Merge modular diffusers with main (#11893)

* [CI] Fix big GPU test marker (#11786)

* update

* update

* First Block Cache (#11180)

* update

* modify flux single blocks to make compatible with cache techniques (without too much model-specific intrusion code)

* remove debug logs

* update

* cache context for different batches of data

* fix hs residual bug for single return outputs; support ltx

* fix controlnet flux

* support flux, ltx i2v, ltx condition

* update

* update

* Update docs/source/en/api/cache.md

* Update src/diffusers/hooks/hooks.py

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

* address review comments pt. 1

* address review comments pt. 2

* cache context refacotr; address review pt. 3

* address review comments

* metadata registration with decorators instead of centralized

* support cogvideox

* support mochi

* fix

* remove unused function

* remove central registry based on review

* update

---------

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

* fix

---------

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

.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
@@ -25,7 +25,7 @@ jobs:
       group: aws-g6e-4xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -38,8 +38,9 @@ jobs:
         run: |
           apt update
           apt install -y libpq-dev postgresql-client
-          uv pip install -e ".[quality]"
-          uv pip install -r benchmarks/requirements.txt
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install -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=(
-            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
+          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

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

.github/workflows/nightly_tests.yml

@@ -7,7 +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
@@ -61,7 +61,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -71,11 +71,10 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
-          uv pip install pytest-reportlog
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -85,8 +84,8 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-             -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \
             tests/pipelines/${{ matrix.module }}
@@ -108,7 +107,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -125,12 +124,11 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
-        uv pip install peft@git+https://github.com/huggingface/peft.git
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
-        uv pip install pytest-reportlog
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install pytest-reportlog
     - name: Environment
       run: python utils/print_env.py
 
@@ -141,8 +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 \
-          -k "not Flax and not Onnx" \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           --report-log=tests_torch_${{ matrix.module }}_cuda.log \
           tests/${{ matrix.module }}
@@ -154,8 +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 \
-          --make-reports=examples_torch_cuda \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v --make-reports=examples_torch_cuda \
           --report-log=examples_torch_cuda.log \
           examples/
 
@@ -180,7 +178,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -193,9 +191,8 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -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: ${{ failure() }}
       run: cat reports/tests_torch_compile_cuda_failures_short.txt
@@ -225,7 +222,7 @@ jobs:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -235,12 +232,11 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip install peft@git+https://github.com/huggingface/peft.git
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
-          uv pip install pytest-reportlog
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -251,7 +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 \
@@ -274,7 +270,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -286,11 +282,10 @@ jobs:
 
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip install peft@git+https://github.com/huggingface/peft.git
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
       - name: Environment
         run: |
@@ -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 \
-            -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_torch_minimum_version_cuda \
             tests/models/test_modeling_common.py \
             tests/pipelines/test_pipelines_common.py \
@@ -338,21 +333,18 @@ jobs:
             additional_deps: ["peft"]
           - backend: "gguf"
             test_location: "gguf"
-            additional_deps: ["peft", "kernels"]
+            additional_deps: ["peft"]
           - backend: "torchao"
             test_location: "torchao"
             additional_deps: []
           - backend: "optimum_quanto"
             test_location: "quanto"
             additional_deps: []
-          - backend: "nvidia_modelopt"
-            test_location: "modelopt"
-            additional_deps: []
     runs-on:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus all
+      options: --shm-size "20gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -362,14 +354,13 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip install -U ${{ matrix.config.backend }}
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install -U ${{ matrix.config.backend }}
           if [ "${{ join(matrix.config.additional_deps, ' ') }}" != "" ]; then
-              uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
+              python -m uv pip install ${{ join(matrix.config.additional_deps, ' ') }}
           fi
-          uv pip install pytest-reportlog
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
+          python -m uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -380,7 +371,7 @@ jobs:
           CUBLAS_WORKSPACE_CONFIG: :16:8
           BIG_GPU_MEMORY: 40
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
             --make-reports=tests_${{ matrix.config.backend }}_torch_cuda \
             --report-log=tests_${{ matrix.config.backend }}_torch_cuda.log \
             tests/quantization/${{ matrix.config.test_location }}
@@ -405,7 +396,7 @@ jobs:
       group: aws-g6e-xlarge-plus
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "20gb" --ipc host --gpus all
+      options: --shm-size "20gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -415,11 +406,10 @@ jobs:
         run: nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip install -U bitsandbytes optimum_quanto
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1 
-          uv pip install pytest-reportlog
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install -U bitsandbytes optimum_quanto
+          python -m uv pip install pytest-reportlog
       - name: Environment
         run: |
           python utils/print_env.py
@@ -430,7 +420,7 @@ 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 +520,11 @@ jobs:
 #      - name: Install dependencies
 #        shell: arch -arch arm64 bash {0}
 #        run: |
-#          ${CONDA_RUN} pip install --upgrade pip uv
-#          ${CONDA_RUN} uv pip install -e ".[quality]"
-#          ${CONDA_RUN} uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} uv pip install pytest-reportlog
+#          ${CONDA_RUN} python -m pip install --upgrade pip uv
+#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
+#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
+#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
 #      - name: Environment
 #        shell: arch -arch arm64 bash {0}
 #        run: |
@@ -545,7 +535,7 @@ 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 +576,11 @@ jobs:
 #      - name: Install dependencies
 #        shell: arch -arch arm64 bash {0}
 #        run: |
-#          ${CONDA_RUN} pip install --upgrade pip uv
-#          ${CONDA_RUN} uv pip install -e ".[quality]"
-#          ${CONDA_RUN} uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-#          ${CONDA_RUN} uv pip install accelerate@git+https://github.com/huggingface/accelerate
-#          ${CONDA_RUN} uv pip install pytest-reportlog
+#          ${CONDA_RUN} python -m pip install --upgrade pip uv
+#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
+#          ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+#          ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate
+#          ${CONDA_RUN} python -m uv pip install pytest-reportlog
 #      - name: Environment
 #        shell: arch -arch arm64 bash {0}
 #        run: |
@@ -601,7 +591,7 @@ 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 .
-          pip install pytest
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m pip install --upgrade pip uv
+          python -m uv pip install -e .
+          python -m uv pip install pytest
       - name: Check for soft dependencies
         run: |
-            pytest tests/others/test_dependencies.py
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          pytest tests/others/test_dependencies.py

