Add stable diffusion weights

.github/ISSUE_TEMPLATE/bug-report.yml

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+name: "\U0001F41B Bug Report"
+description: Report a bug on diffusers
+labels: [ "bug" ]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+  - type: textarea
+    id: bug-description
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is. If you intend to submit a pull request for this issue, tell us in the description. Thanks!
+      placeholder: Bug description
+    validations:
+      required: true
+  - type: textarea
+    id: reproduction
+    attributes:
+      label: Reproduction
+      description: Please provide a minimal reproducible code which we can copy/paste and reproduce the issue.
+      placeholder: Reproduction
+  - type: textarea
+    id: logs
+    attributes:
+      label: Logs
+      description: "Please include the Python logs if you can."
+      render: shell
+  - type: textarea
+    id: system-info
+    attributes:
+      label: System Info
+      description: Please share your system info with us. You can run the command `diffusers-cli env` and copy-paste its output below.
+      placeholder: diffusers version, platform, python version, ...
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,4 @@
+contact_links:
+  - name: Blank issue
+    url: https://github.com/huggingface/diffusers/issues/new
+    about: General usage questions and community discussions

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: "\U0001F680 Feature request"
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/feedback.md

@@ -0,0 +1,12 @@
+---
+name: "💬 Feedback about API Design"
+about: Give feedback about the current API design
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**What API design would you like to have changed or added to the library? Why?**
+
+**What use case would this enable or better enable? Can you give us a code example?**

.github/ISSUE_TEMPLATE/new-model-addition.yml

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+name: "\U0001F31F New model/pipeline/scheduler addition"
+description: Submit a proposal/request to implement a new diffusion model / pipeline / scheduler
+labels: [ "New model/pipeline/scheduler" ]
+
+body:
+  - type: textarea
+    id: description-request
+    validations:
+      required: true
+    attributes:
+      label: Model/Pipeline/Scheduler description
+      description: |
+        Put any and all important information relative to the model/pipeline/scheduler
+
+  - type: checkboxes
+    id: information-tasks
+    attributes:
+      label: Open source status
+      description: |
+          Please note that if the model implementation isn't available or if the weights aren't open-source, we are less likely to implement it in `diffusers`.
+      options:
+        - label: "The model implementation is available"
+        - label: "The model weights are available (Only relevant if addition is not a scheduler)."
+
+  - type: textarea
+    id: additional-info
+    attributes:
+      label: Provide useful links for the implementation
+      description: |
+        Please provide information regarding the implementation, the weights, and the authors.
+        Please mention the authors by @gh-username if you're aware of their usernames.

.github/actions/setup-miniconda/action.yml

@@ -0,0 +1,146 @@
+name: Set up conda environment for testing
+
+description: Sets up miniconda in your ${RUNNER_TEMP} environment and gives you the ${CONDA_RUN} environment variable so you don't have to worry about polluting non-empeheral runners anymore
+
+inputs:
+  python-version:
+    description: If set to any value, dont use sudo to clean the workspace
+    required: false
+    type: string
+    default: "3.9"
+  miniconda-version:
+    description: Miniconda version to install
+    required: false
+    type: string
+    default: "4.12.0"
+  environment-file:
+    description: Environment file to install dependencies from
+    required: false
+    type: string
+    default: ""
+
+runs:
+  using: composite
+  steps:
+      # Use the same trick from https://github.com/marketplace/actions/setup-miniconda
+      # to refresh the cache daily. This is kind of optional though
+      - name: Get date
+        id: get-date
+        shell: bash
+        run: echo "::set-output name=today::$(/bin/date -u '+%Y%m%d')d"
+      - name: Setup miniconda cache
+        id: miniconda-cache
+        uses: actions/cache@v2
+        with:
+          path: ${{ runner.temp }}/miniconda
+          key: miniconda-${{ runner.os }}-${{ runner.arch }}-${{ inputs.python-version }}-${{ steps.get-date.outputs.today }}
+      - name: Install miniconda (${{ inputs.miniconda-version }})
+        if: steps.miniconda-cache.outputs.cache-hit != 'true'
+        env:
+          MINICONDA_VERSION: ${{ inputs.miniconda-version }}
+        shell: bash -l {0}
+        run: |
+          MINICONDA_INSTALL_PATH="${RUNNER_TEMP}/miniconda"
+          mkdir -p "${MINICONDA_INSTALL_PATH}"
+          case ${RUNNER_OS}-${RUNNER_ARCH} in
+            Linux-X64)
+              MINICONDA_ARCH="Linux-x86_64"
+              ;;
+            macOS-ARM64)
+              MINICONDA_ARCH="MacOSX-arm64"
+              ;;
+            macOS-X64)
+              MINICONDA_ARCH="MacOSX-x86_64"
+              ;;
+            *)
+            echo "::error::Platform ${RUNNER_OS}-${RUNNER_ARCH} currently unsupported using this action"
+              exit 1
+              ;;
+          esac
+          MINICONDA_URL="https://repo.anaconda.com/miniconda/Miniconda3-py39_${MINICONDA_VERSION}-${MINICONDA_ARCH}.sh"
+          curl -fsSL "${MINICONDA_URL}" -o "${MINICONDA_INSTALL_PATH}/miniconda.sh"
+          bash "${MINICONDA_INSTALL_PATH}/miniconda.sh" -b -u -p "${MINICONDA_INSTALL_PATH}"
+          rm -rf "${MINICONDA_INSTALL_PATH}/miniconda.sh"
+      - name: Update GitHub path to include miniconda install
+        shell: bash
+        run: |
+          MINICONDA_INSTALL_PATH="${RUNNER_TEMP}/miniconda"
+          echo "${MINICONDA_INSTALL_PATH}/bin" >> $GITHUB_PATH
+      - name: Setup miniconda env cache (with env file)
+        id: miniconda-env-cache-env-file
+        if: ${{ runner.os }} == 'macOS' && ${{ inputs.environment-file }} != ''
+        uses: actions/cache@v2
+        with:
+          path: ${{ runner.temp }}/conda-python-${{ inputs.python-version }}
+          key: miniconda-env-${{ runner.os }}-${{ runner.arch }}-${{ inputs.python-version }}-${{ steps.get-date.outputs.today }}-${{ hashFiles(inputs.environment-file) }}
+      - name: Setup miniconda env cache (without env file)
+        id: miniconda-env-cache
+        if: ${{ runner.os }} == 'macOS' && ${{ inputs.environment-file }} == ''
+        uses: actions/cache@v2
+        with:
+          path: ${{ runner.temp }}/conda-python-${{ inputs.python-version }}
+          key: miniconda-env-${{ runner.os }}-${{ runner.arch }}-${{ inputs.python-version }}-${{ steps.get-date.outputs.today }}
+      - name: Setup conda environment with python (v${{ inputs.python-version }})
+        if: steps.miniconda-env-cache-env-file.outputs.cache-hit != 'true' && steps.miniconda-env-cache.outputs.cache-hit != 'true'
+        shell: bash
+        env:
+          PYTHON_VERSION: ${{ inputs.python-version }}
+          ENV_FILE: ${{ inputs.environment-file }}
+        run: |
+          CONDA_BASE_ENV="${RUNNER_TEMP}/conda-python-${PYTHON_VERSION}"
+          ENV_FILE_FLAG=""
+          if [[ -f "${ENV_FILE}" ]]; then
+            ENV_FILE_FLAG="--file ${ENV_FILE}"
+          elif [[ -n "${ENV_FILE}" ]]; then
+            echo "::warning::Specified env file (${ENV_FILE}) not found, not going to include it"
+          fi
+          conda create \
+            --yes \
+            --prefix "${CONDA_BASE_ENV}" \
+            "python=${PYTHON_VERSION}" \
+            ${ENV_FILE_FLAG} \
+            cmake=3.22 \
+            conda-build=3.21 \
+            ninja=1.10 \
+            pkg-config=0.29 \
+            wheel=0.37
+      - name: Clone the base conda environment and update GitHub env
+        shell: bash
+        env:
+          PYTHON_VERSION: ${{ inputs.python-version }}
+          CONDA_BASE_ENV: ${{ runner.temp }}/conda-python-${{ inputs.python-version }}
+        run: |
+          CONDA_ENV="${RUNNER_TEMP}/conda_environment_${GITHUB_RUN_ID}"
+          conda create \
+            --yes \
+            --prefix "${CONDA_ENV}" \
+            --clone "${CONDA_BASE_ENV}"
+          # TODO: conda-build could not be cloned because it hardcodes the path, so it
+          # could not be cached
+          conda install --yes -p ${CONDA_ENV} conda-build=3.21
+          echo "CONDA_ENV=${CONDA_ENV}" >> "${GITHUB_ENV}"
+          echo "CONDA_RUN=conda run -p ${CONDA_ENV} --no-capture-output" >> "${GITHUB_ENV}"
+          echo "CONDA_BUILD=conda run -p ${CONDA_ENV} conda-build" >> "${GITHUB_ENV}"
+          echo "CONDA_INSTALL=conda install -p ${CONDA_ENV}" >> "${GITHUB_ENV}"
+      - name: Get disk space usage and throw an error for low disk space
+        shell: bash
+        run: |
+          echo "Print the available disk space for manual inspection"
+          df -h
+          # Set the minimum requirement space to 4GB
+          MINIMUM_AVAILABLE_SPACE_IN_GB=4
+          MINIMUM_AVAILABLE_SPACE_IN_KB=$(($MINIMUM_AVAILABLE_SPACE_IN_GB * 1024 * 1024))
+          # Use KB to avoid floating point warning like 3.1GB
+          df -k | tr -s ' ' | cut -d' ' -f 4,9 | while read -r LINE;
+          do
+            AVAIL=$(echo $LINE | cut -f1 -d' ')
+            MOUNT=$(echo $LINE | cut -f2 -d' ')
+            if [ "$MOUNT" = "/" ]; then
+              if [ "$AVAIL" -lt "$MINIMUM_AVAILABLE_SPACE_IN_KB" ]; then
+                echo "There is only ${AVAIL}KB free space left in $MOUNT, which is less than the minimum requirement of ${MINIMUM_AVAILABLE_SPACE_IN_KB}KB. Please help create an issue to PyTorch Release Engineering via https://github.com/pytorch/test-infra/issues and provide the link to the workflow run."
+                exit 1;
+              else
+                echo "There is ${AVAIL}KB free space left in $MOUNT, continue"
+              fi
+            fi
+          done

.github/workflows/build_docker_images.yml

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+name: Build Docker images (nightly)
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 0 * * *" # every day at midnight
+
+concurrency:
+  group: docker-image-builds
+  cancel-in-progress: false
+
+env:
+  REGISTRY: diffusers
+
+jobs:
+  build-docker-images:
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: read
+      packages: write
+
+    strategy:
+      fail-fast: false
+      matrix:
+        image-name:
+          - diffusers-pytorch-cpu
+          - diffusers-pytorch-cuda
+          - diffusers-flax-cpu
+          - diffusers-flax-tpu
+          - diffusers-onnxruntime-cpu
+          - diffusers-onnxruntime-cuda
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ env.REGISTRY }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Build and push
+        uses: docker/build-push-action@v3
+        with:
+          no-cache: true
+          context: ./docker/${{ matrix.image-name }}
+          push: true
+          tags: ${{ env.REGISTRY }}/${{ matrix.image-name }}:latest

