update

* refactor 3d rope

* repeat -> expand

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -63,23 +63,27 @@ body:
 
         Please tag a maximum of 2 people.
 
-        Questions on DiffusionPipeline (Saving, Loading, From pretrained, ...):
+        Questions on DiffusionPipeline (Saving, Loading, From pretrained, ...): @sayakpaul @DN6
 
         Questions on pipelines:
-        - Stable Diffusion @yiyixuxu @DN6 @sayakpaul 
+        - Stable Diffusion @yiyixuxu @asomoza
         - Stable Diffusion XL @yiyixuxu @sayakpaul @DN6
+        - Stable Diffusion 3: @yiyixuxu @sayakpaul @DN6 @asomoza
         - Kandinsky @yiyixuxu
         - ControlNet @sayakpaul @yiyixuxu @DN6
         - T2I Adapter @sayakpaul @yiyixuxu @DN6
         - IF @DN6
-        - Text-to-Video / Video-to-Video @DN6 @sayakpaul 
+        - Text-to-Video / Video-to-Video @DN6 @a-r-r-o-w
         - Wuerstchen @DN6
         - Other: @yiyixuxu @DN6
+        - Improving generation quality: @asomoza
 
         Questions on models:
         - UNet @DN6 @yiyixuxu @sayakpaul
         - VAE @sayakpaul @DN6 @yiyixuxu
-        - Transformers/Attention @DN6 @yiyixuxu @sayakpaul @DN6 
+        - Transformers/Attention @DN6 @yiyixuxu @sayakpaul
+
+        Questions on single file checkpoints: @DN6
 
         Questions on Schedulers: @yiyixuxu
 
@@ -99,7 +103,7 @@ body:
 
         Questions on JAX- and MPS-related things: @pcuenca
 
-        Questions on audio pipelines: @DN6 
+        Questions on audio pipelines: @sanchit-gandhi
 
 
 

.github/PULL_REQUEST_TEMPLATE.md

@@ -39,7 +39,7 @@ members/contributors who may be interested in your PR.
 Core library:
 
 - Schedulers: @yiyixuxu
-- Pipelines:  @sayakpaul @yiyixuxu @DN6
+- Pipelines and pipeline callbacks: @yiyixuxu and @asomoza
 - Training examples: @sayakpaul
 - Docs: @stevhliu and @sayakpaul
 - JAX and MPS: @pcuenca
@@ -48,7 +48,8 @@ Core library:
 
 Integrations:
 
-- deepspeed: HF Trainer/Accelerate: @pacman100
+- deepspeed: HF Trainer/Accelerate: @SunMarc
+- PEFT: @sayakpaul @BenjaminBossan
 
 HF projects:
 

.github/workflows/benchmark.yml

@@ -13,14 +13,17 @@ env:
 
 jobs:
   torch_pipelines_cuda_benchmark_tests:
+    env:
+      SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_BENCHMARK }}
     name: Torch Core Pipelines CUDA Benchmarking Tests
     strategy:
       fail-fast: false
       max-parallel: 1
-    runs-on: [single-gpu, nvidia-gpu, a10, ci]
+    runs-on:
+      group: aws-g6-4xlarge-plus
     container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0
+      image: diffusers/diffusers-pytorch-compile-cuda
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -51,3 +54,13 @@ jobs:
         with:
           name: benchmark_test_reports
           path: benchmarks/benchmark_outputs
+
+      - name: Report success status
+        if: ${{ success() }}
+        run: |
+          pip install requests && python utils/notify_benchmarking_status.py --status=success
+
+      - name: Report failure status
+        if: ${{ failure() }}
+        run: |
+          pip install requests && python utils/notify_benchmarking_status.py --status=failure

.github/workflows/build_docker_images.yml

@@ -20,7 +20,8 @@ env:
 
 jobs:
   test-build-docker-images:
-    runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]
+    runs-on:
+      group: aws-general-8-plus
     if: github.event_name == 'pull_request'
     steps:
       - name: Set up Docker Buildx
@@ -50,7 +51,8 @@ jobs:
         if: steps.file_changes.outputs.all != ''
 
   build-and-push-docker-images:
-    runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]
+    runs-on:
+      group: aws-general-8-plus
     if: github.event_name != 'pull_request'
 
     permissions:

.github/workflows/mirror_community_pipeline.yml

@@ -22,6 +22,9 @@ on:
 
 jobs:
   mirror_community_pipeline:
+    env:
+      SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_COMMUNITY_MIRROR }}
+
     runs-on: ubuntu-latest
     steps:
       # Checkout to correct ref
@@ -54,8 +57,12 @@ jobs:
           else
             # e.g. refs/tags/v0.28.1 -> v0.28.1
             echo "CHECKOUT_REF=${{ github.ref }}" >> $GITHUB_ENV
-            echo "PATH_IN_REPO=${${{ github.ref }}#refs/tags/}" >> $GITHUB_ENV
+            echo "PATH_IN_REPO=$(echo ${{ github.ref }} | sed 's/^refs\/tags\///')" >> $GITHUB_ENV
           fi
+      - name: Print env vars
+        run: |
+          echo "CHECKOUT_REF: ${{ env.CHECKOUT_REF }}"
+          echo "PATH_IN_REPO: ${{ env.PATH_IN_REPO }}"
       - uses: actions/checkout@v3
         with:
           ref: ${{ env.CHECKOUT_REF }}
@@ -67,8 +74,8 @@ jobs:
           python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m pip install uv
-          uv pip install --upgrade huggingface_hub
+          python -m pip install --upgrade pip
+          pip install --upgrade huggingface_hub
 
       # Check secret is set
       - name: whoami
@@ -83,3 +90,13 @@ jobs:
         env:
             PATH_IN_REPO: ${{ env.PATH_IN_REPO }}
             HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}
+
+      - name: Report success status
+        if: ${{ success() }}
+        run: |
+          pip install requests && python utils/notify_community_pipelines_mirror.py --status=success
+
+      - name: Report failure status
+        if: ${{ failure() }}
+        run: |
+          pip install requests && python utils/notify_community_pipelines_mirror.py --status=failure

.github/workflows/nightly_tests.yml

@@ -7,7 +7,7 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HOME: /mnt/cache
+  HF_HUB_ENABLE_HF_TRANSFER: 1
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 600
@@ -18,8 +18,11 @@ env:
 
 jobs:
   setup_torch_cuda_pipeline_matrix:
-    name: Setup Torch Pipelines Matrix
-    runs-on: diffusers/diffusers-pytorch-cpu
+    name: Setup Torch Pipelines CUDA Slow Tests Matrix
+    runs-on:
+      group: aws-general-8-plus
+    container:
+      image: diffusers/diffusers-pytorch-cpu
     outputs:
       pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
     steps:
@@ -27,13 +30,9 @@ jobs:
         uses: actions/checkout@v3
         with:
           fetch-depth: 2
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.8"
       - name: Install dependencies
         run: |
-          pip install -e .
+          pip install -e .[test]
           pip install huggingface_hub
       - name: Fetch Pipeline Matrix
         id: fetch_pipeline_matrix
@@ -50,16 +49,18 @@ jobs:
           path: reports
 
   run_nightly_tests_for_torch_pipelines:
-    name: Torch Pipelines CUDA Nightly Tests
+    name: Nightly Torch Pipelines CUDA Tests
     needs: setup_torch_cuda_pipeline_matrix
     strategy:
       fail-fast: false
+      max-parallel: 8
       matrix:
         module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on:
+      group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -67,19 +68,16 @@ jobs:
           fetch-depth: 2
       - name: NVIDIA-SMI
         run: nvidia-smi
-
       - name: Install dependencies
         run: |
           python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
           python -m uv pip install -e [quality,test]
           python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
           python -m uv pip install pytest-reportlog
-
       - name: Environment
         run: |
           python utils/print_env.py
-
-      - name: Nightly PyTorch CUDA checkpoint (pipelines) tests
+      - name: Pipeline CUDA Test
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
@@ -90,38 +88,38 @@ jobs:
             --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
             --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \
             tests/pipelines/${{ matrix.module }}
-
       - name: Failure short reports
         if: ${{ failure() }}
         run: |
           cat reports/tests_pipeline_${{ matrix.module }}_cuda_stats.txt
           cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
-
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
         with:
           name: pipeline_${{ matrix.module }}_test_reports
           path: reports
-
       - name: Generate Report and Notify Channel
         if: always()
         run: |
           pip install slack_sdk tabulate
-          python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
   run_nightly_tests_for_other_torch_modules:
-    name: Torch Non-Pipelines CUDA Nightly Tests
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    name: Nightly Torch CUDA Tests
+    runs-on:
+      group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
     strategy:
+      fail-fast: false
+      max-parallel: 2
       matrix:
-        module: [models, schedulers, others, examples]
+        module: [models, schedulers, lora, others, single_file, examples]
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
@@ -133,8 +131,8 @@ jobs:
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
         python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
         python -m uv pip install pytest-reportlog
-
     - name: Environment
       run: python utils/print_env.py
 
@@ -158,7 +156,6 @@ jobs:
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v --make-reports=examples_torch_cuda \
           --report-log=examples_torch_cuda.log \
@@ -181,64 +178,7 @@ jobs:
       if: always()
       run: |
         pip install slack_sdk tabulate
-        python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY
-
-  run_lora_nightly_tests:
-    name: Nightly LoRA Tests with PEFT and TORCH
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
-        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
-        python -m uv pip install pytest-reportlog
-
-    - name: Environment
-      run: python utils/print_env.py
-
-    - name: Run nightly LoRA tests with PEFT and Torch
-      env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_torch_lora_cuda \
-          --report-log=tests_torch_lora_cuda.log \
-          tests/lora
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_torch_lora_cuda_stats.txt
-        cat reports/tests_torch_lora_cuda_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v2
-      with:
-        name: torch_lora_cuda_test_reports
-        path: reports
-
-    - name: Generate Report and Notify Channel
-      if: always()
-      run: |
-        pip install slack_sdk tabulate
-        python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
   run_flax_tpu_tests:
     name: Nightly Flax TPU Tests
@@ -294,14 +234,15 @@ jobs:
       if: always()
       run: |
         pip install slack_sdk tabulate
-        python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
   run_nightly_onnx_tests:
     name: Nightly ONNXRuntime CUDA tests on Ubuntu
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on:
+      group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-onnxruntime-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -318,11 +259,10 @@ jobs:
         python -m uv pip install -e [quality,test]
         python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
         python -m uv pip install pytest-reportlog
-
     - name: Environment
       run: python utils/print_env.py
 
-    - name: Run nightly ONNXRuntime CUDA tests
+    - name: Run Nightly ONNXRuntime CUDA tests
       env:
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
       run: |
@@ -349,66 +289,120 @@ jobs:
       if: always()
       run: |
         pip install slack_sdk tabulate
-        python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
-  run_nightly_tests_apple_m1:
-    name: Nightly PyTorch MPS tests on MacOS
-    runs-on: [ self-hosted, apple-m1 ]
-    if: github.event_name == 'schedule'
-
-    steps:
-      - name: Checkout diffusers
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      - name: Clean checkout
-        shell: arch -arch arm64 bash {0}
-        run: |
-          git clean -fxd
-
-      - name: Setup miniconda
-        uses: ./.github/actions/setup-miniconda
-        with:
-          python-version: 3.9
-
-      - name: Install dependencies
-        shell: arch -arch arm64 bash {0}
-        run: |
-          ${CONDA_RUN} python -m pip install --upgrade pip uv
-          ${CONDA_RUN} python -m uv pip install -e [quality,test]
-          ${CONDA_RUN} python -m uv pip install 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: |
-          ${CONDA_RUN} python utils/print_env.py
-
-      - name: Run nightly PyTorch tests on M1 (MPS)
-        shell: arch -arch arm64 bash {0}
-        env:
-          HF_HOME: /System/Volumes/Data/mnt/cache
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        run: |
-          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
-            --report-log=tests_torch_mps.log \
-            tests/
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: cat reports/tests_torch_mps_failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: torch_mps_test_reports
-          path: reports
-
-      - name: Generate Report and Notify Channel
-        if: always()
-        run: |
-          pip install slack_sdk tabulate
-          python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY
+# M1 runner currently not well supported
+# TODO: (Dhruv) add these back when we setup better testing for Apple Silicon
+#  run_nightly_tests_apple_m1:
+#    name: Nightly PyTorch MPS tests on MacOS
+#    runs-on: [ self-hosted, apple-m1 ]
+#    if: github.event_name == 'schedule'
+#
+#    steps:
+#      - name: Checkout diffusers
+#        uses: actions/checkout@v3
+#        with:
+#          fetch-depth: 2
+#
+#      - name: Clean checkout
+#        shell: arch -arch arm64 bash {0}
+#        run: |
+#          git clean -fxd
+#      - name: Setup miniconda
+#        uses: ./.github/actions/setup-miniconda
+#        with:
+#          python-version: 3.9
+#
+#      - name: Install dependencies
+#        shell: arch -arch arm64 bash {0}
+#        run: |
+#          ${CONDA_RUN} python -m pip install --upgrade pip uv
+#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
+#          ${CONDA_RUN} python -m uv pip install 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: |
+#          ${CONDA_RUN} python utils/print_env.py
+#      - name: Run nightly PyTorch tests on M1 (MPS)
+#        shell: arch -arch arm64 bash {0}
+#        env:
+#          HF_HOME: /System/Volumes/Data/mnt/cache
+#          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+#        run: |
+#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
+#            --report-log=tests_torch_mps.log \
+#            tests/
+#      - name: Failure short reports
+#        if: ${{ failure() }}
+#        run: cat reports/tests_torch_mps_failures_short.txt
+#
+#      - name: Test suite reports artifacts
+#        if: ${{ always() }}
+#        uses: actions/upload-artifact@v2
+#        with:
+#          name: torch_mps_test_reports
+#          path: reports
+#
+#      - name: Generate Report and Notify Channel
+#        if: always()
+#        run: |
+#          pip install slack_sdk tabulate
+#          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY  run_nightly_tests_apple_m1:
+#    name: Nightly PyTorch MPS tests on MacOS
+#    runs-on: [ self-hosted, apple-m1 ]
+#    if: github.event_name == 'schedule'
+#
+#    steps:
+#      - name: Checkout diffusers
+#        uses: actions/checkout@v3
+#        with:
+#          fetch-depth: 2
+#
+#      - name: Clean checkout
+#        shell: arch -arch arm64 bash {0}
+#        run: |
+#          git clean -fxd
+#      - name: Setup miniconda
+#        uses: ./.github/actions/setup-miniconda
+#        with:
+#          python-version: 3.9
+#
+#      - name: Install dependencies
+#        shell: arch -arch arm64 bash {0}
+#        run: |
+#          ${CONDA_RUN} python -m pip install --upgrade pip uv
+#          ${CONDA_RUN} python -m uv pip install -e [quality,test]
+#          ${CONDA_RUN} python -m uv pip install 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: |
+#          ${CONDA_RUN} python utils/print_env.py
+#      - name: Run nightly PyTorch tests on M1 (MPS)
+#        shell: arch -arch arm64 bash {0}
+#        env:
+#          HF_HOME: /System/Volumes/Data/mnt/cache
+#          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+#        run: |
+#          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \
+#            --report-log=tests_torch_mps.log \
+#            tests/
+#      - name: Failure short reports
+#        if: ${{ failure() }}
+#        run: cat reports/tests_torch_mps_failures_short.txt
+#
+#      - name: Test suite reports artifacts
+#        if: ${{ always() }}
+#        uses: actions/upload-artifact@v2
+#        with:
+#          name: torch_mps_test_reports
+#          path: reports
+#
+#      - name: Generate Report and Notify Channel
+#        if: always()
+#        run: |
+#          pip install slack_sdk tabulate
+#          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

.github/workflows/pr_dependency_test.yml

@@ -33,4 +33,3 @@ jobs:
         run: |
           python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
           pytest tests/others/test_dependencies.py
-      

.github/workflows/pr_test_fetcher.yml

@@ -15,7 +15,8 @@ concurrency:
 jobs:
   setup_pr_tests:
     name: Setup PR Tests
-    runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]
+    runs-on:
+      group: aws-general-8-plus
     container:
       image: diffusers/diffusers-pytorch-cpu
       options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
@@ -73,7 +74,8 @@ jobs:
       max-parallel: 2
       matrix:
         modules: ${{ fromJson(needs.setup_pr_tests.outputs.matrix) }}
-    runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]
+    runs-on:
+      group: aws-general-8-plus
     container:
       image: diffusers/diffusers-pytorch-cpu
       options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
@@ -123,12 +125,13 @@ jobs:
         config:
           - name: Hub tests for models, schedulers, and pipelines
             framework: hub_tests_pytorch
-            runner: [ self-hosted, intel-cpu, 8-cpu, ci ]
+            runner: aws-general-8-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_hub
 
     name: ${{ matrix.config.name }}
-    runs-on: ${{ matrix.config.runner }}
+    runs-on:
+      group: ${{ matrix.config.runner }}
     container:
       image: ${{ matrix.config.image }}
       options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/

.github/workflows/pr_test_peft_backend.yml

@@ -71,7 +71,8 @@ jobs:
 
     name: LoRA - ${{ matrix.lib-versions }}
 
-    runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]
+    runs-on:
+      group: aws-general-8-plus
 
     container:
       image: diffusers/diffusers-pytorch-cpu

.github/workflows/pr_tests.yml

@@ -77,28 +77,29 @@ jobs:
         config:
           - name: Fast PyTorch Pipeline CPU tests
             framework: pytorch_pipelines
-            runner: [ self-hosted, intel-cpu, 32-cpu, 256-ram, ci ]
+            runner: aws-highmemory-32-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu_pipelines
           - name: Fast PyTorch Models & Schedulers CPU tests
             framework: pytorch_models
-            runner: [ self-hosted, intel-cpu, 8-cpu, ci ]
+            runner: aws-general-8-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu_models_schedulers
           - name: Fast Flax CPU tests
             framework: flax
-            runner: [ self-hosted, intel-cpu, 8-cpu, ci ]
+            runner: aws-general-8-plus
             image: diffusers/diffusers-flax-cpu
             report: flax_cpu
           - name: PyTorch Example CPU tests
             framework: pytorch_examples
-            runner: [ self-hosted, intel-cpu, 8-cpu, ci ]
+            runner: aws-general-8-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_example_cpu
 
     name: ${{ matrix.config.name }}
 
-    runs-on: ${{ matrix.config.runner }}
+    runs-on:
+      group: ${{ matrix.config.runner }}
 
     container:
       image: ${{ matrix.config.image }}
@@ -180,7 +181,8 @@ jobs:
         config:
           - name: Hub tests for models, schedulers, and pipelines
             framework: hub_tests_pytorch
-            runner: [ self-hosted, intel-cpu, 8-cpu, ci ]
+            runner:
+              group: aws-general-8-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_hub
 

.github/workflows/push_tests.yml

@@ -1,4 +1,4 @@
-name: Slow Tests on main
+name: Fast GPU Tests on main
 
 on:
   push:
@@ -11,17 +11,16 @@ on:
 
 env:
   DIFFUSERS_IS_CI: yes
-  HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 600
-  RUN_SLOW: yes
   PIPELINE_USAGE_CUTOFF: 50000
 
 jobs:
   setup_torch_cuda_pipeline_matrix:
     name: Setup Torch Pipelines CUDA Slow Tests Matrix
-    runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]
+    runs-on:
+      group: aws-general-8-plus
     container:
       image: diffusers/diffusers-pytorch-cpu
     outputs:
@@ -52,17 +51,18 @@ jobs:
           path: reports
 
   torch_pipelines_cuda_tests:
-    name: Torch Pipelines CUDA Slow Tests
+    name: Torch Pipelines CUDA Tests
     needs: setup_torch_cuda_pipeline_matrix
     strategy:
       fail-fast: false
       max-parallel: 8
       matrix:
         module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on:
+      group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus 0
     steps:
       - name: Checkout diffusers
         uses: actions/checkout@v3
@@ -103,14 +103,17 @@ jobs:
 
   torch_cuda_tests:
     name: Torch CUDA Tests
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on:
+      group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0
+      options: --shm-size "16gb" --ipc host --gpus 0
     defaults:
       run:
         shell: bash
     strategy:
+      fail-fast: false
+      max-parallel: 2
       matrix:
         module: [models, schedulers, lora, others, single_file]
     steps:
@@ -124,12 +127,13 @@ jobs:
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
         python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
 
     - name: Environment
       run: |
         python utils/print_env.py
 
-    - name: Run slow PyTorch CUDA tests
+    - name: Run PyTorch CUDA tests
       env:
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
@@ -153,61 +157,6 @@ jobs:
         name: torch_cuda_test_reports
         path: reports
 
-  peft_cuda_tests:
-    name: PEFT CUDA Tests
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
-    container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0
-    defaults:
-      run:
-        shell: bash
-    steps:
-    - name: Checkout diffusers
-      uses: actions/checkout@v3
-      with:
-        fetch-depth: 2
-
-    - name: Install dependencies
-      run: |
-        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
-        python -m uv pip install -e [quality,test]
-        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
-        python -m pip install -U peft@git+https://github.com/huggingface/peft.git
-
-    - name: Environment
-      run: |
-        python utils/print_env.py
-
-    - name: Run slow PEFT CUDA tests
-      env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx and not PEFTLoRALoading" \
-          --make-reports=tests_peft_cuda \
-          tests/lora/
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "lora and not Flax and not Onnx and not PEFTLoRALoading" \
-          --make-reports=tests_peft_cuda_models_lora \
-          tests/models/
-
-    - name: Failure short reports
-      if: ${{ failure() }}
-      run: |
-        cat reports/tests_peft_cuda_stats.txt
-        cat reports/tests_peft_cuda_failures_short.txt
-        cat reports/tests_peft_cuda_models_lora_failures_short.txt
-
-    - name: Test suite reports artifacts
-      if: ${{ always() }}
-      uses: actions/upload-artifact@v2
-      with:
-        name: torch_peft_test_reports
-        path: reports
-
   flax_tpu_tests:
     name: Flax TPU Tests
     runs-on: docker-tpu
@@ -257,7 +206,8 @@ jobs:
 
   onnx_cuda_tests:
     name: ONNX CUDA Tests
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on:
+      group: aws-g4dn-2xlarge
     container:
       image: diffusers/diffusers-onnxruntime-cuda
       options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --gpus 0
@@ -305,11 +255,12 @@ jobs:
   run_torch_compile_tests:
     name: PyTorch Compile CUDA tests
 
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on:
+      group: aws-g4dn-2xlarge
 
     container:
       image: diffusers/diffusers-pytorch-compile-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -330,6 +281,7 @@ jobs:
     - name: Run example tests on GPU
       env:
         HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        RUN_COMPILE: yes
       run: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
     - name: Failure short reports
@@ -346,11 +298,12 @@ jobs:
   run_xformers_tests:
     name: PyTorch xformers CUDA tests
 