.github/workflows/pr_flax_dependency_test.yml

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

.github/workflows/pr_modular_tests.yml

@@ -1,139 +0,0 @@
-name: Fast PR tests for Modular
-
-on:
-  pull_request:
-    branches: [main]
-    paths:
-      - "src/diffusers/modular_pipelines/**.py"
-      - "src/diffusers/models/modeling_utils.py"
-      - "src/diffusers/models/model_loading_utils.py"
-      - "src/diffusers/pipelines/pipeline_utils.py"
-      - "src/diffusers/pipeline_loading_utils.py"
-      - "src/diffusers/loaders/lora_base.py"
-      - "src/diffusers/loaders/lora_pipeline.py"
-      - "src/diffusers/loaders/peft.py"
-      - "tests/modular_pipelines/**.py"
-      - ".github/**.yml"
-      - "utils/**.py"
-      - "setup.py"
-  push:
-    branches:
-      - ci-*
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  DIFFUSERS_IS_CI: yes
-  HF_XET_HIGH_PERFORMANCE: 1
-  OMP_NUM_THREADS: 4
-  MKL_NUM_THREADS: 4
-  PYTEST_TIMEOUT: 60
-
-jobs:
-  check_code_quality:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          pip install --upgrade pip
-          pip install .[quality]
-      - name: Check quality
-        run: make quality
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
-
-  check_repository_consistency:
-    needs: check_code_quality
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          pip install --upgrade pip
-          pip install .[quality]
-      - name: Check repo consistency
-        run: |
-          python utils/check_copies.py
-          python utils/check_dummies.py
-          python utils/check_support_list.py
-          make deps_table_check_updated
-      - name: Check if failure
-        if: ${{ failure() }}
-        run: |
-          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
-
-  run_fast_tests:
-    needs: [check_code_quality, check_repository_consistency]
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - name: Fast PyTorch Modular Pipeline CPU tests
-            framework: pytorch_pipelines
-            runner: aws-highmemory-32-plus
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_cpu_modular_pipelines
-
-    name: ${{ matrix.config.name }}
-
-    runs-on:
-      group: ${{ matrix.config.runner }}
-
-    container:
-      image: ${{ matrix.config.image }}
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
-
-    defaults:
-      run:
-        shell: bash
-
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        uv pip install -e ".[quality]"
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run fast PyTorch Pipeline CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
-      run: |
-        pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/modular_pipelines
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v4
-      with:
-        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
-        path: reports
-
-

.github/workflows/pr_test_fetcher.yml

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

.github/workflows/pr_tests.yml

@@ -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]"
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run fast PyTorch Pipeline CPU tests
       if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
       run: |
-        pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/pipelines
 
     - name: Run fast PyTorch Model Scheduler CPU tests
       if: ${{ matrix.config.framework == 'pytorch_models' }}
       run: |
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx and not Dependency" \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx and not Dependency" \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/models tests/schedulers tests/others
 
     - name: Run example PyTorch CPU tests
       if: ${{ matrix.config.framework == 'pytorch_examples' }}
       run: |
-        uv pip install ".[training]"
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install peft timm
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
           examples
 
@@ -191,16 +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
-        uv pip install -U tokenizers
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+        python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
+        python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        python -m uv pip install -U tokenizers
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run fast PyTorch LoRA tests with PEFT
       run: |
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          \
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v \
           --make-reports=tests_peft_main \
           tests/lora/
-        pytest -n 4 --max-worker-restart=0 --dist=loadfile \
-          \
+        python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \
+          -s -v \
           --make-reports=tests_models_lora_peft_main \
           tests/models/ -k "lora"
 

.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:
@@ -13,7 +13,6 @@ on:
       - "src/diffusers/loaders/peft.py"
       - "tests/pipelines/test_pipelines_common.py"
       - "tests/models/test_modeling_common.py"
-      - "examples/**/*.py"
   workflow_dispatch:
 
 concurrency:
@@ -24,7 +23,7 @@ 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 +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
@@ -59,7 +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: |
@@ -71,7 +70,7 @@ 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'" >> $GITHUB_STEP_SUMMARY
-
+  
   setup_torch_cuda_pipeline_matrix:
     needs: [check_code_quality, check_repository_consistency]
     name: Setup Torch Pipelines CUDA Slow Tests Matrix
@@ -88,7 +87,8 @@ jobs:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -117,7 +117,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -129,10 +129,10 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
 
       - name: Environment
         run: |
@@ -150,18 +150,18 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          if [ "${{ matrix.module }}" = "ip_adapters" ]; then
-              pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -k "not Flax and not Onnx" \
+          if [ "${{ matrix.module }}" = "ip_adapters" ]; then 
+              python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+              -s -v -k "not Flax and not Onnx" \
               --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
               tests/pipelines/${{ matrix.module }}
-          else
+          else 
               pattern=$(cat ${{ steps.extract_tests.outputs.pattern_file }})
-              pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-              -k "not Flax and not Onnx and $pattern" \
+              python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+              -s -v -k "not Flax and not Onnx and $pattern" \
               --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
               tests/pipelines/${{ matrix.module }}
-          fi
+          fi 
 
       - name: Failure short reports
         if: ${{ failure() }}
@@ -182,13 +182,13 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
     strategy:
       fail-fast: false
-      max-parallel: 4
+      max-parallel: 2
       matrix:
         module: [models, schedulers, lora, others]
     steps:
@@ -199,11 +199,11 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
-        uv pip install peft@git+https://github.com/huggingface/peft.git
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
 
     - name: Environment
       run: |
@@ -224,11 +224,11 @@ 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/${{ matrix.module }} \
-          --make-reports=tests_torch_cuda_${{ 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 }}  
         else
-          pytest -n 1  --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
-          --make-reports=tests_torch_cuda_${{ matrix.module }}
+          python -m pytest -n 1 -sv --max-worker-restart=0 --dist=loadfile -k "not Flax and not Onnx and $pattern" tests/${{ matrix.module }} \
+          --make-reports=tests_torch_cuda_${{ matrix.module }}  
         fi
 
     - name: Failure short reports
@@ -252,7 +252,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
@@ -264,20 +264,22 @@ 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
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
-        uv pip install -e ".[quality,training]"
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        python -m uv pip install -e [quality,test,training]
 
     - name: Environment
       run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python utils/print_env.py
 
     - name: Run example tests on GPU
       env:
         HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        uv pip install ".[training]"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install timm
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/pr_torch_dependency_test.yml

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

.github/workflows/push_tests.yml

@@ -14,7 +14,7 @@ env:
   DIFFUSERS_IS_CI: yes
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  HF_XET_HIGH_PERFORMANCE: 1
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   PYTEST_TIMEOUT: 600
   PIPELINE_USAGE_CUTOFF: 50000
 
@@ -34,7 +34,8 @@ jobs:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -63,7 +64,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -74,10 +75,9 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
       - name: Environment
         run: |
           python utils/print_env.py
@@ -87,8 +87,8 @@ jobs:
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
-          pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -109,7 +109,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -126,11 +126,10 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
-        uv pip install peft@git+https://github.com/huggingface/peft.git
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
     - name: Environment
       run: |
@@ -142,8 +141,8 @@ jobs:
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -k "not Flax and not Onnx" \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_cuda_${{ matrix.module }} \
           tests/${{ matrix.module }}
 
@@ -168,7 +167,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -181,9 +180,8 @@ jobs:
         nvidia-smi
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality,training]"
-        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
     - name: Environment
       run: |
         python utils/print_env.py
@@ -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
@@ -212,7 +210,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -225,7 +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
@@ -253,7 +252,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
@@ -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: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        uv pip install ".[training]"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install timm
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/push_tests_fast.yml

