[img2img, inpainting] fix fp16 inference (#769)

* handle dtype in vae and image2image pipeline

* fix inpaint in fp16

* dtype should be handled in add_noise

* style

* address review comments

* add simple fast tests to check fp16

* fix test name

* put mask in fp16

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -5,20 +5,7 @@ body:
   - type: markdown
     attributes:
       value: |
-        Thanks a lot for taking the time to file this issue 🤗.
-        Issues do not only help to improve the library, but also publicly document common problems, questions, workflows for the whole community!
-        Thus, issues are of the same importance as pull requests when contributing to this library ❤️.
-        In order to make your issue as **useful for the community as possible**, let's try to stick to some simple guidelines:
-        - 1. Please try to be as precise and concise as possible.
-             *Give your issue a fitting title. Assume that someone which very limited knowledge of diffusers can understand your issue. Add links to the source code, documentation other issues, pull requests etc...*
-        - 2. If your issue is about something not working, **always** provide a reproducible code snippet. The reader should be able to reproduce your issue by **only copy-pasting your code snippet into a Python shell**.
-             *The community cannot solve your issue if it cannot reproduce it. If your bug is related to training, add your training script and make everything needed to train public. Otherwise, just add a simple Python code snippet.*
-        - 3. Add the **minimum amount of code / context that is needed to understand, reproduce your issue**.
-             *Make the life of maintainers easy. `diffusers` is getting many issues every day. Make sure your issue is about one bug and one bug only. Make sure you add only the context, code needed to understand your issues - nothing more. Generally, every issue is a way of documenting this library, try to make it a good documentation entry.*
-  - type: markdown
-    attributes:
-      value: |
-        For more in-detail information on how to write good issues you can have a look [here](https://huggingface.co/course/chapter8/5?fw=pt)
+        Thanks for taking the time to fill out this bug report!
   - type: textarea
     id: bug-description
     attributes:
@@ -33,8 +20,6 @@ body:
       label: Reproduction
       description: Please provide a minimal reproducible code which we can copy/paste and reproduce the issue.
       placeholder: Reproduction
-    validations:
-      required: true
   - type: textarea
     id: logs
     attributes:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,7 +1,7 @@
 contact_links:
+  - name: Forum
+    url: https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63
+    about: General usage questions and community discussions
   - name: Blank issue
     url: https://github.com/huggingface/diffusers/issues/new
-    about: Other
-  - name: Forum
-    url: https://discuss.huggingface.co/
-    about: General usage questions and community discussions
+    about: Please note that the Forum is in most places the right place for discussions

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

@@ -1,146 +0,0 @@
-name: Set up conda environment for testing
-
-description: Sets up miniconda in your ${RUNNER_TEMP} environment and gives you the ${CONDA_RUN} environment variable so you don't have to worry about polluting non-empeheral runners anymore
-
-inputs:
-  python-version:
-    description: If set to any value, dont use sudo to clean the workspace
-    required: false
-    type: string
-    default: "3.9"
-  miniconda-version:
-    description: Miniconda version to install
-    required: false
-    type: string
-    default: "4.12.0"
-  environment-file:
-    description: Environment file to install dependencies from
-    required: false
-    type: string
-    default: ""
-
-runs:
-  using: composite
-  steps:
-      # Use the same trick from https://github.com/marketplace/actions/setup-miniconda
-      # to refresh the cache daily. This is kind of optional though
-      - name: Get date
-        id: get-date
-        shell: bash
-        run: echo "today=$(/bin/date -u '+%Y%m%d')d" >> $GITHUB_OUTPUT
-      - 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

@@ -1,50 +0,0 @@
-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

@@ -5,7 +5,7 @@ on:
     branches:
       - main
       - doc-builder*
-      - v*-patch
+      - v*-release
 
 jobs:
    build:
@@ -13,7 +13,5 @@ jobs:
     with:
       commit_sha: ${{ github.sha }}
       package: diffusers
-      notebook_folder: diffusers_doc
-      languages: en ko
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}

.github/workflows/build_pr_documentation.yml

@@ -14,4 +14,3 @@ jobs:
       commit_sha: ${{ github.event.pull_request.head.sha }}
       pr_number: ${{ github.event.number }}
       package: diffusers
-      languages: en ko

.github/workflows/nightly_tests.yml

@@ -1,162 +0,0 @@
-name: Nightly tests on main
-
-on:
-  schedule:
-    - cron: "0 0 * * *" # every day at midnight
-
-env:
-  DIFFUSERS_IS_CI: yes
-  HF_HOME: /mnt/cache
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  PYTEST_TIMEOUT: 600
-  RUN_SLOW: yes
-  RUN_NIGHTLY: yes
-
-jobs:
-  run_nightly_tests:
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - name: Nightly PyTorch CUDA tests on Ubuntu
-            framework: pytorch
-            runner: docker-gpu
-            image: diffusers/diffusers-pytorch-cuda
-            report: torch_cuda
-          - name: Nightly Flax TPU tests on Ubuntu
-            framework: flax
-            runner: docker-tpu
-            image: diffusers/diffusers-flax-tpu
-            report: flax_tpu
-          - name: Nightly 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 -U git+https://github.com/huggingface/transformers
-          python -m pip install git+https://github.com/huggingface/accelerate
-
-      - name: Environment
-        run: |
-          python utils/print_env.py
-
-      - name: Run nightly 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 nightly 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 nightly 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_nightly_tests_apple_m1:
-    name: Nightly 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 torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/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 nightly PyTorch tests on M1 (MPS)
-        shell: arch -arch arm64 bash {0}
-        env:
-          HF_HOME: /System/Volumes/Data/mnt/cache
-          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
-        run: |
-          ${CONDA_RUN} python -m pytest -n 1 -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: torch_mps_test_reports
-          path: reports

.github/workflows/pr_quality.yml

@@ -27,24 +27,7 @@ jobs:
           pip install .[quality]
       - name: Check quality
         run: |
-          black --check examples tests src utils scripts
-          ruff examples tests src utils scripts
+          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
-          make deps_table_check_updated

.github/workflows/pr_tests.yml

@@ -1,4 +1,4 @@
-name: Fast tests for PRs
+name: Run non-slow tests
 
 on:
   pull_request:
@@ -10,50 +10,19 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  DIFFUSERS_IS_CI: yes
-  OMP_NUM_THREADS: 4
-  MKL_NUM_THREADS: 4
+  HF_HOME: /mnt/cache
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 60
 
 jobs:
-  run_fast_tests:
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - name: Fast PyTorch Pipeline CPU tests
-            framework: pytorch_pipelines
-            runner: docker-cpu
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_cpu_pipelines
-          - name: Fast PyTorch Models & Schedulers CPU tests
-            framework: pytorch_models
-            runner: docker-cpu
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_cpu_models_schedulers
-          - name: Fast Flax CPU tests
-            framework: flax
-            runner: docker-cpu
-            image: diffusers/diffusers-flax-cpu
-            report: flax_cpu
-          - name: PyTorch Example CPU tests
-            framework: pytorch_examples
-            runner: docker-cpu
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_example_cpu
-
-    name: ${{ matrix.config.name }}
-
-    runs-on: ${{ matrix.config.runner }}
-
+  run_tests_cpu:
+    name: Diffusers tests
+    runs-on: [ self-hosted, docker-gpu ]
     container:
-      image: ${{ matrix.config.image }}
+      image: python:3.7
       options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
 
-    defaults:
-      run:
-        shell: bash
-
     steps:
     - name: Checkout diffusers
       uses: actions/checkout@v3
@@ -62,51 +31,25 @@ jobs:
 
     - name: Install dependencies
       run: |
-        apt-get update && apt-get install libsndfile1-dev -y
+        python -m pip install --upgrade pip
+        python -m pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
         python -m pip install -e .[quality,test]
 
     - name: Environment
       run: |
         python utils/print_env.py
 
-    - name: Run fast PyTorch Pipeline CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
+    - name: Run all non-slow selected tests on CPU
       run: |
-        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/pipelines
-
-    - name: Run fast PyTorch Model Scheduler CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_models' }}
-      run: |
-        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/models tests/schedulers tests/others
-
-    - 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 example PyTorch CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_examples' }}
-      run: |
-        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
-          --make-reports=tests_${{ matrix.config.report }} \
-          examples/test_examples.py 
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=tests_torch_cpu tests/
 