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on:
+      group: aws-g4dn-2xlarge
 
     container:
       image: diffusers/diffusers-pytorch-xformers-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers
@@ -387,11 +340,12 @@ jobs:
   run_examples_tests:
     name: Examples PyTorch CUDA tests on Ubuntu
 
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on:
+      group: aws-g4dn-2xlarge
 
     container:
       image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus 0 --shm-size "16gb" --ipc host
 
     steps:
     - name: Checkout diffusers

.github/workflows/push_tests_fast.yml

@@ -29,28 +29,29 @@ jobs:
         config:
           - name: Fast PyTorch CPU tests on Ubuntu
             framework: pytorch
-            runner: [ self-hosted, intel-cpu, 8-cpu, ci ]
+            runner: aws-general-8-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu
           - name: Fast Flax CPU tests on Ubuntu
             framework: flax
-            runner: [ self-hosted, intel-cpu, 8-cpu, ci ]
+            runner: aws-general-8-plus
             image: diffusers/diffusers-flax-cpu
             report: flax_cpu
           - name: Fast ONNXRuntime CPU tests on Ubuntu
             framework: onnxruntime
-            runner: [ self-hosted, intel-cpu, 8-cpu, ci ]
+            runner: aws-general-8-plus
             image: diffusers/diffusers-onnxruntime-cpu
             report: onnx_cpu
           - name: PyTorch Example CPU tests on Ubuntu
             framework: pytorch_examples
-            runner: [ self-hosted, intel-cpu, 8-cpu, ci ]
+            runner: aws-general-8-plus
             image: diffusers/diffusers-pytorch-cpu
             report: torch_example_cpu
 
     name: ${{ matrix.config.name }}
 
-    runs-on: ${{ matrix.config.runner }}
+    runs-on:
+      group: ${{ matrix.config.runner }}
 
     container:
       image: ${{ matrix.config.image }}

.github/workflows/release_tests_fast.yml

@@ -0,0 +1,389 @@
+# Duplicate workflow to push_tests.yml that is meant to run on release/patch branches as a final check
+# Creating a duplicate workflow here is simpler than adding complex path/branch parsing logic to push_tests.yml
+# Needs to be updated if push_tests.yml updated
+name: (Release) Fast GPU Tests on main
+
+on:
+  push:
+    branches:
+      - "v*.*.*-release"
+      - "v*.*.*-patch"
+
+env:
+  DIFFUSERS_IS_CI: yes
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  PYTEST_TIMEOUT: 600
+  PIPELINE_USAGE_CUTOFF: 50000
+
+jobs:
+  setup_torch_cuda_pipeline_matrix:
+    name: Setup Torch Pipelines CUDA Slow Tests Matrix
+    runs-on:
+      group: aws-general-8-plus
+    container:
+      image: diffusers/diffusers-pytorch-cpu
+    outputs:
+      pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+      - name: Install dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+      - name: Environment
+        run: |
+          python utils/print_env.py
+      - name: Fetch Pipeline Matrix
+        id: fetch_pipeline_matrix
+        run: |
+          matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)
+          echo $matrix
+          echo "pipeline_test_matrix=$matrix" >> $GITHUB_OUTPUT
+      - name: Pipeline Tests Artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: test-pipelines.json
+          path: reports
+
+  torch_pipelines_cuda_tests:
+    name: Torch Pipelines CUDA Tests
+    needs: setup_torch_cuda_pipeline_matrix
+    strategy:
+      fail-fast: false
+      max-parallel: 8
+      matrix:
+        module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}
+    runs-on:
+      group: aws-g4dn-2xlarge
+    container:
+      image: diffusers/diffusers-pytorch-cuda
+      options: --shm-size "16gb" --ipc host --gpus 0
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+      - name: Install dependencies
+        run: |
+          python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+          python -m uv pip install -e [quality,test]
+          python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
+      - name: Environment
+        run: |
+          python utils/print_env.py
+      - name: Slow PyTorch CUDA checkpoint tests on Ubuntu
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
+          CUBLAS_WORKSPACE_CONFIG: :16:8
+        run: |
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
+            --make-reports=tests_pipeline_${{ matrix.module }}_cuda \
+            tests/pipelines/${{ matrix.module }}
+      - name: Failure short reports
+        if: ${{ failure() }}
+        run: |
+          cat reports/tests_pipeline_${{ matrix.module }}_cuda_stats.txt
+          cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: pipeline_${{ matrix.module }}_test_reports
+          path: reports
+
+  torch_cuda_tests:
+    name: Torch CUDA Tests
+    runs-on:
+      group: aws-g4dn-2xlarge
+    container:
+      image: diffusers/diffusers-pytorch-cuda
+      options: --shm-size "16gb" --ipc host --gpus 0
+    defaults:
+      run:
+        shell: bash
+    strategy:
+      fail-fast: false
+      max-parallel: 2
+      matrix:
+        module: [models, schedulers, lora, others, single_file]
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
+        python -m uv pip install peft@git+https://github.com/huggingface/peft.git
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run PyTorch CUDA tests
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
+        CUBLAS_WORKSPACE_CONFIG: :16:8
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
+          --make-reports=tests_torch_cuda \
+          tests/${{ matrix.module }}
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_torch_cuda_stats.txt
+        cat reports/tests_torch_cuda_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: torch_cuda_test_reports
+        path: reports
+
+  flax_tpu_tests:
+    name: Flax TPU Tests
+    runs-on: docker-tpu
+    container:
+      image: diffusers/diffusers-flax-tpu
+      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --privileged
+    defaults:
+      run:
+        shell: bash
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run slow Flax TPU tests
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: |
+        python -m pytest -n 0 \
+          -s -v -k "Flax" \
+          --make-reports=tests_flax_tpu \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_flax_tpu_stats.txt
+        cat reports/tests_flax_tpu_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: flax_tpu_test_reports
+        path: reports
+
+  onnx_cuda_tests:
+    name: ONNX CUDA Tests
+    runs-on:
+      group: aws-g4dn-2xlarge
+    container:
+      image: diffusers/diffusers-onnxruntime-cuda
+      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --gpus 0
+    defaults:
+      run:
+        shell: bash
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run slow ONNXRuntime CUDA tests
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_onnx_cuda \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/tests_onnx_cuda_stats.txt
+        cat reports/tests_onnx_cuda_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: onnx_cuda_test_reports
+        path: reports
+
+  run_torch_compile_tests:
+    name: PyTorch Compile CUDA tests
+
+    runs-on:
+      group: aws-g4dn-2xlarge
+
+    container:
+      image: diffusers/diffusers-pytorch-compile-cuda
+      options: --gpus 0 --shm-size "16gb" --ipc host
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: NVIDIA-SMI
+      run: |
+        nvidia-smi
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
+    - name: Environment
+      run: |
+        python utils/print_env.py
+    - name: Run example tests on GPU
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        RUN_COMPILE: yes
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "compile" --make-reports=tests_torch_compile_cuda tests/
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_torch_compile_cuda_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: torch_compile_test_reports
+        path: reports
+
+  run_xformers_tests:
+    name: PyTorch xformers CUDA tests
+
+    runs-on:
+      group: aws-g4dn-2xlarge
+
+    container:
+      image: diffusers/diffusers-pytorch-xformers-cuda
+      options: --gpus 0 --shm-size "16gb" --ipc host
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: NVIDIA-SMI
+      run: |
+        nvidia-smi
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test,training]
+    - name: Environment
+      run: |
+        python utils/print_env.py
+    - name: Run example tests on GPU
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k "xformers" --make-reports=tests_torch_xformers_cuda tests/
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_torch_xformers_cuda_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: torch_xformers_test_reports
+        path: reports
+
+  run_examples_tests:
+    name: Examples PyTorch CUDA tests on Ubuntu
+
+    runs-on:
+      group: aws-g4dn-2xlarge
+
+    container:
+      image: diffusers/diffusers-pytorch-cuda
+      options: --gpus 0 --shm-size "16gb" --ipc host
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: NVIDIA-SMI
+      run: |
+        nvidia-smi
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        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: ${{ secrets.HF_TOKEN }}
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install timm
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: |
+        cat reports/examples_torch_cuda_stats.txt
+        cat reports/examples_torch_cuda_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: examples_test_reports
+        path: reports

.github/workflows/run_tests_from_a_pr.yml

@@ -26,7 +26,8 @@ env:
 jobs:
   run_tests:
     name: "Run a test on our runner from a PR"
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    runs-on:
+      group: aws-g4dn-2xlarge
     container:
       image: ${{ github.event.inputs.docker_image }}
       options: --gpus 0 --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/

.github/workflows/ssh-pr-runner.yml

@@ -0,0 +1,40 @@
+name: SSH into PR runners
+
+on:
+  workflow_dispatch:
+    inputs:
+      docker_image:
+        description: 'Name of the Docker image'
+        required: true
+
+env:
+  IS_GITHUB_CI: "1"
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  HF_HOME: /mnt/cache
+  DIFFUSERS_IS_CI: yes
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  RUN_SLOW: yes
+
+jobs:
+  ssh_runner:
+    name: "SSH"
+    runs-on:
+      group: aws-highmemory-32-plus
+    container:
+      image: ${{ github.event.inputs.docker_image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --privileged
+
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+
+      - name: Tailscale # In order to be able to SSH when a test fails
+        uses: huggingface/tailscale-action@main
+        with:
+          authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
+          slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
+          slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
+          waitForSSH: true

.github/workflows/ssh-runner.yml

@@ -1,4 +1,4 @@
-name: SSH into runners
+name: SSH into GPU runners
 
 on:
   workflow_dispatch:
@@ -22,7 +22,8 @@ env:
 jobs:
   ssh_runner:
     name: "SSH"
-    runs-on: [single-gpu, nvidia-gpu, "${{ github.event.inputs.runner_type }}", ci]
+    runs-on:
+      group: "${{ github.event.inputs.runner_type }}"
     container:
       image: ${{ github.event.inputs.docker_image }}
       options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0 --privileged

.github/workflows/trufflehog.yml

@@ -0,0 +1,15 @@
+on:
+  push:
+
+name: Secret Leaks
+
+jobs:
+  trufflehog:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+    - name: Secret Scanning
+      uses: trufflesecurity/trufflehog@main

CONTRIBUTING.md

@@ -57,13 +57,13 @@ Any question or comment related to the Diffusers library can be asked on the [di
 - ...
 
 Every question that is asked on the forum or on Discord actively encourages the community to publicly
-share knowledge and might very well help a beginner in the future that has the same question you're
+share knowledge and might very well help a beginner in the future who has the same question you're
 having. Please do pose any questions you might have.
 In the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from.
 
 **Please** keep in mind that the more effort you put into asking or answering a question, the higher
 the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database.
-In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
+In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formatted/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
 
 **NOTE about channels**:
 [*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago.
@@ -245,7 +245,7 @@ The official training examples are maintained by the Diffusers' core maintainers
 This is because of the same reasons put forward in [6. Contribute a community pipeline](#6-contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models.
 If the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the `research_projects` folder and maintained by the author.
 
-Both official training and research examples consist of a directory that contains one or more training scripts, a requirements.txt file, and a README.md file. In order for the user to make use of the
+Both official training and research examples consist of a directory that contains one or more training scripts, a `requirements.txt` file, and a `README.md` file. In order for the user to make use of the
 training examples, it is required to clone the repository:
 
 ```bash
@@ -255,7 +255,8 @@ git clone https://github.com/huggingface/diffusers
 as well as to install all additional dependencies required for training:
 
 ```bash
-pip install -r /examples/<your-example-folder>/requirements.txt
+cd diffusers
+pip install -r examples/<your-example-folder>/requirements.txt
 ```
 
 Therefore when adding an example, the `requirements.txt` file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the [DreamBooth `requirements.txt` file](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt).

PHILOSOPHY.md

@@ -15,7 +15,7 @@ specific language governing permissions and limitations under the License.
 🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities.
 Its purpose is to serve as a **modular toolbox** for both inference and training.
 
-We aim at building a library that stands the test of time and therefore take API design very seriously.
+We aim to build a library that stands the test of time and therefore take API design very seriously.
 
 In a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones:
 
@@ -70,7 +70,7 @@ The following design principles are followed:
 - Pipelines should be used **only** for inference.
 - Pipelines should be very readable, self-explanatory, and easy to tweak.
 - Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs.
-- Pipelines are **not** intended to be feature-complete user interfaces. For future complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner).
+- Pipelines are **not** intended to be feature-complete user interfaces. For feature-complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner).
 - Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines.
 - Pipelines should be named after the task they are intended to solve.
 - In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file.
@@ -81,7 +81,7 @@ Models are designed as configurable toolboxes that are natural extensions of [Py
 
 The following design principles are followed:
 - Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.
-- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...
+- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unets/unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_2d_condition.py), [`transformers/transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_2d.py), etc...
 - Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.
 - Models intend to expose complexity, just like PyTorch's `Module` class, and give clear error messages.
 - Models all inherit from `ModelMixin` and `ConfigMixin`.
@@ -90,7 +90,7 @@ The following design principles are followed:
 - To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different.
 - Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and "foreseeing" future changes, *e.g.* it is usually better to add `string` "...type" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work.
 - The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and
-readable long-term, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+readable long-term, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
 
 ### Schedulers
 

README.md

@@ -20,21 +20,11 @@ limitations under the License.
     <br>
 <p>
 <p align="center">
-    <a href="https://github.com/huggingface/diffusers/blob/main/LICENSE">
-        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue">
-    </a>
-    <a href="https://github.com/huggingface/diffusers/releases">
-        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg">
-    </a>
-    <a href="https://pepy.tech/project/diffusers">
-        <img alt="GitHub release" src="https://static.pepy.tech/badge/diffusers/month">
-    </a>
-    <a href="CODE_OF_CONDUCT.md">
-        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg">
-    </a>
-    <a href="https://twitter.com/diffuserslib">
-        <img alt="X account" src="https://img.shields.io/twitter/url/https/twitter.com/diffuserslib.svg?style=social&label=Follow%20%40diffuserslib">
-    </a>
+    <a href="https://github.com/huggingface/diffusers/blob/main/LICENSE"><img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue"></a>
+    <a href="https://github.com/huggingface/diffusers/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg"></a>
+    <a href="https://pepy.tech/project/diffusers"><img alt="GitHub release" src="https://static.pepy.tech/badge/diffusers/month"></a>
+    <a href="CODE_OF_CONDUCT.md"><img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg"></a>
+    <a href="https://twitter.com/diffuserslib"><img alt="X account" src="https://img.shields.io/twitter/url/https/twitter.com/diffuserslib.svg?style=social&label=Follow%20%40diffuserslib"></a>
 </p>
 
 🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
@@ -77,7 +67,7 @@ Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggi
 
 ## Quickstart
 
-Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 25.000+ checkpoints):
+Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 30,000+ checkpoints):
 
 ```python
 from diffusers import DiffusionPipeline
@@ -212,6 +202,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 
 - https://github.com/microsoft/TaskMatrix
 - https://github.com/invoke-ai/InvokeAI
+- https://github.com/InstantID/InstantID
 - https://github.com/apple/ml-stable-diffusion
 - https://github.com/Sanster/lama-cleaner
 - https://github.com/IDEA-Research/Grounded-Segment-Anything
@@ -219,7 +210,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 - https://github.com/deep-floyd/IF
 - https://github.com/bentoml/BentoML
 - https://github.com/bmaltais/kohya_ss
-- +11.000 other amazing GitHub repositories 💪
+- +14,000 other amazing GitHub repositories 💪
 
 Thank you for using us ❤️.
 

benchmarks/run_all.py

@@ -40,7 +40,7 @@ def main():
         print(f"****** Running file: {file} ******")
 
         # Run with canonical settings.
-        if file != "benchmark_text_to_image.py":
+        if file != "benchmark_text_to_image.py" and file != "benchmark_ip_adapters.py":
             command = f"python {file}"
             run_command(command.split())
 
@@ -49,6 +49,10 @@ def main():
 
     # Run variants.
     for file in python_files:
+        # See: https://github.com/pytorch/pytorch/issues/129637
+        if file == "benchmark_ip_adapters.py":
+            continue
+
         if file == "benchmark_text_to_image.py":
             for ckpt in ALL_T2I_CKPTS:
                 command = f"python {file} --ckpt {ckpt}"

docker/diffusers-doc-builder/Dockerfile

@@ -42,7 +42,7 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         huggingface-hub \
         Jinja2 \
         librosa \
-        numpy \
+        numpy==1.26.4 \
         scipy \
         tensorboard \
         transformers \

docker/diffusers-flax-cpu/Dockerfile

@@ -40,7 +40,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         huggingface-hub \
         Jinja2 \
         librosa \
-        numpy \
+        numpy==1.26.4 \
         scipy \
         tensorboard \
         transformers

docker/diffusers-flax-tpu/Dockerfile

@@ -42,7 +42,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         huggingface-hub \
         Jinja2 \
         librosa \
-        numpy \
+        numpy==1.26.4 \
         scipy \
         tensorboard \
         transformers

docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -40,7 +40,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         huggingface-hub \
         Jinja2 \
         librosa \
-        numpy \
+        numpy==1.26.4 \
         scipy \
         tensorboard \
         transformers

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -38,9 +38,10 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
+        hf_transfer \
         Jinja2 \
         librosa \
-        numpy \
+        numpy==1.26.4 \
         scipy \
         tensorboard \
         transformers

docker/diffusers-pytorch-compile-cuda/Dockerfile

@@ -17,6 +17,7 @@ RUN apt install -y bash \
     libsndfile1-dev \
     libgl1 \
     python3.10 \
+    python3.10-dev \
     python3-pip \
     python3.10-venv && \
     rm -rf /var/lib/apt/lists
@@ -37,9 +38,10 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     datasets \
     hf-doc-builder \
     huggingface-hub \
+    hf_transfer \
     Jinja2 \
     librosa \
-    numpy \
+    numpy==1.26.4 \
     scipy \
     tensorboard \
     transformers

docker/diffusers-pytorch-cpu/Dockerfile

@@ -16,6 +16,7 @@ RUN apt install -y bash \
                    ca-certificates \
                    libsndfile1-dev \
                    python3.10 \
+                   python3.10-dev \
                    python3-pip \
                    libgl1 \
                    python3.10-venv && \
@@ -40,7 +41,7 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         huggingface-hub \
         Jinja2 \
         librosa \
-        numpy \
+        numpy==1.26.4 \
         scipy \
         tensorboard \
         transformers matplotlib

docker/diffusers-pytorch-cuda/Dockerfile

@@ -17,6 +17,7 @@ RUN apt install -y bash \
     libsndfile1-dev \
     libgl1 \
     python3.10 \
+    python3.10-dev \
     python3-pip \
     python3.10-venv && \
     rm -rf /var/lib/apt/lists
@@ -37,9 +38,10 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     datasets \
     hf-doc-builder \
     huggingface-hub \
+    hf_transfer \
     Jinja2 \
     librosa \
-    numpy \
+    numpy==1.26.4 \
     scipy \
     tensorboard \
     transformers \

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -17,6 +17,7 @@ RUN apt install -y bash \
                    libsndfile1-dev \
                    libgl1 \
                    python3.10 \
+                   python3.10-dev \
                    python3-pip \
                    python3.10-venv && \
     rm -rf /var/lib/apt/lists
@@ -37,9 +38,10 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
+        hf_transfer \
         Jinja2 \
         librosa \
-        numpy \
+        numpy==1.26.4 \
         scipy \
         tensorboard \
         transformers \