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

.github/workflows/push_tests_mps.yml

@@ -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:$PATH"
+          python -m uv pip install -e [quality,test]
       - name: Environment
         run: |
           python utils/print_env.py
@@ -61,7 +62,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -72,8 +73,9 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
       - name: Environment
         run: |
           python utils/print_env.py
@@ -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 \
-            -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             tests/pipelines/${{ matrix.module }}
       - name: Failure short reports
@@ -105,7 +107,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -122,9 +124,10 @@ jobs:
 
     - name: Install dependencies
       run: |
-        uv pip install -e ".[quality]"
-        uv pip install peft@git+https://github.com/huggingface/peft.git
-        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
     - name: Environment
       run: |
@@ -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 \
-          -k "not Flax and not Onnx" \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
           --make-reports=tests_torch_${{ matrix.module }}_cuda \
           tests/${{ matrix.module }}
 
@@ -160,7 +163,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-minimum-cuda
-      options: --shm-size "16gb" --ipc host --gpus all
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
@@ -172,9 +175,10 @@ jobs:
 
       - name: Install dependencies
         run: |
-          uv pip install -e ".[quality]"
-          uv pip install peft@git+https://github.com/huggingface/peft.git
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+          pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
 
       - name: Environment
         run: |
@@ -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 \
-            -k "not Flax and not Onnx" \
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
             --make-reports=tests_torch_minimum_cuda \
             tests/models/test_modeling_common.py \
             tests/pipelines/test_pipelines_common.py \
@@ -218,7 +222,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -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
@@ -260,7 +265,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -273,7 +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
@@ -301,7 +307,7 @@ jobs:
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus all --shm-size "16gb" --ipc host
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -315,18 +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: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
       run: |
-        uv pip install ".[training]"
-        pytest -n 1 --max-worker-restart=0 --dist=loadfile --make-reports=examples_torch_cuda examples/
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install timm
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
 
     - name: Failure short reports
       if: ${{ failure() }}

.github/workflows/run_tests_from_a_pr.yml

@@ -30,7 +30,7 @@ jobs:
       group: aws-g4dn-2xlarge
     container:
       image: ${{ github.event.inputs.docker_image }}
-      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
 
     steps:
       - name: Validate test files input
@@ -63,8 +63,9 @@ jobs:
 
       - name: Install pytest
         run: |
-          uv pip install -e ".[quality]"
-          uv pip install peft
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install peft
 
       - name: Run tests
         env:

.github/workflows/ssh-runner.yml

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

.gitignore

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

README.md

@@ -37,7 +37,7 @@ limitations under the License.
 
 ## Installation
 
-We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/), please refer to their official documentation.
+We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.
 
 ### PyTorch
 
@@ -53,6 +53,14 @@ With `conda` (maintained by the community):
 conda install -c conda-forge diffusers
 ```
 
+### Flax
+
+With `pip` (official package):
+
+```bash
+pip install --upgrade diffusers[flax]
+```
+
 ### Apple Silicon (M1/M2) support
 
 Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.
@@ -171,7 +179,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
   <tr style="border-top: 2px solid black">
     <td>Text-guided Image Inpainting</td>
     <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpainting</a></td>
-      <td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting"> stable-diffusion-v1-5/stable-diffusion-inpainting </a></td>
+      <td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
   </tr>
   <tr style="border-top: 2px solid black">
     <td>Image Variation</td>

benchmarks/README.md

@@ -31,7 +31,7 @@ pip install -r requirements.txt
 We need to be authenticated to access some of the checkpoints used during benchmarking:
 