     - name: Failure short reports
       if: ${{ failure() }}
-      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+      run: cat reports/tests_torch_cpu_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
       uses: actions/upload-artifact@v2
       with:
-        name: pr_${{ matrix.config.report }}_test_reports
+        name: pr_torch_test_reports
         path: reports

.github/workflows/push_tests.yml

@@ -1,4 +1,4 @@
-name: Slow tests on main
+name: Run all tests
 
 on:
   push:
@@ -6,46 +6,19 @@ on:
       - main
 
 env:
-  DIFFUSERS_IS_CI: yes
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  PYTEST_TIMEOUT: 600
+  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 }}
-
+  run_tests_single_gpu:
+    name: Diffusers tests
+    runs-on: [ self-hosted, docker-gpu, single-gpu ]
     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
+      image: nvcr.io/nvidia/pytorch:22.07-py3
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache
 
     steps:
     - name: Checkout diffusers
@@ -54,70 +27,45 @@ jobs:
         fetch-depth: 2
 
     - name: NVIDIA-SMI
-      if : ${{ matrix.config.runner == 'docker-gpu' }}
       run: |
         nvidia-smi
 
     - name: Install dependencies
       run: |
+        python -m pip install --upgrade pip
+        python -m pip uninstall -y torch torchvision torchtext
+        python -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu116
         python -m pip install -e .[quality,test]
 
     - name: Environment
       run: |
         python utils/print_env.py
 
-    - name: Run slow PyTorch CUDA tests
-      if: ${{ matrix.config.framework == 'pytorch' }}
-      env:
-        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
-        # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
-        CUBLAS_WORKSPACE_CONFIG: :16:8 
-
-      run: |
-        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/
-
-    - name: Run slow Flax TPU tests
-      if: ${{ matrix.config.framework == 'flax' }}
+    - name: Run all (incl. slow) tests on GPU
       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/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=tests_torch_gpu tests/
 
     - name: Failure short reports
       if: ${{ failure() }}
-      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+      run: cat reports/tests_torch_gpu_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}
       uses: actions/upload-artifact@v2
       with:
-        name: ${{ matrix.config.report }}_test_reports
+        name: torch_test_reports
         path: reports
 
-  run_examples_tests:
-    name: Examples PyTorch CUDA tests on Ubuntu
 
-    runs-on: docker-gpu
 
+  run_examples_single_gpu:
+    name: Examples tests
+    runs-on: [ self-hosted, docker-gpu, single-gpu ]
     container:
-      image: diffusers/diffusers-pytorch-cuda
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+      image: nvcr.io/nvidia/pytorch:22.07-py3
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache
 
     steps:
     - name: Checkout diffusers
@@ -131,6 +79,9 @@ jobs:
 
     - name: Install dependencies
       run: |
+        python -m pip install --upgrade pip
+        python -m pip uninstall -y torch torchvision torchtext
+        python -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu116
         python -m pip install -e .[quality,test,training]
 
     - name: Environment
@@ -141,11 +92,11 @@ jobs:
       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/
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_gpu examples/
 
     - name: Failure short reports
       if: ${{ failure() }}
-      run: cat reports/examples_torch_cuda_failures_short.txt
+      run: cat reports/examples_torch_gpu_failures_short.txt
 
     - name: Test suite reports artifacts
       if: ${{ always() }}

.github/workflows/push_tests_fast.yml

@@ -1,110 +0,0 @@
-name: Fast tests on main
-
-on:
-  push:
-    branches:
-      - main
-
-env:
-  DIFFUSERS_IS_CI: yes
-  HF_HOME: /mnt/cache
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  PYTEST_TIMEOUT: 600
-  RUN_SLOW: no
-
-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: PyTorch Example CPU tests on Ubuntu
-            framework: pytorch_examples
-            runner: docker-cpu
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_example_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: |
-        apt-get update && apt-get install libsndfile1-dev -y
-        python -m pip install -e .[quality,test]
-
-    - 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: Run example PyTorch CPU tests
-      if: ${{ matrix.config.framework == 'pytorch_examples' }}
-      run: |
-        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
-          --make-reports=tests_${{ matrix.config.report }} \
-          examples/test_examples.py 
-
-    - 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

.github/workflows/push_tests_mps.yml

@@ -1,68 +0,0 @@
-name: Fast mps tests on main
-
-on:
-  push:
-    branches:
-      - main
-
-env:
-  DIFFUSERS_IS_CI: yes
-  HF_HOME: /mnt/cache
-  OMP_NUM_THREADS: 8
-  MKL_NUM_THREADS: 8
-  PYTEST_TIMEOUT: 600
-  RUN_SLOW: no
-
-jobs:
-  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 torch torchvision torchaudio
-        ${CONDA_RUN} python -m pip install accelerate --upgrade
-        ${CONDA_RUN} python -m pip install transformers --upgrade
-
-    - 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}
-      env:
-        HF_HOME: /System/Volumes/Data/mnt/cache
-        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
-      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

.gitignore

@@ -163,14 +163,4 @@ tags
 *.lock
 
 # DS_Store (MacOS)
-.DS_Store
-# RL pipelines may produce mp4 outputs
-*.mp4
-
-# dependencies
-/transformers
-
-# ruff
-.ruff_cache
-
-wandb
+.DS_Store

CITATION.cff

@@ -1,40 +0,0 @@
-cff-version: 1.2.0
-title: 'Diffusers: State-of-the-art diffusion models'
-message: >-
-  If you use this software, please cite it using the
-  metadata from this file.
-type: software
-authors:
-  - given-names: Patrick
-    family-names: von Platen
-  - given-names: Suraj
-    family-names: Patil
-  - given-names: Anton
-    family-names: Lozhkov
-  - given-names: Pedro
-    family-names: Cuenca
-  - given-names: Nathan
-    family-names: Lambert
-  - given-names: Kashif
-    family-names: Rasul
-  - given-names: Mishig
-    family-names: Davaadorj
-  - given-names: Thomas
-    family-names: Wolf
-repository-code: 'https://github.com/huggingface/diffusers'
-abstract: >-
-  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.
-keywords:
-  - deep-learning
-  - pytorch
-  - image-generation
-  - diffusion
-  - text2image
-  - image2image
-  - score-based-generative-modeling
-  - stable-diffusion
-license: Apache-2.0
-version: 0.12.1

CODE_OF_CONDUCT.md

@@ -24,7 +24,7 @@ community include:
 * 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 diffusers community
+  overall community
 
 Examples of unacceptable behavior include:
 
@@ -34,7 +34,6 @@ Examples of unacceptable behavior include:
 * Public or private harassment
 * Publishing others' private information, such as a physical or email
   address, without their explicit permission
-* Spamming issues or PRs with links to projects unrelated to this library
 * Other conduct which could reasonably be considered inappropriate in a
   professional setting
 

CONTRIBUTING.md

@@ -1,350 +1,94 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!---
+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
+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
+    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.
+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 🧨
+# How to contribute to diffusers?
 
-We ❤️ contributions from the open-source community! Everyone is welcome, and all types of participation –not just code– are valued and appreciated. Answering questions, helping others, reaching out, and improving the documentation are all immensely valuable to the community, so don't be afraid and get involved if you're up for it!
+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.
 
-Everyone is encouraged to start by saying 👋 in our public Discord channel. We discuss the latest trends in diffusion models, ask questions, show off personal projects, help each other with contributions, or just hang out ☕. <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>
+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, we strive to be part of an open, welcoming, and kind community. Please, read our [code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md) and be mindful to respect it during your interactions. We also recommend you become familiar with the [ethical guidelines](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines) that guide our project and ask you to adhere to the same principles of transparency and responsibility.
+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).
 
-We enormously value feedback from the community, so please do not be afraid to speak up if you believe you have valuable feedback that can help improve the library - every message, comment, issue, and pull request (PR) is read and considered.
+## You can contribute in so many ways!
 