docs/source/en/_toctree.yml

@@ -21,6 +21,8 @@
     title: Load LoRAs for inference
   - local: tutorials/fast_diffusion
     title: Accelerate inference of text-to-image diffusion models
+  - local: tutorials/inference_with_big_models
+    title: Working with big models
   title: Tutorials
 - sections:
   - local: using-diffusers/loading
@@ -81,6 +83,8 @@
     title: Kandinsky
   - local: using-diffusers/ip_adapter
     title: IP-Adapter
+  - local: using-diffusers/pag
+    title: PAG
   - local: using-diffusers/controlnet
     title: ControlNet
   - local: using-diffusers/t2i_adapter
@@ -107,7 +111,8 @@
     title: Create a dataset for training
   - local: training/adapt_a_model
     title: Adapt a model to a new task
-  - sections:
+  - isExpanded: false
+    sections:
     - local: training/unconditional_training
       title: Unconditional image generation
     - local: training/text2image
@@ -125,8 +130,8 @@
     - local: training/instructpix2pix
       title: InstructPix2Pix
     title: Models
-    isExpanded: false
-  - sections:
+  - isExpanded: false
+    sections:
     - local: training/text_inversion
       title: Textual Inversion
     - local: training/dreambooth
@@ -140,7 +145,6 @@
     - local: training/ddpo
       title: Reinforcement learning training with DDPO
     title: Methods
-    isExpanded: false
   title: Training
 - sections:
   - local: optimization/fp16
@@ -187,7 +191,12 @@
     title: Evaluating Diffusion Models
   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
@@ -195,8 +204,8 @@
     - local: api/outputs
       title: Outputs
     title: Main Classes
-    isExpanded: false
-  - sections:
+  - isExpanded: false
+    sections:
     - local: api/loaders/ip_adapter
       title: IP-Adapter
     - local: api/loaders/lora
@@ -210,10 +219,53 @@
     - local: api/loaders/peft
       title: PEFT
     title: Loaders
-    isExpanded: false
-  - sections:
+  - isExpanded: false
+    sections:
     - local: api/models/overview
       title: Overview
+    - sections:
+      - local: api/models/controlnet
+        title: ControlNetModel
+      - local: api/models/controlnet_flux
+        title: FluxControlNetModel
+      - local: api/models/controlnet_hunyuandit
+        title: HunyuanDiT2DControlNetModel
+      - local: api/models/controlnet_sd3
+        title: SD3ControlNetModel
+      - local: api/models/controlnet_sparsectrl
+        title: SparseControlNetModel
+      title: ControlNets
+    - sections:
+      - local: api/models/aura_flow_transformer2d
+        title: AuraFlowTransformer2DModel
+      - local: api/models/cogvideox_transformer3d
+        title: CogVideoXTransformer3DModel
+      - local: api/models/dit_transformer2d
+        title: DiTTransformer2DModel
+      - local: api/models/flux_transformer
+        title: FluxTransformer2DModel
+      - local: api/models/hunyuan_transformer2d
+        title: HunyuanDiT2DModel
+      - local: api/models/latte_transformer3d
+        title: LatteTransformer3DModel
+      - local: api/models/lumina_nextdit2d
+        title: LuminaNextDiT2DModel
+      - local: api/models/pixart_transformer2d
+        title: PixArtTransformer2DModel
+      - local: api/models/prior_transformer
+        title: PriorTransformer
+      - local: api/models/sd3_transformer2d
+        title: SD3Transformer2DModel
+      - local: api/models/stable_audio_transformer
+        title: StableAudioDiTModel
+      - local: api/models/transformer2d
+        title: Transformer2DModel
+      - local: api/models/transformer_temporal
+        title: TransformerTemporalModel
+      title: Transformers
+    - sections:
+      - local: api/models/stable_cascade_unet
+        title: StableCascadeUNet
       - local: api/models/unet
         title: UNet1DModel
       - local: api/models/unet2d
@@ -226,33 +278,26 @@
         title: UNetMotionModel
       - local: api/models/uvit2d
         title: UViT2DModel
-    - local: api/models/vq
-      title: VQModel
+      title: UNets
+    - sections:
       - local: api/models/autoencoderkl
         title: AutoencoderKL
+      - local: api/models/autoencoderkl_cogvideox
+        title: AutoencoderKLCogVideoX
       - local: api/models/asymmetricautoencoderkl
         title: AsymmetricAutoencoderKL
-    - local: api/models/autoencoder_tiny
-      title: Tiny AutoEncoder
       - local: api/models/consistency_decoder_vae
         title: ConsistencyDecoderVAE
-    - local: api/models/transformer2d
-      title: Transformer2DModel
-    - local: api/models/pixart_transformer2d
-      title: PixArtTransformer2DModel
-    - local: api/models/dit_transformer2d
-      title: DiTTransformer2DModel
-    - local: api/models/hunyuan_transformer2d
-      title: HunyuanDiT2DModel
-    - local: api/models/transformer_temporal
-      title: TransformerTemporalModel
-    - local: api/models/prior_transformer
-      title: PriorTransformer
-    - local: api/models/controlnet
-      title: ControlNetModel
+      - local: api/models/autoencoder_oobleck
+        title: Oobleck AutoEncoder
+      - local: api/models/autoencoder_tiny
+        title: Tiny AutoEncoder
+      - local: api/models/vq
+        title: VQModel
+      title: VAEs
     title: Models
-    isExpanded: false
-  - sections:
+  - isExpanded: false
+    sections:
     - local: api/pipelines/overview
       title: Overview
     - local: api/pipelines/amused
@@ -265,14 +310,24 @@
       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/cogvideox
+      title: CogVideoX
     - 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/controlnetxs
@@ -291,6 +346,8 @@
       title: DiffEdit
     - local: api/pipelines/dit
       title: DiT
+    - local: api/pipelines/flux
+      title: Flux
     - local: api/pipelines/hunyuandit
       title: Hunyuan-DiT
     - local: api/pipelines/i2vgenxl
@@ -303,18 +360,26 @@
       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/lumina
+      title: Lumina-T2X
     - local: api/pipelines/marigold
       title: Marigold
     - local: api/pipelines/panorama
       title: MultiDiffusion
     - local: api/pipelines/musicldm
       title: MusicLDM
+    - local: api/pipelines/pag
+      title: PAG
     - local: api/pipelines/paint_by_example
       title: Paint by Example
     - local: api/pipelines/pia
@@ -329,6 +394,8 @@
       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:
@@ -350,6 +417,8 @@
         title: Safe Stable Diffusion
       - local: api/pipelines/stable_diffusion/stable_diffusion_2
         title: Stable Diffusion 2
+      - local: api/pipelines/stable_diffusion/stable_diffusion_3
+        title: Stable Diffusion 3
       - local: api/pipelines/stable_diffusion/stable_diffusion_xl
         title: Stable Diffusion XL
       - local: api/pipelines/stable_diffusion/sdxl_turbo
@@ -382,14 +451,16 @@
     - local: api/pipelines/wuerstchen
       title: Wuerstchen
     title: Pipelines
-    isExpanded: false
-  - sections:
+  - isExpanded: false
+    sections:
     - local: api/schedulers/overview
       title: Overview
     - local: api/schedulers/cm_stochastic_iterative
       title: CMStochasticIterativeScheduler
     - local: api/schedulers/consistency_decoder
       title: ConsistencyDecoderScheduler
+    - local: api/schedulers/cosine_dpm
+      title: CosineDPMSolverMultistepScheduler
     - local: api/schedulers/ddim_inverse
       title: DDIMInverseScheduler
     - local: api/schedulers/ddim
@@ -414,6 +485,10 @@
       title: EulerAncestralDiscreteScheduler
     - local: api/schedulers/euler
       title: EulerDiscreteScheduler
+    - local: api/schedulers/flow_match_euler_discrete
+      title: FlowMatchEulerDiscreteScheduler
+    - local: api/schedulers/flow_match_heun_discrete
+      title: FlowMatchHeunDiscreteScheduler
     - local: api/schedulers/heun
       title: HeunDiscreteScheduler
     - local: api/schedulers/ipndm
@@ -443,8 +518,8 @@
     - local: api/schedulers/vq_diffusion
       title: VQDiffusionScheduler
     title: Schedulers
-    isExpanded: false
-  - sections:
+  - isExpanded: false
+    sections:
     - local: api/internal_classes_overview
       title: Overview
     - local: api/attnprocessor
@@ -460,5 +535,4 @@
     - local: api/video_processor
       title: Video Processor
     title: Internal classes
-    isExpanded: false
   title: API

docs/source/en/api/attnprocessor.md

@@ -41,12 +41,6 @@ An attention processor is a class for applying different types of attention mech
 ## FusedAttnProcessor2_0
 [[autodoc]] models.attention_processor.FusedAttnProcessor2_0
 
-## LoRAAttnAddedKVProcessor
-[[autodoc]] models.attention_processor.LoRAAttnAddedKVProcessor
-
-## LoRAXFormersAttnProcessor
-[[autodoc]] models.attention_processor.LoRAXFormersAttnProcessor
-
 ## SlicedAttnProcessor
 [[autodoc]] models.attention_processor.SlicedAttnProcessor
 

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

@@ -12,10 +12,13 @@ specific language governing permissions and limitations under the License.
 
 # LoRA
 
-LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the UNet, text encoder or both. There are two classes for loading LoRA weights:
+LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text encoder or both. The denoiser usually corresponds to a UNet ([`UNet2DConditionModel`], for example) or a Transformer ([`SD3Transformer2DModel`], for example). There are several classes for loading LoRA weights:
 
-- [`LoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
-- [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`LoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.
+- [`StableDiffusionLoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
+- [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`StableDiffusionLoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.
+- [`SD3LoraLoaderMixin`] provides similar functions for [Stable Diffusion 3](https://huggingface.co/blog/sd3).
+- [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
+- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
 <Tip>
 
@@ -23,10 +26,22 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 </Tip>
 
-## LoraLoaderMixin
+## StableDiffusionLoraLoaderMixin
 
-[[autodoc]] loaders.lora.LoraLoaderMixin
+[[autodoc]] loaders.lora_pipeline.StableDiffusionLoraLoaderMixin
 
 ## StableDiffusionXLLoraLoaderMixin
 
-[[autodoc]] loaders.lora.StableDiffusionXLLoraLoaderMixin
+[[autodoc]] loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin
+
+## SD3LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.SD3LoraLoaderMixin
+
+## AmusedLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
+
+## LoraBaseMixin
+
+[[autodoc]] loaders.lora_base.LoraBaseMixin

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

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # PEFT
 
-Diffusers supports loading adapters such as [LoRA](../../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`] to load 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>
 

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

@@ -22,6 +22,7 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 
 ## Supported pipelines
 
+- [`CogVideoXPipeline`]
 - [`StableDiffusionPipeline`]
 - [`StableDiffusionImg2ImgPipeline`]
 - [`StableDiffusionInpaintPipeline`]
@@ -35,6 +36,7 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 - [`StableDiffusionXLInstructPix2PixPipeline`]
 - [`StableDiffusionXLControlNetPipeline`]
 - [`StableDiffusionXLKDiffusionPipeline`]
+- [`StableDiffusion3Pipeline`]
 - [`LatentConsistencyModelPipeline`]
 - [`LatentConsistencyModelImg2ImgPipeline`]
 - [`StableDiffusionControlNetXSPipeline`]
@@ -48,7 +50,10 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 - [`UNet2DConditionModel`]
 - [`StableCascadeUNet`]
 - [`AutoencoderKL`]
+- [`AutoencoderKLCogVideoX`]
 - [`ControlNetModel`]
+- [`SD3Transformer2DModel`]
+- [`FluxTransformer2DModel`]
 
 ## FromSingleFileMixin
 

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

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # UNet
 
-Some training methods - like LoRA and Custom Diffusion - typically target the UNet's attention layers, but these training methods can also target other non-attention layers. Instead of training all of a model's parameters, only a subset of the parameters are trained, which is faster and more efficient. This class is useful if you're *only* loading weights into a UNet. If you need to load weights into the text encoder or a text encoder and UNet, try using the [`~loaders.LoraLoaderMixin.load_lora_weights`] function instead.
+Some training methods - like LoRA and Custom Diffusion - typically target the UNet's attention layers, but these training methods can also target other non-attention layers. Instead of training all of a model's parameters, only a subset of the parameters are trained, which is faster and more efficient. This class is useful if you're *only* loading weights into a UNet. If you need to load weights into the text encoder or a text encoder and UNet, try using the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] function instead.
 
 The [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.
 

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

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# AuraFlowTransformer2DModel
+
+A Transformer model for image-like data from [AuraFlow](https://blog.fal.ai/auraflow/).
+
+## AuraFlowTransformer2DModel
+
+[[autodoc]] AuraFlowTransformer2DModel

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

@@ -0,0 +1,38 @@
+<!--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.
+-->
+
+# AutoencoderOobleck
+
+The Oobleck variational autoencoder (VAE) model with KL loss was introduced in [Stability-AI/stable-audio-tools](https://github.com/Stability-AI/stable-audio-tools) and [Stable Audio Open](https://huggingface.co/papers/2407.14358) by Stability AI. The model is used in 🤗 Diffusers to encode audio waveforms into latents and to decode latent representations into audio waveforms.
+
+The abstract from the paper is:
+
+*Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.*
+
+## AutoencoderOobleck
+
+[[autodoc]] AutoencoderOobleck
+    - decode
+    - encode
+    - all
+
+## OobleckDecoderOutput
+
+[[autodoc]] models.autoencoders.autoencoder_oobleck.OobleckDecoderOutput
+
+## OobleckDecoderOutput
+
+[[autodoc]] models.autoencoders.autoencoder_oobleck.OobleckDecoderOutput
+
+## AutoencoderOobleckOutput
+
+[[autodoc]] models.autoencoders.autoencoder_oobleck.AutoencoderOobleckOutput

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

@@ -21,7 +21,7 @@ The abstract from the paper is:
 ## Loading from the original format
 
 By default the [`AutoencoderKL`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded
-from the original format using [`FromOriginalVAEMixin.from_single_file`] as follows:
+from the original format using [`FromOriginalModelMixin.from_single_file`] as follows:
 
 ```py
 from diffusers import AutoencoderKL

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

@@ -0,0 +1,37 @@
+<!--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. -->
+
+# AutoencoderKLCogVideoX
+
+The 3D variational autoencoder (VAE) model with KL loss used in [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLCogVideoX
+
+vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-2b", subfolder="vae", torch_dtype=torch.float16).to("cuda")
+```
+
+## AutoencoderKLCogVideoX
+
+[[autodoc]] AutoencoderKLCogVideoX
+    - decode
+    - encode
+    - all
+
+## AutoencoderKLOutput
+
+[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/cogvideox_transformer3d.md

@@ -0,0 +1,30 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# CogVideoXTransformer3DModel
+
+A Diffusion Transformer model for 3D data from [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import CogVideoXTransformer3DModel
+
+vae = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
+```
+
+## CogVideoXTransformer3DModel
+
+[[autodoc]] CogVideoXTransformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -21,7 +21,7 @@ The abstract from the paper is:
 ## Loading from the original format
 
 By default the [`ControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded
-from the original format using [`FromOriginalControlnetMixin.from_single_file`] as follows:
+from the original format using [`FromOriginalModelMixin.from_single_file`] as follows:
 
 ```py
 from diffusers import StableDiffusionControlNetPipeline, ControlNetModel

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

@@ -0,0 +1,45 @@
+<!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+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.
+-->
+
+# FluxControlNetModel
+
+FluxControlNetModel is an implementation of ControlNet for Flux.1.
+
+The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
+
+The abstract from the paper is:
+
+*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
+
+## Loading from the original format
+
+By default the [`FluxControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`].
+
+```py
+from diffusers import FluxControlNetPipeline
+from diffusers.models import FluxControlNetModel, FluxMultiControlNetModel
+
+controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
+pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
+
+controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
+controlnet = FluxMultiControlNetModel([controlnet])
+pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
+```
+
+## FluxControlNetModel
+
+[[autodoc]] FluxControlNetModel
+
+## FluxControlNetOutput
+
+[[autodoc]] models.controlnet_flux.FluxControlNetOutput

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

@@ -0,0 +1,37 @@
+<!--Copyright 2024 The HuggingFace Team and Tencent Hunyuan Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+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.
+-->
+
+# HunyuanDiT2DControlNetModel
+
+HunyuanDiT2DControlNetModel is an implementation of ControlNet for [Hunyuan-DiT](https://arxiv.org/abs/2405.08748).
+
+ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
+
+With a ControlNet model, you can provide an additional control image to condition and control Hunyuan-DiT generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
+
+The abstract from the paper is:
+
+*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
+
+This 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).
+
+## Example For Loading HunyuanDiT2DControlNetModel
+
+```py
+from diffusers import HunyuanDiT2DControlNetModel
+import torch
+controlnet = HunyuanDiT2DControlNetModel.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose", torch_dtype=torch.float16)
+```
+
+## HunyuanDiT2DControlNetModel
+
+[[autodoc]] HunyuanDiT2DControlNetModel

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

@@ -0,0 +1,42 @@
+<!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+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.
+-->
+
+# SD3ControlNetModel
+
+SD3ControlNetModel is an implementation of ControlNet for Stable Diffusion 3.
+
+The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
+
+The abstract from the paper is:
+
+*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
+
+## Loading from the original format
+
+By default the [`SD3ControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`].
+
+```py
+from diffusers import StableDiffusion3ControlNetPipeline
+from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
+
+controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny")
+pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet)
+```
+
+## SD3ControlNetModel
+
+[[autodoc]] SD3ControlNetModel
+
+## SD3ControlNetOutput
+
+[[autodoc]] models.controlnet_sd3.SD3ControlNetOutput
+

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

@@ -0,0 +1,46 @@
+<!-- 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. -->
+
+# SparseControlNetModel
+
+SparseControlNetModel is an implementation of ControlNet for [AnimateDiff](https://arxiv.org/abs/2307.04725).
+
+ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
+
+The SparseCtrl version of ControlNet was introduced in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://arxiv.org/abs/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
+
+The abstract from the paper is:
+
+*The development of text-to-video (T2V), i.e., generating videos with a given text prompt, has been significantly advanced in recent years. However, relying solely on text prompts often results in ambiguous frame composition due to spatial uncertainty. The research community thus leverages the dense structure signals, e.g., per-frame depth/edge sequences, to enhance controllability, whose collection accordingly increases the burden of inference. In this work, we present SparseCtrl to enable flexible structure control with temporally sparse signals, requiring only one or a few inputs, as shown in Figure 1. It incorporates an additional condition encoder to process these sparse signals while leaving the pre-trained T2V model untouched. The proposed approach is compatible with various modalities, including sketches, depth maps, and RGB images, providing more practical control for video generation and promoting applications such as storyboarding, depth rendering, keyframe animation, and interpolation. Extensive experiments demonstrate the generalization of SparseCtrl on both original and personalized T2V generators. Codes and models will be publicly available at [this https URL](https://guoyww.github.io/projects/SparseCtrl).*
+
+## Example for loading SparseControlNetModel
+
+```python
+import torch
+from diffusers import SparseControlNetModel
+
+# fp32 variant in float16
+# 1. Scribble checkpoint
+controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectrl-scribble", torch_dtype=torch.float16)
+
+# 2. RGB checkpoint
+controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectrl-rgb", torch_dtype=torch.float16)
+
+# For loading fp16 variant, pass `variant="fp16"` as an additional parameter
+```
+
+## SparseControlNetModel
+
+[[autodoc]] SparseControlNetModel
+
+## SparseControlNetOutput
+
+[[autodoc]] models.controlnet_sparsectrl.SparseControlNetOutput

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

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# FluxTransformer2DModel
+
+A Transformer model for image-like data from [Flux](https://blackforestlabs.ai/announcing-black-forest-labs/).
+
+## FluxTransformer2DModel
+
+[[autodoc]] FluxTransformer2DModel

docs/source/en/api/models/latte_transformer3d.md

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+## LatteTransformer3DModel
+
+A Diffusion Transformer model for 3D data from [Latte](https://github.com/Vchitect/Latte).
+
+## LatteTransformer3DModel
+
+[[autodoc]] LatteTransformer3DModel

docs/source/en/api/models/lumina_nextdit2d.md

@@ -0,0 +1,20 @@
+<!--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.
+-->
+
+# LuminaNextDiT2DModel
+
+A Next Version of Diffusion Transformer model for 2D data from [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X).
+
+## LuminaNextDiT2DModel
+
+[[autodoc]] LuminaNextDiT2DModel
+

docs/source/en/api/models/sd3_transformer2d.md

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# SD3 Transformer Model
+
+The Transformer model introduced in [Stable Diffusion 3](https://hf.co/papers/2403.03206). Its novelty lies in the MMDiT transformer block.
+
+## SD3Transformer2DModel
+
+[[autodoc]] SD3Transformer2DModel

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

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# StableAudioDiTModel
+
+A Transformer model for audio waveforms from [Stable Audio Open](https://huggingface.co/papers/2407.14358).
+
+## StableAudioDiTModel
+
+[[autodoc]] StableAudioDiTModel

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

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# StableCascadeUNet
+
+A UNet model from the [Stable Cascade pipeline](../pipelines/stable_cascade.md).
+
+## StableCascadeUNet
+
+[[autodoc]] models.unets.unet_stable_cascade.StableCascadeUNet

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

@@ -38,4 +38,4 @@ It is assumed one of the input classes is the masked latent pixel. The predicted
 
 ## Transformer2DModelOutput
 
-[[autodoc]] models.transformers.transformer_2d.Transformer2DModelOutput
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -25,6 +25,9 @@ The abstract of the paper is the following:
 | Pipeline | Tasks | Demo
 |---|---|:---:|
 | [AnimateDiffPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff.py) | *Text-to-Video Generation with AnimateDiff* |
+| [AnimateDiffControlNetPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py) | *Controlled Video-to-Video Generation with AnimateDiff using ControlNet* |
+| [AnimateDiffSparseControlNetPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py) | *Controlled Video-to-Video Generation with AnimateDiff using SparseCtrl* |
+| [AnimateDiffSDXLPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py) | *Video-to-Video Generation with AnimateDiff* |
 | [AnimateDiffVideoToVideoPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py) | *Video-to-Video Generation with AnimateDiff* |
 