 ```sh
-hf auth login
+huggingface-cli login
 ```
 
 We use an L40 GPU with 128GB RAM to run the benchmark CI. As such, the benchmarks are configured to run on NVIDIA GPUs. So, make sure you have access to a similar machine (or modify the benchmarking scripts accordingly).

docker/diffusers-doc-builder/Dockerfile

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

docker/diffusers-flax-cpu/Dockerfile

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

docker/diffusers-flax-tpu/Dockerfile

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

docker/diffusers-onnxruntime-cpu/Dockerfile

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

docker/diffusers-pytorch-cuda/Dockerfile

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

docker/diffusers-pytorch-minimum-cuda/Dockerfile

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

docker/diffusers-pytorch-xformers-cuda/Dockerfile

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

docs/source/en/_toctree.yml

@@ -1,36 +1,36 @@
 - sections:
   - local: index
-    title: Diffusers
+    title: 🧨 Diffusers
+  - local: quicktour
+    title: Quicktour
+  - local: stable_diffusion
+    title: Effective and efficient diffusion
   - local: installation
     title: Installation
-  - local: quicktour
-    title: Quickstart
-  - local: stable_diffusion
-    title: Basic performance
   title: Get started
-- isExpanded: false
-  sections:
-  - local: using-diffusers/loading
-    title: DiffusionPipeline
+- sections:
+  - local: tutorials/tutorial_overview
+    title: Overview
+  - local: using-diffusers/write_own_pipeline
+    title: Understanding pipelines, models and schedulers
   - local: tutorials/autopipeline
     title: AutoPipeline
+  - local: tutorials/basic_training
+    title: Train a diffusion model
+  title: Tutorials
+- sections:
+  - local: using-diffusers/loading
+    title: Load pipelines
   - local: using-diffusers/custom_pipeline_overview
-    title: Community pipelines and components
-  - local: using-diffusers/callback
-    title: Pipeline callbacks
-  - local: using-diffusers/reusing_seeds
-    title: Reproducibility
+    title: Load community pipelines and components
   - local: using-diffusers/schedulers
-    title: Schedulers
-  - local: using-diffusers/automodel
-    title: AutoModel
+    title: Load schedulers and models
   - local: using-diffusers/other-formats
-    title: Model formats
+    title: Model files and layouts
   - local: using-diffusers/push_to_hub
-    title: Sharing pipelines and models
-  title: Pipelines
-- isExpanded: false
-  sections:
+    title: Push files to the Hub
+  title: Load pipelines and adapters
+- sections:
   - local: tutorials/using_peft_for_inference
     title: LoRA
   - local: using-diffusers/ip_adapter
@@ -44,52 +44,46 @@
   - local: using-diffusers/textual_inversion_inference
     title: Textual inversion
   title: Adapters
-- isExpanded: false
-  sections:
-  - local: using-diffusers/weighted_prompts
-    title: Prompting
+  isExpanded: false
+- sections:
+  - local: using-diffusers/unconditional_image_generation
+    title: Unconditional image generation
+  - local: using-diffusers/conditional_image_generation
+    title: Text-to-image
+  - local: using-diffusers/img2img
+    title: Image-to-image
+  - local: using-diffusers/inpaint
+    title: Inpainting
+  - local: using-diffusers/text-img2vid
+    title: Video generation
+  - local: using-diffusers/depth2img
+    title: Depth-to-image
+  title: Generative tasks
+- sections:
+  - local: using-diffusers/overview_techniques
+    title: Overview
   - local: using-diffusers/create_a_server
     title: Create a server
   - local: using-diffusers/batched_inference
     title: Batch inference
   - local: training/distributed_inference
     title: Distributed inference
-  title: Inference
-- isExpanded: false
-  sections:
-  - local: optimization/fp16
-    title: Accelerate inference
-  - local: optimization/cache
-    title: Caching
-  - local: optimization/attention_backends
-    title: Attention backends
-  - local: optimization/memory
-    title: Reduce memory usage
-  - local: optimization/speed-memory-optims
-    title: Compiling and offloading quantized models
-  - sections:
-    - local: optimization/pruna
-      title: Pruna
-    - local: optimization/xformers
-      title: xFormers
-    - local: optimization/tome
-      title: Token merging
-    - local: optimization/deepcache
-      title: DeepCache
-    - local: optimization/cache_dit
-      title: CacheDiT
-    - local: optimization/tgate
-      title: TGATE
-    - local: optimization/xdit
-      title: xDiT
-    - local: optimization/para_attn
-      title: ParaAttention
-    - local: using-diffusers/image_quality
-      title: FreeU
-    title: Community optimizations
-  title: Inference optimization
-- isExpanded: false
-  sections:
+  - local: using-diffusers/scheduler_features
+    title: Scheduler features
+  - local: using-diffusers/callback
+    title: Pipeline callbacks
+  - local: using-diffusers/reusing_seeds
+    title: Reproducible pipelines
+  - local: using-diffusers/image_quality
+    title: Controlling image quality
+  - local: using-diffusers/weighted_prompts
+    title: Prompt techniques
+  title: Inference techniques
+- sections:
+  - local: advanced_inference/outpaint
+    title: Outpainting
+  title: Advanced inference
+- sections:
   - local: hybrid_inference/overview
     title: Overview
   - local: hybrid_inference/vae_decode
@@ -99,108 +93,17 @@
   - local: hybrid_inference/api_reference
     title: API Reference
   title: Hybrid Inference
-- isExpanded: false
-  sections:
-  - local: modular_diffusers/overview
-    title: Overview
-  - local: modular_diffusers/quickstart
-    title: Quickstart
-  - local: modular_diffusers/modular_diffusers_states
-    title: States
-  - local: modular_diffusers/pipeline_block
-    title: ModularPipelineBlocks
-  - local: modular_diffusers/sequential_pipeline_blocks
-    title: SequentialPipelineBlocks
-  - local: modular_diffusers/loop_sequential_pipeline_blocks
-    title: LoopSequentialPipelineBlocks
-  - local: modular_diffusers/auto_pipeline_blocks
-    title: AutoPipelineBlocks
-  - local: modular_diffusers/modular_pipeline
-    title: ModularPipeline
+- sections:
+  - local: modular_diffusers/getting_started
+    title: Getting Started
   - local: modular_diffusers/components_manager
-    title: ComponentsManager
-  - local: modular_diffusers/guiders
-    title: Guiders
-  - local: modular_diffusers/custom_blocks
-    title: Building Custom Blocks
+    title: Components Manager
+  - local: modular_diffusers/write_own_pipeline_block
+    title: Write your own pipeline block
+  - local: modular_diffusers/end_to_end_guide
+    title: End-to-End Developer Guide
   title: Modular Diffusers
-- isExpanded: false
-  sections:
-  - local: training/overview
-    title: Overview
-  - local: training/create_dataset
-    title: Create a dataset for training
-  - local: training/adapt_a_model
-    title: Adapt a model to a new task
-  - local: tutorials/basic_training
-    title: Train a diffusion model
-  - sections:
-    - local: training/unconditional_training
-      title: Unconditional image generation
-    - local: training/text2image
-      title: Text-to-image
-    - local: training/sdxl
-      title: Stable Diffusion XL
-    - local: training/kandinsky
-      title: Kandinsky 2.2
-    - local: training/wuerstchen
-      title: Wuerstchen
-    - local: training/controlnet
-      title: ControlNet
-    - local: training/t2i_adapters
-      title: T2I-Adapters
-    - local: training/instructpix2pix
-      title: InstructPix2Pix
-    - local: training/cogvideox
-      title: CogVideoX
-    title: Models
-  - sections:
-    - local: training/text_inversion
-      title: Textual Inversion
-    - local: training/dreambooth
-      title: DreamBooth
-    - local: training/lora
-      title: LoRA
-    - local: training/custom_diffusion
-      title: Custom Diffusion
-    - local: training/lcm_distill
-      title: Latent Consistency Distillation
-    - local: training/ddpo
-      title: Reinforcement learning training with DDPO
-    title: Methods
-  title: Training
-- isExpanded: false
-  sections:
-  - local: quantization/overview
-    title: Getting started
-  - local: quantization/bitsandbytes
-    title: bitsandbytes
-  - local: quantization/gguf
-    title: gguf
-  - local: quantization/torchao
-    title: torchao
-  - local: quantization/quanto
-    title: quanto
-  - local: quantization/modelopt
-    title: NVIDIA ModelOpt
-  title: Quantization
-- isExpanded: false