.github/workflows/build_documentation.yml

@@ -0,0 +1,17 @@
+name: Build documentation
+
+on:
+  push:
+    branches:
+      - main
+      - doc-builder*
+      - v*-release
+
+jobs:
+   build:
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
+    with:
+      commit_sha: ${{ github.sha }}
+      package: diffusers
+    secrets:
+      token: ${{ secrets.HUGGINGFACE_PUSH }}

.github/workflows/build_pr_documentation.yml

@@ -0,0 +1,16 @@
+name: Build PR Documentation
+
+on:
+  pull_request:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  build:
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
+    with:
+      commit_sha: ${{ github.event.pull_request.head.sha }}
+      pr_number: ${{ github.event.number }}
+      package: diffusers

.github/workflows/delete_doc_comment.yml

@@ -0,0 +1,13 @@
+name: Delete dev documentation
+
+on:
+  pull_request:
+    types: [ closed ]
+
+
+jobs:
+  delete:
+    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment.yml@main
+    with:
+      pr_number: ${{ github.event.number }}
+      package: diffusers

.github/workflows/pr_quality.yml

@@ -0,0 +1,50 @@
+name: Run code quality checks
+
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - main
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  check_code_quality:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.7"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check quality
+        run: |
+          black  --check --preview examples tests src utils scripts
+          isort --check-only examples tests src utils scripts
+          flake8 examples tests src utils scripts
+          doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
+
+  check_repository_consistency:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.7"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check quality
+        run: |
+          python utils/check_copies.py
+          python utils/check_dummies.py

.github/workflows/pr_tests.yml

@@ -0,0 +1,150 @@
+name: Run fast tests
+
+on:
+  pull_request:
+    branches:
+      - main
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  DIFFUSERS_IS_CI: yes
+  OMP_NUM_THREADS: 4
+  MKL_NUM_THREADS: 4
+  PYTEST_TIMEOUT: 60
+  MPS_TORCH_VERSION: 1.13.0
+
+jobs:
+  run_fast_tests:
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - name: Fast PyTorch CPU tests on Ubuntu
+            framework: pytorch
+            runner: docker-cpu
+            image: diffusers/diffusers-pytorch-cpu
+            report: torch_cpu
+          - name: Fast Flax CPU tests on Ubuntu
+            framework: flax
+            runner: docker-cpu
+            image: diffusers/diffusers-flax-cpu
+            report: flax_cpu
+          - name: Fast ONNXRuntime CPU tests on Ubuntu
+            framework: onnxruntime
+            runner: docker-cpu
+            image: diffusers/diffusers-onnxruntime-cpu
+            report: onnx_cpu
+
+    name: ${{ matrix.config.name }}
+
+    runs-on: ${{ matrix.config.runner }}
+
+    container:
+      image: ${{ matrix.config.image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m pip install -e .[quality,test]
+        python -m pip install git+https://github.com/huggingface/accelerate
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run fast PyTorch CPU tests
+      if: ${{ matrix.config.framework == 'pytorch' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run fast Flax TPU tests
+      if: ${{ matrix.config.framework == 'flax' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Flax" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run fast ONNXRuntime CPU tests
+      if: ${{ matrix.config.framework == 'onnxruntime' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: pr_${{ matrix.config.report }}_test_reports
+        path: reports
+
+  run_fast_tests_apple_m1:
+    name: Fast PyTorch MPS tests on MacOS
+    runs-on: [ self-hosted, apple-m1 ]
+
+    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
+        ${CONDA_RUN} python -m pip install -e .[quality,test]
+        ${CONDA_RUN} python -m pip install --pre torch==${MPS_TORCH_VERSION} --extra-index-url https://download.pytorch.org/whl/test/cpu
+        ${CONDA_RUN} python -m pip install git+https://github.com/huggingface/accelerate
+
+    - name: Environment
+      shell: arch -arch arm64 bash {0}
+      run: |
+        ${CONDA_RUN} python utils/print_env.py
+
+    - name: Run fast PyTorch tests on M1 (MPS)
+      shell: arch -arch arm64 bash {0}
+      run: |
+        ${CONDA_RUN} python -m pytest -n 0 -s -v --make-reports=tests_torch_mps 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: pr_torch_mps_test_reports
+        path: reports

.github/workflows/push_tests.yml

@@ -0,0 +1,154 @@
+name: Run all tests
+
+on:
+  push:
+    branches:
+      - main
+
+env:
+  DIFFUSERS_IS_CI: yes
+  HF_HOME: /mnt/cache
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  PYTEST_TIMEOUT: 1000
+  RUN_SLOW: yes
+
+jobs:
+  run_slow_tests:
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - name: Slow PyTorch CUDA tests on Ubuntu
+            framework: pytorch
+            runner: docker-gpu
+            image: diffusers/diffusers-pytorch-cuda
+            report: torch_cuda
+          - name: Slow Flax TPU tests on Ubuntu
+            framework: flax
+            runner: docker-tpu
+            image: diffusers/diffusers-flax-tpu
+            report: flax_tpu
+          - name: Slow ONNXRuntime CUDA tests on Ubuntu
+            framework: onnxruntime
+            runner: docker-gpu
+            image: diffusers/diffusers-onnxruntime-cuda
+            report: onnx_cuda
+
+    name: ${{ matrix.config.name }}
+
+    runs-on: ${{ matrix.config.runner }}
+
+    container:
+      image: ${{ matrix.config.image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ ${{ matrix.config.runner == 'docker-tpu' && '--privileged' || '--gpus 0'}}
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: NVIDIA-SMI
+      if : ${{ matrix.config.runner == 'docker-gpu' }}
+      run: |
+        nvidia-smi
+
+    - name: Install dependencies
+      run: |
+        python -m pip install -e .[quality,test]
+        python -m pip install git+https://github.com/huggingface/accelerate
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run slow PyTorch CUDA tests
+      if: ${{ matrix.config.framework == 'pytorch' }}
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run slow Flax TPU tests
+      if: ${{ matrix.config.framework == 'flax' }}
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        python -m pytest -n 0 \
+          -s -v -k "Flax" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run slow ONNXRuntime CUDA tests
+      if: ${{ matrix.config.framework == 'onnxruntime' }}
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: ${{ matrix.config.report }}_test_reports
+        path: reports
+
+  run_examples_tests:
+    name: Examples PyTorch CUDA tests on Ubuntu
+
+    runs-on: docker-gpu
+
+    container:
+      image: diffusers/diffusers-pytorch-cuda
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: NVIDIA-SMI
+      run: |
+        nvidia-smi
+
+    - name: Install dependencies
+      run: |
+        python -m pip install -e .[quality,test,training]
+        python -m pip install git+https://github.com/huggingface/accelerate
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run example tests on GPU
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        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_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: examples_test_reports
+        path: reports

.github/workflows/stale.yml

@@ -0,0 +1,27 @@
+name: Stale Bot
+
+on:
+  schedule:
+    - cron: "0 15 * * *"
+
+jobs:
+  close_stale_issues:
+    name: Close Stale Issues
+    if: github.repository == 'huggingface/diffusers'
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Setup Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.7
+
+    - name: Install requirements
+      run: |
+        pip install PyGithub
+    - name: Close stale issues
+      run: |
+        python utils/stale.py

.github/workflows/typos.yml

@@ -0,0 +1,14 @@
+name: Check typos
+
+on:
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: typos-action
+        uses: crate-ci/[email protected]

.gitignore

@@ -0,0 +1,168 @@
+# Initially taken from Github's Python gitignore file
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# tests and logs
+tests/fixtures/cached_*_text.txt
+logs/
+lightning_logs/
+lang_code_data/
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# vscode
+.vs
+.vscode
+
+# Pycharm
+.idea
+
+# TF code
+tensorflow_code
+
+# Models
+proc_data
+
+# examples
+runs
+/runs_old
+/wandb
+/examples/runs
+/examples/**/*.args
+/examples/rag/sweep
+
+# data
+/data
+serialization_dir
+
+# emacs
+*.*~
+debug.env
+
+# vim
+.*.swp
+
+#ctags
+tags
+
+# pre-commit
+.pre-commit*
+
+# .lock
+*.lock
+
+# DS_Store (MacOS)
+.DS_Store
+# RL pipelines may produce mp4 outputs
+*.mp4

CODE_OF_CONDUCT.md

@@ -0,0 +1,129 @@
+
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+[email protected].
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