 ## Available checkpoints
@@ -78,7 +81,6 @@ output = pipe(
 )
 frames = output.frames[0]
 export_to_gif(frames, "animation.gif")
-
 ```
 
 Here are some sample outputs:
@@ -101,6 +103,266 @@ AnimateDiff tends to work better with finetuned Stable Diffusion models. If you
 
 </Tip>
 
+### AnimateDiffControlNetPipeline
+
+AnimateDiff can also be used with ControlNets ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide depth maps, the ControlNet model generates a video that'll preserve the spatial information from the depth maps. It is a more flexible and accurate way to control the video generation process.
+
+```python
+import torch
+from diffusers import AnimateDiffControlNetPipeline, AutoencoderKL, ControlNetModel, MotionAdapter, LCMScheduler
+from diffusers.utils import export_to_gif, load_video
+
+# Additionally, you will need a preprocess videos before they can be used with the ControlNet
+# HF maintains just the right package for it: `pip install controlnet_aux`
+from controlnet_aux.processor import ZoeDetector
+
+# Download controlnets from https://huggingface.co/lllyasviel/ControlNet-v1-1 to use .from_single_file
+# Download Diffusers-format controlnets, such as https://huggingface.co/lllyasviel/sd-controlnet-depth, to use .from_pretrained()
+controlnet = ControlNetModel.from_single_file("control_v11f1p_sd15_depth.pth", torch_dtype=torch.float16)
+
+# We use AnimateLCM for this example but one can use the original motion adapters as well (for example, https://huggingface.co/guoyww/animatediff-motion-adapter-v1-5-3)
+motion_adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM")
+
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
+pipe: AnimateDiffControlNetPipeline = AnimateDiffControlNetPipeline.from_pretrained(
+    "SG161222/Realistic_Vision_V5.1_noVAE",
+    motion_adapter=motion_adapter,
+    controlnet=controlnet,
+    vae=vae,
+).to(device="cuda", dtype=torch.float16)
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
+pipe.load_lora_weights("wangfuyun/AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm-lora")
+pipe.set_adapters(["lcm-lora"], [0.8])
+
+depth_detector = ZoeDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
+video = load_video("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif")
+conditioning_frames = []
+
+with pipe.progress_bar(total=len(video)) as progress_bar:
+    for frame in video:
+        conditioning_frames.append(depth_detector(frame))
+        progress_bar.update()
+
+prompt = "a panda, playing a guitar, sitting in a pink boat, in the ocean, mountains in background, realistic, high quality"
+negative_prompt = "bad quality, worst quality"
+
+video = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_frames=len(video),
+    num_inference_steps=10,
+    guidance_scale=2.0,
+    conditioning_frames=conditioning_frames,
+    generator=torch.Generator().manual_seed(42),
+).frames[0]
+
+export_to_gif(video, "animatediff_controlnet.gif", fps=8)
+```
+
+Here are some sample outputs:
+
+<table align="center">
+    <tr>
+      <th align="center">Source Video</th>
+      <th align="center">Output Video</th>
+    </tr>
+    <tr>
+        <td align="center">
+          raccoon playing a guitar
+          <br />
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif" alt="racoon playing a guitar" />
+        </td>
+        <td align="center">
+          a panda, playing a guitar, sitting in a pink boat, in the ocean, mountains in background, realistic, high quality
+          <br/>
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-controlnet-output.gif" alt="a panda, playing a guitar, sitting in a pink boat, in the ocean, mountains in background, realistic, high quality" />
+        </td>
+    </tr>
+</table>
+
+### AnimateDiffSparseControlNetPipeline
+
+[SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://arxiv.org/abs/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
+
+The abstract from the paper is:
+
+*The development of text-to-video (T2V), i.e., generating videos with a given text prompt, has been significantly advanced in recent years. However, relying solely on text prompts often results in ambiguous frame composition due to spatial uncertainty. The research community thus leverages the dense structure signals, e.g., per-frame depth/edge sequences, to enhance controllability, whose collection accordingly increases the burden of inference. In this work, we present SparseCtrl to enable flexible structure control with temporally sparse signals, requiring only one or a few inputs, as shown in Figure 1. It incorporates an additional condition encoder to process these sparse signals while leaving the pre-trained T2V model untouched. The proposed approach is compatible with various modalities, including sketches, depth maps, and RGB images, providing more practical control for video generation and promoting applications such as storyboarding, depth rendering, keyframe animation, and interpolation. Extensive experiments demonstrate the generalization of SparseCtrl on both original and personalized T2V generators. Codes and models will be publicly available at [this https URL](https://guoyww.github.io/projects/SparseCtrl).*
+
+SparseCtrl introduces the following checkpoints for controlled text-to-video generation:
+
+- [SparseCtrl Scribble](https://huggingface.co/guoyww/animatediff-sparsectrl-scribble)
+- [SparseCtrl RGB](https://huggingface.co/guoyww/animatediff-sparsectrl-rgb)
+
+#### Using SparseCtrl Scribble
+
+```python
+import torch
+
+from diffusers import AnimateDiffSparseControlNetPipeline
+from diffusers.models import AutoencoderKL, MotionAdapter, SparseControlNetModel
+from diffusers.schedulers import DPMSolverMultistepScheduler
+from diffusers.utils import export_to_gif, load_image
+
+
+model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
+motion_adapter_id = "guoyww/animatediff-motion-adapter-v1-5-3"
+controlnet_id = "guoyww/animatediff-sparsectrl-scribble"
+lora_adapter_id = "guoyww/animatediff-motion-lora-v1-5-3"
+vae_id = "stabilityai/sd-vae-ft-mse"
+device = "cuda"
+
+motion_adapter = MotionAdapter.from_pretrained(motion_adapter_id, torch_dtype=torch.float16).to(device)
+controlnet = SparseControlNetModel.from_pretrained(controlnet_id, torch_dtype=torch.float16).to(device)
+vae = AutoencoderKL.from_pretrained(vae_id, torch_dtype=torch.float16).to(device)
+scheduler = DPMSolverMultistepScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    beta_schedule="linear",
+    algorithm_type="dpmsolver++",
+    use_karras_sigmas=True,
+)
+pipe = AnimateDiffSparseControlNetPipeline.from_pretrained(
+    model_id,
+    motion_adapter=motion_adapter,
+    controlnet=controlnet,
+    vae=vae,
+    scheduler=scheduler,
+    torch_dtype=torch.float16,
+).to(device)
+pipe.load_lora_weights(lora_adapter_id, adapter_name="motion_lora")
+pipe.fuse_lora(lora_scale=1.0)
+
+prompt = "an aerial view of a cyberpunk city, night time, neon lights, masterpiece, high quality"
+negative_prompt = "low quality, worst quality, letterboxed"
+
+image_files = [
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-scribble-1.png",
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-scribble-2.png",
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-scribble-3.png"
+]
+condition_frame_indices = [0, 8, 15]
+conditioning_frames = [load_image(img_file) for img_file in image_files]
+
+video = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=25,
+    conditioning_frames=conditioning_frames,
+    controlnet_conditioning_scale=1.0,
+    controlnet_frame_indices=condition_frame_indices,
+    generator=torch.Generator().manual_seed(1337),
+).frames[0]
+export_to_gif(video, "output.gif")
+```
+
+Here are some sample outputs:
+
+<table align="center">
+    <tr>
+        <center>
+          <b>an aerial view of a cyberpunk city, night time, neon lights, masterpiece, high quality</b>
+        </center>
+    </tr>
+    <tr>
+        <td>
+          <center>
+            <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-scribble-1.png" alt="scribble-1" />
+          </center>
+        </td>
+        <td>
+          <center>
+            <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-scribble-2.png" alt="scribble-2" />
+          </center>
+        </td>
+        <td>
+          <center>
+            <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-scribble-3.png" alt="scribble-3" />
+          </center>
+        </td>
+    </tr>
+    <tr>
+        <td colspan=3>
+          <center>
+            <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-sparsectrl-scribble-results.gif" alt="an aerial view of a cyberpunk city, night time, neon lights, masterpiece, high quality" />
+          </center>
+        </td>
+    </tr>
+</table>
+
+#### Using SparseCtrl RGB
+
+```python
+import torch
+
+from diffusers import AnimateDiffSparseControlNetPipeline
+from diffusers.models import AutoencoderKL, MotionAdapter, SparseControlNetModel
+from diffusers.schedulers import DPMSolverMultistepScheduler
+from diffusers.utils import export_to_gif, load_image
+
+
+model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
+motion_adapter_id = "guoyww/animatediff-motion-adapter-v1-5-3"
+controlnet_id = "guoyww/animatediff-sparsectrl-rgb"
+lora_adapter_id = "guoyww/animatediff-motion-lora-v1-5-3"
+vae_id = "stabilityai/sd-vae-ft-mse"
+device = "cuda"
+
+motion_adapter = MotionAdapter.from_pretrained(motion_adapter_id, torch_dtype=torch.float16).to(device)
+controlnet = SparseControlNetModel.from_pretrained(controlnet_id, torch_dtype=torch.float16).to(device)
+vae = AutoencoderKL.from_pretrained(vae_id, torch_dtype=torch.float16).to(device)
+scheduler = DPMSolverMultistepScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    beta_schedule="linear",
+    algorithm_type="dpmsolver++",
+    use_karras_sigmas=True,
+)
+pipe = AnimateDiffSparseControlNetPipeline.from_pretrained(
+    model_id,
+    motion_adapter=motion_adapter,
+    controlnet=controlnet,
+    vae=vae,
+    scheduler=scheduler,
+    torch_dtype=torch.float16,
+).to(device)
+pipe.load_lora_weights(lora_adapter_id, adapter_name="motion_lora")
+
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-firework.png")
+
+video = pipe(
+    prompt="closeup face photo of man in black clothes, night city street, bokeh, fireworks in background",
+    negative_prompt="low quality, worst quality",
+    num_inference_steps=25,
+    conditioning_frames=image,
+    controlnet_frame_indices=[0],
+    controlnet_conditioning_scale=1.0,
+    generator=torch.Generator().manual_seed(42),
+).frames[0]
+export_to_gif(video, "output.gif")
+```
+
+Here are some sample outputs:
+
+<table align="center">
+    <tr>
+        <center>
+          <b>closeup face photo of man in black clothes, night city street, bokeh, fireworks in background</b>
+        </center>
+    </tr>
+    <tr>
+        <td>
+          <center>
+            <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-firework.png" alt="closeup face photo of man in black clothes, night city street, bokeh, fireworks in background" />
+          </center>
+        </td>
+        <td>
+          <center>
+            <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-sparsectrl-rgb-result.gif" alt="closeup face photo of man in black clothes, night city street, bokeh, fireworks in background" />
+          </center>
+        </td>
+    </tr>
+</table>
+
 ### AnimateDiffSDXLPipeline
 
 AnimateDiff can also be used with SDXL models. This is currently an experimental feature as only a beta release of the motion adapter checkpoint is available.
@@ -303,7 +565,6 @@ output = pipe(
 )
 frames = output.frames[0]
 export_to_gif(frames, "animation.gif")
-
 ```
 