-  sections:
-  - local: optimization/onnx
-    title: ONNX
-  - local: optimization/open_vino
-    title: OpenVINO
-  - local: optimization/coreml
-    title: Core ML
-  - local: optimization/mps
-    title: Metal Performance Shaders (MPS)
-  - local: optimization/habana
-    title: Intel Gaudi
-  - local: optimization/neuron
-    title: AWS Neuron
-  title: Model accelerators and hardware
-- isExpanded: false
-  sections:
+- sections:
   - local: using-diffusers/consisid
     title: ConsisID
   - local: using-diffusers/sdxl
@@ -226,28 +129,105 @@
   - local: using-diffusers/marigold_usage
     title: Marigold Computer Vision
   title: Specific pipeline examples
-- isExpanded: false
-  sections:
-  - sections:
-    - local: using-diffusers/unconditional_image_generation
+- sections:
+  - local: training/overview
+    title: Overview
+  - local: training/create_dataset
+    title: Create a dataset for training
+  - local: training/adapt_a_model
+    title: Adapt a model to a new task
+  - isExpanded: false
+    sections:
+    - local: training/unconditional_training
       title: Unconditional image generation
-    - local: using-diffusers/conditional_image_generation
+    - local: training/text2image
       title: Text-to-image
-    - local: using-diffusers/img2img
-      title: Image-to-image
-    - local: using-diffusers/inpaint
-      title: Inpainting
-    - local: advanced_inference/outpaint
-      title: Outpainting
-    - local: using-diffusers/text-img2vid
-      title: Video generation
-    - local: using-diffusers/depth2img
-      title: Depth-to-image
-    title: Task recipes
-  - local: using-diffusers/write_own_pipeline
-    title: Understanding pipelines, models and schedulers
-  - local: community_projects
-    title: Projects built with Diffusers
+    - local: training/sdxl
+      title: Stable Diffusion XL
+    - local: training/kandinsky
+      title: Kandinsky 2.2
+    - local: training/wuerstchen
+      title: Wuerstchen
+    - local: training/controlnet
+      title: ControlNet
+    - local: training/t2i_adapters
+      title: T2I-Adapters
+    - local: training/instructpix2pix
+      title: InstructPix2Pix
+    - local: training/cogvideox
+      title: CogVideoX
+    title: Models
+  - isExpanded: false
+    sections:
+    - local: training/text_inversion
+      title: Textual Inversion
+    - local: training/dreambooth
+      title: DreamBooth
+    - local: training/lora
+      title: LoRA
+    - local: training/custom_diffusion
+      title: Custom Diffusion
+    - local: training/lcm_distill
+      title: Latent Consistency Distillation
+    - local: training/ddpo
+      title: Reinforcement learning training with DDPO
+    title: Methods
+  title: Training
+- sections:
+  - local: quantization/overview
+    title: Getting Started
+  - local: quantization/bitsandbytes
+    title: bitsandbytes
+  - local: quantization/gguf
+    title: gguf
+  - local: quantization/torchao
+    title: torchao
+  - local: quantization/quanto
+    title: quanto
+  title: Quantization Methods
+- sections:
+  - local: optimization/fp16
+    title: Accelerate inference
+  - local: optimization/cache
+    title: Caching
+  - local: optimization/memory
+    title: Reduce memory usage
+  - local: optimization/speed-memory-optims
+    title: Compile and offloading quantized models
+  - local: optimization/pruna
+    title: Pruna
+  - local: optimization/xformers
+    title: xFormers
+  - local: optimization/tome
+    title: Token merging
+  - local: optimization/deepcache
+    title: DeepCache
+  - local: optimization/tgate
+    title: TGATE
+  - local: optimization/xdit
+    title: xDiT
+  - local: optimization/para_attn
+    title: ParaAttention
+  - sections:
+    - local: using-diffusers/stable_diffusion_jax_how_to
+      title: JAX/Flax
+    - local: optimization/onnx
+      title: ONNX
+    - local: optimization/open_vino
+      title: OpenVINO
+    - local: optimization/coreml
+      title: Core ML
+    title: Optimized model formats
+  - sections:
+    - local: optimization/mps
+      title: Metal Performance Shaders (MPS)
+    - local: optimization/habana
+      title: Intel Gaudi
+    - local: optimization/neuron
+      title: AWS Neuron
+    title: Optimized hardware
+  title: Accelerate inference and reduce memory
+- sections:
   - local: conceptual/philosophy
     title: Philosophy
   - local: using-diffusers/controlling_generation
@@ -258,10 +238,14 @@
     title: Diffusers' Ethical Guidelines
   - local: conceptual/evaluation
     title: Evaluating Diffusion Models
-  title: Resources
-- isExpanded: false
-  sections:
-  - sections:
+  title: Conceptual Guides
+- sections:
+  - local: community_projects
+    title: Projects built with Diffusers
+  title: Community Projects
+- sections:
+  - isExpanded: false
+    sections:
     - local: api/configuration
       title: Configuration
     - local: api/logging
@@ -270,22 +254,9 @@
       title: Outputs
     - local: api/quantization
       title: Quantization
-    - local: api/parallel
-      title: Parallel inference
     title: Main Classes
-  - sections:
-    - local: api/modular_diffusers/pipeline
-      title: Pipeline
-    - local: api/modular_diffusers/pipeline_blocks
-      title: Blocks
-    - local: api/modular_diffusers/pipeline_states
-      title: States
-    - local: api/modular_diffusers/pipeline_components
-      title: Components and configs
-    - local: api/modular_diffusers/guiders
-      title: Guiders
-    title: Modular
-  - sections:
+  - isExpanded: false
+    sections:
     - local: api/loaders/ip_adapter
       title: IP-Adapter
     - local: api/loaders/lora
@@ -301,7 +272,8 @@
     - local: api/loaders/peft
       title: PEFT
     title: Loaders
-  - sections:
+  - isExpanded: false
+    sections:
     - local: api/models/overview
       title: Overview
     - local: api/models/auto_model
@@ -327,14 +299,8 @@
         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
@@ -349,16 +315,12 @@
         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_video_transformer_3d
         title: HunyuanVideoTransformer3DModel
       - local: api/models/latte_transformer3d
@@ -377,24 +339,16 @@
         title: PixArtTransformer2DModel
       - local: api/models/prior_transformer
         title: PriorTransformer
-      - local: api/models/qwenimage_transformer2d
-        title: QwenImageTransformer2DModel
       - local: api/models/sana_transformer2d
         title: SanaTransformer2DModel
-      - local: api/models/sana_video_transformer3d
-        title: SanaVideoTransformer3DModel
       - local: api/models/sd3_transformer2d
         title: SD3Transformer2DModel
-      - local: api/models/skyreels_v2_transformer_3d
-        title: SkyReelsV2Transformer3DModel
       - local: api/models/stable_audio_transformer
         title: StableAudioDiTModel
       - local: api/models/transformer2d
         title: Transformer2DModel
       - local: api/models/transformer_temporal
         title: TransformerTemporalModel
-      - local: api/models/wan_animate_transformer_3d
-        title: WanAnimateTransformer3DModel
       - local: api/models/wan_transformer_3d
         title: WanTransformer3DModel
       title: Transformers
@@ -427,10 +381,6 @@
         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/autoencoderkl_ltx_video
@@ -439,8 +389,6 @@
         title: AutoencoderKLMagvit
       - local: api/models/autoencoderkl_mochi
         title: AutoencoderKLMochi
-      - local: api/models/autoencoderkl_qwenimage
-        title: AutoencoderKLQwenImage
       - local: api/models/autoencoder_kl_wan
         title: AutoencoderKLWan
       - local: api/models/consistency_decoder_vae
@@ -453,230 +401,205 @@
         title: VQModel
       title: VAEs
     title: Models
-  - sections:
+  - isExpanded: false
+    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
+    - local: api/pipelines/blip_diffusion
+      title: BLIP-Diffusion
+    - local: api/pipelines/chroma
+      title: Chroma
+    - local: api/pipelines/cogvideox
+      title: CogVideoX
+    - local: api/pipelines/cogview3
+      title: CogView3
+    - local: api/pipelines/cogview4
+      title: CogView4
+    - local: api/pipelines/consisid
+      title: ConsisID
+    - local: api/pipelines/consistency_models
+      title: Consistency Models
+    - local: api/pipelines/controlnet
+      title: ControlNet
+    - local: api/pipelines/controlnet_flux
+      title: ControlNet with Flux.1
+    - local: api/pipelines/controlnet_hunyuandit
+      title: ControlNet with Hunyuan-DiT
+    - local: api/pipelines/controlnet_sd3