-## Overview
+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.
 
-You can contribute in many ways ranging from answering questions on issues to adding new diffusion models to
-the core library.
+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.
 
-In the following, we give an overview of different ways to contribute, ranked by difficulty in ascending order. All of them are valuable to the community.
+*All are equally valuable to the community.*
 
-* 1. Asking and answering questions on [the Diffusers discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers) or on [Discord](https://discord.gg/G7tWnz98XR).
-* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose)
-* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues)
-* 4. Fix a simple issue, marked by the "Good first issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).
-* 5. Contribute to the [documentation](https://github.com/huggingface/diffusers/tree/main/docs/source).
-* 6. Contribute a [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples)
-* 7. Contribute to the [examples](https://github.com/huggingface/diffusers/tree/main/examples).
-* 8. Fix a more difficult issue, marked by the "Good second issue" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22).
-* 9. Add a new pipeline, model, or scheduler, see ["New Pipeline/Model"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) and ["New scheduler"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) issues. For this contribution, please have a look at [Design Philosophy](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md).
+## Submitting a new issue or feature request
 
-As said before, **all contributions are valuable to the community**.
-In the following, we will explain each contribution a bit more in detail.
+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.
 
-For all contributions 4.-9. you will need to open a PR. It is explained in detail how to do so in [Opening a pull requst](#how-to-open-a-pr)
-
-### 1. Asking and answering questions on the Diffusers discussion forum or on the Diffusers Discord
-
-Any question or comment related to the Diffusers library can be asked on the [discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/) or on [Discord](https://discord.gg/G7tWnz98XR). Such questions and comments include (but are not limited to):
-- Reports of training or inference experiments in an attempt to share knowledge
-- Presentation of personal projects
-- Questions to non-official training examples
-- Project proposals
-- General feedback
-- Paper summaries
-- Asking for help on personal projects that build on top of the Diffusers library
-- General questions
-- Ethical questions regarding diffusion models
-- ...
-
-Every question that is asked on the forum or on Discord actively encourages the community to publicly
-share knowledge and might very well help a beginner in the future that has the same question you're
-having. Please do pose any questions you might have.
-In the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from.
-
-**Please** keep in mind that the more effort you put into asking or answering a question, the higher
-the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database.
-In short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accesible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
-
-**NOTE about channels**:
-[*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago.
-In addition, questions and answers posted in the forum can easily be linked to.
-In contrast, *Discord* has a chat-like format that invites fast back-and-forth communication.
-While it will most likely take less time for you to get an answer to your question on Discord, your
-question won't be visible anymore over time. Also, it's much harder to find information that was posted a while back on Discord. We therefore strongly recommend using the forum for high-quality questions and answers in an attempt to create long-lasting knowledge for the community. If discussions on Discord lead to very interesting answers and conclusions, we recommend posting the results on the forum to make the information more available for future readers.
-
-### 2. Opening new issues on the GitHub issues tab
+### 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.
 
-Remember, GitHub issues are reserved for technical questions directly related to the Diffusers library, bug reports, feature requests, or feedback on the library design.
+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).
 
-In a nutshell, this means that everything that is **not** related to the **code of the Diffusers library** (including the documentation) should **not** be asked on GitHub, but rather on either the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR).
+### Do you want to implement a new diffusion pipeline / diffusion model?
 
-**Please consider the following guidelines when opening a new issue**:
-- Make sure you have searched whether your issue has already been asked before (use the search bar on GitHub under Issues).
-- Please never report a new issue on another (related) issue. If another issue is highly related, please
-open a new issue nevertheless and link to the related issue.
-- Make sure your issue is written in English. Please use one of the great, free online translation services, such as [DeepL](https://www.deepl.com/translator) to translate from your native language to English if you are not comfortable in English.
-- Check whether your issue might be solved by updating to the newest Diffusers version. Before posting your issue, please make sure that `python -c "import diffusers; print(diffusers.__version__)"` is higher or matches the latest Diffusers version.
-- Remember that the more effort you put into opening a new issue, the higher the quality of your answer will be and the better the overall quality of the Diffusers issues.
+Awesome! Please provide the following information:
 
-New issues usually include the following.
+* 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.
 
-#### 2.1. Reproducible, minimal bug reports.
+If you are willing to contribute the model yourself, let us know so we can best
+guide you.
 
-A bug report should always have a reproducible code snippet and be as minimal and concise as possible.
-This means in more detail:
-- Narrow the bug down as much as you can, **do not just dump your whole code file**
-- Format your code
-- Do not include any external libraries except for Diffusers depending on them.
-- **Always** provide all necessary information about your environment; for this, you can run: `diffusers-cli env` in your shell and copy-paste the displayed information to the issue.
-- Explain the issue. If the reader doesn't know what the issue is and why it is an issue, she cannot solve it.
-- **Always** make sure the reader can reproduce your issue with as little effort as possible. If your code snippet cannot be run because of missing libraries or undefined variables, the reader cannot help you. Make sure your reproducible code snippet is as minimal as possible and can be copy-pasted into a simple Python shell.
-- If in order to reproduce your issue a model and/or dataset is required, make sure the reader has access to that model or dataset. You can always upload your model or dataset to the [Hub](https://huggingface.co) to make it easily downloadable. Try to keep your model and dataset as small as possible, to make the reproduction of your issue as effortless as possible.
-
-For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.
-
-You can open a bug report [here](https://github.com/huggingface/diffusers/issues/new/choose).
-
-#### 2.2. Feature requests.
+### 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.
+  * 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.
 
-You can open a feature request [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=).
+If your issue is well written we're already 80% of the way there by the time you
+post it.
 