CONTRIBUTING.md

@@ -0,0 +1,294 @@
+<!---
+Copyright 2022 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.
+-->
+
+# How to contribute to diffusers?
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code
+is thus not the only way to help the community. Answering questions, helping
+others, reaching out and improving the documentations are immensely valuable to
+the community.
+
+It also helps us if you spread the word: reference the library from blog posts
+on the awesome projects it made possible, shout out on Twitter every time it has
+helped you, or simply star the repo to say "thank you".
+
+Whichever way you choose to contribute, please be mindful to respect our
+[code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md).
+
+## You can contribute in so many ways!
+
+There are 4 ways you can contribute to diffusers:
+* Fixing outstanding issues with the existing code;
+* Implementing [new diffusion pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines#contribution), [new schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) or [new models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)
+* [Contributing to the examples](https://github.com/huggingface/diffusers/tree/main/examples) or to the documentation;
+* Submitting issues related to bugs or desired new features.
+
+In particular there is a special [Good First Issue](https://github.com/huggingface/diffusers/contribute) listing. 
+It will give you a list of open Issues that are open to anybody to work on. Just comment in the issue that you'd like to work on it. 
+In that same listing you will also find some Issues with `Good Second Issue` label. These are
+typically slightly more complicated than the Issues with just `Good First Issue` label. But if you
+feel you know what you're doing, go for it.
+
+*All are equally valuable to the community.*
+
+## Submitting a new issue or feature request
+
+Do your best to follow these guidelines when submitting an issue or a feature
+request. It will make it easier for us to come back to you quickly and with good
+feedback.
+
+### Did you find a bug?
+
+The 🧨 Diffusers library is robust and reliable thanks to the users who notify us of
+the problems they encounter. So thank you for reporting an issue.
+
+First, we would really appreciate it if you could **make sure the bug was not
+already reported** (use the search bar on Github under Issues).
+
+### Do you want to implement a new diffusion pipeline / diffusion model?
+
+Awesome! Please provide the following information:
+
+* Short description of the diffusion pipeline and link to the paper;
+* Link to the implementation if it is open-source;
+* Link to the model weights if they are available.
+
+If you are willing to contribute the model yourself, let us know so we can best
+guide you.
+
+### Do you want a new feature (that is not a model)?
+
+A world-class feature request addresses the following points:
+
+1. Motivation first:
+  * Is it related to a problem/frustration with the library? If so, please explain
+    why. Providing a code snippet that demonstrates the problem is best.
+  * Is it related to something you would need for a project? We'd love to hear
+    about it!
+  * Is it something you worked on and think could benefit the community?
+    Awesome! Tell us what problem it solved for you.
+2. Write a *full paragraph* describing the feature;
+3. Provide a **code snippet** that demonstrates its future use;
+4. In case this is related to a paper, please attach a link;
+5. Attach any additional information (drawings, screenshots, etc.) you think may help.
+
+If your issue is well written we're already 80% of the way there by the time you
+post it.
+
+## Start contributing! (Pull Requests)
+
+Before writing code, we strongly advise you to search through the existing PRs or
+issues to make sure that nobody is already working on the same thing. If you are
+unsure, it is always a good idea to open an issue to get some feedback.
+
+You will need basic `git` proficiency to be able to contribute to
+🧨 Diffusers. `git` is not the easiest tool to use but it has the greatest
+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#L426)):
+
+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
+   under your GitHub user account.
+
+2. Clone your fork to your local disk, and add the base repository as a remote:
+
+   ```bash
+   $ git clone [email protected]:<your Github handle>/diffusers.git
+   $ cd diffusers
+   $ git remote add upstream https://github.com/huggingface/diffusers.git
+   ```
+
+3. Create a new branch to hold your development changes:
+
+   ```bash
+   $ git checkout -b a-descriptive-name-for-my-changes
+   ```
+
+   **Do not** work on the `main` branch.
+
+4. Set up a development environment by running the following command in a virtual environment:
+
+   ```bash
+   $ pip install -e ".[dev]"
+   ```
+
+   (If diffusers was already installed in the virtual environment, remove
+   it with `pip uninstall diffusers` before reinstalling it in editable
+   mode with the `-e` flag.)
+
+   To run the full test suite, you might need the additional dependency on `transformers` and `datasets` which requires a separate source
+   install:
+
+   ```bash
+   $ git clone https://github.com/huggingface/transformers
+   $ cd transformers
+   $ pip install -e .
+   ```
+
+   ```bash
+   $ git clone https://github.com/huggingface/datasets
+   $ cd datasets
+   $ pip install -e .
+   ```
+
+   If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `datasets`
+   library.
+
+5. Develop the features on your branch.
+
+   As you work on the features, you should make sure that the test suite
+   passes. You should run the tests impacted by your changes like this:
+
+   ```bash
+   $ pytest tests/<TEST_TO_RUN>.py
+   ```
+
+   You can also run the full suite with the following command, but it takes
+   a beefy machine to produce a result in a decent amount of time now that
+   Diffusers has grown a lot. Here is the command for it:
+
+   ```bash
+   $ make test
+   ```
+
+   For more information about tests, check out the
+   [dedicated documentation](https://huggingface.co/docs/diffusers/testing)
+
+   🧨 Diffusers relies on `black` and `isort` to format its source code
+   consistently. After you make changes, apply automatic style corrections and code verifications
+   that can't be automated in one go with:
+
+   ```bash
+   $ make style
+   ```
+
+   🧨 Diffusers also uses `flake8` and a few custom scripts to check for coding mistakes. Quality
+   control runs in CI, however you can also run the same checks with:
+
+   ```bash
+   $ make quality
+   ```
+
+   Once you're happy with your changes, add changed files using `git add` and
+   make a commit with `git commit` to record your changes locally:
+
+   ```bash
+   $ git add modified_file.py
+   $ git commit
+   ```
+
+   It is a good idea to sync your copy of the code with the original
+   repository regularly. This way you can quickly account for changes:
+
+   ```bash
+   $ git fetch upstream
+   $ git rebase upstream/main
+   ```
+
+   Push the changes to your account using:
+
+   ```bash
+   $ git push -u origin a-descriptive-name-for-my-changes
+   ```
+
+6. Once you are satisfied (**and the checklist below is happy too**), go to the
+   webpage of your fork on GitHub. Click on 'Pull request' to send your changes
+   to the project maintainers for review.
+
+7. It's ok if maintainers ask you for changes. It happens to core contributors
+   too! So everyone can see the changes in the Pull request, work in your local
+   branch and push the changes to your fork. They will automatically appear in
+   the pull request.
+
+
+### Checklist
+
+1. The title of your pull request should be a summary of its contribution;
+2. If your pull request addresses an issue, please mention the issue number in
+   the pull request description to make sure they are linked (and people
+   consulting the issue know you are working on it);
+3. To indicate a work in progress please prefix the title with `[WIP]`. These
+   are useful to avoid duplicated work, and to differentiate it from PRs ready
+   to be merged;
+4. Make sure existing tests pass;
+5. Add high-coverage tests. No quality testing = no merge.
+   - If you are adding new `@slow` tests, make sure they pass using
+     `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.
+   - If you are adding a new tokenizer, write tests, and make sure
+     `RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
+   CircleCI does not run the slow tests, but github actions does every night!
+6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_bert.py` for an
+   example.
+7. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
+   the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference 
+   them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
+   If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
+   to this dataset.
+
+### Tests
+
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
+the [tests folder](https://github.com/huggingface/diffusers/tree/main/tests).
+
+We like `pytest` and `pytest-xdist` because it's faster. From the root of the
+repository, here's how to run tests with `pytest` for the library:
+
+```bash
+$ python -m pytest -n auto --dist=loadfile -s -v ./tests/
+```
+
+In fact, that's how `make test` is implemented (sans the `pip install` line)!
+
+You can specify a smaller set of tests in order to test only the feature
+you're working on.
+
+By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to
+`yes` to run them. This will download many gigabytes of models — make sure you
+have enough disk space and a good Internet connection, or a lot of patience!
+
+```bash
+$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
+```
+
+This means `unittest` is fully supported. Here's how to run tests with
+`unittest`:
+
+```bash
+$ python -m unittest discover -s tests -t . -v
+$ python -m unittest discover -s examples -t examples -v
+```
+
+
+### Style guide
+
+For documentation strings, 🧨 Diffusers follows the [google style](https://google.github.io/styleguide/pyguide.html).
+
+**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).**
+
+### Syncing forked main with upstream (HuggingFace) main
+
+To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,
+when syncing the main branch of a forked repository, please, follow these steps:
+1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked main.
+2. If a PR is absolutely necessary, use the following steps after checking out your branch:
+```
+$ git checkout -b your-branch-for-syncing
+$ git pull --squash --no-commit upstream main
+$ git commit -m '<your message without GitHub references>'
+$ git push --set-upstream origin your-branch-for-syncing
+```

LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
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MANIFEST.in

@@ -0,0 +1,2 @@
+include LICENSE
+include src/diffusers/utils/model_card_template.md

Makefile

@@ -0,0 +1,96 @@
+.PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples
+
+# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
+export PYTHONPATH = src
+
+check_dirs := examples scripts src tests utils
+
+modified_only_fixup:
+	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))
+	@if test -n "$(modified_py_files)"; then \
+		echo "Checking/fixing $(modified_py_files)"; \
+		black --preview $(modified_py_files); \
+		isort $(modified_py_files); \
+		flake8 $(modified_py_files); \
+	else \
+		echo "No library .py files were modified"; \
+	fi
+
+# Update src/diffusers/dependency_versions_table.py
+
+deps_table_update:
+	@python setup.py deps_table_update
+
+deps_table_check_updated:
+	@md5sum src/diffusers/dependency_versions_table.py > md5sum.saved
+	@python setup.py deps_table_update
+	@md5sum -c --quiet md5sum.saved || (printf "\nError: the version dependency table is outdated.\nPlease run 'make fixup' or 'make style' and commit the changes.\n\n" && exit 1)
+	@rm md5sum.saved
+
+# autogenerating code
+
+autogenerate_code: deps_table_update
+
+# Check that the repo is in a good state
+
+repo-consistency:
+	python utils/check_dummies.py
+	python utils/check_repo.py
+	python utils/check_inits.py
+
+# this target runs checks on all files
+
+quality:
+	black --check --preview $(check_dirs)
+	isort --check-only $(check_dirs)
+	flake8 $(check_dirs)
+	doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
+
+# Format source code automatically and check is there are any problems left that need manual fixing
+
+extra_style_checks:
+	python utils/custom_init_isort.py
+	doc-builder style src/diffusers docs/source --max_len 119 --path_to_docs docs/source
+
+# this target runs checks on all files and potentially modifies some of them
+
+style:
+	black --preview $(check_dirs)
+	isort $(check_dirs)
+	${MAKE} autogenerate_code
+	${MAKE} extra_style_checks
+
+# Super fast fix and check target that only works on relevant modified files since the branch was made
+
+fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency
+
+# Make marked copies of snippets of codes conform to the original
+
+fix-copies:
+	python utils/check_copies.py --fix_and_overwrite
+	python utils/check_dummies.py --fix_and_overwrite
+
+# Run tests for the library
+
+test:
+	python -m pytest -n auto --dist=loadfile -s -v ./tests/
+
+# Run tests for examples
+
+test-examples:
+	python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/
+
+
+# Release stuff
+
+pre-release:
+	python utils/release.py
+
+pre-patch:
+	python utils/release.py --patch
+
+post-release:
+	python utils/release.py --post_release
+
+post-patch:
+	python utils/release.py --post_release --patch

