
+from diffusers.utils import load_image
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16
+).to("cuda")
+
+input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png")
+
+images = pipeline(
+    prompt="pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics",
+    image=input_image,
+    num_images_per_prompt=4
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+Combine both approaches to generate different variations of different prompts.
+
+```py
+input_images = [
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"),
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png")
+]
+
+prompts = [
+    "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+    "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+images = pipeline(
+    prompt=prompts,
+    image=input_images,
+    num_images_per_prompt=2,
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+</hfoption>
+</hfoptions>
 
 ## Deterministic generation
 
 Enable reproducible batch generation by passing a list of [Generator’s](https://pytorch.org/docs/stable/generated/torch.Generator.html) to the pipeline and tie each `Generator` to a seed to reuse it.
 
-> [!TIP]
+Use a list comprehension to iterate over the batch size specified in `range()` to create a unique `Generator` object for each image in the batch.
-> Refer to the [Reproducibility](./reusing_seeds) docs to learn more about deterministic algorithms and the `Generator` object.
 
-Use a list comprehension to iterate over the batch size specified in `range()` to create a unique `Generator` object for each image in the batch. Don't multiply the `Generator` by the batch size because that only creates one `Generator` object that is used sequentially for each image in the batch.
+Don't multiply the `Generator` by the batch size because that only creates one `Generator` object that is used sequentially for each image in the batch.
 
 ```py
 generator = [torch.Generator(device="cuda").manual_seed(0)] * 3
@@ -141,16 +234,14 @@ from diffusers import DiffusionPipeline
 
 pipeline = DiffusionPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
-    torch_dtype=torch.float16,
+    torch_dtype=torch.float16
-    device_map="cuda"
+).to("cuda")
-)
 
 generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(3)]
 prompts = [
-    "Cinematic shot of a cozy coffee shop interior, warm pastel light streaming through a window where a cat rests. Shallow depth of field, glowing cups in soft focus, dreamy lofi-inspired mood, nostalgic tones, framed like a quiet film scene.",
+    "cinematic photo of A beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed",
-    "Polaroid-style photograph of a cozy coffee shop interior, bathed in warm pastel light. A cat sits on the windowsill near steaming mugs. Soft, slightly faded tones and dreamy blur evoke nostalgia, a lofi mood, and the intimate, imperfect charm of instant film.",
+    "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
-    "Soft watercolor illustration of a cozy coffee shop interior, pastel washes of color filling the space. A cat rests peacefully on the windowsill as warm light glows through. Gentle brushstrokes create a dreamy, lofi-inspired atmosphere with whimsical textures and nostalgic calm.",
+    "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
-    "Isometric pixel-art illustration of a cozy coffee shop interior in detailed 8-bit style. Warm pastel light fills the space as a cat rests on the windowsill. Blocky furniture and tiny mugs add charm, low-res retro graphics enhance the nostalgic, lofi-inspired game aesthetic."
 ]
 
 images = pipeline(
@@ -170,4 +261,4 @@ plt.tight_layout()
 plt.show()
 ```
 
-You can use this to select an image associated with a seed and iteratively improve on it by crafting a more detailed prompt.
+You can use this to iteratively select an image associated with a seed and then improve on it by crafting a more detailed prompt.

docs/source/en/using-diffusers/controlling_generation.md

@@ -70,6 +70,32 @@ For convenience, we provide a table to denote which methods are inference-only a
 [InstructPix2Pix](../api/pipelines/pix2pix) is fine-tuned from Stable Diffusion to support editing input images. It takes as inputs an image and a prompt describing an edit, and it outputs the edited image.
 InstructPix2Pix has been explicitly trained to work well with [InstructGPT](https://openai.com/blog/instruction-following/)-like prompts.
 
+## Pix2Pix Zero
+
+[Paper](https://huggingface.co/papers/2302.03027)
+
+[Pix2Pix Zero](../api/pipelines/pix2pix_zero) allows modifying an image so that one concept or subject is translated to another one while preserving general image semantics.
+
+The denoising process is guided from one conceptual embedding towards another conceptual embedding. The intermediate latents are optimized during the denoising process to push the attention maps towards reference attention maps. The reference attention maps are from the denoising process of the input image and are used to encourage semantic preservation.
+
+Pix2Pix Zero can be used both to edit synthetic images as well as real images.
+
+- To edit synthetic images, one first generates an image given a caption.
+  Next, we generate image captions for the concept that shall be edited and for the new target concept. We can use a model like [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) for this purpose. Then, "mean" prompt embeddings for both the source and target concepts are created via the text encoder. Finally, the pix2pix-zero algorithm is used to edit the synthetic image.
+- To edit a real image, one first generates an image caption using a model like [BLIP](https://huggingface.co/docs/transformers/model_doc/blip). Then one applies DDIM inversion on the prompt and image to generate "inverse" latents. Similar to before, "mean" prompt embeddings for both source and target concepts are created and finally the pix2pix-zero algorithm in combination with the "inverse" latents is used to edit the image.
+
+> [!TIP]
+> Pix2Pix Zero is the first model that allows "zero-shot" image editing. This means that the model
+> can edit an image in less than a minute on a consumer GPU as shown [here](../api/pipelines/pix2pix_zero#usage-example).
+
+As mentioned above, Pix2Pix Zero includes optimizing the latents (and not any of the UNet, VAE, or the text encoder) to steer the generation toward a specific concept. This means that the overall
+pipeline might require more memory than a standard [StableDiffusionPipeline](../api/pipelines/stable_diffusion/text2img).
+
+> [!TIP]
+> An important distinction between methods like InstructPix2Pix and Pix2Pix Zero is that the former
+> involves fine-tuning the pre-trained weights while the latter does not. This means that you can
+> apply Pix2Pix Zero to any of the available Stable Diffusion models.
+
 ## Attend and Excite
 
 [Paper](https://huggingface.co/papers/2301.13826)
@@ -152,6 +178,14 @@ multi-concept training by design. Like DreamBooth and Textual Inversion, Custom
 teach a pre-trained text-to-image diffusion model about new concepts to generate outputs involving the
 concept(s) of interest.
 
+## Model Editing
+
+[Paper](https://huggingface.co/papers/2303.08084)
+
+The [text-to-image model editing pipeline](../api/pipelines/model_editing) helps you mitigate some of the incorrect implicit assumptions a pre-trained text-to-image
+diffusion model might make about the subjects present in the input prompt. For example, if you prompt Stable Diffusion to generate images for "A pack of roses", the roses in the generated images
+are more likely to be red. This pipeline helps you change that assumption.
+
 ## DiffEdit
 
 [Paper](https://huggingface.co/papers/2210.11427)

docs/source/en/using-diffusers/inference_with_lcm.md

@@ -215,7 +215,7 @@ from diffusers import AutoPipelineForInpainting, LCMScheduler
 from diffusers.utils import load_image, make_image_grid
 
 pipe = AutoPipelineForInpainting.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting",
+    "runwayml/stable-diffusion-inpainting",
     torch_dtype=torch.float16,
     variant="fp16",
 ).to("cuda")
@@ -257,7 +257,7 @@ LCMs are compatible with adapters like LoRA, ControlNet, T2I-Adapter, and Animat
 
 ### LoRA
 
-[LoRA](../tutorials/using_peft_for_inference) adapters can be rapidly finetuned to learn a new style from just a few images and plugged into a pretrained model to generate images in that style.
+[LoRA](../using-diffusers/loading_adapters#lora) adapters can be rapidly finetuned to learn a new style from just a few images and plugged into a pretrained model to generate images in that style.
 
 <hfoptions id="lcm-lora">
 <hfoption id="LCM">