-#### 2.3 Feedback.
-
-Feedback about the library design and why it is good or not good helps the core maintainers immensely to build a user-friendly library. To understand the philosophy behind the current design philosophy, please have a look [here](https://huggingface.co/docs/diffusers/conceptual/philosophy). If you feel like a certain design choice does not fit with the current design philosophy, please explain why and how it should be changed. If a certain design choice follows the design philosophy too much, hence restricting use cases, explain why and how it should be changed.
-If a certain design choice is very useful for you, please also leave a note as this is great feedback for future design decisions.
-
-You can open an issue about feedback [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).
-
-#### 2.4 Technical questions.
-
-Technical questions are mainly about why certain code of the library was written in a certain way, or what a certain part of the code does. Please make sure to link to the code in question and please provide detail on
-why this part of the code is difficult to understand.
-
-You can open an issue about a technical question [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml).
-
-#### 2.5 Proposal to add a new model, scheduler, or pipeline.
-
-If the diffusion model community released a new model, pipeline, or scheduler that you would like to see in the Diffusers library, please provide the following information:
-
-* Short description of the diffusion pipeline, model, or scheduler and link to the paper or public release.
-* Link to any of its open-source implementation.
-* Link to the model weights if they are available.
-
-If you are willing to contribute to the model yourself, let us know so we can best guide you. Also, don't forget
-to tag the original author of the component (model, scheduler, pipeline, etc.) by GitHub handle if you can find it.
-
-You can open a request for a model/pipeline/scheduler [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=New+model%2Fpipeline%2Fscheduler&template=new-model-addition.yml).
-
-### 3. Answering issues on the GitHub issues tab
-
-Answering issues on GitHub might require some technical knowledge of Diffusers, but we encourage everybody to give it a try even if you are not 100% certain that your answer is correct.
-Some tips to give a high-quality answer to an issue:
-- Be as concise and minimal as possible
-- Stay on topic. An answer to the issue should concern the issue and only the issue.
-- Provide links to code, papers, or other sources that prove or encourage your point.
-- Answer in code. If a simple code snippet is the answer to the issue or shows how the issue can be solved, please provide a fully reproducible code snippet.
-
-Also, many issues tend to be simply off-topic, duplicates of other issues, or irrelevant. It is of great
-help to the maintainers if you can answer such issues, encouraging the author of the issue to be
-more precise, provide the link to a duplicated issue or redirect them to [the forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR)
-
-If you have verified that the issued bug report is correct and requires a correction in the source code,
-please have a look at the next sections.
-
-For all of the following contributions, you will need to open a PR. It is explained in detail how to do so in the [Opening a pull requst](#how-to-open-a-pr) section.
-
-### 4. Fixing a "Good first issue"
-
-*Good first issues* are marked by the [Good first issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) label. Usually, the issue already
-explains how a potential solution should look so that it is easier to fix.
-If the issue hasn't been closed and you would like to try to fix this issue, you can just leave a message "I would like to try this issue.". There are usually three scenarios:
-- a.) The issue description already proposes a fix. In this case and if the solution makes sense to you, you can open a PR or draft PR to fix it.
-- b.) The issue description does not propose a fix. In this case, you can ask what a proposed fix could look like and someone from the Diffusers team should answer shortly. If you have a good idea of how to fix it, feel free to directly open a PR.
-- c.) There is already an open PR to fix the issue, but the issue hasn't been closed yet. If the PR has gone stale, you can simply open a new PR and link to the stale PR. PRs often go stale if the original contributor who wanted to fix the issue suddenly cannot find the time anymore to proceed. This often happens in open-source and is very normal. In this case, the community will be very happy if you give it a new try and leverage the knowledge of the existing PR. If there is already a PR and it is active, you can help the author by giving suggestions, reviewing the PR or even asking whether you can contribute to the PR.
-
-
-### 5. Contribute to the documentation
-
-A good library **always** has good documentation! The official documentation is often one of the first points of contact for new users of the library, and therefore contributing to the documentation is a **highly
-valuable contribution**.
-
-Contributing to the library can have many forms:
-
-- Correcting spelling or grammatical errors.
-- Correct incorrect formatting of the docstring. If you see that the official documentation is weirdly displayed or a link is broken, we are very happy if you take some time to correct it.
-- Correct the shape or dimensions of a docstring input or output tensor.
-- Clarify documentation that is hard to understand or incorrect.
-- Update outdated code examples.
-- Translating the documentation to another language.
-
-Anything displayed on [the official Diffusers doc page](https://huggingface.co/docs/diffusers/index) is part of the official documentation and can be corrected, adjusted in the respective [documentation source](https://github.com/huggingface/diffusers/tree/main/docs/source).
-
-Please have a look at [this page](https://github.com/huggingface/diffusers/tree/main/docs) on how to verify changes made to the documentation locally.
-
-
-### 6. Contribute a community pipeline
-
-[Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) are usually the first point of contact between the Diffusers library and the user.
-Pipelines are examples of how to use Diffusers [models](https://huggingface.co/docs/diffusers/api/models) and [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview).
-We support two types of pipelines:
-
-- Official Pipelines
-- Community Pipelines
-
-Both official and community pipelines follow the same design and consist of the same type of components.
-
-Official pipelines are tested and maintained by the core maintainers of Diffusers. Their code
-resides in [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
-In contrast, community pipelines are contributed and maintained purely by the **community** and are **not** tested.
-They reside in [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and while they can be accessed via the [PyPI diffusers package](https://pypi.org/project/diffusers/), their code is not part of the PyPI distribution.
-
-The reason for the distinction is that the core maintainers of the Diffusers library cannot maintain and test all
-possible ways diffusion models can be used for inference, but some of them may be of interest to the community.
-Officially released diffusion pipelines,
-such as Stable Diffusion are added to the core src/diffusers/pipelines package which ensures
-high quality of maintenance, no backward-breaking code changes, and testing.
-More bleeding edge pipelines should be added as community pipelines. If usage for a community pipeline is high, the pipeline can be moved to the official pipelines upon request from the community. This is one of the ways we strive to be a community-driven library.
-
-To add a community pipeline, one should add a <name-of-the-community>.py file to [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and adapt the [examples/community/README.md](https://github.com/huggingface/diffusers/tree/main/examples/community/README.md) to include an example of the new pipeline.
-
-An example can be seen [here](https://github.com/huggingface/diffusers/pull/2400).
-
-Community pipeline PRs are only checked at a superficial level and ideally they should be maintained by their original authors.
-
-Contributing a community pipeline is a great way to understand how Diffusers models and schedulers work. Having contributed a community pipeline is usually the first stepping stone to contributing an official pipeline to the
-core package.
-
-### 7. Contribute to training examples
-
-Diffusers examples are a collection of training scripts that reside in [examples](https://github.com/huggingface/diffusers/tree/main/examples).
-
-We support two types of training examples:
-
-- Official training examples
-- Research training examples
-
-Research training examples are located in [examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects) whereas official training examples include all folders under [examples](https://github.com/huggingface/diffusers/tree/main/examples) except the `research_projects` and `community` folders.
-The official training examples are maintained by the Diffusers' core maintainers whereas the research training examples are maintained by the community.
-This is because of the same reasons put forward in [6. Contribute a community pipeline](#contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models.
-If the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the `research_projects` folder and maintained by the author.
-
-Both official training and research examples consist of a directory that contains one or more training scripts, a requirements.txt file, and a README.md file. In order for the user to make use of the
-training examples, it is required to clone the repository:
-
-```
-git clone https://github.com/huggingface/diffusers
-```
-
-as well as to install all additional dependencies required for training:
-
-```
-pip install -r /examples/<your-example-folder>/requirements.txt
-```
-
-Therefore when adding an example, the `requirements.txt` file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the [DreamBooth `requirements.txt` file](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt).
-
-Training examples of the Diffusers library should adhere to the following philosophy:
-- All the code necessary to run the examples should be found in a single Python file
-- One should be able to run the example from the command line with `python <your-example>.py --args`
-- Examples should be kept simple and serve as **an example** on how to use Diffusers for training. The purpose of example scripts is **not** to create state-of-the-art diffusion models, but rather to reproduce known training schemes without adding too much custom logic. As a byproduct of this point, our examples also strive to serve as good educational materials.
-
-To contribute an example, it is highly recommended to look at already existing examples such as [dreambooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) to get an idea of how they should look like.
-We strongly advise contributors to make use of the [Accelerate library](https://github.com/huggingface/accelerate) as it's tightly integrated
-with Diffusers.
-Once an example script works, please make sure to add a comprehensive `README.md` that states how to use the example exactly. This README should include:
-- An example command on how to run the example script as shown [here e.g.](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#running-locally-with-pytorch).
-- A link to some training results (logs, models, ...) that show what the user can expect as shown [here e.g.](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5).
-- If you are adding a non-official/research training example, **please don't forget** to add a sentence that you are maintaining this training example which includes your git handle as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/intel_opts#diffusers-examples-with-intel-optimizations).
-
-If you are contributing to the official training examples, please also make sure to add a test to [examples/test_examples.py](https://github.com/huggingface/diffusers/blob/main/examples/test_examples.py). This is not necessary for non-official training examples.
-
-### 8. Fixing a "Good second issue"
-
-*Good second issues* are marked by the [Good second issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) label. Good second issues are
-usually more complicated to solve than [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).
-The issue description usually gives less guidance on how to fix the issue and requires
-a decent understanding of the library by the interested contributor.
-If you are interested in tackling a second good issue, feel free to open a PR to fix it and link the PR to the issue. If you see that a PR has already been opened for this issue but did not get merged, have a look to understand why it wasn't merged and try to open an improved PR.
-Good second issues are usually more difficult to get merged compared to good first issues, so don't hesitate to ask for help from the core maintainers. If your PR is almost finished the core maintainers can also jump into your PR and commit to it in order to get it merged.
-
-### 9. Adding pipelines, models, schedulers
-
-Pipelines, models, and schedulers are the most important pieces of the Diffusers library.
-They provide easy access to state-of-the-art diffusion technologies and thus allow the community to
-build powerful generative AI applications.
-
-By adding a new model, pipeline, or scheduler you might enable a new powerful use case for any of the user interfaces relying on Diffusers which can be of immense value for the whole generative AI ecosystem.
-
-Diffusers has a couple of open feature requests for all three components - feel free to gloss over them
-if you don't know yet what specific component you would like to add:
-- [Model or pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22)
-- [Scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)
-
-Before adding any of the three components, it is strongly recommended that you give the [Philosophy guide](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) a read to better understand the design of any of the three components. Please be aware that
-we cannot merge model, scheduler, or pipeline additions that strongly diverge from our design philosophy
-as it will lead to API inconsistencies. If you fundamentally disagree with a design choice, please
-open a [Feedback issue](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=) instead so that it can be discussed whether a certain design
-pattern/design choice shall be changed everywhere in the library and whether we shall update our design philosophy. Consistency across the library is very important for us.
-
-Please make sure to add links to the original codebase/paper to the PR and ideally also ping the
-original author directly on the PR so that they can follow the progress and potentially help with questions.
-
-If you are unsure or stuck in the PR, don't hesitate to leave a message to ask for a first review or help.
-
-## How to write a good issue
-
-**The better your issue is written, the higher the chances that it will be quickly resolved.**
-
-1. Make sure that you've used the correct template for your issue. You can pick between *Bug Report*, *Feature Request*, *Feedback about API Design*, *New model/pipeline/scheduler addition*, *Forum*, or a blank issue. Make sure to pick the correct one when opening [a new issue](https://github.com/huggingface/diffusers/issues/new/choose).
-2. **Be precise**: Give your issue a fitting title. Try to formulate your issue description as simple as possible. The more precise you are when submitting an issue, the less time it takes to understand the issue and potentially solve it. Make sure to open an issue for one issue only and not for multiple issues. If you found multiple issues, simply open multiple issues. If your issue is a bug, try to be as precise as possible about what bug it is - you should not just write "Error in diffusers".
-3. **Reproducibility**: No reproducible code snippet == no solution. If you encounter a bug, maintainers **have to be able to reproduce** it. Make sure that you include a code snippet that can be copy-pasted into a Python interpreter to reproduce the issue. Make sure that your code snippet works, *i.e.* that there are no missing imports or missing links to images, ... Your issue should contain an error message **and** a code snippet that can be copy-pasted without any changes to reproduce the exact same error message. If your issue is using local model weights or local data that cannot be accessed by the reader, the issue cannot be solved. If you cannot share your data or model, try to make a dummy model or dummy data.
-4. **Minimalistic**: Try to help the reader as much as you can to understand the issue as quickly as possible by staying as concise as possible. Remove all code / all information that is irrelevant to the issue. If you have found a bug, try to create the easiest code example you can to demonstrate your issue, do not just dump your whole workflow into the issue as soon as you have found a bug. E.g., if you train a model and get an error at some point during the training, you should first try to understand what part of the training code is responsible for the error and try to reproduce it with a couple of lines. Try to use dummy data instead of full datasets.
-5. Add links. If you are referring to a certain naming, method, or model make sure to provide a link so that the reader can better understand what you mean. If you are referring to a specific PR or issue, make sure to link it to your issue. Do not assume that the reader knows what you are talking about. The more links you add to your issue the better.
-6. Formatting. Make sure to nicely format your issue by formatting code into Python code syntax, and error messages into normal code syntax. See the [official GitHub formatting docs](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) for more information.
-7. Think of your issue not as a ticket to be solved, but rather as a beautiful entry to a well-written encyclopedia. Every added issue is a contribution to publicly available knowledge. By adding a nicely written issue you not only make it easier for maintainers to solve your issue, but you are helping the whole community to better understand a certain aspect of the library.
-
-## How to write a good PR
-
-1. Be a chameleon. Understand existing design patterns and syntax and make sure your code additions flow seamlessly into the existing code base. Pull requests that significantly diverge from existing design patterns or user interfaces will not be merged.
-2. Be laser focused. A pull request should solve one problem and one problem only. Make sure to not fall into the trap of "also fixing another problem while we're adding it". It is much more difficult to review pull requests that solve multiple, unrelated problems at once.
-3. If helpful, try to add a code snippet that displays an example of how your addition can be used.
-4. The title of your pull request should be a summary of its contribution.
-5. 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);
-6. 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;
-7. Try to formulate and format your text as explained in [How to write a good issue](#how-to-write-a-good-issue).
-8. Make sure existing tests pass;
-9. 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`.
-CircleCI does not run the slow tests, but GitHub actions does every night!
-10. All public methods must have informative docstrings that work nicely with markdown. See `[pipeline_latent_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py)` for an example.
-11. 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
-[`hf-internal-testing`](https://huggingface.co/hf-internal-testing) or [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images) to place these files.
-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.
-
-## How to open a PR
+## 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
@@ -355,105 +99,146 @@ You will need basic `git` proficiency to be able to contribute to
 manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
 Git](https://git-scm.com/book/en/v2) is a very good reference.
 
-Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/main/setup.py#L244)):
+Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/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.
+   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 git@github.com:<your Github handle>/diffusers.git
- $ cd diffusers
- $ git remote add upstream https://github.com/huggingface/diffusers.git
- ```
+   ```bash
+   $ git clone git@github.com:<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
- ```
+   ```bash
+   $ git checkout -b a-descriptive-name-for-my-changes
+   ```
 
-**Do not** work on the `main` branch.
+   **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]"
- ```
+   ```bash
+   $ pip install -e ".[dev]"
+   ```
 
-If you have already cloned the repo, you might need to `git pull` to get the most recent changes in the
-library.
+   (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:
+   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
- ```
+   ```bash
+   $ pytest tests/<TEST_TO_RUN>.py
+   ```
 
-Before you run the tests, please make sure you install the dependencies required for testing. You can do so
-with this command:
+   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
- $ pip install -e ".[test]"
- ```
+   ```bash
+   $ make test
+   ```
 
-You can run the full test 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:
+   For more information about tests, check out the
+   [dedicated documentation](https://huggingface.co/docs/diffusers/testing)
 
- ```bash
- $ make test
- ```
+   🧨 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:
 
-🧨 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
+   ```
 
- ```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:
 
-🧨 Diffusers also uses `ruff` 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
+   ```
 
- ```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:
 
-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
+   ```
 
- ```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:
 
-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
+   ```
 
- ```bash
- $ git pull upstream main
- ```
+   Push the changes to your account using:
 
-Push the changes to your account using:
+   ```bash
+   $ git push -u origin a-descriptive-name-for-my-changes
+   ```
 
- ```bash
- $ git push -u origin a-descriptive-name-for-my-changes
- ```
-
-6. Once you are satisfied, go to the
-webpage of your fork on GitHub. Click on 'Pull request' to send your changes
-to the project maintainers for review.
+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.
+   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
 
@@ -467,7 +252,7 @@ repository, here's how to run tests with `pytest` for the library:
 $ python -m pytest -n auto --dist=loadfile -s -v ./tests/
 ```
 
-In fact, that's how `make test` is implemented!
+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.
@@ -480,18 +265,26 @@ have enough disk space and a good Internet connection, or a lot of patience!
 $ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
 ```
 
-`unittest` is fully supported, here's how to run tests with it:
+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.
+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
@@ -499,7 +292,3 @@ $ git pull --squash --no-commit upstream main
 $ git commit -m '<your message without GitHub references>'
 $ git push --set-upstream origin your-branch-for-syncing
 ```
-
-### Style guide
-
-For documentation strings, 🧨 Diffusers follows the [google style](https://google.github.io/styleguide/pyguide.html).