README.md

@@ -1,3 +1,492 @@
----
-license: openrail
----
+<p align="center">
+    <br>
+    <img src="https://github.com/huggingface/diffusers/raw/main/docs/source/imgs/diffusers_library.jpg" width="400"/>
+    <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="CODE_OF_CONDUCT.md">
+        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg">
+    </a>
+</p>
+
+🤗 Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves
+as a modular toolbox for inference and training of diffusion models.
+
+More precisely, 🤗 Diffusers offers:
+
+- State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)). Check [this overview](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/README.md#pipelines-summary) to see all supported pipelines and their corresponding official papers.
+- Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)).
+- Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)).
+- Training examples to show how to train the most popular diffusion model tasks (see [examples](https://github.com/huggingface/diffusers/tree/main/examples), *e.g.* [unconditional-image-generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)).
+
+## Installation
+
+### For PyTorch
+
+**With `pip`**
+    
+```bash
+pip install --upgrade diffusers[torch]
+```
+
+**With `conda`**
+
+```sh
+conda install -c conda-forge diffusers
+```
+
+### For Flax
+
+**With `pip`**
+
+```bash
+pip install --upgrade diffusers[flax]
+```
+
+**Apple Silicon (M1/M2) support**
+
+Please, refer to [the documentation](https://huggingface.co/docs/diffusers/optimization/mps).
+
+## Contributing
+
+We ❤️  contributions from the open-source community! 
+If you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md).
+You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library.
+- See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute
+- See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines
+- See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)
+
+Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or
+just hang out ☕.
+
+## Quickstart
+
+In order to get started, we recommend taking a look at two notebooks:
+
+- The [Getting started with Diffusers](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) notebook, which showcases an end-to-end example of usage for diffusion models, schedulers and pipelines.
+  Take a look at this notebook to learn how to use the pipeline abstraction, which takes care of everything (model, scheduler, noise handling) for you, and also to understand each independent building block in the library.
+- The [Training a diffusers model](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) notebook summarizes diffusion models training methods. This notebook takes a step-by-step approach to training your
+  diffusion models on an image dataset, with explanatory graphics. 
+  
+## Stable Diffusion is fully compatible with `diffusers`!  
+
+Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [LAION](https://laion.ai/) and [RunwayML](https://runwayml.com/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 4GB VRAM.
+See the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information.
+
+You need to accept the model license before downloading or using the Stable Diffusion weights. Please, visit the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license carefully and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section](https://huggingface.co/docs/hub/security-tokens) of the documentation.
+
+
+### Text-to-Image generation with Stable Diffusion
+
+First let's install
+```bash
+pip install --upgrade diffusers transformers scipy
+```
+
+Run this command to log in with your HF Hub token if you haven't before (you can skip this step if you prefer to run the model locally, follow [this](#running-the-model-locally) instead)
+```bash
+huggingface-cli login
+```
+
+We recommend using the model in [half-precision (`fp16`)](https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/) as it gives almost always the same results as full
+precision while being roughly twice as fast and requiring half the amount of GPU RAM.
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, revision="fp16")
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]  
+```
+
+#### Running the model locally
+If you don't want to login to Hugging Face, you can also simply download the model folder
+(after having [accepted the license](https://huggingface.co/runwayml/stable-diffusion-v1-5)) and pass
+the path to the local folder to the `StableDiffusionPipeline`.
+
+```
+git lfs install
+git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
+```
+
+Assuming the folder is stored locally under `./stable-diffusion-v1-5`, you can also run stable diffusion
+without requiring an authentication token:
+
+```python
+pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]  
+```
+
+If you are limited by GPU memory, you might want to consider chunking the attention computation in addition 
+to using `fp16`.
+The following snippet should result in less than 4GB VRAM.
+
+```python
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", 
+    revision="fp16", 
+    torch_dtype=torch.float16,
+)
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+pipe.enable_attention_slicing()
+image = pipe(prompt).images[0]  
+```
+
+If you wish to use a different scheduler (e.g.: DDIM, LMS, PNDM/PLMS), you can instantiate
+it before the pipeline and pass it to `from_pretrained`.
+    
+```python
+from diffusers import LMSDiscreteScheduler
+
+pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]  
+    
+image.save("astronaut_rides_horse.png")
+```
+
+If you want to run Stable Diffusion on CPU or you want to have maximum precision on GPU, 
+please run the model in the default *full-precision* setting:
+
+```python
+# make sure you're logged in with `huggingface-cli login`
+from diffusers import StableDiffusionPipeline
+
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+
+# disable the following line if you run on CPU
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]  
+    
+image.save("astronaut_rides_horse.png")
+```
+
+### JAX/Flax
+
+Diffusers offers a JAX / Flax implementation of Stable Diffusion for very fast inference. JAX shines specially on TPU hardware because each TPU server has 8 accelerators working in parallel, but it runs great on GPUs too.
+
+Running the pipeline with the default PNDMScheduler:
+
+```python
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+
+from diffusers import FlaxStableDiffusionPipeline
+
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", revision="flax", dtype=jax.numpy.bfloat16
+)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+prompt_ids = pipeline.prepare_inputs(prompt)
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+```
+
+**Note**:
+If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch.
+
+```python
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+
+from diffusers import FlaxStableDiffusionPipeline
+
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jax.numpy.bfloat16
+)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+prompt_ids = pipeline.prepare_inputs(prompt)
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+```
+
+### Image-to-Image text-guided generation with Stable Diffusion
+
+The `StableDiffusionImg2ImgPipeline` lets you pass a text prompt and an initial image to condition the generation of new images.
+
+```python
+import requests
+import torch
+from PIL import Image
+from io import BytesIO
+
+from diffusers import StableDiffusionImg2ImgPipeline
+
+# load the pipeline
+device = "cuda"
+model_id_or_path = "runwayml/stable-diffusion-v1-5"
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    model_id_or_path,
+    revision="fp16", 
+    torch_dtype=torch.float16,
+)
+# or download via git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
+# and pass `model_id_or_path="./stable-diffusion-v1-5"`.
+pipe = pipe.to(device)
+
+# let's download an initial image
+url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+
+response = requests.get(url)
+init_image = Image.open(BytesIO(response.content)).convert("RGB")
+init_image = init_image.resize((768, 512))
+
+prompt = "A fantasy landscape, trending on artstation"
+
+images = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5).images
+
+images[0].save("fantasy_landscape.png")
+```
+You can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
+
+### In-painting using Stable Diffusion
+
+The `StableDiffusionInpaintPipeline` lets you edit specific parts of an image by providing a mask and a text prompt. It uses a model optimized for this particular task, whose license you need to accept before use.
+
+Please, visit the [model card](https://huggingface.co/runwayml/stable-diffusion-inpainting), read the license carefully and tick the checkbox if you agree. Note that this is an additional license, you need to accept it even if you accepted the text-to-image Stable Diffusion license in the past. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section](https://huggingface.co/docs/hub/security-tokens) of the documentation.
+
+
+```python
+import PIL
+import requests
+import torch
+from io import BytesIO
+
+from diffusers import StableDiffusionInpaintPipeline
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+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 = download_image(img_url).resize((512, 512))
+mask_image = download_image(mask_url).resize((512, 512))
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    revision="fp16",
+    torch_dtype=torch.float16,
+)
+pipe = pipe.to("cuda")
+
+prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
+```
+
+### Tweak prompts reusing seeds and latents
+
+You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. [This notebook](https://github.com/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) shows how to do it step by step. You can also run it in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb).
+
+
+For more details, check out [the Stable Diffusion notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb)
+and have a look into the [release notes](https://github.com/huggingface/diffusers/releases/tag/v0.2.0).
+
+## Fine-Tuning Stable Diffusion
+
+Fine-tuning techniques make it possible to adapt Stable Diffusion to your own dataset, or add new subjects to it. These are some of the techniques supported in `diffusers`:
+
+Textual Inversion is a technique for capturing novel concepts from a small number of example images in a way that can later be used to control text-to-image pipelines. It does so by learning new 'words' in the embedding space of the pipeline's text encoder. These special words can then be used within text prompts to achieve very fine-grained control of the resulting images. 
+
+- Textual Inversion. Capture novel concepts from a small set of sample images, and associate them with new "words" in the embedding space of the text encoder. Please, refer to [our training examples](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) or [documentation](https://huggingface.co/docs/diffusers/training/text_inversion) to try for yourself.
+
+- Dreambooth. Another technique to capture new concepts in Stable Diffusion. This method fine-tunes the UNet (and, optionally, also the text encoder) of the pipeline to achieve impressive results. Please, refer to [our training example](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) and [training report](https://huggingface.co/blog/dreambooth) for additional details and training recommendations.
+
+- Full Stable Diffusion fine-tuning. If you have a more sizable dataset with a specific look or style, you can fine-tune Stable Diffusion so that it outputs images following those examples. This was the approach taken to create [a Pokémon Stable Diffusion model](https://huggingface.co/justinpinkney/pokemon-stable-diffusion) (by Justing Pinkney / Lambda Labs), [a Japanese specific version of Stable Diffusion](https://huggingface.co/spaces/rinna/japanese-stable-diffusion) (by [Rinna Co.](https://github.com/rinnakk/japanese-stable-diffusion/) and others. You can start at [our text-to-image fine-tuning example](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) and go from there.
+
+
+## Stable Diffusion Community Pipelines
+
+The release of Stable Diffusion as an open source model has fostered a lot of interesting ideas and experimentation. 
+Our [Community Examples folder](https://github.com/huggingface/diffusers/tree/main/examples/community) contains many ideas worth exploring, like interpolating to create animated videos, using CLIP Guidance for additional prompt fidelity, term weighting, and much more! [Take a look](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview) and [contribute your own](https://huggingface.co/docs/diffusers/using-diffusers/contribute_pipeline).
+
+## Other Examples
+
+There are many ways to try running Diffusers! Here we outline code-focused tools (primarily using `DiffusionPipeline`s and Google Colab) and interactive web-tools.
+
+### Running Code
+
+If you want to run the code yourself 💻, you can try out:
+- [Text-to-Image Latent Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256)
+```python
+# !pip install diffusers["torch"] transformers
+from diffusers import DiffusionPipeline
+
+device = "cuda"
+model_id = "CompVis/ldm-text2im-large-256"
+
+# load model and scheduler
+ldm = DiffusionPipeline.from_pretrained(model_id)
+ldm = ldm.to(device)
+
+# run pipeline in inference (sample random noise and denoise)
+prompt = "A painting of a squirrel eating a burger"
+image = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6).images[0]
+
+# save image
+image.save("squirrel.png")
+```
+- [Unconditional Diffusion with discrete scheduler](https://huggingface.co/google/ddpm-celebahq-256)
+```python
+# !pip install diffusers["torch"]
+from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
+
+model_id = "google/ddpm-celebahq-256"
+device = "cuda"
+
+# load model and scheduler
+ddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
+ddpm.to(device)
+
+# run pipeline in inference (sample random noise and denoise)
+image = ddpm().images[0]
+
+# save image
+image.save("ddpm_generated_image.png")
+```
+- [Unconditional Latent Diffusion](https://huggingface.co/CompVis/ldm-celebahq-256)
+- [Unconditional Diffusion with continuous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)
+
+**Other Image Notebooks**:
+* [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+* [tweak images via repeated Stable Diffusion seeds](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+
+**Diffusers for Other Modalities**:
+* [Molecule conformation generation](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+* [Model-based reinforcement learning](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+
+### Web Demos
+If you just want to play around with some web demos, you can try out the following 🚀 Spaces:
+| Model                          	| Hugging Face Spaces                                                                                                                                               	|
+|--------------------------------	|-------------------------------------------------------------------------------------------------------------------------------------------------------------------	|
+| Text-to-Image Latent Diffusion 	| [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CompVis/text2img-latent-diffusion) 	|
+| Faces generator                	| [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CompVis/celeba-latent-diffusion)    	|
+| DDPM with different schedulers 	| [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/fusing/celeba-diffusion)           	|
+| Conditional generation from sketch  	| [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/huggingface/diffuse-the-rest)           	|
+| Composable diffusion | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Shuang59/Composable-Diffusion)           	|
+
+## Definitions
+
+**Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image.
+*Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet
+
+<p align="center">
+    <img src="https://user-images.githubusercontent.com/10695622/174349667-04e9e485-793b-429a-affe-096e8199ad5b.png" width="800"/>
+    <br>
+    <em> Figure from DDPM paper (https://arxiv.org/abs/2006.11239). </em>
+<p>
+    
+**Schedulers**: Algorithm class for both **inference** and **training**.
+The class provides functionality to compute previous image according to alpha, beta schedule as well as predict noise for training. Also known as **Samplers**.
+*Examples*: [DDPM](https://arxiv.org/abs/2006.11239), [DDIM](https://arxiv.org/abs/2010.02502), [PNDM](https://arxiv.org/abs/2202.09778), [DEIS](https://arxiv.org/abs/2204.13902)
+
+<p align="center">
+    <img src="https://user-images.githubusercontent.com/10695622/174349706-53d58acc-a4d1-4cda-b3e8-432d9dc7ad38.png" width="800"/>
+    <br>
+    <em> Sampling and training algorithms. Figure from DDPM paper (https://arxiv.org/abs/2006.11239). </em>
+<p>
+    
+
+**Diffusion Pipeline**: End-to-end pipeline that includes multiple diffusion models, possible text encoders, ...
+*Examples*: Glide, Latent-Diffusion, Imagen, DALL-E 2
+
+<p align="center">
+    <img src="https://user-images.githubusercontent.com/10695622/174348898-481bd7c2-5457-4830-89bc-f0907756f64c.jpeg" width="550"/>
+    <br>
+    <em> Figure from ImageGen (https://imagen.research.google/). </em>
+<p>
+    
+## Philosophy
+
+- Readability and clarity is preferred over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
+- Diffusers is **modality independent** and focuses on providing pretrained models and tools to build systems that generate **continuous outputs**, *e.g.* vision and audio.
+- Diffusion models and schedulers are provided as concise, elementary building blocks. In contrast, diffusion pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box, should stay as close as possible to their original implementation and can include components of another library, such as text-encoders. Examples for diffusion pipelines are [Glide](https://github.com/openai/glide-text2im) and [Latent Diffusion](https://github.com/CompVis/latent-diffusion).
+
+## In the works
+
+For the first release, 🤗 Diffusers focuses on text-to-image diffusion techniques. However, diffusers can be used for much more than that! Over the upcoming releases, we'll be focusing on:
+
+- Diffusers for audio
+- Diffusers for reinforcement learning (initial work happening in https://github.com/huggingface/diffusers/pull/105).
+- Diffusers for video generation
+- Diffusers for molecule generation (initial work happening in https://github.com/huggingface/diffusers/pull/54)
+
+A few pipeline components are already being worked on, namely:
+
+- BDDMPipeline for spectrogram-to-sound vocoding
+- GLIDEPipeline to support OpenAI's GLIDE model
+- Grad-TTS for text to audio generation / conditional audio generation
+
+We want diffusers to be a toolbox useful for diffusers models in general; if you find yourself limited in any way by the current API, or would like to see additional models, schedulers, or techniques, please open a [GitHub issue](https://github.com/huggingface/diffusers/issues) mentioning what you would like to see.
+
+## Credits
+
+This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today:
+
+- @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion)
+- @hojonathanho original DDPM implementation, available [here](https://github.com/hojonathanho/diffusion) as well as the extremely useful translation into PyTorch by @pesser, available [here](https://github.com/pesser/pytorch_diffusion)
+- @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim).
+- @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch)
+
+We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available [here](https://github.com/heejkoo/Awesome-Diffusion-Models) as well as @crowsonkb and @rromb for useful discussions and insights.
+
+## Citation
+
+```bibtex
+@misc{von-platen-etal-2022-diffusers,
+  author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
+  title = {Diffusers: State-of-the-art diffusion models},
+  year = {2022},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/huggingface/diffusers}}
+}
+```