 <table>
@@ -378,7 +639,6 @@ output = pipe(
 )
 frames = output.frames[0]
 export_to_gif(frames, "animation.gif")
-
 ```
 
 <table>
@@ -563,12 +823,37 @@ export_to_gif(frames, "animatelcm-motion-lora.gif")
 </table>
 
 
+## Using `from_single_file` with the MotionAdapter
+
+`diffusers>=0.30.0` supports loading the AnimateDiff checkpoints into the `MotionAdapter` in their original format via `from_single_file`
+
+```python
+from diffusers import MotionAdapter
+
+ckpt_path = "https://huggingface.co/Lightricks/LongAnimateDiff/blob/main/lt_long_mm_32_frames.ckpt"
+
+adapter = MotionAdapter.from_single_file(ckpt_path, torch_dtype=torch.float16)
+pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter)
+```
+
 ## AnimateDiffPipeline
 
 [[autodoc]] AnimateDiffPipeline
   - all
   - __call__
 
+## AnimateDiffControlNetPipeline
+
+[[autodoc]] AnimateDiffControlNetPipeline
+  - all
+  - __call__
+
+## AnimateDiffSparseControlNetPipeline
+
+[[autodoc]] AnimateDiffSparseControlNetPipeline
+  - all
+  - __call__
+
 ## AnimateDiffSDXLPipeline
 
 [[autodoc]] AnimateDiffSDXLPipeline

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

@@ -0,0 +1,29 @@
+<!--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.
+-->
+
+# AuraFlow
+
+AuraFlow is inspired by [Stable Diffusion 3](../pipelines/stable_diffusion/stable_diffusion_3.md) and is by far the largest text-to-image generation model that comes with an Apache 2.0 license. This model achieves state-of-the-art results on the [GenEval](https://github.com/djghosh13/geneval) benchmark.
+
+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>
+
+## AuraFlowPipeline
+
+[[autodoc]] AuraFlowPipeline
+	- all
+	- __call__

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

@@ -0,0 +1,92 @@
+<!--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.
+-->
+
+# CogVideoX
+
+[CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://arxiv.org/abs/2408.06072) from Tsinghua University & ZhipuAI, by Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang, Yean Cheng, Ting Liu, Bin Xu, Yuxiao Dong, Jie Tang.
+
+The abstract from the paper is:
+
+*We introduce CogVideoX, a large-scale diffusion transformer model designed for generating videos based on text prompts. To efficently model video data, we propose to levearge a 3D Variational Autoencoder (VAE) to compresses videos along both spatial and temporal dimensions. To improve the text-video alignment, we propose an expert transformer with the expert adaptive LayerNorm to facilitate the deep fusion between the two modalities. By employing a progressive training technique, CogVideoX is adept at producing coherent, long-duration videos characterized by significant motion. In addition, we develop an effectively text-video data processing pipeline that includes various data preprocessing strategies and a video captioning method. It significantly helps enhance the performance of CogVideoX, improving both generation quality and semantic alignment. Results show that CogVideoX demonstrates state-of-the-art performance across both multiple machine metrics and human evaluations. The model weight of CogVideoX-2B is publicly available at https://github.com/THUDM/CogVideo.*
+
+<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 [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).
+
+There are two models available that can be used with the CogVideoX pipeline:
+- [`THUDM/CogVideoX-2b`](https://huggingface.co/THUDM/CogVideoX-2b)
+- [`THUDM/CogVideoX-5b`](https://huggingface.co/THUDM/CogVideoX-5b)
+
+## Inference
+
+Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
+
+First, load the pipeline:
+
+```python
+import torch
+from diffusers import CogVideoXPipeline
+from diffusers.utils import export_to_video
+
+pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b").to("cuda")
+```
+
+Then change the memory layout of the pipelines `transformer` component to `torch.channels_last`:
+
+```python
+pipe.transformer.to(memory_format=torch.channels_last)
+```
+
+Finally, compile the components and run inference:
+
+```python
+pipe.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
+
+# CogVideoX works well with long and well-described prompts
+prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
+video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
+```
+
+The [benchmark](https://gist.github.com/a-r-r-o-w/5183d75e452a368fd17448fcc810bd3f) results on an 80GB A100 machine are:
+
+```
+Without torch.compile(): Average inference time: 96.89 seconds.
+With torch.compile(): Average inference time: 76.27 seconds.
+```
+
+### Memory optimization
+
+CogVideoX-2b requires about 19 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to [this](https://gist.github.com/a-r-r-o-w/3959a03f15be5c9bd1fe545b09dfcc93) script.
+
+- `pipe.enable_model_cpu_offload()`:
+  - Without enabling cpu offloading, memory usage is `33 GB`
+  - With enabling cpu offloading, memory usage is `19 GB`
+- `pipe.vae.enable_tiling()`:
+  - With enabling cpu offloading and tiling, memory usage is `11 GB`
+- `pipe.vae.enable_slicing()`
+
+## CogVideoXPipeline
+
+[[autodoc]] CogVideoXPipeline
+  - all
+  - __call__
+
+## CogVideoXPipelineOutput
+
+[[autodoc]] pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput

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

@@ -0,0 +1,48 @@
+<!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+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.
+-->
+
+# ControlNet with Flux.1
+
+FluxControlNetPipeline is an implementation of ControlNet for Flux.1.
+
+ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
+
+With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
+
+The abstract from the paper is:
+
+*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
+
+This controlnet code is implemented by [The InstantX Team](https://huggingface.co/InstantX). You can find pre-trained checkpoints for Flux-ControlNet in the table below:
+
+
+| ControlNet type | Developer | Link |
+| -------- | ---------- | ---- |
+| Canny | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny) |
+| Depth | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Depth) |
+| Union | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union) |
+
+
+<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-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+## FluxControlNetPipeline
+[[autodoc]] FluxControlNetPipeline
+	- all
+	- __call__
+
+
+## FluxPipelineOutput
+[[autodoc]] pipelines.flux.pipeline_output.FluxPipelineOutput

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

@@ -0,0 +1,36 @@
+<!--Copyright 2024 The HuggingFace Team and Tencent Hunyuan Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+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.
+-->
+
+# ControlNet with Hunyuan-DiT
+
+HunyuanDiTControlNetPipeline is an implementation of ControlNet for [Hunyuan-DiT](https://arxiv.org/abs/2405.08748).
+
+ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
+
+With a ControlNet model, you can provide an additional control image to condition and control Hunyuan-DiT generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
+
+The abstract from the paper is:
+
+*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
+
+This 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-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+## HunyuanDiTControlNetPipeline
+[[autodoc]] HunyuanDiTControlNetPipeline
+	- all
+	- __call__

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

@@ -0,0 +1,53 @@
+<!--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.
+-->
+
+# ControlNet with Stable Diffusion 3
+
+StableDiffusion3ControlNetPipeline is an implementation of ControlNet for Stable Diffusion 3.
+
+ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
+
+With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
+
+The abstract from the paper is:
+
+*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
+
+This controlnet code is mainly implemented by [The InstantX Team](https://huggingface.co/InstantX). The inpainting-related code was developed by [The Alimama Creative Team](https://huggingface.co/alimama-creative). You can find pre-trained checkpoints for SD3-ControlNet in the table below:
+
+
+| ControlNet type | Developer | Link |
+| -------- | ---------- | ---- |
+| Canny | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Canny) |
+| Pose | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Pose) |
+| Tile | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Tile) |
+| 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-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+## StableDiffusion3ControlNetPipeline
+[[autodoc]] StableDiffusion3ControlNetPipeline
+	- all
+	- __call__
+
+## StableDiffusion3ControlNetInpaintingPipeline
+[[autodoc]] pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet_inpainting.StableDiffusion3ControlNetInpaintingPipeline
+	- all
+	- __call__
+
+## StableDiffusion3PipelineOutput
+[[autodoc]] pipelines.stable_diffusion_3.pipeline_output.StableDiffusion3PipelineOutput

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

@@ -0,0 +1,165 @@
+<!--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.
+-->
+
+# Flux
+
+Flux is a series of text-to-image generation models based on diffusion transformers. To know more about Flux, check out the original [blog post](https://blackforestlabs.ai/announcing-black-forest-labs/) by the creators of Flux, Black Forest Labs.
+
+Original model checkpoints for Flux can be found [here](https://huggingface.co/black-forest-labs). Original inference code can be found [here](https://github.com/black-forest-labs/flux).
+
+<Tip>
+
+Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.  For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
+
+</Tip>
+
+Flux comes in two variants:
+
+* Timestep-distilled (`black-forest-labs/FLUX.1-schnell`)
+* Guidance-distilled (`black-forest-labs/FLUX.1-dev`)
+
+Both checkpoints have slightly difference usage which we detail below.
+
+### Timestep-distilled
+
+* `max_sequence_length` cannot be more than 256.
+* `guidance_scale` needs to be 0.
+* As this is a timestep-distilled model, it benefits from fewer sampling steps.
+
+```python
+import torch
+from diffusers import FluxPipeline
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload()
+
+prompt = "A cat holding a sign that says hello world"
+out = pipe(
+    prompt=prompt,
+    guidance_scale=0.,
+    height=768,
+    width=1360,
+    num_inference_steps=4,
+    max_sequence_length=256,
+).images[0]
+out.save("image.png")
+```
+
+### Guidance-distilled
+
+* The guidance-distilled variant takes about 50 sampling steps for good-quality generation.
+* It doesn't have any limitations around the `max_sequence_length`.
+
+```python
+import torch
+from diffusers import FluxPipeline
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload()
+
+prompt = "a tiny astronaut hatching from an egg on the moon"
+out = pipe(
+    prompt=prompt,
+    guidance_scale=3.5,
+    height=768,
+    width=1360,
+    num_inference_steps=50,
+).images[0]
+out.save("image.png")
+```
+
+## Running FP16 inference
+Flux can generate high-quality images with FP16 (i.e. to accelerate inference on Turing/Volta GPUs) but produces different outputs compared to FP32/BF16. The issue is that some activations in the text encoders have to be clipped when running in FP16, which affects the overall image. Forcing text encoders to run with FP32 inference thus removes this output difference. See [here](https://github.com/huggingface/diffusers/pull/9097#issuecomment-2272292516) for details.
+
+FP16 inference code:
+```python
+import torch
+from diffusers import FluxPipeline
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) # can replace schnell with dev
+# to run on low vram GPUs (i.e. between 4 and 32 GB VRAM)
+pipe.enable_sequential_cpu_offload()
+pipe.vae.enable_slicing()
+pipe.vae.enable_tiling()
+
+pipe.to(torch.float16) # casting here instead of in the pipeline constructor because doing so in the constructor loads all models into CPU memory at once
+
+prompt = "A cat holding a sign that says hello world"
+out = pipe(
+    prompt=prompt,
+    guidance_scale=0.,
+    height=768,
+    width=1360,
+    num_inference_steps=4,
+    max_sequence_length=256,
+).images[0]
+out.save("image.png")
+```
+
+## Single File Loading for the `FluxTransformer2DModel`
+
+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>
+
+The following example demonstrates how to run Flux with less than 16GB of VRAM.
+
+First install `optimum-quanto`
+
+```shell
+pip install optimum-quanto
+```
+
+Then run the following example
+
+```python
+import torch
+from diffusers import FluxTransformer2DModel, FluxPipeline
+from transformers import T5EncoderModel, CLIPTextModel
+from optimum.quanto import freeze, qfloat8, quantize
+
+bfl_repo = "black-forest-labs/FLUX.1-dev"
+dtype = torch.bfloat16
+
+transformer = FluxTransformer2DModel.from_single_file("https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8.safetensors", torch_dtype=dtype)
+quantize(transformer, weights=qfloat8)
+freeze(transformer)
+
+text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
+quantize(text_encoder_2, weights=qfloat8)
+freeze(text_encoder_2)
+
+pipe = FluxPipeline.from_pretrained(bfl_repo, transformer=None, text_encoder_2=None, torch_dtype=dtype)
+pipe.transformer = transformer
+pipe.text_encoder_2 = text_encoder_2
+
+pipe.enable_model_cpu_offload()
+
+prompt = "A cat holding a sign that says hello world"
+image = pipe(
+    prompt,
+    guidance_scale=3.5,
+    output_type="pil",
+    num_inference_steps=20,
+    generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+
+image.save("flux-fp8-dev.png")
+```
+
+## FluxPipeline
+
+[[autodoc]] FluxPipeline
+	- all
+	- __call__

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

@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team and Tencent Hunyuan Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -34,6 +34,12 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.m
 
 </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
 
 You can optimize the pipeline's runtime and memory consumption with torch.compile and feed-forward chunking. To learn about other optimization methods, check out the [Speed up inference](../../optimization/fp16) and [Reduce memory usage](../../optimization/memory) guides.

docs/source/en/api/pipelines/kandinsky3.md

@@ -11,7 +11,7 @@ specific language governing permissions and limitations under the License.
 
 Kandinsky 3 is created by [Vladimir Arkhipkin](https://github.com/oriBetelgeuse),[Anastasia Maltseva](https://github.com/NastyaMittseva),[Igor Pavlov](https://github.com/boomb0om),[Andrei Filatov](https://github.com/anvilarth),[Arseniy Shakhmatov](https://github.com/cene555),[Andrey Kuznetsov](https://github.com/kuznetsoffandrey),[Denis Dimitrov](https://github.com/denndimitrov), [Zein Shaheen](https://github.com/zeinsh)
 
-The description from it's Github page:
+The description from it's GitHub page:
 
 *Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.*
 

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

@@ -0,0 +1,107 @@
+<!--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.
+-->
+
+# Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis
+
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kolors/kolors_header_collage.png)
+
+Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by [the Kuaishou Kolors team](https://github.com/Kwai-Kolors/Kolors). Trained on billions of text-image pairs, Kolors exhibits significant advantages over both open-source and closed-source models in visual quality, complex semantic accuracy, and text rendering for both Chinese and English characters. Furthermore, Kolors supports both Chinese and English inputs, demonstrating strong performance in understanding and generating Chinese-specific content. For more details, please refer to this [technical report](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf).
+
+The abstract from the technical report is:
+
+*We present Kolors, a latent diffusion model for text-to-image synthesis, characterized by its profound understanding of both English and Chinese, as well as an impressive degree of photorealism. There are three key insights contributing to the development of Kolors. Firstly, unlike large language model T5 used in Imagen and Stable Diffusion 3, Kolors is built upon the General Language Model (GLM), which enhances its comprehension capabilities in both English and Chinese. Moreover, we employ a multimodal large language model to recaption the extensive training dataset for fine-grained text understanding. These strategies significantly improve Kolors’ ability to comprehend intricate semantics, particularly those involving multiple entities, and enable its advanced text rendering capabilities. Secondly, we divide the training of Kolors into two phases: the concept learning phase with broad knowledge and the quality improvement phase with specifically curated high-aesthetic data. Furthermore, we investigate the critical role of the noise schedule and introduce a novel schedule to optimize high-resolution image generation. These strategies collectively enhance the visual appeal of the generated high-resolution images. Lastly, we propose a category-balanced benchmark KolorsPrompts, which serves as a guide for the training and evaluation of Kolors. Consequently, even when employing the commonly used U-Net backbone, Kolors has demonstrated remarkable performance in human evaluations, surpassing the existing open-source models and achieving Midjourney-v6 level performance, especially in terms of visual appeal. We will release the code and weights of Kolors at <https://github.com/Kwai-Kolors/Kolors>, and hope that it will benefit future research and applications in the visual generation community.*
+
+## Usage Example
+
+```python
+import torch
+
+from diffusers import DPMSolverMultistepScheduler, KolorsPipeline
+
+pipe = KolorsPipeline.from_pretrained("Kwai-Kolors/Kolors-diffusers", torch_dtype=torch.float16, variant="fp16")
+pipe.to("cuda")
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
+
+image = pipe(
+    prompt='一张瓢虫的照片，微距，变焦，高质量，电影，拿着一个牌子，写着"可图"',
+    negative_prompt="",
+    guidance_scale=6.5,
+    num_inference_steps=25,
+).images[0]
+
+image.save("kolors_sample.png")
+```
+
+### IP Adapter
+
+Kolors needs a different IP Adapter to work, and it uses [Openai-CLIP-336](https://huggingface.co/openai/clip-vit-large-patch14-336) as an image encoder.
+
+<Tip>
+
+Using an IP Adapter with Kolors requires more than 24GB of VRAM. To use it, we recommend using [`~DiffusionPipeline.enable_model_cpu_offload`] on consumer GPUs.
+
+</Tip>
+
+<Tip>
+
+While Kolors is integrated in Diffusers, you need to load the image encoder from a revision to use the safetensor files. You can still use the main branch of the original repository if you're comfortable loading pickle checkpoints.
+
+</Tip>
+
+```python
+import torch
+from transformers import CLIPVisionModelWithProjection
+
+from diffusers import DPMSolverMultistepScheduler, KolorsPipeline
+from diffusers.utils import load_image
+
+image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+    "Kwai-Kolors/Kolors-IP-Adapter-Plus",
+    subfolder="image_encoder",
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.float16,
+    revision="refs/pr/4",
+)
+
+pipe = KolorsPipeline.from_pretrained(
+    "Kwai-Kolors/Kolors-diffusers", image_encoder=image_encoder, torch_dtype=torch.float16, variant="fp16"
+)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
+
+pipe.load_ip_adapter(
+    "Kwai-Kolors/Kolors-IP-Adapter-Plus",
+    subfolder="",
+    weight_name="ip_adapter_plus_general.safetensors",
+    revision="refs/pr/4",
+    image_encoder_folder=None,
+)
+pipe.enable_model_cpu_offload()
+
+ipa_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kolors/cat_square.png")
+
+image = pipe(
+    prompt="best quality, high quality",
+    negative_prompt="",
+    guidance_scale=6.5,
+    num_inference_steps=25,
+    ip_adapter_image=ipa_image,
+).images[0]
+
+image.save("kolors_ipa_sample.png")
+```
+
+## KolorsPipeline
+
+[[autodoc]] KolorsPipeline
+
+- all
+- __call__

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

@@ -0,0 +1,77 @@
+<!-- # 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. -->
+
+# Latte
+
+![latte text-to-video](https://github.com/Vchitect/Latte/blob/52bc0029899babbd6e9250384c83d8ed2670ff7a/visuals/latte.gif?raw=true)
+
+[Latte: Latent Diffusion Transformer for Video Generation](https://arxiv.org/abs/2401.03048) from Monash University, Shanghai AI Lab, Nanjing University, and Nanyang Technological University.
+
+The abstract from the paper is:
+
+*We propose a novel Latent Diffusion Transformer, namely Latte, for video generation. Latte first extracts spatio-temporal tokens from input videos and then adopts a series of Transformer blocks to model video distribution in the latent space. In order to model a substantial number of tokens extracted from videos, four efficient variants are introduced from the perspective of decomposing the spatial and temporal dimensions of input videos. To improve the quality of generated videos, we determine the best practices of Latte through rigorous experimental analysis, including video clip patch embedding, model variants, timestep-class information injection, temporal positional embedding, and learning strategies. Our comprehensive evaluation demonstrates that Latte achieves state-of-the-art performance across four standard video generation datasets, i.e., FaceForensics, SkyTimelapse, UCF101, and Taichi-HD. In addition, we extend Latte to text-to-video generation (T2V) task, where Latte achieves comparable results compared to recent T2V models. We strongly believe that Latte provides valuable insights for future research on incorporating Transformers into diffusion models for video generation.*
+
+**Highlights**: Latte is a latent diffusion transformer proposed as a backbone for modeling different modalities (trained for text-to-video generation here). It achieves state-of-the-art performance across four standard video benchmarks - [FaceForensics](https://arxiv.org/abs/1803.09179), [SkyTimelapse](https://arxiv.org/abs/1709.07592), [UCF101](https://arxiv.org/abs/1212.0402) and [Taichi-HD](https://arxiv.org/abs/2003.00196). To prepare and download the datasets for evaluation, please refer to [this https URL](https://github.com/Vchitect/Latte/blob/main/docs/datasets_evaluation.md).
+
+This pipeline was contributed by [maxin-cn](https://github.com/maxin-cn). The original codebase can be found [here](https://github.com/Vchitect/Latte). The original weights can be found under [hf.co/maxin-cn](https://huggingface.co/maxin-cn).
+
+<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>
+
+### Inference
+
+Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
+
+First, load the pipeline:
+
+```python
+import torch
+from diffusers import LattePipeline
+
+pipeline = LattePipeline.from_pretrained(
+	"maxin-cn/Latte-1", torch_dtype=torch.float16
+).to("cuda")
+```
+
+Then change the memory layout of the pipelines `transformer` and `vae` components to `torch.channels-last`:
+
+```python
+pipeline.transformer.to(memory_format=torch.channels_last)
+pipeline.vae.to(memory_format=torch.channels_last)
+```
+
+Finally, compile the components and run inference:
+
+```python
+pipeline.transformer = torch.compile(pipeline.transformer)
+pipeline.vae.decode = torch.compile(pipeline.vae.decode)
+
+video = pipeline(prompt="A dog wearing sunglasses floating in space, surreal, nebulae in background").frames[0]
+```
+
+The [benchmark](https://gist.github.com/a-r-r-o-w/4e1694ca46374793c0361d740a99ff19) results on an 80GB A100 machine are:
+
+```
+Without torch.compile(): Average inference time: 16.246 seconds.
+With torch.compile(): Average inference time: 14.573 seconds.
+```
+
+## LattePipeline
+
+[[autodoc]] LattePipeline
+  - all
+  - __call__

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

@@ -0,0 +1,90 @@
+<!--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.
+-->
+
+# Lumina-T2X
+![concepts](https://github.com/Alpha-VLLM/Lumina-T2X/assets/54879512/9f52eabb-07dc-4881-8257-6d8a5f2a0a5a)
+
+[Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT](https://github.com/Alpha-VLLM/Lumina-T2X/blob/main/assets/lumina-next.pdf) from Alpha-VLLM, OpenGVLab, Shanghai AI Laboratory.
+
+The abstract from the paper is:
+
+*Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers (Flag-DiT) that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions. Despite its promising capabilities, Lumina-T2X still encounters challenges including training instability, slow inference, and extrapolation artifacts. In this paper, we present Lumina-Next, an improved version of Lumina-T2X, showcasing stronger generation performance with increased training and inference efficiency. We begin with a comprehensive analysis of the Flag-DiT architecture and identify several suboptimal components, which we address by introducing the Next-DiT architecture with 3D RoPE and sandwich normalizations. To enable better resolution extrapolation, we thoroughly compare different context extrapolation methods applied to text-to-image generation with 3D RoPE, and propose Frequency- and Time-Aware Scaled RoPE tailored for diffusion transformers. Additionally, we introduce a sigmoid time discretization schedule to reduce sampling steps in solving the Flow ODE and the Context Drop method to merge redundant visual tokens for faster network evaluation, effectively boosting the overall sampling speed. Thanks to these improvements, Lumina-Next not only improves the quality and efficiency of basic text-to-image generation but also demonstrates superior resolution extrapolation capabilities and multilingual generation using decoder-based LLMs as the text encoder, all in a zero-shot manner. To further validate Lumina-Next as a versatile generative framework, we instantiate it on diverse tasks including visual recognition, multi-view, audio, music, and point cloud generation, showcasing strong performance across these domains. By releasing all codes and model weights at https://github.com/Alpha-VLLM/Lumina-T2X, we aim to advance the development of next-generation generative AI capable of universal modeling.*
+
+**Highlights**: Lumina-Next is a next-generation Diffusion Transformer that significantly enhances text-to-image generation, multilingual generation, and multitask performance by introducing the Next-DiT architecture, 3D RoPE, and frequency- and time-aware RoPE, among other improvements.
+
+Lumina-Next has the following components:
+* It improves sampling efficiency with fewer and faster Steps.
+* It uses a Next-DiT as a transformer backbone with Sandwichnorm 3D RoPE, and Grouped-Query Attention.
+* It uses a Frequency- and Time-Aware Scaled RoPE.
+
+---
+
+[Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers](https://arxiv.org/abs/2405.05945) from Alpha-VLLM, OpenGVLab, Shanghai AI Laboratory.
+
+The abstract from the paper is:
+
+*Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details. In this technical report, we introduce the Lumina-T2X family - a series of Flow-based Large Diffusion Transformers (Flag-DiT) equipped with zero-initialized attention, as a unified framework designed to transform noise into images, videos, multi-view 3D objects, and audio clips conditioned on text instructions. By tokenizing the latent spatial-temporal space and incorporating learnable placeholders such as [nextline] and [nextframe] tokens, Lumina-T2X seamlessly unifies the representations of different modalities across various spatial-temporal resolutions. This unified approach enables training within a single framework for different modalities and allows for flexible generation of multimodal data at any resolution, aspect ratio, and length during inference. Advanced techniques like RoPE, RMSNorm, and flow matching enhance the stability, flexibility, and scalability of Flag-DiT, enabling models of Lumina-T2X to scale up to 7 billion parameters and extend the context window to 128K tokens. This is particularly beneficial for creating ultra-high-definition images with our Lumina-T2I model and long 720p videos with our Lumina-T2V model. Remarkably, Lumina-T2I, powered by a 5-billion-parameter Flag-DiT, requires only 35% of the training computational costs of a 600-million-parameter naive DiT. Our further comprehensive analysis underscores Lumina-T2X's preliminary capability in resolution extrapolation, high-resolution editing, generating consistent 3D views, and synthesizing videos with seamless transitions. We expect that the open-sourcing of Lumina-T2X will further foster creativity, transparency, and diversity in the generative AI community.*
+
+
+You can find the original codebase at [Alpha-VLLM](https://github.com/Alpha-VLLM/Lumina-T2X) and all the available checkpoints at [Alpha-VLLM Lumina Family](https://huggingface.co/collections/Alpha-VLLM/lumina-family-66423205bedb81171fd0644b).
+
+**Highlights**: Lumina-T2X supports Any Modality, Resolution, and Duration.
+
+Lumina-T2X has the following components:
+* It uses a Flow-based Large Diffusion Transformer as the backbone
+* It supports different any modalities with one backbone and corresponding encoder, decoder.
+
+This pipeline was contributed by [PommesPeter](https://github.com/PommesPeter). The original codebase can be found [here](https://github.com/Alpha-VLLM/Lumina-T2X). The original weights can be found under [hf.co/Alpha-VLLM](https://huggingface.co/Alpha-VLLM).
+
+<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>
+
+### Inference (Text-to-Image)
+
+Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
+
+First, load the pipeline:
+
+```python
+from diffusers import LuminaText2ImgPipeline
+import torch
+
+pipeline = LuminaText2ImgPipeline.from_pretrained(
+	"Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16
+).to("cuda")
+```
+
+Then change the memory layout of the pipelines `transformer` and `vae` components to `torch.channels-last`:
+
+```python
+pipeline.transformer.to(memory_format=torch.channels_last)
+pipeline.vae.to(memory_format=torch.channels_last)
+```
+
+Finally, compile the components and run inference:
+
+```python
+pipeline.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
+pipeline.vae.decode = torch.compile(pipeline.vae.decode, mode="max-autotune", fullgraph=True)
+
+image = pipeline(prompt="Upper body of a young woman in a Victorian-era outfit with brass goggles and leather straps. Background shows an industrial revolution cityscape with smoky skies and tall, metal structures").images[0]
+```
+
+## LuminaText2ImgPipeline
+
+[[autodoc]] LuminaText2ImgPipeline
+	- all
+	- __call__
+

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

@@ -71,6 +71,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 | [Semantic Guidance](semantic_stable_diffusion) | text2image |
 | [Shap-E](shap_e) | text-to-3D, image-to-3D |
 | [Spectrogram Diffusion](spectrogram_diffusion) |  |
+| [Stable Audio](stable_audio) | text2audio |
 | [Stable Diffusion](stable_diffusion/overview) | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
 | [Stable Diffusion Model Editing](model_editing) | model editing |
 | [Stable Diffusion XL](stable_diffusion/stable_diffusion_xl) | text2image, image2image, inpainting |

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

@@ -0,0 +1,95 @@
+<!--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.
+-->
+
+# Perturbed-Attention Guidance
+
+[Perturbed-Attention Guidance (PAG)](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) is a new diffusion sampling guidance that improves sample quality across both unconditional and conditional settings, achieving this without requiring further training or the integration of external modules.
+
+PAG was introduced in [Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance](https://huggingface.co/papers/2403.17377) by Donghoon Ahn, Hyoungwon Cho, Jaewon Min, Wooseok Jang, Jungwoo Kim, SeonHwa Kim, Hyun Hee Park, Kyong Hwan Jin and Seungryong Kim.
+
+The abstract from the paper is:
+
+*Recent studies have demonstrated that diffusion models are capable of generating high-quality samples, but their quality heavily depends on sampling guidance techniques, such as classifier guidance (CG) and classifier-free guidance (CFG). These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration. In this paper, we propose a novel sampling guidance, called Perturbed-Attention Guidance (PAG), which improves diffusion sample quality across both unconditional and conditional settings, achieving this without requiring additional training or the integration of external modules. PAG is designed to progressively enhance the structure of samples throughout the denoising process. It involves generating intermediate samples with degraded structure by substituting selected self-attention maps in diffusion U-Net with an identity matrix, by considering the self-attention mechanisms' ability to capture structural information, and guiding the denoising process away from these degraded samples. In both ADM and Stable Diffusion, PAG surprisingly improves sample quality in conditional and even unconditional scenarios. Moreover, PAG significantly improves the baseline performance in various downstream tasks where existing guidances such as CG or CFG cannot be fully utilized, including ControlNet with empty prompts and image restoration such as inpainting and deblurring.*
+
+PAG can be used by specifying the `pag_applied_layers` as a parameter when instantiating a PAG pipeline. It can be a single string or a list of strings. Each string can be a unique layer identifier or a regular expression to identify one or more layers.
+
+- Full identifier as a normal string: `down_blocks.2.attentions.0.transformer_blocks.0.attn1.processor`
+- Full identifier as a RegEx: `down_blocks.2.(attentions|motion_modules).0.transformer_blocks.0.attn1.processor`
+- Partial identifier as a RegEx: `down_blocks.2`, or `attn1`
+- List of identifiers (can be combo of strings and ReGex): `["blocks.1", "blocks.(14|20)", r"down_blocks\.(2,3)"]`
+
+<Tip warning={true}>
+
+Since RegEx is supported as a way for matching layer identifiers, it is crucial to use it correctly otherwise there might be unexpected behaviour. The recommended way to use PAG is by specifying layers as `blocks.{layer_index}` and `blocks.({layer_index_1|layer_index_2|...})`. Using it in any other way, while doable, may bypass our basic validation checks and give you unexpected results.
+
+</Tip>
+
+## AnimateDiffPAGPipeline
+[[autodoc]] AnimateDiffPAGPipeline
+  - all
+  - __call__
+
+## HunyuanDiTPAGPipeline
+[[autodoc]] HunyuanDiTPAGPipeline
+  - all
+  - __call__
+
+## KolorsPAGPipeline
+[[autodoc]] KolorsPAGPipeline
+  - all
+  - __call__
+
+## StableDiffusionPAGPipeline
+[[autodoc]] StableDiffusionPAGPipeline
+	- all
+	- __call__
+
+## StableDiffusionControlNetPAGPipeline
+[[autodoc]] StableDiffusionControlNetPAGPipeline
+	- all
+	- __call__
+
+## StableDiffusionXLPAGPipeline
+[[autodoc]] StableDiffusionXLPAGPipeline
+	- all
+	- __call__
+
+## StableDiffusionXLPAGImg2ImgPipeline
+[[autodoc]] StableDiffusionXLPAGImg2ImgPipeline
+	- all
+	- __call__
+
+## StableDiffusionXLPAGInpaintPipeline
+[[autodoc]] StableDiffusionXLPAGInpaintPipeline
+	- all
+	- __call__
+
+## StableDiffusionXLControlNetPAGPipeline
+[[autodoc]] StableDiffusionXLControlNetPAGPipeline
+	- all
+	- __call__
+
+## StableDiffusionXLControlNetPAGImg2ImgPipeline
+[[autodoc]] StableDiffusionXLControlNetPAGImg2ImgPipeline
+	- all
+	- __call__
+
+## StableDiffusion3PAGPipeline
+[[autodoc]] StableDiffusion3PAGPipeline
+	- all
+	- __call__
+
+
+## PixArtSigmaPAGPipeline
+[[autodoc]] PixArtSigmaPAGPipeline
+	- all
+	- __call__

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

@@ -37,6 +37,12 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 
 </Tip>
 
+<Tip>
+
+You can further improve generation quality by passing the generated image from [`PixArtSigmaPipeline`] to the [SDXL refiner](../../using-diffusers/sdxl#base-to-refiner-model) model.
+
+</Tip>
+
 ## Inference with under 8GB GPU VRAM
 
 Run the [`PixArtSigmaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example.

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

@@ -0,0 +1,42 @@
+<!--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.
+-->
+
+# Stable Audio
+
+Stable Audio was proposed in [Stable Audio Open](https://arxiv.org/abs/2407.14358) by Zach Evans et al. . it takes a text prompt as input and predicts the corresponding sound or music sample.
+
+Stable Audio Open generates variable-length (up to 47s) stereo audio at 44.1kHz from text prompts. It comprises three components: an autoencoder that compresses waveforms into a manageable sequence length, a T5-based text embedding for text conditioning, and a transformer-based diffusion (DiT) model that operates in the latent space of the autoencoder.
+
+Stable Audio is trained on a corpus of around 48k audio recordings, where around 47k are from Freesound and the rest are from the Free Music Archive (FMA). All audio files are licensed under CC0, CC BY, or CC Sampling+. This data is used to train the autoencoder and the DiT.
+
+The abstract of the paper is the following:
+*Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.*
+
+This pipeline was contributed by [Yoach Lacombe](https://huggingface.co/ylacombe). The original codebase can be found at [Stability-AI/stable-audio-tools](https://github.com/Stability-AI/stable-audio-tools).
+
+## Tips
+
+When constructing a prompt, keep in mind:
+
+* Descriptive prompt inputs work best; use adjectives to describe the sound (for example, "high quality" or "clear") and make the prompt context specific where possible (e.g. "melodic techno with a fast beat and synths" works better than "techno").
+* Using a *negative prompt* can significantly improve the quality of the generated audio. Try using a negative prompt of "low quality, average quality".
+
+During inference:
+
+* The _quality_ of the generated audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference.
+* Multiple waveforms can be generated in one go: set `num_waveforms_per_prompt` to a value greater than 1 to enable. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly.
+
+
+## StableAudioPipeline
+[[autodoc]] StableAudioPipeline
+	- all
+	- __call__

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

@@ -48,7 +48,7 @@ from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 import torch
 
 repo_id = "stabilityai/stable-diffusion-2-base"
-pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
+pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, variant="fp16")
 
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 pipe = pipe.to("cuda")
@@ -72,7 +72,7 @@ init_image = load_image(img_url).resize((512, 512))
 mask_image = load_image(mask_url).resize((512, 512))
 
 repo_id = "stabilityai/stable-diffusion-2-inpainting"
-pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
+pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, variant="fp16")
 
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 pipe = pipe.to("cuda")

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

@@ -0,0 +1,315 @@
+<!--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.
+-->
+
+# Stable Diffusion 3
+
+Stable Diffusion 3 (SD3) was proposed in [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://arxiv.org/pdf/2403.03206.pdf) by Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Muller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach.
+
+The abstract from the paper is:
+
+*Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations.*
+
+
+## Usage Example
+
+_As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), 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
+huggingface-cli login
+```
+
+<Tip>
+
+The SD3 pipeline uses three text encoders to generate an image. Model offloading is necessary in order for it to run on most commodity hardware. Please use the `torch.float16` data type for additional memory savings.
+
+</Tip>
+
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline
+
+pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
+pipe.to("cuda")
+
+image = pipe(
+    prompt="a photo of a cat holding a sign that says hello world",
+    negative_prompt="",
+    num_inference_steps=28,
+    height=1024,
+    width=1024,
+    guidance_scale=7.0,
+).images[0]
+
+image.save("sd3_hello_world.png")
+```
+
+## Memory Optimisations for SD3
+
+SD3 uses three text encoders, one if which is the very large T5-XXL model. This makes it challenging to run the model on GPUs with less than 24GB of VRAM, even when using `fp16` precision. The following section outlines a few memory optimizations in Diffusers that make it easier to run SD3 on low resource hardware.
+
+### Running Inference with Model Offloading
+
+The most basic memory optimization available in Diffusers allows you to offload the components of the model to CPU during inference in order to save memory, while seeing a slight increase in inference latency. Model offloading will only move a model component onto the GPU when it needs to be executed, while keeping the remaining components on the CPU.
+
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline
+
+pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
+pipe.enable_model_cpu_offload()
+
+image = pipe(
+    prompt="a photo of a cat holding a sign that says hello world",
+    negative_prompt="",
+    num_inference_steps=28,
+    height=1024,
+    width=1024,
+    guidance_scale=7.0,
+).images[0]
+
+image.save("sd3_hello_world.png")
+```
+
+### Dropping the T5 Text Encoder during Inference
+
+Removing the memory-intensive 4.7B parameter T5-XXL text encoder during inference can significantly decrease the memory requirements for SD3 with only a slight loss in performance.
+
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline
+
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3-medium-diffusers",
+    text_encoder_3=None,
+    tokenizer_3=None,
+    torch_dtype=torch.float16
+)
+pipe.to("cuda")
+
+image = pipe(
+    prompt="a photo of a cat holding a sign that says hello world",
+    negative_prompt="",
+    num_inference_steps=28,
+    height=1024,
+    width=1024,
+    guidance_scale=7.0,
+).images[0]
+
+image.save("sd3_hello_world-no-T5.png")
+```
+
+### Using a Quantized Version of the T5 Text Encoder
+
+We can leverage the `bitsandbytes` library to load and quantize the T5-XXL text encoder to 8-bit precision. This allows you to keep using all three text encoders while only slightly impacting performance.
+
+First install the `bitsandbytes` library.
+
+```shell
+pip install bitsandbytes
+```
+
+Then load the T5-XXL model using the `BitsAndBytesConfig`.
+
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline
+from transformers import T5EncoderModel, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+
+model_id = "stabilityai/stable-diffusion-3-medium-diffusers"
+text_encoder = T5EncoderModel.from_pretrained(
+    model_id,
+    subfolder="text_encoder_3",
+    quantization_config=quantization_config,
+)
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    model_id,
+    text_encoder_3=text_encoder,
+    device_map="balanced",
+    torch_dtype=torch.float16
+)
+
+image = pipe(
+    prompt="a photo of a cat holding a sign that says hello world",
+    negative_prompt="",
+    num_inference_steps=28,
+    height=1024,
+    width=1024,
+    guidance_scale=7.0,
+).images[0]
+
+image.save("sd3_hello_world-8bit-T5.png")
+```
+
+You can find the end-to-end script [here](https://gist.github.com/sayakpaul/82acb5976509851f2db1a83456e504f1).
+
+## Performance Optimizations for SD3
+
+### Using Torch Compile to Speed Up Inference
+
+Using compiled components in the SD3 pipeline can speed up inference by as much as 4X. The following code snippet demonstrates how to compile the Transformer and VAE components of the SD3 pipeline.
+
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline
+
+torch.set_float32_matmul_precision("high")
+
+torch._inductor.config.conv_1x1_as_mm = True
+torch._inductor.config.coordinate_descent_tuning = True
+torch._inductor.config.epilogue_fusion = False
+torch._inductor.config.coordinate_descent_check_all_directions = True
+
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3-medium-diffusers",
+    torch_dtype=torch.float16
+).to("cuda")
+pipe.set_progress_bar_config(disable=True)
+
+pipe.transformer.to(memory_format=torch.channels_last)
+pipe.vae.to(memory_format=torch.channels_last)
+
+pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
+pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=True)
+
+# Warm Up
+prompt = "a photo of a cat holding a sign that says hello world"
+for _ in range(3):
+    _ = pipe(prompt=prompt, generator=torch.manual_seed(1))
+
+# Run Inference
+image = pipe(prompt=prompt, generator=torch.manual_seed(1)).images[0]
+image.save("sd3_hello_world.png")
+```
+
+Check out the full script [here](https://gist.github.com/sayakpaul/508d89d7aad4f454900813da5d42ca97).
+
+## Using Long Prompts with the T5 Text Encoder
+
+By default, the T5 Text Encoder prompt uses a maximum sequence length of `256`. This can be adjusted by setting the `max_sequence_length` to accept fewer or more tokens. Keep in mind that longer sequences require additional resources and result in longer generation times, such as during batch inference.
+
+```python
+prompt = "A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus, basking in a river of melted butter amidst a breakfast-themed landscape. It features the distinctive, bulky body shape of a hippo. However, instead of the usual grey skin, the creature’s body resembles a golden-brown, crispy waffle fresh off the griddle. The skin is textured with the familiar grid pattern of a waffle, each square filled with a glistening sheen of syrup. The environment combines the natural habitat of a hippo with elements of a breakfast table setting, a river of warm, melted butter, with oversized utensils or plates peeking out from the lush, pancake-like foliage in the background, a towering pepper mill standing in for a tree.  As the sun rises in this fantastical world, it casts a warm, buttery glow over the scene. The creature, content in its butter river, lets out a yawn. Nearby, a flock of birds take flight"
+
+image = pipe(
+    prompt=prompt,
+    negative_prompt="",
+    num_inference_steps=28,
+    guidance_scale=4.5,
+    max_sequence_length=512,
+).images[0]
+```
+
+### Sending a different prompt to the T5 Text Encoder
+
+You can send a different prompt to the CLIP Text Encoders and the T5 Text Encoder to prevent the prompt from being truncated by the CLIP Text Encoders and to improve generation.
+
+<Tip>
+
+The prompt with the CLIP Text Encoders is still truncated to the 77 token limit.
+
+</Tip>
+
+```python
+prompt = "A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus, basking in a river of melted butter amidst a breakfast-themed landscape. A river of warm, melted butter, pancake-like foliage in the background, a towering pepper mill standing in for a tree."
+
+prompt_3 = "A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus, basking in a river of melted butter amidst a breakfast-themed landscape. It features the distinctive, bulky body shape of a hippo. However, instead of the usual grey skin, the creature’s body resembles a golden-brown, crispy waffle fresh off the griddle. The skin is textured with the familiar grid pattern of a waffle, each square filled with a glistening sheen of syrup. The environment combines the natural habitat of a hippo with elements of a breakfast table setting, a river of warm, melted butter, with oversized utensils or plates peeking out from the lush, pancake-like foliage in the background, a towering pepper mill standing in for a tree.  As the sun rises in this fantastical world, it casts a warm, buttery glow over the scene. The creature, content in its butter river, lets out a yawn. Nearby, a flock of birds take flight"
+
+image = pipe(
+    prompt=prompt,
+    prompt_3=prompt_3,
+    negative_prompt="",
+    num_inference_steps=28,
+    guidance_scale=4.5,
+    max_sequence_length=512,
+).images[0]
+```
+
+## Tiny AutoEncoder for Stable Diffusion 3
+
+Tiny AutoEncoder for Stable Diffusion (TAESD3) is a tiny distilled version of Stable Diffusion 3's VAE by [Ollin Boer Bohan](https://github.com/madebyollin/taesd) that can decode [`StableDiffusion3Pipeline`] latents almost instantly.
+
+To use with Stable Diffusion 3:
+
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline, AutoencoderTiny
+
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16
+)
+pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd3", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "slice of delicious New York-style berry cheesecake"
+image = pipe(prompt, num_inference_steps=25).images[0]
+image.save("cheesecake.png")
+```
+
+## Loading the original checkpoints via `from_single_file`
+
+The `SD3Transformer2DModel` and `StableDiffusion3Pipeline` classes support loading the original checkpoints via the `from_single_file` method. This method allows you to load the original checkpoint files that were used to train the models.
+
+## Loading the original checkpoints for the `SD3Transformer2DModel`
+
+```python
+from diffusers import SD3Transformer2DModel
+
+model = SD3Transformer2DModel.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium.safetensors")
+```
+
+## Loading the single checkpoint for the `StableDiffusion3Pipeline`
+
+### Loading the single file checkpoint without T5
+
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline
+
+pipe = StableDiffusion3Pipeline.from_single_file(
+    "https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips.safetensors",
+    torch_dtype=torch.float16,
+    text_encoder_3=None
+)
+pipe.enable_model_cpu_offload()
+
+image = pipe("a picture of a cat holding a sign that says hello world").images[0]
+image.save('sd3-single-file.png')
+```
+
+### Loading the single file checkpoint with T5
+
+> [!TIP]
+> The following example loads a checkpoint stored in a 8-bit floating point format which requires PyTorch 2.3 or later.
+
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline
+
+pipe = StableDiffusion3Pipeline.from_single_file(
+    "https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips_t5xxlfp8.safetensors",
+    torch_dtype=torch.float16,
+)
+pipe.enable_model_cpu_offload()
+
+image = pipe("a picture of a cat holding a sign that says hello world").images[0]
+image.save('sd3-single-file-t5-fp8.png')
+```
+
+## StableDiffusion3Pipeline
+
+[[autodoc]] StableDiffusion3Pipeline
+	- all
+	- __call__

docs/source/en/api/schedulers/cosine_dpm.md

@@ -0,0 +1,24 @@
+<!--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.
+-->
+
+# CosineDPMSolverMultistepScheduler
+
+The [`CosineDPMSolverMultistepScheduler`] is a variant of [`DPMSolverMultistepScheduler`] with cosine schedule, proposed by Nichol and Dhariwal (2021).
+It is being used in the [Stable Audio Open](https://arxiv.org/abs/2407.14358) paper and the [Stability-AI/stable-audio-tool](https://github.com/Stability-AI/stable-audio-tool) codebase.
+
+This scheduler was contributed by [Yoach Lacombe](https://huggingface.co/ylacombe).
+
+## CosineDPMSolverMultistepScheduler
+[[autodoc]] CosineDPMSolverMultistepScheduler
+
+## SchedulerOutput
+[[autodoc]] schedulers.scheduling_utils.SchedulerOutput

docs/source/en/api/schedulers/flow_match_euler_discrete.md

@@ -0,0 +1,18 @@
+<!--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.
+-->
+
+# FlowMatchEulerDiscreteScheduler
+
+`FlowMatchEulerDiscreteScheduler` is based on the flow-matching sampling introduced in [Stable Diffusion 3](https://arxiv.org/abs/2403.03206).
+
+## FlowMatchEulerDiscreteScheduler
+[[autodoc]] FlowMatchEulerDiscreteScheduler

docs/source/en/api/schedulers/flow_match_heun_discrete.md

@@ -0,0 +1,18 @@
+<!--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.
+-->
+
+# FlowMatchHeunDiscreteScheduler
+
+`FlowMatchHeunDiscreteScheduler` is based on the flow-matching sampling introduced in [EDM](https://arxiv.org/abs/2403.03206).
+
+## FlowMatchHeunDiscreteScheduler
+[[autodoc]] FlowMatchHeunDiscreteScheduler

docs/source/en/community_projects.md

@@ -0,0 +1,78 @@
+<!--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.
+-->
+
+# Community Projects
+
+Welcome to Community Projects. This space is dedicated to showcasing the incredible work and innovative applications created by our vibrant community using the `diffusers` library.
+
+This section aims to:
+
+- Highlight diverse and inspiring projects built with `diffusers`
+- Foster knowledge sharing within our community
+- Provide real-world examples of how `diffusers` can be leveraged
+
+Happy exploring, and thank you for being part of the Diffusers community!
+
+<table>
+    <tr>
+        <th>Project Name</th>
+        <th>Description</th>
+    </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/carson-katri/dream-textures"> dream-textures </a></td>
+    <td>Stable Diffusion built-in to Blender</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/megvii-research/HiDiffusion"> HiDiffusion </a></td>
+    <td>Increases the resolution and speed of your diffusion model by only adding a single line of code</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/lllyasviel/IC-Light"> IC-Light </a></td>
+    <td>IC-Light is a project to manipulate the illumination of images</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/InstantID/InstantID"> InstantID </a></td>
+    <td>InstantID : Zero-shot Identity-Preserving Generation in Seconds</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/Sanster/IOPaint"> IOPaint </a></td>
+    <td>Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/bmaltais/kohya_ss"> Kohya </a></td>
+    <td>Gradio GUI for Kohya's Stable Diffusion trainers</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/magic-research/magic-animate"> MagicAnimate </a></td>
+    <td>MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/levihsu/OOTDiffusion"> OOTDiffusion </a></td>
+    <td>Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/vladmandic/automatic"> SD.Next </a></td>
+    <td>SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/ashawkey/stable-dreamfusion"> stable-dreamfusion </a></td>
+    <td>Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/HVision-NKU/StoryDiffusion"> StoryDiffusion </a></td>
+    <td>StoryDiffusion can create a magic story by generating consistent images and videos.</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/cumulo-autumn/StreamDiffusion"> StreamDiffusion </a></td>
+    <td>A Pipeline-Level Solution for Real-Time Interactive Generation</td>
+  </tr>
+</table>

docs/source/en/conceptual/contribution.md

@@ -22,14 +22,13 @@ We enormously value feedback from the community, so please do not be afraid to s
 
 ## Overview
 
-You can contribute in many ways ranging from answering questions on issues to adding new diffusion models to
-the core library.
+You can contribute in many ways ranging from answering questions on issues and discussions to adding new diffusion models to the core library.
 
 In the following, we give an overview of different ways to contribute, ranked by difficulty in ascending order. All of them are valuable to the community.
 
 * 1. Asking and answering questions on [the Diffusers discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers) or on [Discord](https://discord.gg/G7tWnz98XR).
-* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose).
-* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues).
+* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose) or new discussions on [the GitHub Discussions tab](https://github.com/huggingface/diffusers/discussions/new/choose).
+* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues) or discussions on [the GitHub Discussions tab](https://github.com/huggingface/diffusers/discussions).
 * 4. Fix a simple issue, marked by the "Good first issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).
 * 5. Contribute to the [documentation](https://github.com/huggingface/diffusers/tree/main/docs/source).
 * 6. Contribute a [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples).
@@ -63,7 +62,7 @@ In the same spirit, you are of immense help to the community by answering such q
 
 **Please** keep in mind that the more effort you put into asking or answering a question, the higher
 the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database.
-In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
+In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formatted/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
 
 **NOTE about channels**:
 [*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago.
@@ -99,7 +98,7 @@ This means in more detail:
 - Format your code.
 - Do not include any external libraries except for Diffusers depending on them.
 - **Always** provide all necessary information about your environment; for this, you can run: `diffusers-cli env` in your shell and copy-paste the displayed information to the issue.
-- Explain the issue. If the reader doesn't know what the issue is and why it is an issue, she cannot solve it.
+- Explain the issue. If the reader doesn't know what the issue is and why it is an issue, (s)he cannot solve it.
 - **Always** make sure the reader can reproduce your issue with as little effort as possible. If your code snippet cannot be run because of missing libraries or undefined variables, the reader cannot help you. Make sure your reproducible code snippet is as minimal as possible and can be copy-pasted into a simple Python shell.
 - If in order to reproduce your issue a model and/or dataset is required, make sure the reader has access to that model or dataset. You can always upload your model or dataset to the [Hub](https://huggingface.co) to make it easily downloadable. Try to keep your model and dataset as small as possible, to make the reproduction of your issue as effortless as possible.
 
@@ -288,7 +287,7 @@ The official training examples are maintained by the Diffusers' core maintainers
 This is because of the same reasons put forward in [6. Contribute a community pipeline](#6-contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models.
 If the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the `research_projects` folder and maintained by the author.
 
-Both official training and research examples consist of a directory that contains one or more training scripts, a requirements.txt file, and a README.md file. In order for the user to make use of the
+Both official training and research examples consist of a directory that contains one or more training scripts, a `requirements.txt` file, and a `README.md` file. In order for the user to make use of the
 training examples, it is required to clone the repository:
 