MANIFEST.in

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

Makefile

@@ -9,8 +9,9 @@ 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 $(modified_py_files); \
-		ruff $(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
@@ -40,23 +41,22 @@ repo-consistency:
 # this target runs checks on all files
 
 quality:
-	black --check $(check_dirs)
-	ruff $(check_dirs)
+	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
-	python utils/check_doc_toc.py
 
 # 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
-	python utils/check_doc_toc.py --fix_and_overwrite
 
 # this target runs checks on all files and potentially modifies some of them
 
 style:
-	black $(check_dirs)
-	ruff $(check_dirs) --fix
+	black --preview $(check_dirs)
+	isort $(check_dirs)
 	${MAKE} autogenerate_code
 	${MAKE} extra_style_checks
 
@@ -67,7 +67,6 @@ 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

PHILOSOPHY.md

@@ -1,110 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Philosophy
-
-🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities.
-Its purpose is to serve as a **modular toolbox** for both inference and training.
-
-We aim at building a library that stands the test of time and therefore take API design very seriously.
-
-In a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones:
-
-## Usability over Performance
-
-- While Diffusers has many built-in performance-enhancing features (see [Memory and Speed](https://huggingface.co/docs/diffusers/optimization/fp16)), models are always loaded with the highest precision and lowest optimization. Therefore, by default diffusion pipelines are always instantiated on CPU with float32 precision if not otherwise defined by the user. This ensures usability across different platforms and accelerators and means that no complex installations are required to run the library.
-- Diffusers aim at being a **light-weight** package and therefore has very few required dependencies, but many soft dependencies that can improve performance (such as `accelerate`, `safetensors`, `onnx`, etc...). We strive to keep the library as lightweight as possible so that it can be added without much concern as a dependency on other packages.
-- Diffusers prefers simple, self-explainable code over condensed, magic code. This means that short-hand code syntaxes such as lambda functions, and advanced PyTorch operators are often not desired.
-
-## Simple over easy
-
-As PyTorch states, **explicit is better than implicit** and **simple is better than complex**. This design philosophy is reflected in multiple parts of the library:
-- We follow PyTorch's API with methods like [`DiffusionPipeline.to`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.to) to let the user handle device management.
-- Raising concise error messages is preferred to silently correct erroneous input. Diffusers aims at teaching the user, rather than making the library as easy to use as possible.
-- Complex model vs. scheduler logic is exposed instead of magically handled inside. Schedulers/Samplers are separated from diffusion models with minimal dependencies on each other. This forces the user to write the unrolled denoising loop. However, the separation allows for easier debugging and gives the user more control over adapting the denoising process or switching out diffusion models or schedulers.
-- Separately trained components of the diffusion pipeline, *e.g.* the text encoder, the unet, and the variational autoencoder, each have their own model class. This forces the user to handle the interaction between the different model components, and the serialization format separates the model components into different files. However, this allows for easier debugging and customization. Dreambooth or textual inversion training
-is very simple thanks to diffusers' ability to separate single components of the diffusion pipeline.
-
-## Tweakable, contributor-friendly over abstraction
-
-For large parts of the library, Diffusers adopts an important design principle of the [Transformers library](https://github.com/huggingface/transformers), which is to prefer copy-pasted code over hasty abstractions. This design principle is very opinionated and stands in stark contrast to popular design principles such as [Don't repeat yourself (DRY)](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself).
-In short, just like Transformers does for modeling files, diffusers prefers to keep an extremely low level of abstraction and very self-contained code for pipelines and schedulers.
-Functions, long code blocks, and even classes can be copied across multiple files which at first can look like a bad, sloppy design choice that makes the library unmaintainable.
-**However**, this design has proven to be extremely successful for Transformers and makes a lot of sense for community-driven, open-source machine learning libraries because:
-- Machine Learning is an extremely fast-moving field in which paradigms, model architectures, and algorithms are changing rapidly, which therefore makes it very difficult to define long-lasting code abstractions.
-- Machine Learning practitioners like to be able to quickly tweak existing code for ideation and research and therefore prefer self-contained code over one that contains many abstractions.
-- Open-source libraries rely on community contributions and therefore must build a library that is easy to contribute to. The more abstract the code, the more dependencies, the harder to read, and the harder to contribute to. Contributors simply stop contributing to very abstract libraries out of fear of breaking vital functionality. If contributing to a library cannot break other fundamental code, not only is it more inviting for potential new contributors, but it is also easier to review and contribute to multiple parts in parallel.
-
-At Hugging Face, we call this design the **single-file policy** which means that almost all of the code of a certain class should be written in a single, self-contained file. To read more about the philosophy, you can have a look
-at [this blog post](https://huggingface.co/blog/transformers-design-philosophy).
-
-In diffusers, we follow this philosophy for both pipelines and schedulers, but only partly for diffusion models. The reason we don't follow this design fully for diffusion models is because almost all diffusion pipelines, such
-as [DDPM](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [UnCLIP (Dalle-2)](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/unclip#overview) and [Imagen](https://imagen.research.google/) all rely on the same diffusion model, the [UNet](https://huggingface.co/docs/diffusers/api/models#diffusers.UNet2DConditionModel).
-
-Great, now you should have generally understood why 🧨 Diffusers is designed the way it is 🤗.
-We try to apply these design principles consistently across the library. Nevertheless, there are some minor exceptions to the philosophy or some unlucky design choices. If you have feedback regarding the design, we would ❤️  to hear it [directly on GitHub](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).
-
-## Design Philosophy in Details
-
-Now, let's look a bit into the nitty-gritty details of the design philosophy. Diffusers essentially consist of three major classes, [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), and [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).
-Let's walk through more in-detail design decisions for each class.
-
-### Pipelines
-
-Pipelines are designed to be easy to use (therefore do not follow [*Simple over easy*](#simple-over-easy) 100%)), are not feature complete, and should loosely be seen as examples of how to use [models](#models) and [schedulers](#schedulers) for inference.
-
-The following design principles are followed:
-- Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [#Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).
-- Pipelines all inherit from [`DiffusionPipeline`]
-- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
-- Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function.
-- Pipelines should be used **only** for inference.
-- Pipelines should be very readable, self-explanatory, and easy to tweak.
-- Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs.
-- Pipelines are **not** intended to be feature-complete user interfaces. For future complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner)
-- Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines.
-- Pipelines should be named after the task they are intended to solve.
-- In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file.
-
-### Models
-
-Models are designed as configurable toolboxes that are natural extensions of [PyTorch's Module class](https://pytorch.org/docs/stable/generated/torch.nn.Module.html). They only partly follow the **single-file policy**.
-
-The following design principles are followed:
-- Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.
-- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...
-- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.
-- Models intend to expose complexity, just like PyTorch's module does, and give clear error messages.
-- Models all inherit from `ModelMixin` and `ConfigMixin`.
-- Models can be optimized for performance when it doesn’t demand major code changes, keeps backward compatibility, and gives significant memory or compute gain.
-- Models should by default have the highest precision and lowest performance setting.
-- To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different.
-- Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and "foreseeing" future changes, *e.g.* it is usually better to add `string` "...type" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work.
-- The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and
-readable longterm, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-
-### Schedulers
-
-Schedulers are responsible to guide the denoising process for inference as well as to define a noise schedule for training. They are designed as individual classes with loadable configuration files and strongly follow the **single-file policy**.
-
-The following design principles are followed:
-- All schedulers are found in [`src/diffusers/schedulers`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).
-- Schedulers are **not** allowed to import from large utils files and shall be kept very self-contained.
-- One scheduler python file corresponds to one scheduler algorithm (as might be defined in a paper).
-- If schedulers share similar functionalities, we can make use of the `#Copied from` mechanism.
-- Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.
-- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.mdx).
-- Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.
-- Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon
-- The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).
-- Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a "black box".
-- In almost all cases, novel schedulers shall be implemented in a new scheduling file.

README.md

@@ -1,6 +1,6 @@
 <p align="center">
     <br>
-    <img src="https://github.com/huggingface/diffusers/blob/main/docs/source/en/imgs/diffusers_library.jpg" width="400"/>
+    <img src="docs/source/imgs/diffusers_library.jpg" width="400"/>
     <br>
 <p>
 <p align="center">
@@ -15,99 +15,37 @@
     </a>
 </p>
 
-🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
+🤗 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.
 
-🤗 Diffusers offers three core components:
+More precisely, 🤗 Diffusers offers:
 
-- State-of-the-art [diffusion pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) that can be run in inference with just a few lines of code.
-- Interchangeable noise [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview) for different diffusion speeds and output quality.
-- Pretrained [models](https://huggingface.co/docs/diffusers/api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
+- 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
 
-We recommend installing 🤗 Diffusers in a virtual environment from PyPi or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/installation.html), please refer to their official documentation.
-
-### PyTorch
-
-With `pip` (official package):
-
+**With `pip`**
+    
 ```bash
-pip install --upgrade diffusers[torch]
+pip install --upgrade diffusers
 ```
 
-With `conda` (maintained by the community):
+**With `conda`**
 
 ```sh
 conda install -c conda-forge diffusers
 ```
 
-### Flax
+**Apple Silicon (M1/M2) support**
 
-With `pip` (official package):
+Please, refer to [the documentation](https://huggingface.co/docs/diffusers/optimization/mps).
 