_typos.toml

@@ -0,0 +1,13 @@
+# Files for typos
+# Instruction:  https://github.com/marketplace/actions/typos-action#getting-started
+
+[default.extend-identifiers]
+
+[default.extend-words]
+NIN="NIN" # NIN is used in scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py
+nd="np" # nd may be np (numpy)
+parms="parms" # parms is used in scripts/convert_original_stable_diffusion_to_diffusers.py
+
+
+[files]
+extend-exclude = ["_typos.toml"]

docker/diffusers-flax-cpu/Dockerfile

@@ -0,0 +1,42 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --upgrade --no-cache-dir \
+        clu \
+        "jax[cpu]>=0.2.16,!=0.3.2" \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        modelcards \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

docker/diffusers-flax-tpu/Dockerfile

@@ -0,0 +1,44 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        "jax[tpu]>=0.2.16,!=0.3.2" \
+        -f https://storage.googleapis.com/jax-releases/libtpu_releases.html && \
+    python3 -m pip install --upgrade --no-cache-dir \
+        clu \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        modelcards \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -0,0 +1,42 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        onnxruntime \
+        --extra-index-url https://download.pytorch.org/whl/cpu && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        modelcards \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -0,0 +1,42 @@
+FROM nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        "onnxruntime-gpu>=1.13.1" \
+        --extra-index-url https://download.pytorch.org/whl/cu117 && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        modelcards \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

docker/diffusers-pytorch-cpu/Dockerfile

@@ -0,0 +1,41 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        --extra-index-url https://download.pytorch.org/whl/cpu && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        modelcards \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

docker/diffusers-pytorch-cuda/Dockerfile

@@ -0,0 +1,41 @@
+FROM nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        --extra-index-url https://download.pytorch.org/whl/cu117 && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        modelcards \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

docs/source/_toctree.yml

@@ -0,0 +1,122 @@
+- sections:
+  - local: index
+    title: "🧨 Diffusers"
+  - local: quicktour
+    title: "Quicktour"
+  - local: installation
+    title: "Installation"
+  title: "Get started"
+- sections:
+  - sections:
+    - local: using-diffusers/loading
+      title: "Loading Pipelines, Models, and Schedulers"
+    - local: using-diffusers/schedulers
+      title: "Using different Schedulers"
+    - local: using-diffusers/configuration
+      title: "Configuring Pipelines, Models, and Schedulers"
+    - local: using-diffusers/custom_pipeline_overview
+      title: "Loading and Adding Custom Pipelines"
+    title: "Loading & Hub"
+  - sections:
+    - local: using-diffusers/unconditional_image_generation
+      title: "Unconditional Image Generation"
+    - local: using-diffusers/conditional_image_generation
+      title: "Text-to-Image Generation"
+    - local: using-diffusers/img2img
+      title: "Text-Guided Image-to-Image"
+    - local: using-diffusers/inpaint
+      title: "Text-Guided Image-Inpainting"
+    - local: using-diffusers/custom_pipeline_examples
+      title: "Community Pipelines"
+    - local: using-diffusers/contribute_pipeline
+      title: "How to contribute a Pipeline"
+    title: "Pipelines for Inference"
+  - sections:
+    - local: using-diffusers/rl
+      title: "Reinforcement Learning"
+    - local: using-diffusers/audio
+      title: "Audio"
+    - local: using-diffusers/other-modalities
+      title: "Other Modalities"
+    title: "Taking Diffusers Beyond Images"
+  title: "Using Diffusers"
+- sections:
+  - local: optimization/fp16
+    title: "Memory and Speed"
+  - local: optimization/onnx
+    title: "ONNX"
+  - local: optimization/open_vino
+    title: "OpenVINO"
+  - local: optimization/mps
+    title: "MPS"
+  title: "Optimization/Special Hardware"
+- sections:
+  - local: training/overview
+    title: "Overview"
+  - local: training/unconditional_training
+    title: "Unconditional Image Generation"
+  - local: training/text_inversion
+    title: "Textual Inversion"
+  - local: training/dreambooth
+    title: "Dreambooth"
+  - local: training/text2image
+    title: "Text-to-image fine-tuning"
+  title: "Training"
+- sections:
+  - local: conceptual/stable_diffusion
+    title: "Stable Diffusion"
+  - local: conceptual/philosophy
+    title: "Philosophy"
+  - local: conceptual/contribution
+    title: "How to contribute?"
+  title: "Conceptual Guides"
+- sections:
+  - sections:
+    - local: api/models
+      title: "Models"
+    - local: api/schedulers
+      title: "Schedulers"
+    - local: api/diffusion_pipeline
+      title: "Diffusion Pipeline"
+    - local: api/logging
+      title: "Logging"
+    - local: api/configuration
+      title: "Configuration"
+    - local: api/outputs
+      title: "Outputs"
+    title: "Main Classes"
+  - sections:
+    - local: api/pipelines/overview
+      title: "Overview"
+    - local: api/pipelines/alt_diffusion
+      title: "AltDiffusion"
+    - local: api/pipelines/cycle_diffusion
+      title: "Cycle Diffusion"
+    - local: api/pipelines/ddim
+      title: "DDIM"
+    - local: api/pipelines/ddpm
+      title: "DDPM"
+    - local: api/pipelines/latent_diffusion
+      title: "Latent Diffusion"
+    - local: api/pipelines/latent_diffusion_uncond
+      title: "Unconditional Latent Diffusion"
+    - local: api/pipelines/pndm
+      title: "PNDM"
+    - local: api/pipelines/score_sde_ve
+      title: "Score SDE VE"
+    - local: api/pipelines/stable_diffusion
+      title: "Stable Diffusion"
+    - local: api/pipelines/stochastic_karras_ve
+      title: "Stochastic Karras VE"
+    - local: api/pipelines/dance_diffusion
+      title: "Dance Diffusion"
+    - local: api/pipelines/vq_diffusion
+      title: "VQ Diffusion"
+    - local: api/pipelines/repaint
+      title: "RePaint"
+    title: "Pipelines"
+  - sections:
+    - local: api/experimental/rl
+      title: "RL Planning"
+    title: "Experimental Features"
+  title: "API"

docs/source/api/configuration.mdx

@@ -0,0 +1,23 @@
+<!--Copyright 2022 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.
+-->
+
+# Configuration
+
+In Diffusers, schedulers of type [`schedulers.scheduling_utils.SchedulerMixin`], and models of type [`ModelMixin`] inherit from [`ConfigMixin`] which conveniently takes care of storing all parameters that are 
+passed to the respective `__init__` methods in a JSON-configuration file.
+
+## ConfigMixin
+
+[[autodoc]] ConfigMixin
+	- load_config
+	- from_config
+	- save_config

docs/source/api/diffusion_pipeline.mdx

@@ -0,0 +1,42 @@
+<!--Copyright 2022 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.
+-->
+
+# Pipelines
+
+The [`DiffusionPipeline`] is the easiest way to load any pretrained diffusion pipeline from the [Hub](https://huggingface.co/models?library=diffusers) and to use it in inference.
+
+<Tip>
+	
+	One should not use the Diffusion Pipeline class for training or fine-tuning a diffusion model. Individual 
+	components of diffusion pipelines are usually trained individually, so we suggest to directly work 
+	with [`UNetModel`] and [`UNetConditionModel`].
+
+</Tip>
+
+Any diffusion pipeline that is loaded with [`~DiffusionPipeline.from_pretrained`] will automatically 
+detect the pipeline type, *e.g.* [`StableDiffusionPipeline`] and consequently load each component of the 
+pipeline and pass them into the `__init__` function of the pipeline, *e.g.* [`~StableDiffusionPipeline.__init__`].
+
+Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrained`].
+
+## DiffusionPipeline
+[[autodoc]] DiffusionPipeline
+	- from_pretrained
+	- save_pretrained
+	- to
+	- device
+	- components
+
+## ImagePipelineOutput
+By default diffusion pipelines return an object of class
+
+[[autodoc]] pipeline_utils.ImagePipelineOutput

docs/source/api/experimental/rl.mdx

@@ -0,0 +1,15 @@
+<!--Copyright 2022 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.
+-->
+
+# TODO
+
+Coming soon!