 ```bash
@@ -298,7 +297,8 @@ git clone https://github.com/huggingface/diffusers
 as well as to install all additional dependencies required for training:
 
 ```bash
-pip install -r /examples/<your-example-folder>/requirements.txt
+cd diffusers
+pip install -r examples/<your-example-folder>/requirements.txt
 ```
 
 Therefore when adding an example, the `requirements.txt` file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the [DreamBooth `requirements.txt` file](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt).
@@ -316,7 +316,7 @@ Once an example script works, please make sure to add a comprehensive `README.md
 - A link to some training results (logs, models, etc.) that show what the user can expect as shown [here](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5).
 - If you are adding a non-official/research training example, **please don't forget** to add a sentence that you are maintaining this training example which includes your git handle as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/intel_opts#diffusers-examples-with-intel-optimizations).
 
-If you are contributing to the official training examples, please also make sure to add a test to [examples/test_examples.py](https://github.com/huggingface/diffusers/blob/main/examples/test_examples.py). This is not necessary for non-official training examples.
+If you are contributing to the official training examples, please also make sure to add a test to its folder such as [examples/dreambooth/test_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/test_dreambooth.py). This is not necessary for non-official training examples.
 
 ### 8. Fixing a "Good second issue"
 
@@ -418,7 +418,7 @@ You will need basic `git` proficiency to be able to contribute to
 manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
 Git](https://git-scm.com/book/en/v2) is a very good reference.
 
-Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/main/setup.py#L244)):
+Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/83bc6c94eaeb6f7704a2a428931cf2d9ad973ae9/setup.py#L270)):
 
 1. Fork the [repository](https://github.com/huggingface/diffusers) by
 clicking on the 'Fork' button on the repository's page. This creates a copy of the code

docs/source/en/conceptual/philosophy.md

@@ -81,7 +81,7 @@ Models are designed as configurable toolboxes that are natural extensions of [Py
 
 The following design principles are followed:
 - Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.
-- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...
+- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unets/unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_2d_condition.py), [`transformers/transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_2d.py), etc...
 - Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.
 - Models intend to expose complexity, just like PyTorch's `Module` class, and give clear error messages.
 - Models all inherit from `ModelMixin` and `ConfigMixin`.
@@ -90,7 +90,7 @@ The following design principles are followed:
 - To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different.
 - Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and "foreseeing" future changes, *e.g.* it is usually better to add `string` "...type" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work.
 - The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and
-readable long-term, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+readable long-term, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unets/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
 
 ### Schedulers
 
@@ -102,7 +102,7 @@ The following design principles are followed:
 - One scheduler Python file corresponds to one scheduler algorithm (as might be defined in a paper).
 - If schedulers share similar functionalities, we can make use of the `# Copied from` mechanism.
 - Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.
-- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](../using-diffusers/schedulers.md).
+- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](../using-diffusers/schedulers).
 - Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.
 - Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon.
 - The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).

docs/source/en/optimization/fp16.md

@@ -125,3 +125,5 @@ image
     <figcaption class="mt-2 text-center text-sm text-gray-500">distilled Stable Diffusion + Tiny AutoEncoder</figcaption>
   </div>
 </div>
+
+More tiny autoencoder models for other Stable Diffusion models, like Stable Diffusion 3, are available from [madebyollin](https://huggingface.co/madebyollin).

docs/source/en/stable_diffusion.md

@@ -238,7 +238,7 @@ Pretty impressive! Let's tweak the second image - corresponding to the `Generato
 ```python
 prompts = [
     "portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
-    "portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
+    "portrait photo of an old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
     "portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
     "portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",
 ]

docs/source/en/training/controlnet.md

@@ -349,7 +349,7 @@ control_image = load_image("./conditioning_image_1.png")
 prompt = "pale golden rod circle with old lace background"
 
 generator = torch.manual_seed(0)
-image = pipe(prompt, num_inference_steps=20, generator=generator, image=control_image).images[0]
+image = pipeline(prompt, num_inference_steps=20, generator=generator, image=control_image).images[0]
 image.save("./output.png")
 ```
 

docs/source/en/training/distributed_inference.md

@@ -48,80 +48,10 @@ accelerate launch run_distributed.py --num_processes=2
 
 <Tip>
 
-To learn more, take a look at the [Distributed Inference with 🤗 Accelerate](https://huggingface.co/docs/accelerate/en/usage_guides/distributed_inference#distributed-inference-with-accelerate) guide.
+Refer to this minimal example [script](https://gist.github.com/sayakpaul/cfaebd221820d7b43fae638b4dfa01ba) for running inference across multiple GPUs. To learn more, take a look at the [Distributed Inference with 🤗 Accelerate](https://huggingface.co/docs/accelerate/en/usage_guides/distributed_inference#distributed-inference-with-accelerate) guide.
 
 </Tip>
 
-### Device placement
-
-> [!WARNING]
-> This feature is experimental and its APIs might change in the future. 
-
-With Accelerate, you can use the `device_map` to determine how to distribute the models of a pipeline across multiple devices. This is useful in situations where you have more than one GPU.
-
-For example, if you have two 8GB GPUs, then using [`~DiffusionPipeline.enable_model_cpu_offload`] may not work so well because:
-
-* it only works on a single GPU
-* a single model might not fit on a single GPU ([`~DiffusionPipeline.enable_sequential_cpu_offload`] might work but it will be extremely slow and it is also limited to a single GPU)
-
-To make use of both GPUs, you can use the "balanced" device placement strategy which splits the models across all available GPUs.
-
-> [!WARNING]
-> Only the "balanced" strategy is supported at the moment, and we plan to support additional mapping strategies in the future.
-
-```diff
-from diffusers import DiffusionPipeline
-import torch
-
-pipeline = DiffusionPipeline.from_pretrained(
--    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True,
-+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True, device_map="balanced"
-)
-image = pipeline("a dog").images[0]
-image
-```
-
-You can also pass a dictionary to enforce the maximum GPU memory that can be used on each device:
-
-```diff
-from diffusers import DiffusionPipeline
-import torch
-
-max_memory = {0:"1GB", 1:"1GB"}
-pipeline = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
-    torch_dtype=torch.float16, 
-    use_safetensors=True, 
-    device_map="balanced",
-+   max_memory=max_memory
-)
-image = pipeline("a dog").images[0]
-image
-```
-
-If a device is not present in `max_memory`, then it will be completely ignored and will not participate in the device placement. 
-
-By default, Diffusers uses the maximum memory of all devices. If the models don't fit on the GPUs, they are offloaded to the CPU. If the CPU doesn't have enough memory, then you might see an error. In that case, you could defer to using [`~DiffusionPipeline.enable_sequential_cpu_offload`] and [`~DiffusionPipeline.enable_model_cpu_offload`].
-
-Call [`~DiffusionPipeline.reset_device_map`] to reset the `device_map` of a pipeline. This is also necessary if you want to use methods like `to()`, [`~DiffusionPipeline.enable_sequential_cpu_offload`], and [`~DiffusionPipeline.enable_model_cpu_offload`] on a pipeline that was device-mapped.
-
-```py
-pipeline.reset_device_map()
-```
-
-Once a pipeline has been device-mapped, you can also access its device map via `hf_device_map`:
-
-```py
-print(pipeline.hf_device_map)
-```
-
-An example device map would look like so:
-
-
-```bash
-{'unet': 1, 'vae': 1, 'safety_checker': 0, 'text_encoder': 0}
-```
-
 ## PyTorch Distributed
 
 PyTorch supports [`DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html) which enables data parallelism.
@@ -176,3 +106,6 @@ Once you've completed the inference script, use the `--nproc_per_node` argument
 ```bash
 torchrun run_distributed.py --nproc_per_node=2
 ```
+
+> [!TIP]
+> You can use `device_map` within a [`DiffusionPipeline`] to distribute its model-level components on multiple devices. Refer to the [Device placement](../tutorials/inference_with_big_models#device-placement) guide to learn more.

docs/source/en/training/instructpix2pix.md

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 [InstructPix2Pix](https://hf.co/papers/2211.09800) is a Stable Diffusion model trained to edit images from human-provided instructions. For example, your prompt can be "turn the clouds rainy" and the model will edit the input image accordingly. This model is conditioned on the text prompt (or editing instruction) and the input image.
 
-This guide will explore the [train_instruct_pix2pix.py](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.
+This guide will explore the [train_instruct_pix2pix.py](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py) training script to help you become familiar with it, and how you can adapt it for your own use case.
 
 Before running the script, make sure you install the library from source:
 
@@ -117,7 +117,7 @@ optimizer = optimizer_cls(
 )
 ```
 
-Next, the edited images and and edit instructions are [preprocessed](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L624) and [tokenized](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L610C24-L610C24). It is important the same image transformations are applied to the original and edited images.
+Next, the edited images and edit instructions are [preprocessed](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L624) and [tokenized](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L610C24-L610C24). It is important the same image transformations are applied to the original and edited images.
 