-```bash
-pip install --upgrade diffusers[flax]
-```
+## Contributing
 
-### Apple Silicon (M1/M2) support
-
-Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.
-
-## Quickstart
-
-Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 4000+ checkpoints):
-
-```python
-from diffusers import DiffusionPipeline
-import torch
-
-pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-pipeline.to("cuda")
-pipeline("An image of a squirrel in Picasso style").images[0]
-```
-
-You can also dig into the models and schedulers toolbox to build your own diffusion system:
-
-```python
-from diffusers import DDPMScheduler, UNet2DModel
-from PIL import Image
-import torch
-import numpy as np
-
-scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
-model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
-scheduler.set_timesteps(50)
-
-sample_size = model.config.sample_size
-noise = torch.randn((1, 3, sample_size, sample_size)).to("cuda")
-input = noise
-
-for t in scheduler.timesteps:
-    with torch.no_grad():
-        noisy_residual = model(input, t).sample
-        prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
-        input = prev_noisy_sample
-
-image = (input / 2 + 0.5).clamp(0, 1)
-image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
-image = Image.fromarray((image * 255).round().astype("uint8"))
-image
-```
-
-Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to launch your diffusion journey today!
-
-## How to navigate the documentation
-
-| **Documentation**                                                   | **What can I learn?**                                                                                                                                                                           |
-|---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview)                                                            | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model.  |
-| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview)                                                             | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers.                                         |
-| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview)                                             | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library.               |
-| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview)                                                        | Guides for how to optimize your diffusion model to run faster and consume less memory.                                                                                                          |
-| [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques.                                                                                               |
-## Contribution
-
-We ❤️  contributions from the open-source community!
+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
@@ -117,86 +55,321 @@ You can look out for [issues](https://github.com/huggingface/diffusers/issues) y
 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
 
-## Popular Tasks & Pipelines
+In order to get started, we recommend taking a look at two notebooks:
 
-<table>
-  <tr>
-    <th>Task</th>
-    <th>Pipeline</th>
-    <th>🤗 Hub</th>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td>Unconditional Image Generation</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/ddpm"> DDPM </a></td>
-    <td><a href="https://huggingface.co/google/ddpm-ema-church-256"> google/ddpm-ema-church-256 </a></td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td>Text-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img">Stable Diffusion Text-to-Image</a></td>
-      <td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
-  </tr>
-  <tr>
-    <td>Text-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/unclip">unclip</a></td>
-      <td><a href="https://huggingface.co/kakaobrain/karlo-v1-alpha"> kakaobrain/karlo-v1-alpha </a></td>
-  </tr>
-  <tr>
-    <td>Text-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/if">if</a></td>
-      <td><a href="https://huggingface.co/DeepFloyd/IF-I-XL-v1.0"> DeepFloyd/IF-I-XL-v1.0 </a></td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td>Text-guided Image-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet">Controlnet</a></td>
-      <td><a href="https://huggingface.co/lllyasviel/sd-controlnet-canny"> lllyasviel/sd-controlnet-canny </a></td>
-  </tr>
-  <tr>
-    <td>Text-guided Image-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/pix2pix">Instruct Pix2Pix</a></td>
-      <td><a href="https://huggingface.co/timbrooks/instruct-pix2pix"> timbrooks/instruct-pix2pix </a></td>
-  </tr>
-  <tr>
-    <td>Text-guided Image-to-Image</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img">Stable Diffusion Image-to-Image</a></td>
-      <td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td>Text-guided Image Inpainting</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpaint</a></td>
-      <td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td>Image Variation</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/image_variation">Stable Diffusion Image Variation</a></td>
-      <td><a href="https://huggingface.co/lambdalabs/sd-image-variations-diffusers"> lambdalabs/sd-image-variations-diffusers </a></td>
-  </tr>
-  <tr style="border-top: 2px solid black">
-    <td>Super Resolution</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/upscale">Stable Diffusion Upscale</a></td>
-      <td><a href="https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler"> stabilityai/stable-diffusion-x4-upscaler </a></td>
-  </tr>
-  <tr>
-    <td>Super Resolution</td>
-    <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/latent_upscale">Stable Diffusion Latent Upscale</a></td>
-      <td><a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler"> stabilityai/sd-x2-latent-upscaler </a></td>
-  </tr>
-</table>
+- 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. 
+  
+## **New** Stable Diffusion is now fully compatible with `diffusers`!  
 
-## Popular libraries using 🧨 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/) and [LAION](https://laion.ai/). 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 10GB VRAM.
+See the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information.
 
-- https://github.com/microsoft/TaskMatrix
-- https://github.com/invoke-ai/InvokeAI
-- https://github.com/apple/ml-stable-diffusion
-- https://github.com/Sanster/lama-cleaner
-- https://github.com/IDEA-Research/Grounded-Segment-Anything
-- https://github.com/ashawkey/stable-dreamfusion
-- https://github.com/deep-floyd/IF
-- https://github.com/bentoml/BentoML
-- https://github.com/bmaltais/kohya_ss
-- +3000 other amazing GitHub repositories 💪
+You need to accept the model license before downloading or using the Stable Diffusion weights. Please, visit the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license 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.
 
-Thank you for using us ❤️
+
+### Text-to-Image generation with Stable Diffusion
+
+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
+# make sure you're logged in with `huggingface-cli login`
+from diffusers import StableDiffusionPipeline
+
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_type=torch.float16, revision="fp16")
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]  
+```
+
+**Note**: If you don't want to use the token, you can also simply download the model weights
+(after having [accepted the license](https://huggingface.co/CompVis/stable-diffusion-v1-4)) and pass
+the path to the local folder to the `StableDiffusionPipeline`.
+
+```
+git lfs install
+git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
+```
+
+Assuming the folder is stored locally under `./stable-diffusion-v1-4`, you can also run stable diffusion
+without requiring an authentication token:
+
+```python
+pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-4")
+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(
+    "CompVis/stable-diffusion-v1-4", 
+    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, you can simply instantiate
+it before the pipeline and pass it to `from_pretrained`.
+    
+```python
+from diffusers import LMSDiscreteScheduler
+
+lms = LMSDiscreteScheduler(
+    beta_start=0.00085, 
+    beta_end=0.012, 
+    beta_schedule="scaled_linear"
+)
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4", 
+    revision="fp16", 
+    torch_dtype=torch.float16,
+    scheduler=lms,
+)
+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")
+```
+
+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("CompVis/stable-diffusion-v1-4")
+
+# 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")
+```
+
+### 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 = "CompVis/stable-diffusion-v1-4"
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    model_id_or_path,
+    revision="fp16", 
+    torch_dtype=torch.float16,
+)
+# or download via git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
+# and pass `model_id_or_path="./stable-diffusion-v1-4"`.
+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 text prompt.
+
+```python
+from io import BytesIO
+
+import torch
+import requests
+import PIL
+
+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))
+
+device = "cuda"
+model_id_or_path = "CompVis/stable-diffusion-v1-4"
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    model_id_or_path,
+    revision="fp16", 
+    torch_dtype=torch.float16,
+)
+# or download via git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
+# and pass `model_id_or_path="./stable-diffusion-v1-4"`.
+pipe = pipe.to(device)
+
+prompt = "a cat sitting on a bench"
+images = pipe(prompt=prompt, init_image=init_image, mask_image=mask_image, strength=0.75).images
+
+images[0].save("cat_on_bench.png")
+```
+
+### 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).
+  
+## 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 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
+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 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),
+
+### 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.
+*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
 
@@ -204,7 +377,7 @@ This library concretizes previous work by many different authors and would not h
 
 - @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)
+- @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.