docs/source/api/logging.mdx

@@ -0,0 +1,98 @@
+<!--Copyright 2020 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.
+-->
+
+# Logging
+
+🧨 Diffusers has a centralized logging system, so that you can setup the verbosity of the library easily.
+
+Currently the default verbosity of the library is `WARNING`.
+
+To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity
+to the INFO level.
+
+```python
+import diffusers
+
+diffusers.logging.set_verbosity_info()
+```
+
+You can also use the environment variable `DIFFUSERS_VERBOSITY` to override the default verbosity. You can set it
+to one of the following: `debug`, `info`, `warning`, `error`, `critical`. For example:
+
+```bash
+DIFFUSERS_VERBOSITY=error ./myprogram.py
+```
+
+Additionally, some `warnings` can be disabled by setting the environment variable
+`DIFFUSERS_NO_ADVISORY_WARNINGS` to a true value, like *1*. This will disable any warning that is logged using
+[`logger.warning_advice`]. For example:
+
+```bash
+DIFFUSERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py
+```
+
+Here is an example of how to use the same logger as the library in your own module or script:
+
+```python
+from diffusers.utils import logging
+
+logging.set_verbosity_info()
+logger = logging.get_logger("diffusers")
+logger.info("INFO")
+logger.warning("WARN")
+```
+
+
+All the methods of this logging module are documented below, the main ones are
+[`logging.get_verbosity`] to get the current level of verbosity in the logger and
+[`logging.set_verbosity`] to set the verbosity to the level of your choice. In order (from the least
+verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are:
+
+- `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL` (int value, 50): only report the most
+  critical errors.
+- `diffusers.logging.ERROR` (int value, 40): only report errors.
+- `diffusers.logging.WARNING` or `diffusers.logging.WARN` (int value, 30): only reports error and
+  warnings. This the default level used by the library.
+- `diffusers.logging.INFO` (int value, 20): reports error, warnings and basic information.
+- `diffusers.logging.DEBUG` (int value, 10): report all information.
+
+By default, `tqdm` progress bars will be displayed during model download. [`logging.disable_progress_bar`] and [`logging.enable_progress_bar`] can be used to suppress or unsuppress this behavior.
+
+## Base setters
+
+[[autodoc]] logging.set_verbosity_error
+
+[[autodoc]] logging.set_verbosity_warning
+
+[[autodoc]] logging.set_verbosity_info
+
+[[autodoc]] logging.set_verbosity_debug
+
+## Other functions
+
+[[autodoc]] logging.get_verbosity
+
+[[autodoc]] logging.set_verbosity
+
+[[autodoc]] logging.get_logger
+
+[[autodoc]] logging.enable_default_handler
+
+[[autodoc]] logging.disable_default_handler
+
+[[autodoc]] logging.enable_explicit_format
+
+[[autodoc]] logging.reset_format
+
+[[autodoc]] logging.enable_progress_bar
+
+[[autodoc]] logging.disable_progress_bar

docs/source/api/models.mdx

@@ -0,0 +1,77 @@
+<!--Copyright 2022 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.
+-->
+
+# Models
+
+Diffusers contains pretrained models for popular algorithms and modules for creating the next set of diffusion models.
+The primary function of these models is to denoise an input sample, by modeling the distribution $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$.
+The models are built on the base class ['ModelMixin'] that is a `torch.nn.module` with basic functionality for saving and loading models both locally and from the HuggingFace hub.
+
+## ModelMixin
+[[autodoc]] ModelMixin
+
+## UNet2DOutput
+[[autodoc]] models.unet_2d.UNet2DOutput
+
+## UNet2DModel
+[[autodoc]] UNet2DModel
+
+## UNet1DOutput
+[[autodoc]] models.unet_1d.UNet1DOutput
+
+## UNet1DModel
+[[autodoc]] UNet1DModel
+
+## UNet2DConditionOutput
+[[autodoc]] models.unet_2d_condition.UNet2DConditionOutput
+
+## UNet2DConditionModel
+[[autodoc]] UNet2DConditionModel
+
+## DecoderOutput
+[[autodoc]] models.vae.DecoderOutput
+
+## VQEncoderOutput
+[[autodoc]] models.vae.VQEncoderOutput
+
+## VQModel
+[[autodoc]] VQModel
+
+## AutoencoderKLOutput
+[[autodoc]] models.vae.AutoencoderKLOutput
+
+## AutoencoderKL
+[[autodoc]] AutoencoderKL
+
+## Transformer2DModel
+[[autodoc]] Transformer2DModel
+
+## Transformer2DModelOutput
+[[autodoc]] models.attention.Transformer2DModelOutput
+
+## FlaxModelMixin
+[[autodoc]] FlaxModelMixin
+
+## FlaxUNet2DConditionOutput
+[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionOutput
+
+## FlaxUNet2DConditionModel
+[[autodoc]] FlaxUNet2DConditionModel
+
+## FlaxDecoderOutput
+[[autodoc]] models.vae_flax.FlaxDecoderOutput
+
+## FlaxAutoencoderKLOutput
+[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
+
+## FlaxAutoencoderKL
+[[autodoc]] FlaxAutoencoderKL

docs/source/api/outputs.mdx

@@ -0,0 +1,55 @@
+<!--Copyright 2022 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.
+-->
+
+# BaseOutputs
+
+All models have outputs that are instances of subclasses of [`~utils.BaseOutput`]. Those are
+data structures containing all the information returned by the model, but that can also be used as tuples or
+dictionaries.
+
+Let's see how this looks in an example:
+
+```python
+from diffusers import DDIMPipeline
+
+pipeline = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
+outputs = pipeline()
+```
+
+The `outputs` object is a [`~pipeline_utils.ImagePipelineOutput`], as we can see in the
+documentation of that class below, it means it has an image attribute.
+
+You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you will get `None`:
+
+```python
+outputs.images
+```
+
+or via keyword lookup
+
+```python
+outputs["images"]
+```
+
+When considering our `outputs` object as tuple, it only considers the attributes that don't have `None` values.
+Here for instance, we could retrieve images via indexing:
+
+```python
+outputs[:1]
+```
+
+which will return the tuple `(outputs.images)` for instance.
+
+## BaseOutput
+
+[[autodoc]] utils.BaseOutput
+    - to_tuple

docs/source/api/pipelines/alt_diffusion.mdx

@@ -0,0 +1,83 @@
+<!--Copyright 2022 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.
+-->
+
+# AltDiffusion
+
+AltDiffusion was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu
+
+The abstract of the paper is the following:
+
+*In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.*
+
+
+*Overview*:
+
+| Pipeline | Tasks | Colab | Demo
+|---|---|:---:|:---:|
+| [pipeline_alt_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py) | *Text-to-Image Generation* | - | -
+| [pipeline_alt_diffusion_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | - |-
+
+## Tips
+
+- AltDiffusion is conceptually exaclty the same as [Stable Diffusion](./api/pipelines/stable_diffusion).
+
+- *Run AltDiffusion*
+
+AltDiffusion can be tested very easily with the [`AltDiffusionPipeline`], [`AltDiffusionImg2ImgPipeline`] and the `"BAAI/AltDiffusion"` checkpoint exactly in the same way it is shown in the [Conditional Image Generation Guide](./using-diffusers/conditional_image_generation) and the [Image-to-Image Generation Guide](./using-diffusers/img2img).
+
+- *How to load and use different schedulers.*
+
+The alt diffusion pipeline uses [`DDIMScheduler`] scheduler by default. But `diffusers` provides many other schedulers that can be used with the alt diffusion pipeline such as [`PNDMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`] etc.
+To use a different scheduler, you can either change it via the [`ConfigMixin.from_config`] method or pass the `scheduler` argument to the `from_pretrained` method of the pipeline. For example, to use the [`EulerDiscreteScheduler`], you can do the following:
+
+```python
+>>> from diffusers import AltDiffusionPipeline, EulerDiscreteScheduler
+
+>>> pipeline = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion")
+>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
+
+>>> # or
+>>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("BAAI/AltDiffusion", subfolder="scheduler")
+>>> pipeline = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion", scheduler=euler_scheduler)
+```
+
+
+- *How to conver all use cases with multiple or single pipeline*
+
+If you want to use all possible use cases in a single `DiffusionPipeline` we recommend using the `components` functionality to instantiate all components in the most memory-efficient way:
+
+```python
+>>> from diffusers import (
+...     AltDiffusionPipeline,
+...     AltDiffusionImg2ImgPipeline,
+... )
+
+>>> text2img = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion")
+>>> img2img = AltDiffusionImg2ImgPipeline(**text2img.components)
+
+>>> # now you can use text2img(...) and img2img(...) just like the call methods of each respective pipeline
+```
+
+## AltDiffusionPipelineOutput
+[[autodoc]] pipelines.alt_diffusion.AltDiffusionPipelineOutput
+
+## AltDiffusionPipeline
+[[autodoc]] AltDiffusionPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+
+## AltDiffusionImg2ImgPipeline
+[[autodoc]] AltDiffusionImg2ImgPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing

docs/source/api/pipelines/cycle_diffusion.mdx

@@ -0,0 +1,99 @@
+<!--Copyright 2022 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.
+-->
+
+# Cycle Diffusion
+
+## Overview
+
+Cycle Diffusion is a Text-Guided Image-to-Image Generation model proposed in [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://arxiv.org/abs/2210.05559) by Chen Henry Wu, Fernando De la Torre.
+
+The abstract of the paper is the following:
+
+*Diffusion models have achieved unprecedented performance in generative modeling. The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually denoised samples, as opposed to the simpler (e.g., Gaussian) latent space of GANs, VAEs, and normalizing flows. This paper provides an alternative, Gaussian formulation of the latent space of various diffusion models, as well as an invertible DPM-Encoder that maps images into the latent space. While our formulation is purely based on the definition of diffusion models, we demonstrate several intriguing consequences. (1) Empirically, we observe that a common latent space emerges from two diffusion models trained independently on related domains. In light of this finding, we propose CycleDiffusion, which uses DPM-Encoder for unpaired image-to-image translation. Furthermore, applying CycleDiffusion to text-to-image diffusion models, we show that large-scale text-to-image diffusion models can be used as zero-shot image-to-image editors. (2) One can guide pre-trained diffusion models and GANs by controlling the latent codes in a unified, plug-and-play formulation based on energy-based models. Using the CLIP model and a face recognition model as guidance, we demonstrate that diffusion models have better coverage of low-density sub-populations and individuals than GANs.*
+
+*Tips*:
+- The Cycle Diffusion pipeline is fully compatible with any [Stable Diffusion](./stable_diffusion) checkpoints
+- Currently Cycle Diffusion only works with the [`DDIMScheduler`].
+
+*Example*:
+
+In the following we should how to best use the [`CycleDiffusionPipeline`]
+
+```python
+import requests
+import torch
+from PIL import Image
+from io import BytesIO
+
+from diffusers import CycleDiffusionPipeline, DDIMScheduler
+
+# load the pipeline
+# make sure you're logged in with `huggingface-cli login`
+model_id_or_path = "CompVis/stable-diffusion-v1-4"
+scheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder="scheduler")
+pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to("cuda")
+
+# let's download an initial image
+url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png"
+response = requests.get(url)
+init_image = Image.open(BytesIO(response.content)).convert("RGB")
+init_image = init_image.resize((512, 512))
+init_image.save("horse.png")
+
+# let's specify a prompt
+source_prompt = "An astronaut riding a horse"
+prompt = "An astronaut riding an elephant"
+
+# call the pipeline
+image = pipe(
+    prompt=prompt,
+    source_prompt=source_prompt,
+    init_image=init_image,
+    num_inference_steps=100,
+    eta=0.1,
+    strength=0.8,
+    guidance_scale=2,
+    source_guidance_scale=1,
+).images[0]
+
+image.save("horse_to_elephant.png")
+
+# let's try another example
+# See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion
+url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png"
+response = requests.get(url)
+init_image = Image.open(BytesIO(response.content)).convert("RGB")
+init_image = init_image.resize((512, 512))
+init_image.save("black.png")
+
+source_prompt = "A black colored car"
+prompt = "A blue colored car"
+
+# call the pipeline
+torch.manual_seed(0)
+image = pipe(
+    prompt=prompt,
+    source_prompt=source_prompt,
+    init_image=init_image,
+    num_inference_steps=100,
+    eta=0.1,
+    strength=0.85,
+    guidance_scale=3,
+    source_guidance_scale=1,
+).images[0]
+
+image.save("black_to_blue.png")
+```
+
+## CycleDiffusionPipeline
+[[autodoc]] CycleDiffusionPipeline
+	- __call__