 ```py
 def preprocess_train(examples):

docs/source/en/training/text2image.md

@@ -181,7 +181,7 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
   --max_train_steps=15000 \
   --learning_rate=1e-05 \
   --max_grad_norm=1 \
-  --enable_xformers_memory_efficient_attention
+  --enable_xformers_memory_efficient_attention \
   --lr_scheduler="constant" --lr_warmup_steps=0 \
   --output_dir="sd-naruto-model" \
   --push_to_hub

docs/source/en/tutorials/basic_training.md

@@ -340,6 +340,7 @@ Now you can wrap all these components together in a training loop with 🤗 Acce
 ...                 loss = F.mse_loss(noise_pred, noise)
 ...                 accelerator.backward(loss)
 
+...                 if accelerator.sync_gradients:
 ...                     accelerator.clip_grad_norm_(model.parameters(), 1.0)
 ...                 optimizer.step()
 ...                 lr_scheduler.step()

docs/source/en/tutorials/fast_diffusion.md

@@ -34,13 +34,10 @@ Install [PyTorch nightly](https://pytorch.org/) to benefit from the latest and f
 pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121
 ```
 
-<Tip>
+> [!TIP]
+> The results reported below are from a 80GB 400W A100 with its clock rate set to the maximum.
+> If you're interested in the full benchmarking code, take a look at [huggingface/diffusion-fast](https://github.com/huggingface/diffusion-fast).
 
-The results reported below are from a 80GB 400W A100 with its clock rate set to the maximum. <br>
-
-If you're interested in the full benchmarking code, take a look at [huggingface/diffusion-fast](https://github.com/huggingface/diffusion-fast).
-
-</Tip>
 
 ## Baseline
 
@@ -170,6 +167,9 @@ Using SDPA attention and compiling both the UNet and VAE cuts the latency from 3
     <img src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/progressive-acceleration-sdxl/SDXL%2C_Batch_Size%3A_1%2C_Steps%3A_30_3.png" width=500>
 </div>
 
+> [!TIP]
+> From PyTorch 2.3.1, you can control the caching behavior of `torch.compile()`. This is particularly beneficial for compilation modes like `"max-autotune"` which performs a grid-search over several compilation flags to find the optimal configuration. Learn more in the [Compile Time Caching in torch.compile](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) tutorial.
+
 ### Prevent graph breaks
 
 Specifying `fullgraph=True` ensures there are no graph breaks in the underlying model to take full advantage of `torch.compile` without any performance degradation. For the UNet and VAE, this means changing how you access the return variables.

docs/source/en/tutorials/inference_with_big_models.md

@@ -0,0 +1,139 @@
+<!--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.
+-->
+
+# Working with big models
+
+A modern diffusion model, like [Stable Diffusion XL (SDXL)](../using-diffusers/sdxl), is not just a single model, but a collection of multiple models. SDXL has four different model-level components:
+
+* A variational autoencoder (VAE)
+* Two text encoders
+* A UNet for denoising
+
+Usually, the text encoders and the denoiser are much larger compared to the VAE.
+
+As models get bigger and better, it’s possible your model is so big that even a single copy won’t fit in memory. But that doesn’t mean it can’t be loaded. If you have more than one GPU, there is more memory available to store your model. In this case, it’s better to split your model checkpoint into several smaller *checkpoint shards*.
+
+When a text encoder checkpoint has multiple shards, like [T5-xxl for SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers/tree/main/text_encoder_3), it is automatically handled by the [Transformers](https://huggingface.co/docs/transformers/index) library as it is a required dependency of Diffusers when using the [`StableDiffusion3Pipeline`]. More specifically, Transformers will automatically handle the loading of multiple shards within the requested model class and get it ready so that inference can be performed.
+
+The denoiser checkpoint can also have multiple shards and supports inference thanks to the [Accelerate](https://huggingface.co/docs/accelerate/index) library.
+
+> [!TIP]
+> Refer to the [Handling big models for inference](https://huggingface.co/docs/accelerate/main/en/concept_guides/big_model_inference) guide for general guidance when working with big models that are hard to fit into memory.
+
+For example, let's save a sharded checkpoint for the [SDXL UNet](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main/unet):
+
+```python
+from diffusers import UNet2DConditionModel
+
+unet = UNet2DConditionModel.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet"
+)
+unet.save_pretrained("sdxl-unet-sharded", max_shard_size="5GB")
+```
+
+The size of the fp32 variant of the SDXL UNet checkpoint is ~10.4GB. Set the `max_shard_size` parameter to 5GB to create 3 shards. After saving, you can load them in [`StableDiffusionXLPipeline`]:
+
+```python
+from diffusers import UNet2DConditionModel, StableDiffusionXLPipeline
+import torch
+
+unet = UNet2DConditionModel.from_pretrained(
+    "sayakpaul/sdxl-unet-sharded", torch_dtype=torch.float16
+)
+pipeline = StableDiffusionXLPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16
+).to("cuda")
+
+image = pipeline("a cute dog running on the grass", num_inference_steps=30).images[0]
+image.save("dog.png")
+```
+
+If placing all the model-level components on the GPU at once is not feasible, use [`~DiffusionPipeline.enable_model_cpu_offload`] to help you:
+
+```diff
+- pipeline.to("cuda")
++ pipeline.enable_model_cpu_offload()
+```
+
+In general, we recommend sharding when a checkpoint is more than 5GB (in fp32).
+
+## Device placement
+
+On distributed setups, you can run inference across multiple GPUs with Accelerate.
+
+> [!WARNING]
+> This feature is experimental and its APIs might change in the future.
+
+With Accelerate, you can use the `device_map` to determine how to distribute the models of a pipeline across multiple devices. This is useful in situations where you have more than one GPU.
+
+For example, if you have two 8GB GPUs, then using [`~DiffusionPipeline.enable_model_cpu_offload`] may not work so well because:
+
+* it only works on a single GPU
+* a single model might not fit on a single GPU ([`~DiffusionPipeline.enable_sequential_cpu_offload`] might work but it will be extremely slow and it is also limited to a single GPU)
+
+To make use of both GPUs, you can use the "balanced" device placement strategy which splits the models across all available GPUs.
+
+> [!WARNING]
+> Only the "balanced" strategy is supported at the moment, and we plan to support additional mapping strategies in the future.
+
+```diff
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained(
+-    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True,
++    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True, device_map="balanced"
+)
+image = pipeline("a dog").images[0]
+image
+```
+
+You can also pass a dictionary to enforce the maximum GPU memory that can be used on each device:
+
+```diff
+from diffusers import DiffusionPipeline
+import torch
+
+max_memory = {0:"1GB", 1:"1GB"}
+pipeline = DiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+    device_map="balanced",
++   max_memory=max_memory
+)
+image = pipeline("a dog").images[0]
+image
+```
+
+If a device is not present in `max_memory`, then it will be completely ignored and will not participate in the device placement.
+
+By default, Diffusers uses the maximum memory of all devices. If the models don't fit on the GPUs, they are offloaded to the CPU. If the CPU doesn't have enough memory, then you might see an error. In that case, you could defer to using [`~DiffusionPipeline.enable_sequential_cpu_offload`] and [`~DiffusionPipeline.enable_model_cpu_offload`].
+
+Call [`~DiffusionPipeline.reset_device_map`] to reset the `device_map` of a pipeline. This is also necessary if you want to use methods like `to()`, [`~DiffusionPipeline.enable_sequential_cpu_offload`], and [`~DiffusionPipeline.enable_model_cpu_offload`] on a pipeline that was device-mapped.
+
+```py
+pipeline.reset_device_map()
+```
+
+Once a pipeline has been device-mapped, you can also access its device map via `hf_device_map`:
+
+```py
+print(pipeline.hf_device_map)
+```
+
+An example device map would look like so:
+
+
+```bash
+{'unet': 1, 'vae': 1, 'safety_checker': 0, 'text_encoder': 0}
+```

docs/source/en/tutorials/using_peft_for_inference.md

@@ -34,7 +34,7 @@ pipe_id = "stabilityai/stable-diffusion-xl-base-1.0"
 pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda")
 ```
 
-Next, load a [CiroN2022/toy-face](https://huggingface.co/CiroN2022/toy-face) adapter with the [`~diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] method. With the 🤗 PEFT integration, you can assign a specific `adapter_name` to the checkpoint, which let's you easily switch between different LoRA checkpoints. Let's call this adapter `"toy"`.
+Next, load a [CiroN2022/toy-face](https://huggingface.co/CiroN2022/toy-face) adapter with the [`~diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] method. With the 🤗 PEFT integration, you can assign a specific `adapter_name` to the checkpoint, which lets you easily switch between different LoRA checkpoints. Let's call this adapter `"toy"`.
 
 ```python
 pipe.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
@@ -191,7 +191,7 @@ image
 
 ## Manage active adapters
 
-You have attached multiple adapters in this tutorial, and if you're feeling a bit lost on what adapters have been attached to the pipeline's components, use the [`~diffusers.loaders.LoraLoaderMixin.get_active_adapters`] method to check the list of active adapters:
+You have attached multiple adapters in this tutorial, and if you're feeling a bit lost on what adapters have been attached to the pipeline's components, use the [`~diffusers.loaders.StableDiffusionLoraLoaderMixin.get_active_adapters`] method to check the list of active adapters:
 
 ```py
 active_adapters = pipe.get_active_adapters()
@@ -199,7 +199,7 @@ active_adapters
 ["toy", "pixel"]
 ```
 
-You can also get the active adapters of each pipeline component with [`~diffusers.loaders.LoraLoaderMixin.get_list_adapters`]:
+You can also get the active adapters of each pipeline component with [`~diffusers.loaders.StableDiffusionLoraLoaderMixin.get_list_adapters`]:
 
 ```py
 list_adapters_component_wise = pipe.get_list_adapters()

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

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # Pipeline callbacks
 
-The denoising loop of a pipeline can be modified with custom defined functions using the `callback_on_step_end` parameter. The callback function is executed at the end of each step, and modifies the pipeline attributes and variables for the next step. This is really useful for *dynamically* adjusting certain pipeline attributes or modifying tensor variables. This versatility allows for interesting use-cases such as changing the prompt embeddings at each timestep, assigning different weights to the prompt embeddings, and editing the guidance scale. With callbacks, you can implement new features without modifying the underlying code!
+The denoising loop of a pipeline can be modified with custom defined functions using the `callback_on_step_end` parameter. The callback function is executed at the end of each step, and modifies the pipeline attributes and variables for the next step. This is really useful for *dynamically* adjusting certain pipeline attributes or modifying tensor variables. This versatility allows for interesting use cases such as changing the prompt embeddings at each timestep, assigning different weights to the prompt embeddings, and editing the guidance scale. With callbacks, you can implement new features without modifying the underlying code!
 
 > [!TIP]
 > 🤗 Diffusers currently only supports `callback_on_step_end`, but feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you have a cool use-case and require a callback function with a different execution point!
@@ -75,7 +75,7 @@ out.images[0].save("official_callback.png")
     <figcaption class="mt-2 text-center text-sm text-gray-500">without SDXLCFGCutoffCallback</figcaption>
   </div>
   <div>
-    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/with_cfg_callback.png" alt="generated image of a a sports car at the road with cfg callback" />
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/with_cfg_callback.png" alt="generated image of a sports car at the road with cfg callback" />
     <figcaption class="mt-2 text-center text-sm text-gray-500">with SDXLCFGCutoffCallback</figcaption>
   </div>
 </div>

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

@@ -256,7 +256,7 @@ make_image_grid([init_image, mask_image, output], rows=1, cols=3)
 
 ## Guess mode
 
-[Guess mode](https://github.com/lllyasviel/ControlNet/discussions/188) does not require supplying a prompt to a ControlNet at all! This forces the ControlNet encoder to do it's best to "guess" the contents of the input control map (depth map, pose estimation, canny edge, etc.).
+[Guess mode](https://github.com/lllyasviel/ControlNet/discussions/188) does not require supplying a prompt to a ControlNet at all! This forces the ControlNet encoder to do its best to "guess" the contents of the input control map (depth map, pose estimation, canny edge, etc.).
 
 Guess mode adjusts the scale of the output residuals from a ControlNet by a fixed ratio depending on the block depth. The shallowest `DownBlock` corresponds to 0.1, and as the blocks get deeper, the scale increases exponentially such that the scale of the `MidBlock` output becomes 1.0.
 

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

@@ -289,9 +289,9 @@ scheduler = DPMSolverMultistepScheduler.from_pretrained(pipe_id, subfolder="sche
 3. Load an image processor:
 
 ```python
-from transformers import CLIPFeatureExtractor
+from transformers import CLIPImageProcessor
 
-feature_extractor = CLIPFeatureExtractor.from_pretrained(pipe_id, subfolder="feature_extractor")
+feature_extractor = CLIPImageProcessor.from_pretrained(pipe_id, subfolder="feature_extractor")
 ```
 
 <Tip warning={true}>

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

@@ -64,7 +64,7 @@ image
 </hfoption>
 <hfoption id="LCM-LoRA">
 
-To use LCM-LoRAs, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt to generate an image in just 4 steps.
+To use LCM-LoRAs, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt to generate an image in just 4 steps.
 
 A couple of notes to keep in mind when using LCM-LoRAs are:
 
@@ -156,7 +156,7 @@ image
 </hfoption>
 <hfoption id="LCM-LoRA">
 
-To use LCM-LoRAs for image-to-image, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt and initial image to generate an image in just 4 steps.
+To use LCM-LoRAs for image-to-image, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt and initial image to generate an image in just 4 steps.
 
 > [!TIP]
 > Experiment with different values for `num_inference_steps`, `strength`, and `guidance_scale` to get the best results.
@@ -207,7 +207,7 @@ image
 
 ## Inpainting
 
-To use LCM-LoRAs for inpainting, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt, initial image, and mask image to generate an image in just 4 steps.
+To use LCM-LoRAs for inpainting, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt, initial image, and mask image to generate an image in just 4 steps.
 
 ```py
 import torch
@@ -262,7 +262,7 @@ LCMs are compatible with adapters like LoRA, ControlNet, T2I-Adapter, and Animat
 <hfoptions id="lcm-lora">
 <hfoption id="LCM">
 
-Load the LCM checkpoint for your supported model into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Then you can use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the LoRA weights into the LCM and generate a styled image in a few steps.
+Load the LCM checkpoint for your supported model into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Then you can use the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method to load the LoRA weights into the LCM and generate a styled image in a few steps.
 
 ```python
 from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler
@@ -294,7 +294,7 @@ image
 </hfoption>
 <hfoption id="LCM-LoRA">
 
-Replace the scheduler with the [`LCMScheduler`]. Then you can use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights and the style LoRA you want to use. Combine both LoRA adapters with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method and generate a styled image in a few steps.
+Replace the scheduler with the [`LCMScheduler`]. Then you can use the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights and the style LoRA you want to use. Combine both LoRA adapters with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method and generate a styled image in a few steps.
 
 ```py
 import torch
@@ -389,7 +389,7 @@ make_image_grid([canny_image, image], rows=1, cols=2)
 </hfoption>
 <hfoption id="LCM-LoRA">
 
-Load a ControlNet model trained on canny images and pass it to the [`ControlNetModel`]. Then you can load a Stable Diffusion v1.5 model into [`StableDiffusionControlNetPipeline`] and replace the scheduler with the [`LCMScheduler`]. Use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights, and pass the canny image to the pipeline and generate an image.
+Load a ControlNet model trained on canny images and pass it to the [`ControlNetModel`]. Then you can load a Stable Diffusion v1.5 model into [`StableDiffusionControlNetPipeline`] and replace the scheduler with the [`LCMScheduler`]. Use the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights, and pass the canny image to the pipeline and generate an image.
 
 > [!TIP]
 > Experiment with different values for `num_inference_steps`, `controlnet_conditioning_scale`, `cross_attention_kwargs`, and `guidance_scale` to get the best results.
@@ -525,7 +525,7 @@ image = pipe(
 </hfoption>
 <hfoption id="LCM-LoRA">
 
-Load a T2IAdapter trained on canny images and pass it to the [`StableDiffusionXLAdapterPipeline`]. Replace the scheduler with the [`LCMScheduler`], and use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights. Pass the canny image to the pipeline and generate an image.
+Load a T2IAdapter trained on canny images and pass it to the [`StableDiffusionXLAdapterPipeline`]. Replace the scheduler with the [`LCMScheduler`], and use the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights. Pass the canny image to the pipeline and generate an image.
 
 ```py
 import torch

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

@@ -212,14 +212,14 @@ TCD-LoRA is very versatile, and it can be combined with other adapter types like
 import torch
 import numpy as np
 from PIL import Image
-from transformers import DPTFeatureExtractor, DPTForDepthEstimation
+from transformers import DPTImageProcessor, DPTForDepthEstimation
 from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
 from diffusers.utils import load_image, make_image_grid
 from scheduling_tcd import TCDScheduler
 
 device = "cuda"
 depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
-feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
+feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
 
 def get_depth_map(image):
     image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)

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

@@ -116,7 +116,7 @@ import torch
 pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
 ```
 
-Then use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the [ostris/super-cereal-sdxl-lora](https://huggingface.co/ostris/super-cereal-sdxl-lora) weights and specify the weights filename from the repository:
+Then use the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method to load the [ostris/super-cereal-sdxl-lora](https://huggingface.co/ostris/super-cereal-sdxl-lora) weights and specify the weights filename from the repository:
 
 ```py
 pipeline.load_lora_weights("ostris/super-cereal-sdxl-lora", weight_name="cereal_box_sdxl_v1.safetensors")
@@ -129,7 +129,7 @@ image
     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_lora.png" />
 </div>
 
-The [`~loaders.LoraLoaderMixin.load_lora_weights`] method loads LoRA weights into both the UNet and text encoder. It is the preferred way for loading LoRAs because it can handle cases where:
+The [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method loads LoRA weights into both the UNet and text encoder. It is the preferred way for loading LoRAs because it can handle cases where:
 
 - the LoRA weights don't have separate identifiers for the UNet and text encoder
 - the LoRA weights have separate identifiers for the UNet and text encoder
@@ -153,7 +153,7 @@ image
     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_attn_proc.png" />
 </div>
 
-To unload the LoRA weights, use the [`~loaders.LoraLoaderMixin.unload_lora_weights`] method to discard the LoRA weights and restore the model to its original weights:
+To unload the LoRA weights, use the [`~loaders.StableDiffusionLoraLoaderMixin.unload_lora_weights`] method to discard the LoRA weights and restore the model to its original weights:
 
 ```py
 pipeline.unload_lora_weights()
@@ -161,9 +161,9 @@ pipeline.unload_lora_weights()
 
 ### Adjust LoRA weight scale
 
-For both [`~loaders.LoraLoaderMixin.load_lora_weights`] and [`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`], you can pass the `cross_attention_kwargs={"scale": 0.5}` parameter to adjust how much of the LoRA weights to use. A value of `0` is the same as only using the base model weights, and a value of `1` is equivalent to using the fully finetuned LoRA.
+For both [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] and [`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`], you can pass the `cross_attention_kwargs={"scale": 0.5}` parameter to adjust how much of the LoRA weights to use. A value of `0` is the same as only using the base model weights, and a value of `1` is equivalent to using the fully finetuned LoRA.
 
-For more granular control on the amount of LoRA weights used per layer, you can use [`~loaders.LoraLoaderMixin.set_adapters`] and pass a dictionary specifying by how much to scale the weights in each layer by.
+For more granular control on the amount of LoRA weights used per layer, you can use [`~loaders.StableDiffusionLoraLoaderMixin.set_adapters`] and pass a dictionary specifying by how much to scale the weights in each layer by.
 ```python
 pipe = ... # create pipeline
 pipe.load_lora_weights(..., adapter_name="my_adapter")
@@ -186,7 +186,7 @@ This also works with multiple adapters - see [this guide](https://huggingface.co
 
 <Tip warning={true}>
 
-Currently, [`~loaders.LoraLoaderMixin.set_adapters`] only supports scaling attention weights. If a LoRA has other parts (e.g., resnets or down-/upsamplers), they will keep a scale of 1.0.
+Currently, [`~loaders.StableDiffusionLoraLoaderMixin.set_adapters`] only supports scaling attention weights. If a LoRA has other parts (e.g., resnets or down-/upsamplers), they will keep a scale of 1.0.
 
 </Tip>
 
@@ -203,7 +203,7 @@ To load a Kohya LoRA, let's download the [Blueprintify SD XL 1.0](https://civita
 !wget https://civitai.com/api/download/models/168776 -O blueprintify-sd-xl-10.safetensors
 ```
 
-Load the LoRA checkpoint with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method, and specify the filename in the `weight_name` parameter:
+Load the LoRA checkpoint with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method, and specify the filename in the `weight_name` parameter:
 
 ```py
 from diffusers import AutoPipelineForText2Image
@@ -227,7 +227,7 @@ image
 Some limitations of using Kohya LoRAs with 🤗 Diffusers include:
 
 - Images may not look like those generated by UIs - like ComfyUI - for multiple reasons, which are explained [here](https://github.com/huggingface/diffusers/pull/4287/#issuecomment-1655110736).
-- [LyCORIS checkpoints](https://github.com/KohakuBlueleaf/LyCORIS) aren't fully supported. The [`~loaders.LoraLoaderMixin.load_lora_weights`] method loads LyCORIS checkpoints with LoRA and LoCon modules, but Hada and LoKR are not supported.
+- [LyCORIS checkpoints](https://github.com/KohakuBlueleaf/LyCORIS) aren't fully supported. The [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method loads LyCORIS checkpoints with LoRA and LoCon modules, but Hada and LoKR are not supported.
 
 </Tip>
 

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

@@ -14,9 +14,9 @@ specific language governing permissions and limitations under the License.
 
 It can be fun and creative to use multiple [LoRAs]((https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora)) together to generate something entirely new and unique. This works by merging multiple LoRA weights together to produce images that are a blend of different styles. Diffusers provides a few methods to merge LoRAs depending on *how* you want to merge their weights, which can affect image quality.
 
-This guide will show you how to merge LoRAs using the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] and [`~peft.LoraModel.add_weighted_adapter`] methods. To improve inference speed and reduce memory-usage of merged LoRAs, you'll also see how to use the [`~loaders.LoraLoaderMixin.fuse_lora`] method to fuse the LoRA weights with the original weights of the underlying model.
+This guide will show you how to merge LoRAs using the [`~loaders.PeftAdapterMixin.set_adapters`] and [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) methods. To improve inference speed and reduce memory-usage of merged LoRAs, you'll also see how to use the [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] method to fuse the LoRA weights with the original weights of the underlying model.
 
-For this guide, load a Stable Diffusion XL (SDXL) checkpoint and the [KappaNeuro/studio-ghibli-style]() and [Norod78/sdxl-chalkboarddrawing-lora]() LoRAs with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method. You'll need to assign each LoRA an `adapter_name` to combine them later.
+For this guide, load a Stable Diffusion XL (SDXL) checkpoint and the [KappaNeuro/studio-ghibli-style](https://huggingface.co/KappaNeuro/studio-ghibli-style) and [Norod78/sdxl-chalkboarddrawing-lora](https://huggingface.co/Norod78/sdxl-chalkboarddrawing-lora) LoRAs with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method. You'll need to assign each LoRA an `adapter_name` to combine them later.
 
 ```py
 from diffusers import DiffusionPipeline
@@ -29,7 +29,7 @@ pipeline.load_lora_weights("lordjia/by-feng-zikai", weight_name="fengzikai_v1.0_
 
 ## set_adapters
 
-The [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method merges LoRA adapters by concatenating their weighted matrices. Use the adapter name to specify which LoRAs to merge, and the `adapter_weights` parameter to control the scaling for each LoRA. For example, if `adapter_weights=[0.5, 0.5]`, then the merged LoRA output is an average of both LoRAs. Try adjusting the adapter weights to see how it affects the generated image!
+The [`~loaders.PeftAdapterMixin.set_adapters`] method merges LoRA adapters by concatenating their weighted matrices. Use the adapter name to specify which LoRAs to merge, and the `adapter_weights` parameter to control the scaling for each LoRA. For example, if `adapter_weights=[0.5, 0.5]`, then the merged LoRA output is an average of both LoRAs. Try adjusting the adapter weights to see how it affects the generated image!
 