+      title: ControlNet with Stable Diffusion 3
+    - local: api/pipelines/controlnet_sdxl
+      title: ControlNet with Stable Diffusion XL
+    - local: api/pipelines/controlnet_sana
+      title: ControlNet-Sana
+    - local: api/pipelines/controlnetxs
+      title: ControlNet-XS
+    - local: api/pipelines/controlnetxs_sdxl
+      title: ControlNet-XS with Stable Diffusion XL
+    - local: api/pipelines/controlnet_union
+      title: ControlNetUnion
+    - local: api/pipelines/cosmos
+      title: Cosmos
+    - local: api/pipelines/dance_diffusion
+      title: Dance Diffusion
+    - local: api/pipelines/ddim
+      title: DDIM
+    - local: api/pipelines/ddpm
+      title: DDPM
+    - local: api/pipelines/deepfloyd_if
+      title: DeepFloyd IF
+    - local: api/pipelines/diffedit
+      title: DiffEdit
+    - local: api/pipelines/dit
+      title: DiT
+    - local: api/pipelines/easyanimate
+      title: EasyAnimate
+    - local: api/pipelines/flux
+      title: Flux
+    - local: api/pipelines/control_flux_inpaint
+      title: FluxControlInpaint
+    - local: api/pipelines/framepack
+      title: Framepack
+    - local: api/pipelines/hidream
+      title: HiDream-I1
+    - local: api/pipelines/hunyuandit
+      title: Hunyuan-DiT
+    - local: api/pipelines/hunyuan_video
+      title: HunyuanVideo
+    - local: api/pipelines/i2vgenxl
+      title: I2VGen-XL
+    - local: api/pipelines/pix2pix
+      title: InstructPix2Pix
+    - local: api/pipelines/kandinsky
+      title: Kandinsky 2.1
+    - local: api/pipelines/kandinsky_v22
+      title: Kandinsky 2.2
+    - local: api/pipelines/kandinsky3
+      title: Kandinsky 3
+    - local: api/pipelines/kolors
+      title: Kolors
+    - local: api/pipelines/latent_consistency_models
+      title: Latent Consistency Models
+    - local: api/pipelines/latent_diffusion
+      title: Latent Diffusion
+    - local: api/pipelines/latte
+      title: Latte
+    - local: api/pipelines/ledits_pp
+      title: LEDITS++
+    - local: api/pipelines/ltx_video
+      title: LTXVideo
+    - local: api/pipelines/lumina2
+      title: Lumina 2.0
+    - local: api/pipelines/lumina
+      title: Lumina-T2X
+    - local: api/pipelines/marigold
+      title: Marigold
+    - local: api/pipelines/mochi
+      title: Mochi
+    - local: api/pipelines/panorama
+      title: MultiDiffusion
+    - local: api/pipelines/musicldm
+      title: MusicLDM
+    - local: api/pipelines/omnigen
+      title: OmniGen
+    - local: api/pipelines/pag
+      title: PAG
+    - local: api/pipelines/paint_by_example
+      title: Paint by Example
+    - local: api/pipelines/pia
+      title: Personalized Image Animator (PIA)
+    - local: api/pipelines/pixart
+      title: PixArt-α
+    - local: api/pipelines/pixart_sigma
+      title: PixArt-Σ
+    - local: api/pipelines/sana
+      title: Sana
+    - local: api/pipelines/sana_sprint
+      title: Sana Sprint
+    - local: api/pipelines/self_attention_guidance
+      title: Self-Attention Guidance
+    - local: api/pipelines/semantic_stable_diffusion
+      title: Semantic Guidance
+    - local: api/pipelines/shap_e
+      title: Shap-E
+    - local: api/pipelines/stable_audio
+      title: Stable Audio
+    - local: api/pipelines/stable_cascade
+      title: Stable Cascade
     - sections:
-      - local: api/pipelines/audioldm
-        title: AudioLDM
-      - local: api/pipelines/audioldm2
-        title: AudioLDM 2
-      - local: api/pipelines/dance_diffusion
-        title: Dance Diffusion
-      - local: api/pipelines/musicldm
-        title: MusicLDM
-      - local: api/pipelines/stable_audio
-        title: Stable Audio
-      title: Audio
-    - sections:
-      - local: api/pipelines/amused
-        title: aMUSEd
-      - local: api/pipelines/animatediff
-        title: AnimateDiff
-      - local: api/pipelines/attend_and_excite
-        title: Attend-and-Excite
-      - local: api/pipelines/aura_flow
-        title: AuraFlow
-      - local: api/pipelines/blip_diffusion
-        title: BLIP-Diffusion
-      - local: api/pipelines/bria_3_2
-        title: Bria 3.2
-      - local: api/pipelines/bria_fibo
-        title: Bria Fibo
-      - local: api/pipelines/chroma
-        title: Chroma
-      - local: api/pipelines/cogview3
-        title: CogView3
-      - local: api/pipelines/cogview4
-        title: CogView4
-      - local: api/pipelines/consistency_models
-        title: Consistency Models
-      - local: api/pipelines/controlnet
-        title: ControlNet
-      - local: api/pipelines/controlnet_flux
-        title: ControlNet with Flux.1
-      - local: api/pipelines/controlnet_hunyuandit
-        title: ControlNet with Hunyuan-DiT
-      - local: api/pipelines/controlnet_sd3
-        title: ControlNet with Stable Diffusion 3
-      - local: api/pipelines/controlnet_sdxl
-        title: ControlNet with Stable Diffusion XL
-      - local: api/pipelines/controlnet_sana
-        title: ControlNet-Sana
-      - local: api/pipelines/controlnetxs
-        title: ControlNet-XS
-      - local: api/pipelines/controlnetxs_sdxl
-        title: ControlNet-XS with Stable Diffusion XL
-      - local: api/pipelines/controlnet_union
-        title: ControlNetUnion
-      - local: api/pipelines/cosmos
-        title: Cosmos
-      - local: api/pipelines/ddim
-        title: DDIM
-      - local: api/pipelines/ddpm
-        title: DDPM
-      - local: api/pipelines/deepfloyd_if
-        title: DeepFloyd IF
-      - local: api/pipelines/diffedit
-        title: DiffEdit
-      - local: api/pipelines/dit
-        title: DiT
-      - local: api/pipelines/easyanimate
-        title: EasyAnimate
-      - local: api/pipelines/flux
-        title: Flux
-      - local: api/pipelines/flux2
-        title: Flux2
-      - local: api/pipelines/control_flux_inpaint
-        title: FluxControlInpaint
-      - local: api/pipelines/hidream
-        title: HiDream-I1
-      - local: api/pipelines/hunyuandit
-        title: Hunyuan-DiT
-      - local: api/pipelines/hunyuanimage21
-        title: HunyuanImage2.1
-      - local: api/pipelines/pix2pix
-        title: InstructPix2Pix
-      - local: api/pipelines/kandinsky
-        title: Kandinsky 2.1
-      - local: api/pipelines/kandinsky_v22
-        title: Kandinsky 2.2
-      - local: api/pipelines/kandinsky3
-        title: Kandinsky 3
-      - local: api/pipelines/kolors
-        title: Kolors
-      - local: api/pipelines/latent_consistency_models
-        title: Latent Consistency Models
-      - local: api/pipelines/latent_diffusion
-        title: Latent Diffusion
-      - local: api/pipelines/ledits_pp
-        title: LEDITS++
-      - local: api/pipelines/lumina2
-        title: Lumina 2.0
-      - local: api/pipelines/lumina
-        title: Lumina-T2X
-      - local: api/pipelines/marigold
-        title: Marigold
-      - local: api/pipelines/panorama
-        title: MultiDiffusion
-      - local: api/pipelines/omnigen
-        title: OmniGen
-      - local: api/pipelines/pag
-        title: PAG
-      - local: api/pipelines/paint_by_example
-        title: Paint by Example
-      - local: api/pipelines/pixart
-        title: PixArt-α
-      - local: api/pipelines/pixart_sigma
-        title: PixArt-Σ
-      - local: api/pipelines/prx
-        title: PRX
-      - local: api/pipelines/qwenimage
-        title: QwenImage
-      - local: api/pipelines/sana
-        title: Sana
-      - local: api/pipelines/sana_sprint
-        title: Sana Sprint
-      - local: api/pipelines/sana_video
-        title: Sana Video
-      - local: api/pipelines/self_attention_guidance
-        title: Self-Attention Guidance
-      - local: api/pipelines/semantic_stable_diffusion
-        title: Semantic Guidance
-      - local: api/pipelines/shap_e
-        title: Shap-E
-      - local: api/pipelines/stable_cascade
-        title: Stable Cascade
-      - sections:
-        - local: api/pipelines/stable_diffusion/overview
-          title: Overview
-        - local: api/pipelines/stable_diffusion/depth2img
-          title: Depth-to-image
-        - local: api/pipelines/stable_diffusion/gligen
-          title: GLIGEN (Grounded Language-to-Image Generation)
-        - local: api/pipelines/stable_diffusion/image_variation
-          title: Image variation
-        - local: api/pipelines/stable_diffusion/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
-      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/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/overview
+        title: Overview
+      - local: api/pipelines/stable_diffusion/depth2img