docs/source/api/pipelines/dance_diffusion.mdx

@@ -0,0 +1,33 @@
+<!--Copyright 2022 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.
+-->
+
+# Dance Diffusion
+
+## Overview
+
+[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) by Zach Evans.
+
+Dance Diffusion is the first in a suite of generative audio tools for producers and musicians to be released by Harmonai.
+For more info or to get involved in the development of these tools, please visit https://harmonai.org and fill out the form on the front page.
+
+The original codebase of this implementation can be found [here](https://github.com/Harmonai-org/sample-generator).
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_dance_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py) | *Unconditional Audio Generation* | - |
+
+
+## DanceDiffusionPipeline
+[[autodoc]] DanceDiffusionPipeline
+    - __call__

docs/source/api/pipelines/ddim.mdx

@@ -0,0 +1,35 @@
+<!--Copyright 2022 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.
+-->
+
+# DDIM
+
+## Overview
+
+[Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.
+
+The abstract of the paper is the following:
+
+Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.
+
+The original codebase of this paper can be found here: [ermongroup/ddim](https://github.com/ermongroup/ddim).
+For questions, feel free to contact the author on [tsong.me](https://tsong.me/).
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_ddim.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddim/pipeline_ddim.py) | *Unconditional Image Generation* | - |
+
+
+## DDIMPipeline
+[[autodoc]] DDIMPipeline
+    - __call__

docs/source/api/pipelines/ddpm.mdx

@@ -0,0 +1,36 @@
+<!--Copyright 2022 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.
+-->
+
+# DDPM
+
+## Overview
+
+[Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 
+ (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes the diffusion based model of the same name, but in the context of the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline.
+
+The abstract of the paper is the following:
+
+We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.
+
+The original codebase of this paper can be found [here](https://github.com/hojonathanho/diffusion).
+
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_ddpm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddpm/pipeline_ddpm.py) | *Unconditional Image Generation* | - |
+
+
+# DDPMPipeline
+[[autodoc]] DDPMPipeline
+    - __call__

docs/source/api/pipelines/latent_diffusion.mdx

@@ -0,0 +1,47 @@
+<!--Copyright 2022 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.
+-->
+
+# Latent Diffusion
+
+## Overview
+
+Latent Diffusion was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.
+
+The abstract of the paper is the following:
+
+*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.*
+
+The original codebase can be found [here](https://github.com/CompVis/latent-diffusion).
+
+## Tips:
+
+- 
+- 
+- 
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_latent_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py) | *Text-to-Image Generation* | - |
+| [pipeline_latent_diffusion_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py) | *Super Resolution* | - |
+
+## Examples:
+
+
+## LDMTextToImagePipeline
+[[autodoc]] LDMTextToImagePipeline
+    - __call__
+
+## LDMSuperResolutionPipeline
+[[autodoc]] LDMSuperResolutionPipeline
+    - __call__

docs/source/api/pipelines/latent_diffusion_uncond.mdx

@@ -0,0 +1,41 @@
+<!--Copyright 2022 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.
+-->
+
+# Unconditional Latent Diffusion
+
+## Overview
+
+Unconditional Latent Diffusion was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.
+
+The abstract of the paper is the following:
+
+*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.*
+
+The original codebase can be found [here](https://github.com/CompVis/latent-diffusion).
+
+## Tips:
+
+- 
+- 
+- 
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_latent_diffusion_uncond.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py) | *Unconditional Image Generation* | - |
+
+## Examples:
+
+## LDMPipeline
+[[autodoc]] LDMPipeline
+    - __call__

docs/source/api/pipelines/overview.mdx

@@ -0,0 +1,191 @@
+<!--Copyright 2022 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.
+-->
+
+# Pipelines
+
+Pipelines provide a simple way to run state-of-the-art diffusion models in inference.
+Most diffusion systems consist of multiple independently-trained models and highly adaptable scheduler 
+components - all of which are needed to have a functioning end-to-end diffusion system.
+
+As an example, [Stable Diffusion](https://huggingface.co/blog/stable_diffusion) has three independently trained models:
+- [Autoencoder](./api/models#vae)
+- [Conditional Unet](./api/models#UNet2DConditionModel)
+- [CLIP text encoder](https://huggingface.co/docs/transformers/v4.21.2/en/model_doc/clip#transformers.CLIPTextModel)
+- a scheduler component, [scheduler](./api/scheduler#pndm), 
+- a [CLIPFeatureExtractor](https://huggingface.co/docs/transformers/v4.21.2/en/model_doc/clip#transformers.CLIPFeatureExtractor),
+- as well as a [safety checker](./stable_diffusion#safety_checker).
+All of these components are necessary to run stable diffusion in inference even though they were trained 
+or created independently from each other.
+
+To that end, we strive to offer all open-sourced, state-of-the-art diffusion system under a unified API. 
+More specifically, we strive to provide pipelines that
+- 1. can load the officially published weights and yield 1-to-1 the same outputs as the original implementation according to the corresponding paper (*e.g.* [LDMTextToImagePipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_diffusion), uses the officially released weights of [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)),
+- 2. have a simple user interface to run the model in inference (see the [Pipelines API](#pipelines-api) section), 
+- 3. are easy to understand with code that is self-explanatory and can be read along-side the official paper (see [Pipelines summary](#pipelines-summary)),
+- 4. can easily be contributed by the community (see the [Contribution](#contribution) section).
+
+**Note** that pipelines do not (and should not) offer any training functionality. 
+If you are looking for *official* training examples, please have a look at [examples](https://github.com/huggingface/diffusers/tree/main/examples).
+
+## 🧨 Diffusers Summary
+
+The following table summarizes all officially supported pipelines, their corresponding paper, and if 
+available a colab notebook to directly try them out.
+
+
+| Pipeline | Paper | Tasks | Colab
+|---|---|:---:|:---:|
+| [alt_diffusion](./api/pipelines/alt_diffusion) | [**AltDiffusion**](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation | -
+| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [**Cycle Diffusion**](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
+| [dance_diffusion](./api/pipelines/dance_diffusion) | [**Dance Diffusion**](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
+| [ddpm](./api/pipelines/ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
+| [ddim](./api/pipelines/ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
+| [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation | 
+| [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image | 
+| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation | 
+| [pndm](./api/pipelines/pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation | 
+| [score_sde_ve](./api/pipelines/score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
+| [score_sde_vp](./api/pipelines/score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
+| [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
+| [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
+| [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
+| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
+| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation | 
+
+
+**Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. 
+
+However, most of them can be adapted to use different scheduler components or even different model components. Some pipeline examples are shown in the [Examples](#examples) below.
+
+## Pipelines API
+
+Diffusion models often consist of multiple independently-trained models or other previously existing components. 
+
+
+Each model has been trained independently on a different task and the scheduler can easily be swapped out and replaced with a different one. 
+During inference, we however want to be able to easily load all components and use them in inference - even if one component, *e.g.* CLIP's text encoder, originates from a different library, such as [Transformers](https://github.com/huggingface/transformers). To that end, all pipelines provide the following functionality:
+
+- [`from_pretrained` method](../diffusion_pipeline) that accepts a Hugging Face Hub repository id, *e.g.* [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) or a path to a local directory, *e.g.*
+"./stable-diffusion". To correctly retrieve which models and components should be loaded, one has to provide a `model_index.json` file, *e.g.* [runwayml/stable-diffusion-v1-5/model_index.json](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), which defines all components that should be
+loaded into the pipelines. More specifically, for each model/component one needs to define the format `<name>: ["<library>", "<class name>"]`. `<name>` is the attribute name given to the loaded instance of `<class name>` which can be found in the library or pipeline folder called `"<library>"`.
+- [`save_pretrained`](../diffusion_pipeline) that accepts a local path, *e.g.* `./stable-diffusion` under which all models/components of the pipeline will be saved. For each component/model a folder is created inside the local path that is named after the given attribute name, *e.g.* `./stable_diffusion/unet`. 
+In addition, a `model_index.json` file is created at the root of the local path, *e.g.* `./stable_diffusion/model_index.json` so that the complete pipeline can again be instantiated 
+from the local path.
+- [`to`](../diffusion_pipeline) which accepts a `string` or `torch.device` to move all models that are of type `torch.nn.Module` to the passed device. The behavior is fully analogous to [PyTorch's `to` method](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.to).
+- [`__call__`] method to use the pipeline in inference. `__call__` defines inference logic of the pipeline and should ideally encompass all aspects of it, from pre-processing to forwarding tensors to the different models and schedulers, as well as post-processing. The API of the `__call__` method can strongly vary from pipeline to pipeline. *E.g.* a text-to-image pipeline, such as [`StableDiffusionPipeline`](./stable_diffusion) should accept among other things the text prompt to generate the image. A pure image generation pipeline, such as [DDPMPipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/ddpm) on the other hand can be run without providing any inputs. To better understand what inputs can be adapted for 
+each pipeline, one should look directly into the respective pipeline.
+
+**Note**: All pipelines have PyTorch's autograd disabled by decorating the `__call__` method with a [`torch.no_grad`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should
+not be used for training. If you want to store the gradients during the forward pass, we recommend writing your own pipeline, see also our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/community)
+
+## Contribution
+
+We are more than happy about any contribution to the officially supported pipelines 🤗. We aspire
+all of our pipelines to be  **self-contained**, **easy-to-tweak**, **beginner-friendly** and for **one-purpose-only**.
+
+- **Self-contained**: A pipeline shall be as self-contained as possible. More specifically, this means that all functionality should be either directly defined in the pipeline file itself, should be inherited from (and only from) the [`DiffusionPipeline` class](.../diffusion_pipeline) or be directly attached to the model and scheduler components of the pipeline. 
+- **Easy-to-use**: Pipelines should be extremely easy to use - one should be able to load the pipeline and 
+use it for its designated task, *e.g.* text-to-image generation, in just a couple of lines of code. Most 
+logic including pre-processing, an unrolled diffusion loop, and post-processing should all happen inside the `__call__` method.
+- **Easy-to-tweak**: Certain pipelines will not be able to handle all use cases and tasks that you might like them to. If you want to use a certain pipeline for a specific use case that is not yet supported, you might have to copy the pipeline file and tweak the code to your needs. We try to make the pipeline code as readable as possible so that each part –from pre-processing to diffusing to post-processing– can easily be adapted. If you would like the community to benefit from your customized pipeline, we would love to see a contribution to our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/community). If you feel that an important pipeline should be part of the official pipelines but isn't, a contribution to the [official pipelines](./overview) would be even better.
+- **One-purpose-only**: Pipelines should be used for one task and one task only. Even if two tasks are very similar from a modeling point of view, *e.g.* image2image translation and in-painting, pipelines shall be used for one task only to keep them *easy-to-tweak* and *readable*.
+
+## Examples
+
+### Text-to-Image generation with Stable Diffusion
+
+```python
+# make sure you're logged in with `huggingface-cli login`
+from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
+
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]
+
+image.save("astronaut_rides_horse.png")
+```
+
+### Image-to-Image text-guided generation with Stable Diffusion
+
+The `StableDiffusionImg2ImgPipeline` lets you pass a text prompt and an initial image to condition the generation of new images.
+
+```python
+import requests
+from PIL import Image
+from io import BytesIO
+
+from diffusers import StableDiffusionImg2ImgPipeline
+
+# load the pipeline
+device = "cuda"
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16
+).to(device)
+
+# let's download an initial image
+url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+
+response = requests.get(url)
+init_image = Image.open(BytesIO(response.content)).convert("RGB")
+init_image = init_image.resize((768, 512))
+
+prompt = "A fantasy landscape, trending on artstation"
+
+images = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5).images
+
+images[0].save("fantasy_landscape.png")
+```
+You can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
+
+### Tweak prompts reusing seeds and latents
+
+You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. [This notebook](https://github.com/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) shows how to do it step by step. You can also run it in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb).
+
+
+### In-painting using Stable Diffusion
+
+The `StableDiffusionInpaintPipeline` lets you edit specific parts of an image by providing a mask and text prompt.
+
+```python
+import PIL
+import requests
+import torch
+from io import BytesIO
+
+from diffusers import StableDiffusionInpaintPipeline
+
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+
+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 = download_image(img_url).resize((512, 512))
+mask_image = download_image(mask_url).resize((512, 512))
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    revision="fp16",
+    torch_dtype=torch.float16,
+)
+pipe = pipe.to("cuda")
+
+prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
+```
+
+You can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)