 ```py
 pipeline.set_adapters(["ikea", "feng"], adapter_weights=[0.7, 0.8])
@@ -47,19 +47,19 @@ image
 ## add_weighted_adapter
 
 > [!WARNING]
-> This is an experimental method that adds PEFTs [`~peft.LoraModel.add_weighted_adapter`] method to Diffusers to enable more efficient merging methods. Check out this [issue](https://github.com/huggingface/diffusers/issues/6892) if you're interested in learning more about the motivation and design behind this integration.
+> This is an experimental method that adds PEFTs [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method to Diffusers to enable more efficient merging methods. Check out this [issue](https://github.com/huggingface/diffusers/issues/6892) if you're interested in learning more about the motivation and design behind this integration.
 
-The [`~peft.LoraModel.add_weighted_adapter`] method provides access to more efficient merging method such as [TIES and DARE](https://huggingface.co/docs/peft/developer_guides/model_merging). To use these merging methods, make sure you have the latest stable version of Diffusers and PEFT installed.
+The [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method provides access to more efficient merging method such as [TIES and DARE](https://huggingface.co/docs/peft/developer_guides/model_merging). To use these merging methods, make sure you have the latest stable version of Diffusers and PEFT installed.
 
 ```bash
 pip install -U diffusers peft
 ```
 
-There are three steps to merge LoRAs with the [`~peft.LoraModel.add_weighted_adapter`] method:
+There are three steps to merge LoRAs with the [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method:
 
-1. Create a [`~peft.PeftModel`] from the underlying model and LoRA checkpoint.
+1. Create a [PeftModel](https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel) from the underlying model and LoRA checkpoint.
 2. Load a base UNet model and the LoRA adapters.
-3. Merge the adapters using the [`~peft.LoraModel.add_weighted_adapter`] method and the merging method of your choice.
+3. Merge the adapters using the [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method and the merging method of your choice.
 
 Let's dive deeper into what these steps entail.
 
@@ -92,7 +92,7 @@ pipeline = DiffusionPipeline.from_pretrained(
 pipeline.load_lora_weights("ostris/ikea-instructions-lora-sdxl", weight_name="ikea_instructions_xl_v1_5.safetensors", adapter_name="ikea")
 ```
 
-Now you'll create a [`~peft.PeftModel`] from the loaded LoRA checkpoint by combining the SDXL UNet and the LoRA UNet from the pipeline.
+Now you'll create a [PeftModel](https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel) from the loaded LoRA checkpoint by combining the SDXL UNet and the LoRA UNet from the pipeline.
 
 ```python
 from peft import get_peft_model, LoraConfig
@@ -112,7 +112,7 @@ ikea_peft_model.load_state_dict(original_state_dict, strict=True)
 > [!TIP]
 > You can optionally push the ikea_peft_model to the Hub by calling `ikea_peft_model.push_to_hub("ikea_peft_model", token=TOKEN)`.
 
-Repeat this process to create a [`~peft.PeftModel`] from the [lordjia/by-feng-zikai](https://huggingface.co/lordjia/by-feng-zikai) LoRA.
+Repeat this process to create a [PeftModel](https://huggingface.co/docs/peft/package_reference/peft_model#peft.PeftModel) from the [lordjia/by-feng-zikai](https://huggingface.co/lordjia/by-feng-zikai) LoRA.
 
 ```python
 pipeline.delete_adapters("ikea")
@@ -148,7 +148,7 @@ model = PeftModel.from_pretrained(base_unet, "stevhliu/ikea_peft_model", use_saf
 model.load_adapter("stevhliu/feng_peft_model", use_safetensors=True, subfolder="feng", adapter_name="feng")
 ```
 
-3. Merge the adapters using the [`~peft.LoraModel.add_weighted_adapter`] method and the merging method of your choice (learn more about other merging methods in this [blog post](https://huggingface.co/blog/peft_merging)). For this example, let's use the `"dare_linear"` method to merge the LoRAs.
+3. Merge the adapters using the [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) method and the merging method of your choice (learn more about other merging methods in this [blog post](https://huggingface.co/blog/peft_merging)). For this example, let's use the `"dare_linear"` method to merge the LoRAs.
 
 > [!WARNING]
 > Keep in mind the LoRAs need to have the same rank to be merged!
@@ -182,9 +182,9 @@ image
 
 ## fuse_lora
 
-Both the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] and [`~peft.LoraModel.add_weighted_adapter`] methods require loading the base model and the LoRA adapters separately which incurs some overhead. The [`~loaders.LoraLoaderMixin.fuse_lora`] method allows you to fuse the LoRA weights directly with the original weights of the underlying model. This way, you're only loading the model once which can increase inference and lower memory-usage.
+Both the [`~loaders.PeftAdapterMixin.set_adapters`] and [add_weighted_adapter](https://huggingface.co/docs/peft/package_reference/lora#peft.LoraModel.add_weighted_adapter) methods require loading the base model and the LoRA adapters separately which incurs some overhead. The [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method allows you to fuse the LoRA weights directly with the original weights of the underlying model. This way, you're only loading the model once which can increase inference and lower memory-usage.
 
-You can use PEFT to easily fuse/unfuse multiple adapters directly into the model weights (both UNet and text encoder) using the [`~loaders.LoraLoaderMixin.fuse_lora`] method, which can lead to a speed-up in inference and lower VRAM usage.
+You can use PEFT to easily fuse/unfuse multiple adapters directly into the model weights (both UNet and text encoder) using the [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method, which can lead to a speed-up in inference and lower VRAM usage.
 
 For example, if you have a base model and adapters loaded and set as active with the following adapter weights:
 
@@ -199,13 +199,13 @@ pipeline.load_lora_weights("lordjia/by-feng-zikai", weight_name="fengzikai_v1.0_
 pipeline.set_adapters(["ikea", "feng"], adapter_weights=[0.7, 0.8])
 ```
 
-Fuse these LoRAs into the UNet with the [`~loaders.LoraLoaderMixin.fuse_lora`] method. The `lora_scale` parameter controls how much to scale the output by with the LoRA weights. It is important to make the `lora_scale` adjustments in the [`~loaders.LoraLoaderMixin.fuse_lora`] method because it won’t work if you try to pass `scale` to the `cross_attention_kwargs` in the pipeline.
+Fuse these LoRAs into the UNet with the [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method. The `lora_scale` parameter controls how much to scale the output by with the LoRA weights. It is important to make the `lora_scale` adjustments in the [`~loaders.lora_base.LoraBaseMixin.fuse_lora`] method because it won’t work if you try to pass `scale` to the `cross_attention_kwargs` in the pipeline.
 
 ```py
 pipeline.fuse_lora(adapter_names=["ikea", "feng"], lora_scale=1.0)
 ```
 
-Then you should use [`~loaders.LoraLoaderMixin.unload_lora_weights`] to unload the LoRA weights since they've already been fused with the underlying base model. Finally, call [`~DiffusionPipeline.save_pretrained`] to save the fused pipeline locally or you could call [`~DiffusionPipeline.push_to_hub`] to push the fused pipeline to the Hub.
+Then you should use [`~loaders.StableDiffusionLoraLoaderMixin.unload_lora_weights`] to unload the LoRA weights since they've already been fused with the underlying base model. Finally, call [`~DiffusionPipeline.save_pretrained`] to save the fused pipeline locally or you could call [`~DiffusionPipeline.push_to_hub`] to push the fused pipeline to the Hub.
 
 ```py
 pipeline.unload_lora_weights()
@@ -226,7 +226,7 @@ image = pipeline("A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai"
 image
 ```
 
-You can call [`~loaders.LoraLoaderMixin.unfuse_lora`] to restore the original model's weights (for example, if you want to use a different `lora_scale` value). However, this only works if you've only fused one LoRA adapter to the original model. If you've fused multiple LoRAs, you'll need to reload the model.
+You can call [`~~loaders.lora_base.LoraBaseMixin.unfuse_lora`] to restore the original model's weights (for example, if you want to use a different `lora_scale` value). However, this only works if you've only fused one LoRA adapter to the original model. If you've fused multiple LoRAs, you'll need to reload the model.
 
 ```py
 pipeline.unfuse_lora()

docs/source/en/using-diffusers/other-formats.md

@@ -74,7 +74,7 @@ pipeline = StableDiffusionPipeline.from_single_file(
 
 [LoRA](https://hf.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) is a lightweight adapter that is fast and easy to train, making them especially popular for generating images in a certain way or style. These adapters are commonly stored in a safetensors file, and are widely popular on model sharing platforms like [civitai](https://civitai.com/).
 
-LoRAs are loaded into a base model with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method.
+LoRAs are loaded into a base model with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method.
 
 ```py
 from diffusers import StableDiffusionXLPipeline
@@ -418,7 +418,7 @@ my_local_checkpoint_path = hf_hub_download(
 
 my_local_config_path = snapshot_download(
     repo_id="segmind/SSD-1B",
-    allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
+    allow_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
 )
 
 pipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)
@@ -438,7 +438,7 @@ my_local_checkpoint_path = hf_hub_download(
 
 my_local_config_path = snapshot_download(
     repo_id="segmind/SSD-1B",
-    allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
+    allow_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
     local_dir="my_local_config"
 )
 
@@ -468,11 +468,10 @@ print("My local checkpoint: ", my_local_checkpoint_path)
 
 my_local_config_path = snapshot_download(
     repo_id="segmind/SSD-1B",
-    allowed_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
+    allow_patterns=["*.json", "**/*.json", "*.txt", "**/*.txt"]
     local_dir_use_symlinks=False,
 )
 print("My local config: ", my_local_config_path)
-
 ```
 
 Then you can pass the local paths to the `pretrained_model_link_or_path` and `config` parameters.

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

@@ -0,0 +1,351 @@
+<!--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.
+-->
+
+# Perturbed-Attention Guidance
+
+[Perturbed-Attention Guidance (PAG)](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) is a new diffusion sampling guidance that improves sample quality across both unconditional and conditional settings, achieving this without requiring further training or the integration of external modules. PAG is designed to progressively enhance the structure of synthesized samples throughout the denoising process by considering the self-attention mechanisms' ability to capture structural information. It involves generating intermediate samples with degraded structure by substituting selected self-attention maps in diffusion U-Net with an identity matrix, and guiding the denoising process away from these degraded samples.
+
+This guide will show you how to use PAG for various tasks and use cases.
+
+
+## General tasks
+
+You can apply PAG to the [`StableDiffusionXLPipeline`] for tasks such as text-to-image, image-to-image, and inpainting. To enable PAG for a specific task, load the pipeline using the [AutoPipeline](../api/pipelines/auto_pipeline) API with the `enable_pag=True` flag and the `pag_applied_layers` argument.
+
+> [!TIP]
+> 🤗 Diffusers currently only supports using PAG with selected SDXL pipelines and [`PixArtSigmaPAGPipeline`]. But feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you want to add PAG support to a new pipeline!
+
+<hfoptions id="tasks">
+<hfoption id="Text-to-image">
+
+```py
+from diffusers import AutoPipelineForText2Image
+from diffusers.utils import load_image
+import torch
+
+pipeline = AutoPipelineForText2Image.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    enable_pag=True,
+    pag_applied_layers=["mid"],
+    torch_dtype=torch.float16
+)
+pipeline.enable_model_cpu_offload()
+```
+
+> [!TIP]
+> The `pag_applied_layers` argument allows you to specify which layers PAG is applied to. Additionally, you can use `set_pag_applied_layers` method to update these layers after the pipeline has been created. Check out the [pag_applied_layers](#pag_applied_layers) section to learn more about applying PAG to other layers.
+
+If you already have a pipeline created and loaded, you can enable PAG on it using the `from_pipe` API with the `enable_pag` flag. Internally, a PAG pipeline is created based on the pipeline and task you specified. In the example below, since we used `AutoPipelineForText2Image` and passed a `StableDiffusionXLPipeline`, a `StableDiffusionXLPAGPipeline` is created accordingly. Note that this does not require additional memory, and you will have both `StableDiffusionXLPipeline` and  `StableDiffusionXLPAGPipeline` loaded and ready to use. You can read more about the `from_pipe` API and how to reuse pipelines in diffuser [here](https://huggingface.co/docs/diffusers/using-diffusers/loading#reuse-a-pipeline).
+
+```py
+pipeline_sdxl = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+pipeline = AutoPipelineForText2Image.from_pipe(pipeline_sdxl, enable_pag=True)
+```
+
+To generate an image, you will also need to pass a `pag_scale`. When `pag_scale` increases, images gain more semantically coherent structures and exhibit fewer artifacts. However overly large guidance scale can lead to smoother textures and slight saturation in the images, similarly to CFG. `pag_scale=3.0` is used in the official demo and works well in most of the use cases, but feel free to experiment and select the appropriate value according to your needs! PAG is disabled when `pag_scale=0`.
+
+```py
+prompt = "an insect robot preparing a delicious meal, anime style"
+
+for pag_scale in [0.0, 3.0]:
+    generator = torch.Generator(device="cpu").manual_seed(0)
+    images = pipeline(
+        prompt=prompt,
+        num_inference_steps=25,
+        guidance_scale=7.0,
+        generator=generator,
+        pag_scale=pag_scale,
+    ).images
+```
+
+<div class="flex flex-row gap-4">
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_0.0_cfg_7.0_mid.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">generated image without PAG</figcaption>
+  </div>
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_3.0_cfg_7.0_mid.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">generated image with PAG</figcaption>
+  </div>
+</div>
+
+</hfoption>
+<hfoption id="Image-to-image">
+
+You can use PAG with image-to-image pipelines.
+
+```py
+from diffusers import AutoPipelineForImage2Image
+from diffusers.utils import load_image
+import torch
+
+pipeline = AutoPipelineForImage2Image.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    enable_pag=True,
+    pag_applied_layers=["mid"],
+    torch_dtype=torch.float16
+)
+pipeline.enable_model_cpu_offload()
+```
+
+If you already have a image-to-image pipeline and would like enable PAG on it, you can run this
+
+```py
+pipeline_t2i = AutoPipelineForImage2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_t2i, enable_pag=True)
+```
+
+It is also very easy to directly switch from a text-to-image pipeline to PAG enabled image-to-image pipeline
+
+```py
+pipeline_pag = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_t2i, enable_pag=True)
+```
+
+If you have a PAG enabled text-to-image pipeline, you can directly switch to a image-to-image pipeline with PAG still enabled
+
+```py
+pipeline_pag = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", enable_pag=True, torch_dtype=torch.float16)
+pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_t2i)
+```
+
+Now let's generate an image!
+
+```py
+pag_scales =  4.0
+guidance_scales = 7.0
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
+init_image = load_image(url)
+prompt = "a dog catching a frisbee in the jungle"
+
+generator = torch.Generator(device="cpu").manual_seed(0)
+image = pipeline(
+    prompt,
+    image=init_image,
+    strength=0.8,
+    guidance_scale=guidance_scale,
+    pag_scale=pag_scale,
+    generator=generator).images[0]
+```
+
+</hfoption>
+<hfoption id="Inpainting">
+
+```py
+from diffusers import AutoPipelineForInpainting
+from diffusers.utils import load_image
+import torch
+
+pipeline = AutoPipelineForInpainting.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    enable_pag=True,
+    torch_dtype=torch.float16
+)
+pipeline.enable_model_cpu_offload()
+```
+
+You can enable PAG on an exisiting inpainting pipeline like this
+
+```py
+pipeline_inpaint = AutoPipelineForInpaiting.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+pipeline = AutoPipelineForInpaiting.from_pipe(pipeline_inpaint, enable_pag=True)
+```
+
+This still works when your pipeline has a different task:
+
+```py
+pipeline_t2i = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+pipeline = AutoPipelineForInpaiting.from_pipe(pipeline_t2i, enable_pag=True)
+```
+
+Let's generate an image!
+
+```py
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+init_image = load_image(img_url).convert("RGB")
+mask_image = load_image(mask_url).convert("RGB")
+
+prompt = "A majestic tiger sitting on a bench"
+
+pag_scales =  3.0
+guidance_scales = 7.5
+
+generator = torch.Generator(device="cpu").manual_seed(1)
+images = pipeline(
+    prompt=prompt,
+    image=init_image,
+    mask_image=mask_image,
+    strength=0.8,
+    num_inference_steps=50,
+    guidance_scale=guidance_scale,
+    generator=generator,
+    pag_scale=pag_scale,
+).images
+images[0]
+```
+</hfoption>
+</hfoptions>
+
+## PAG with ControlNet
+
+To use PAG with ControlNet, first create a `controlnet`. Then, pass the `controlnet` and other PAG arguments to the `from_pretrained` method of the AutoPipeline for the specified task.
+
+```py
+from diffusers import AutoPipelineForText2Image, ControlNetModel
+import torch
+
+controlnet = ControlNetModel.from_pretrained(
+    "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
+)
+
+pipeline = AutoPipelineForText2Image.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    enable_pag=True,
+    pag_applied_layers="mid",
+    torch_dtype=torch.float16
+)
+pipeline.enable_model_cpu_offload()
+```
+
+<Tip>
+
+If you already have a controlnet pipeline and want to enable PAG, you can use the `from_pipe` API: `AutoPipelineForText2Image.from_pipe(pipeline_controlnet, enable_pag=True)`
+
+</Tip>
+
+You can use the pipeline in the same way you normally use ControlNet pipelines, with the added option to specify a `pag_scale` parameter. Note that PAG works well for unconditional generation. In this example, we will generate an image without a prompt.
+
+```py
+from diffusers.utils import load_image
+canny_image = load_image(
+    "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_control_input.png"
+)
+
+for pag_scale in [0.0, 3.0]:
+    generator = torch.Generator(device="cpu").manual_seed(1)
+    images = pipeline(
+        prompt="",
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
+        image=canny_image,
+        num_inference_steps=50,
+        guidance_scale=0,
+        generator=generator,
+        pag_scale=pag_scale,
+    ).images
+    images[0]
+```
+
+<div class="flex flex-row gap-4">
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_0.0_controlnet.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">generated image without PAG</figcaption>
+  </div>
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_3.0_controlnet.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">generated image with PAG</figcaption>
+  </div>
+</div>
+
+## PAG with IP-Adapter
+
+[IP-Adapter](https://hf.co/papers/2308.06721) is a popular model that can be plugged into diffusion models to enable image prompting without any changes to the underlying model. You can enable PAG on a pipeline with IP-Adapter loaded.
+
+```py
+from diffusers import AutoPipelineForText2Image
+from diffusers.utils import load_image
+from transformers import CLIPVisionModelWithProjection
+import torch
+
+image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+    "h94/IP-Adapter",
+    subfolder="models/image_encoder",
+    torch_dtype=torch.float16
+)
+
+pipeline = AutoPipelineForText2Image.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    image_encoder=image_encoder,
+    enable_pag=True,
+    torch_dtype=torch.float16
+).to("cuda")
+
+pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter-plus_sdxl_vit-h.bin")
+
+pag_scales = 5.0
+ip_adapter_scales = 0.8
+
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_diner.png")
+
+pipeline.set_ip_adapter_scale(ip_adapter_scale)
+generator = torch.Generator(device="cpu").manual_seed(0)
+images = pipeline(
+    prompt="a polar bear sitting in a chair drinking a milkshake",
+    ip_adapter_image=image,
+    negative_prompt="deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality",
+    num_inference_steps=25,
+    guidance_scale=3.0,
+    generator=generator,
+    pag_scale=pag_scale,
+).images
+images[0]
+
+```
+
+PAG reduces artifacts and improves the overall compposition.
+
+<div class="flex flex-row gap-4">
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_0.0_ipa_0.8.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">generated image without PAG</figcaption>
+  </div>
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_5.0_ipa_0.8.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">generated image with PAG</figcaption>
+  </div>
+</div>
+
+
+## Configure parameters
+
+### pag_applied_layers
+
+The `pag_applied_layers` argument allows you to specify which layers PAG is applied to. By default, it applies only to the mid blocks. Changing this setting will significantly impact the output. You can use the `set_pag_applied_layers` method to adjust the PAG layers after the pipeline is created, helping you find the optimal layers for your model.
+
+As an example, here is the images generated with `pag_layers = ["down.block_2"]` and `pag_layers = ["down.block_2", "up.block_1.attentions_0"]`
+
+```py
+prompt = "an insect robot preparing a delicious meal, anime style"
+pipeline.set_pag_applied_layers(pag_layers)
+generator = torch.Generator(device="cpu").manual_seed(0)
+images = pipeline(
+    prompt=prompt,
+    num_inference_steps=25,
+    guidance_scale=guidance_scale,
+    generator=generator,
+    pag_scale=pag_scale,
+).images
+images[0]
+```
+
+<div class="flex flex-row gap-4">
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_3.0_cfg_7.0_down2_up1a0.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">down.block_2 + up.block1.attentions_0</figcaption>
+  </div>
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_3.0_cfg_7.0_down2.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">down.block_2</figcaption>
+  </div>
+</div>

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

@@ -186,7 +186,7 @@ scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
 pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
     scheduler=scheduler,
-    revision="bf16",
+    variant="bf16",
     dtype=jax.numpy.bfloat16,
 )
 params["scheduler"] = scheduler_state

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

@@ -285,6 +285,12 @@ refiner = DiffusionPipeline.from_pretrained(
 ).to("cuda")
 ```
 
+<Tip warning={true}>
+
+You can use SDXL refiner with a different base model. For example, you can use the [Hunyuan-DiT](../../api/pipelines/hunyuandit) or [PixArt-Sigma](../../api/pipelines/pixart_sigma) pipelines to generate images with better prompt adherence. Once you have generated an image, you can pass it to the SDXL refiner model to enhance final generation quality.
+
+</Tip>
+
 Generate an image from the base model, and set the model output to **latent** space:
 
 ```py

docs/source/en/using-diffusers/shap-e.md

@@ -52,7 +52,7 @@ images = pipe(
 ).images
 ```
 
-Now use the [`~utils.export_to_gif`] function to turn the list of image frames into a gif of the 3D object.
+이제 [`~utils.export_to_gif`] 함수를 사용해 이미지 프레임 리스트를 3D 오브젝트의 gif로 변환합니다.
 
 ```py
 from diffusers.utils import export_to_gif

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

@@ -63,7 +63,7 @@ Flax is a functional framework, so models are stateless and parameters are store
 dtype = jnp.bfloat16
 pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
-    revision="bf16",
+    variant="bf16",
     dtype=dtype,
 )
 ```