+        title: Depth-to-image
+      - local: api/pipelines/stable_diffusion/gligen
+        title: GLIGEN (Grounded Language-to-Image Generation)
+      - local: api/pipelines/stable_diffusion/image_variation
+        title: Image variation
+      - local: api/pipelines/stable_diffusion/img2img
+        title: Image-to-image
       - local: api/pipelines/stable_diffusion/svd
-        title: Stable Video Diffusion
-      - local: api/pipelines/text_to_video
-        title: Text-to-video
-      - local: api/pipelines/text_to_video_zero
-        title: Text2Video-Zero
-      - local: api/pipelines/wan
-        title: Wan
-      title: Video
+        title: Image-to-video
+      - local: api/pipelines/stable_diffusion/inpaint
+        title: Inpainting
+      - local: api/pipelines/stable_diffusion/k_diffusion
+        title: K-Diffusion
+      - local: api/pipelines/stable_diffusion/latent_upscale
+        title: Latent upscaler
+      - local: api/pipelines/stable_diffusion/ldm3d_diffusion
+        title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
+      - local: api/pipelines/stable_diffusion/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/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: Pipelines
-  - sections:
+  - isExpanded: false
+    sections:
     - local: api/schedulers/overview
       title: Overview
     - local: api/schedulers/cm_stochastic_iterative
@@ -746,7 +669,8 @@
     - local: api/schedulers/vq_diffusion
       title: VQDiffusionScheduler
     title: Schedulers
-  - sections:
+  - isExpanded: false
+    sections:
     - local: api/internal_classes_overview
       title: Overview
     - local: api/attnprocessor

docs/source/en/api/configuration.md

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

docs/source/en/api/image_processor.md

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

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

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

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

@@ -26,16 +26,16 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
 - [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
 - [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
-- [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
 - [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
 - [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
-- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen).
-- [`Flux2LoraLoaderMixin`] provides similar functions for [Flux2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux2).
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
-> [!TIP]
-> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
+<Tip>
+
+To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
+
+</Tip>
 
 ## LoraBaseMixin
 
@@ -57,10 +57,6 @@ 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
@@ -96,10 +92,6 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 
 [[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
 
-## SkyReelsV2LoraLoaderMixin
-
-[[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
-
 ## AmusedLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
@@ -108,13 +100,6 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 
 [[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
 
-## QwenImageLoraLoaderMixin
+## WanLoraLoaderMixin
 
-[[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
-
-## KandinskyLoraLoaderMixin
-[[autodoc]] loaders.lora_pipeline.KandinskyLoraLoaderMixin
-
-## LoraBaseMixin
-
-[[autodoc]] loaders.lora_base.LoraBaseMixin
+[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin

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

@@ -12,10 +12,13 @@ specific language governing permissions and limitations under the License.
 
 # PEFT
 
-Diffusers supports loading adapters such as [LoRA](../../tutorials/using_peft_for_inference) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
+Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
 
-> [!TIP]
-> Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
+<Tip>
+
+Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
+
+</Tip>
 
 ## PeftAdapterMixin
 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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/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/overview.md

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

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

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

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

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

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

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

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

@@ -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/unet2d-cond.md

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

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

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

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

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

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

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

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

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

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

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

docs/source/en/api/outputs.md

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

docs/source/en/api/parallel.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. -->
-
-# Parallelism
-
-Parallelism strategies help speed up diffusion transformers by distributing computations across multiple devices, allowing for faster inference/training times. Refer to the [Distributed inferece](../training/distributed_inference) guide to learn more.
-
-## ParallelConfig
-
-[[autodoc]] ParallelConfig
-
-## ContextParallelConfig
-
-[[autodoc]] ContextParallelConfig
-
-[[autodoc]] hooks.apply_context_parallel

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,44 +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 3.2
-
-Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to provide on par results to leading open-source models, and outperforming other licensed models.
-In addition to being built entirely on licensed data, 3.2 provides several advantages for enterprise and commercial use:
-
-- Efficient Compute - the model is X3 smaller than the equivalent models in the market (4B parameters vs 12B parameters other open source models)
-- Architecture Consistency: Same architecture as 3.1—ideal for users looking to upgrade without disruption.
-- Fine-tuning Speedup: 2x faster fine-tuning on L40S and A100.
-
-Original model checkpoints for Bria 3.2 can be found [here](https://huggingface.co/briaai/BRIA-3.2).
-Github repo for Bria 3.2 can be found [here](https://github.com/Bria-AI/BRIA-3.2).
-
-If you want to learn more about the Bria platform, and get free traril access, please visit [bria.ai](https://bria.ai).
-
-
-## Usage
-
-_As the model is gated, before using it with diffusers you first need to go to the [Bria 3.2 Hugging Face page](https://huggingface.co/briaai/BRIA-3.2), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
-
-Use the command below to log in:
-
-```bash
-hf auth login
-```
-
-
-## BriaPipeline
-
-[[autodoc]] BriaPipeline
-	- all
-	- __call__
-

docs/source/en/api/pipelines/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.
-
-its not recommended to use freeform text prompts directly with FIBO, as it will not produce the best results.
-
-you can learn more about FIBO in  [Bria Fibo Hugging Face page](https://huggingface.co/briaai/FIBO).
-
-
-## 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
-```
-
-
-## BriaPipeline
-
-[[autodoc]] BriaPipeline
-	- all
-	- __call__
-

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

@@ -19,22 +19,24 @@ 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:
-* 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)
+Original model checkpoints for Chroma can be found [here](https://huggingface.co/lodestones/Chroma).
 
-> [!TIP]
-> Chroma can use all the same optimizations as Flux.
+<Tip>
+
+Chroma can use all the same optimizations as Flux.
+
+</Tip>
 
 ## Inference
 
+The Diffusers version of Chroma is based on the [`unlocked-v37`](https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors) version of the original model, which is available in the [Chroma repository](https://huggingface.co/lodestones/Chroma).
+
 ```python
 import torch
 from diffusers import ChromaPipeline
 
-pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload()
+pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16)
+pipe.enabe_model_cpu_offload()
 
 prompt = [
     "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
@@ -64,10 +66,10 @@ 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/cogvideox.md

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -21,10 +21,11 @@ Flux is a series of text-to-image generation models based on diffusion transform
 
 Original model checkpoints for Flux can be found [here](https://huggingface.co/black-forest-labs). Original inference code can be found [here](https://github.com/black-forest-labs/flux).
 
-> [!TIP]
-> Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.  For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
->
-> [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
+<Tip>
+
+Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.  For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
+
+</Tip>
 
 Flux comes in the following variants:
 
@@ -313,67 +314,6 @@ if integrity_checker.test_image(image_):
     raise ValueError("Your image has been flagged. Choose another prompt/image or try again.")
 ```
 
-### Kontext Inpainting
-`FluxKontextInpaintPipeline` enables image modification within a fixed mask region. It currently supports both text-based conditioning and image-reference conditioning.
-<hfoptions id="kontext-inpaint">
-<hfoption id="text-only">
-
-
-```python
-import torch
-from diffusers import FluxKontextInpaintPipeline
-from diffusers.utils import load_image
-
-prompt = "Change the yellow dinosaur to green one"
-img_url = (
-    "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/dinosaur_input.jpeg?raw=true"
-)
-mask_url = (
-    "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/dinosaur_mask.png?raw=true"
-)
-
-source = load_image(img_url)
-mask = load_image(mask_url)
-
-pipe = FluxKontextInpaintPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
-)
-pipe.to("cuda")
-
-image = pipe(prompt=prompt, image=source, mask_image=mask, strength=1.0).images[0]
-image.save("kontext_inpainting_normal.png")
-```
-</hfoption>
-<hfoption id="image conditioning">
-
-```python
-import torch
-from diffusers import FluxKontextInpaintPipeline
-from diffusers.utils import load_image
-
-pipe = FluxKontextInpaintPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
-)
-pipe.to("cuda")
-
-prompt = "Replace this ball"
-img_url = "https://images.pexels.com/photos/39362/the-ball-stadion-football-the-pitch-39362.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500"
-mask_url = "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/ball_mask.png?raw=true"
-image_reference_url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTah3x6OL_ECMBaZ5ZlJJhNsyC-OSMLWAI-xw&s"
-
-source = load_image(img_url)
-mask = load_image(mask_url)
-image_reference = load_image(image_reference_url)
-
-mask = pipe.mask_processor.blur(mask, blur_factor=12)
-image = pipe(
-    prompt=prompt, image=source, mask_image=mask, image_reference=image_reference, strength=1.0
-).images[0]
-image.save("kontext_inpainting_ref.png")
-```
-</hfoption>
-</hfoptions>
-
 ## Combining Flux Turbo LoRAs with Flux Control, Fill, and Redux
 
 We can combine Flux Turbo LoRAs with Flux Control and other pipelines like Fill and Redux to enable few-steps' inference. The example below shows how to do that for Flux Control LoRA for depth and turbo LoRA from [`ByteDance/Hyper-SD`](https://hf.co/ByteDance/Hyper-SD).
@@ -417,8 +357,11 @@ When unloading the Control LoRA weights, call `pipe.unload_lora_weights(reset_to
 
 ## IP-Adapter
 
-> [!TIP]
-> Check out [IP-Adapter](../../using-diffusers/ip_adapter) to learn more about how IP-Adapters work.
+<Tip>
+
+Check out [IP-Adapter](../../../using-diffusers/ip_adapter) to learn more about how IP-Adapters work.
+
+</Tip>
 
 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.
 
@@ -598,8 +541,9 @@ image.save("flux.png")
 
 The `FluxTransformer2DModel` supports loading checkpoints in the original format shipped by Black Forest Labs. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.
 
-> [!TIP]
-> `FP8` inference can be brittle depending on the GPU type, CUDA version, and `torch` version that you are using. It is recommended that you use the `optimum-quanto` library in order to run FP8 inference on your machine.
+<Tip>
+`FP8` inference can be brittle depending on the GPU type, CUDA version, and `torch` version that you are using. It is recommended that you use the `optimum-quanto` library in order to run FP8 inference on your machine.
+</Tip>
 
 The following example demonstrates how to run Flux with less than 16GB of VRAM.
 
@@ -700,15 +644,3 @@ image.save("flux-fp8-dev.png")
 [[autodoc]] FluxFillPipeline
 	- all
 	- __call__
-
-## FluxKontextPipeline
-
-[[autodoc]] FluxKontextPipeline
-	- all
-	- __call__
-
-## FluxKontextInpaintPipeline
-
-[[autodoc]] FluxKontextInpaintPipeline
-	- all
-	- __call__

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

@@ -1,33 +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.
-
-## Flux2Pipeline
-
-[[autodoc]] Flux2Pipeline
-	- all
-	- __call__

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

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

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

@@ -16,12 +16,15 @@
 
 [HiDream-I1](https://huggingface.co/HiDream-ai) by HiDream.ai
 
-> [!TIP]
-> [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
+<Tip>
+
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
 
 ## 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_video.md

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

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

@@ -28,11 +28,17 @@ HunyuanDiT has the following components:
 * It uses a diffusion transformer as the backbone
 * It combines two text encoders, a bilingual CLIP and a multilingual T5 encoder
 
-> [!TIP]
-> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+<Tip>
 
-> [!TIP]
-> You can further improve generation quality by passing the generated image from [`HungyuanDiTPipeline`] to the [SDXL refiner](../../using-diffusers/sdxl#base-to-refiner-model) model.
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+<Tip>
+
+You can further improve generation quality by passing the generated image from [`HungyuanDiTPipeline`] to the [SDXL refiner](../../using-diffusers/sdxl#base-to-refiner-model) model.
+
+</Tip>
 
 ## Optimization
 

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