docs/source/api/pipelines/pndm.mdx

@@ -0,0 +1,35 @@
+<!--Copyright 2022 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.
+-->
+
+# PNDM
+
+## Overview
+
+[Pseudo Numerical methods for Diffusion Models on manifolds](https://arxiv.org/abs/2202.09778) (PNDM) by  Luping Liu, Yi Ren, Zhijie Lin and Zhou Zhao.
+
+The abstract of the paper is the following:
+
+Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoising Diffusion Probabilistic Models) or the denoising equation (e.g., Denoising Diffusion Implicit Models (DDIMs)). However, these acceleration methods cannot maintain the quality of samples and even introduce new noise at a high speedup rate, which limit their practicability. To accelerate the inference process while keeping the sample quality, we provide a fresh perspective that DDPMs should be treated as solving differential equations on manifolds. Under such a perspective, we propose pseudo numerical methods for diffusion models (PNDMs). Specifically, we figure out how to solve differential equations on manifolds and show that DDIMs are simple cases of pseudo numerical methods. We change several classical numerical methods to corresponding pseudo numerical methods and find that the pseudo linear multi-step method is the best in most situations. According to our experiments, by directly using pre-trained models on Cifar10, CelebA and LSUN, PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup), significantly outperform DDIMs with 250 steps (by around 0.4 in FID) and have good generalization on different variance schedules. 
+
+The original codebase can be found [here](https://github.com/luping-liu/PNDM).
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_pndm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pndm/pipeline_pndm.py) | *Unconditional Image Generation* | - |
+
+
+## PNDMPipeline
+[[autodoc]] pipelines.pndm.pipeline_pndm.PNDMPipeline
+    - __call__
+

docs/source/api/pipelines/repaint.mdx

@@ -0,0 +1,77 @@
+<!--Copyright 2022 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.
+-->
+
+# RePaint
+
+## Overview
+
+[RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2201.09865) (PNDM) by Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc Van Gool.
+
+The abstract of the paper is the following:
+
+Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks.
+RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions.
+
+The original codebase can be found [here](https://github.com/andreas128/RePaint).
+
+## Available Pipelines:
+
+| Pipeline                                                                                                                      | Tasks              | Colab
+|-------------------------------------------------------------------------------------------------------------------------------|--------------------|:---:|
+| [pipeline_repaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/repaint/pipeline_repaint.py) | *Image Inpainting* | - |
+
+## Usage example
+
+```python
+from io import BytesIO
+
+import torch
+
+import PIL
+import requests
+from diffusers import RePaintPipeline, RePaintScheduler
+
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+
+img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
+mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
+
+# Load the original image and the mask as PIL images
+original_image = download_image(img_url).resize((256, 256))
+mask_image = download_image(mask_url).resize((256, 256))
+
+# Load the RePaint scheduler and pipeline based on a pretrained DDPM model
+scheduler = RePaintScheduler.from_pretrained("google/ddpm-ema-celebahq-256")
+pipe = RePaintPipeline.from_pretrained("google/ddpm-ema-celebahq-256", scheduler=scheduler)
+pipe = pipe.to("cuda")
+
+generator = torch.Generator(device="cuda").manual_seed(0)
+output = pipe(
+    original_image=original_image,
+    mask_image=mask_image,
+    num_inference_steps=250,
+    eta=0.0,
+    jump_length=10,
+    jump_n_sample=10,
+    generator=generator,
+)
+inpainted_image = output.images[0]
+```
+
+## RePaintPipeline
+[[autodoc]] pipelines.repaint.pipeline_repaint.RePaintPipeline
+    - __call__
+

docs/source/api/pipelines/score_sde_ve.mdx

@@ -0,0 +1,36 @@
+<!--Copyright 2022 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.
+-->
+
+# Score SDE VE
+
+## Overview
+
+[Score-Based Generative Modeling through Stochastic Differential Equations](https://arxiv.org/abs/2011.13456) (Score SDE) by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon and Ben Poole.
+
+The abstract of the paper is the following:
+
+Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.
+
+The original codebase can be found [here](https://github.com/yang-song/score_sde_pytorch).
+
+This pipeline implements the Variance Expanding (VE) variant of the method.
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_score_sde_ve.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py) | *Unconditional Image Generation* | - |
+
+## ScoreSdeVePipeline
+[[autodoc]] ScoreSdeVePipeline
+    - __call__
+

docs/source/api/pipelines/stable_diffusion.mdx

@@ -0,0 +1,90 @@
+<!--Copyright 2022 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 pipelines
+
+Stable Diffusion is a text-to-image _latent diffusion_ model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs.
+
+Latent diffusion is the research on top of which Stable Diffusion was built. It was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. You can learn more details about it in the [specific pipeline for latent diffusion](pipelines/latent_diffusion) that is part of 🤗 Diffusers.
+
+For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, please refer to the official [launch announcement post](https://stability.ai/blog/stable-diffusion-announcement) and [this section of our own blog post](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work).
+
+*Tips*:
+- To tweak your prompts on a specific result you liked, you can generate your own latents, as demonstrated in the following notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb)
+
+*Overview*:
+
+| Pipeline | Tasks | Colab | Demo
+|---|---|:---:|:---:|
+| [pipeline_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) | [🤗 Stable Diffusion](https://huggingface.co/spaces/stabilityai/stable-diffusion)
+| [pipeline_stable_diffusion_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) | [🤗 Diffuse the Rest](https://huggingface.co/spaces/huggingface/diffuse-the-rest)
+| [pipeline_stable_diffusion_inpaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | **Experimental** – *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) | Coming soon
+
+## Tips
+
+### How to load and use different schedulers.
+
+The stable diffusion pipeline uses [`PNDMScheduler`] scheduler by default. But `diffusers` provides many other schedulers that can be used with the stable diffusion pipeline such as [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`] etc.
+To use a different scheduler, you can either change it via the [`ConfigMixin.from_config`] method or pass the `scheduler` argument to the `from_pretrained` method of the pipeline. For example, to use the [`EulerDiscreteScheduler`], you can do the following:
+
+```python
+>>> from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+
+>>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
+
+>>> # or
+>>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
+>>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=euler_scheduler)
+```
+
+
+### How to conver all use cases with multiple or single pipeline
+
+If you want to use all possible use cases in a single `DiffusionPipeline` you can either:
+- Make use of the [Stable Diffusion Mega Pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#stable-diffusion-mega) or 
+- Make use of the `components` functionality to instantiate all components in the most memory-efficient way:
+
+```python
+>>> from diffusers import (
+...     StableDiffusionPipeline,
+...     StableDiffusionImg2ImgPipeline,
+...     StableDiffusionInpaintPipeline,
+... )
+
+>>> text2img = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+>>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
+>>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)
+
+>>> # now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline
+```
+
+## StableDiffusionPipelineOutput
+[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
+
+## StableDiffusionPipeline
+[[autodoc]] StableDiffusionPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+
+## StableDiffusionImg2ImgPipeline
+[[autodoc]] StableDiffusionImg2ImgPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+
+## StableDiffusionInpaintPipeline
+[[autodoc]] StableDiffusionInpaintPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing

docs/source/api/pipelines/stochastic_karras_ve.mdx

@@ -0,0 +1,35 @@
+<!--Copyright 2022 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.
+-->
+
+# Stochastic Karras VE
+
+## Overview
+
+[Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) by Tero Karras, Miika Aittala, Timo Aila and Samuli Laine.
+
+The abstract of the paper is the following:
+
+We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices. This lets us identify several changes to both the sampling and training processes, as well as preconditioning of the score networks. Together, our improvements yield new state-of-the-art FID of 1.79 for CIFAR-10 in a class-conditional setting and 1.97 in an unconditional setting, with much faster sampling (35 network evaluations per image) than prior designs. To further demonstrate their modular nature, we show that our design changes dramatically improve both the efficiency and quality obtainable with pre-trained score networks from previous work, including improving the FID of an existing ImageNet-64 model from 2.07 to near-SOTA 1.55.
+
+This pipeline implements the Stochastic sampling tailored to the Variance-Expanding (VE) models.
+
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_stochastic_karras_ve.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py) | *Unconditional Image Generation* | - |
+
+
+## KarrasVePipeline
+[[autodoc]] KarrasVePipeline
+    - __call__

docs/source/api/pipelines/vq_diffusion.mdx

@@ -0,0 +1,34 @@
+<!--Copyright 2022 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.
+-->
+
+# VQDiffusion
+
+## Overview
+
+[Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) by Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo
+
+The abstract of the paper is the following:
+
+We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.
+
+The original codebase can be found [here](https://github.com/microsoft/VQ-Diffusion).
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_vq_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py) | *Text-to-Image Generation* | - |
+
+
+## VQDiffusionPipeline
+[[autodoc]] pipelines.vq_diffusion.pipeline_vq_diffusion.VQDiffusionPipeline
+    - __call__