docker/diffusers-flax-cpu/Dockerfile

@@ -1,44 +0,0 @@
-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 \
-                   libsndfile1-dev \
-                   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 \
-        Jinja2 \
-        librosa \
-        numpy \
-        scipy \
-        tensorboard \
-        transformers
-
-CMD ["/bin/bash"]

docker/diffusers-flax-tpu/Dockerfile

@@ -1,46 +0,0 @@
-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 \
-                   libsndfile1-dev \
-                   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 \
-        Jinja2 \
-        librosa \        
-        numpy \
-        scipy \
-        tensorboard \
-        transformers
-
-CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -1,44 +0,0 @@
-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 \
-                   libsndfile1-dev \
-                   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 \
-        Jinja2 \
-        librosa \
-        numpy \
-        scipy \
-        tensorboard \
-        transformers
-
-CMD ["/bin/bash"]

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -1,44 +0,0 @@
-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 \
-                   libsndfile1-dev \
-                   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 \
-        Jinja2 \
-        librosa \
-        numpy \
-        scipy \
-        tensorboard \
-        transformers
-
-CMD ["/bin/bash"]

docker/diffusers-pytorch-cpu/Dockerfile

@@ -1,43 +0,0 @@
-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 \
-                   libsndfile1-dev \
-                   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 \
-        Jinja2 \
-        librosa \
-        numpy \
-        scipy \
-        tensorboard \
-        transformers
-
-CMD ["/bin/bash"]

docker/diffusers-pytorch-cuda/Dockerfile

@@ -1,45 +0,0 @@
-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 \
-                   libsndfile1-dev \
-                   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 && \
-    python3 -m pip install --no-cache-dir \
-        accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        Jinja2 \
-        librosa \
-        numpy \
-        scipy \
-        tensorboard \
-        transformers \
-        omegaconf \
-        pytorch-lightning \
-        xformers
-
-CMD ["/bin/bash"]

docs/README.md

@@ -1,271 +0,0 @@
-<!---
-Copyright 2023- 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.
--->
-
-# Generating the documentation
-
-To generate the documentation, you first have to build it. Several packages are necessary to build the doc, 
-you can install them with the following command, at the root of the code repository:
-
-```bash
-pip install -e ".[docs]"
-```
-
-Then you need to install our open source documentation builder tool:
-
-```bash
-pip install git+https://github.com/huggingface/doc-builder
-```
-
----
-**NOTE**
-
-You only need to generate the documentation to inspect it locally (if you're planning changes and want to
-check how they look before committing for instance). You don't have to commit the built documentation.
-
----
-
-## Previewing the documentation
-
-To preview the docs, first install the `watchdog` module with:
-
-```bash
-pip install watchdog
-```
-
-Then run the following command:
-
-```bash
-doc-builder preview {package_name} {path_to_docs}
-```
-
-For example:
-
-```bash
-doc-builder preview diffusers docs/source/en
-```
-
-The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
-
----
-**NOTE**
-
-The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
-
----
-
-## Adding a new element to the navigation bar
-
-Accepted files are Markdown (.md or .mdx).
-
-Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
-the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/diffusers/blob/main/docs/source/_toctree.yml) file.
-
-## Renaming section headers and moving sections
-
-It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
-
-Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
-
-So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
-
-```
-Sections that were moved:
-
-[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
-```
-and of course, if you moved it to another file, then:
-
-```
-Sections that were moved:
-
-[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
-```
-
-Use the relative style to link to the new file so that the versioned docs continue to work.
-
-For an example of a rich moved section set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.mdx).
-
-
-## Writing Documentation - Specification
-
-The `huggingface/diffusers` documentation follows the
-[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
-although we can write them directly in Markdown.
-
-### Adding a new tutorial
-
-Adding a new tutorial or section is done in two steps:
-
-- Add a new file under `docs/source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
-- Link that file in `docs/source/_toctree.yml` on the correct toc-tree.
-
-Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
-depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or four.
-
-### Adding a new pipeline/scheduler
-
-When adding a new pipeline:
-
-- create a file `xxx.mdx` under `docs/source/api/pipelines` (don't hesitate to copy an existing file as template).
-- Link that file in (*Diffusers Summary*) section in `docs/source/api/pipelines/overview.mdx`, along with the link to the paper, and a colab notebook (if available).
-- Write a short overview of the diffusion model:
-    - Overview with paper & authors
-    - Paper abstract
-    - Tips and tricks and how to use it best
-    - Possible an end-to-end example of how to use it
-- Add all the pipeline classes that should be linked in the diffusion model. These classes should be added using our Markdown syntax. By default as follows:
-
-```
-## XXXPipeline
-
-[[autodoc]] XXXPipeline
-    - all
-	- __call__
-```
-
-This will include every public method of the pipeline that is documented, as well as the  `__call__` method that is not documented by default. If you just want to add additional methods that are not documented, you can put the list of all methods to add in a list that contains `all`.
-
-```
-[[autodoc]] XXXPipeline
-    - all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-    - enable_xformers_memory_efficient_attention 
-    - disable_xformers_memory_efficient_attention
-```
-
-You can follow the same process to create a new scheduler under the `docs/source/api/schedulers` folder
-
-### Writing source documentation
-
-Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
-and objects like True, None, or any strings should usually be put in `code`.
-
-When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
-adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or 
-function to be in the main package.
-
-If you want to create a link to some internal class or function, you need to
-provide its path. For instance: \[\`pipelines.ImagePipelineOutput\`\]. This will be converted into a link with
-`pipelines.ImagePipelineOutput` in the description. To get rid of the path and only keep the name of the object you are
-linking to in the description, add a ~: \[\`~pipelines.ImagePipelineOutput\`\] will generate a link with `ImagePipelineOutput` in the description.
-
-The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
-
-#### Defining arguments in a method
-
-Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
-an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
-description:
-
-```
-    Args:
-        n_layers (`int`): The number of layers of the model.
-```
-
-If the description is too long to fit in one line, another indentation is necessary before writing the description
-after the argument.
-
-Here's an example showcasing everything so far:
-
-```
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and
-            [`~PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-```
-
-For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
-following signature:
-
-```
-def my_function(x: str = None, a: float = 1):
-```
-
-then its documentation should look like this:
-
-```
-    Args:
-        x (`str`, *optional*):
-            This argument controls ...
-        a (`float`, *optional*, defaults to 1):
-            This argument is used to ...
-```
-
-Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even
-if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
-however write as many lines as you want in the indented description (see the example above with `input_ids`).
-
-#### Writing a multi-line code block
-
-Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
-
-
-````
-```
-# first line of code
-# second line
-# etc
-```
-````
-
-#### Writing a return block
-
-The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.
-The first line should be the type of the return, followed by a line return. No need to indent further for the elements
-building the return.
-
-Here's an example of a single value return:
-
-```
-    Returns:
-        `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
-```
-
-Here's an example of a tuple return, comprising several objects:
-
-```
-    Returns:
-        `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
-        - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
-          Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
-        - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
-          Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-```
-
-#### Adding an image
-
-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.
-
-## Styling the docstring
-
-We have an automatic script running with the `make style` command that will make sure that:
-- the docstrings fully take advantage of the line width
-- all code examples are formatted using black, like the code of the Transformers library
-
-This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's
-recommended to commit your changes before running `make style`, so you can revert the changes done by that script
-easily.
-

docs/TRANSLATING.md

@@ -1,57 +0,0 @@
-### Translating the Diffusers documentation into your language
-
-As part of our mission to democratize machine learning, we'd love to make the Diffusers library available in many more languages! Follow the steps below if you want to help translate the documentation into your language 🙏.
-
-**🗞️ Open an issue**
-
-To get started, navigate to the [Issues](https://github.com/huggingface/diffusers/issues) page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the "Translation template" from the "New issue" button.
-
-Once an issue exists, post a comment to indicate which chapters you'd like to work on, and we'll add your name to the list.
-
-
-**🍴 Fork the repository**
-
-First, you'll need to [fork the Diffusers repo](https://docs.github.com/en/get-started/quickstart/fork-a-repo). You can do this by clicking on the **Fork** button on the top-right corner of this repo's page.
-
-Once you've forked the repo, you'll want to get the files on your local machine for editing. You can do that by cloning the fork with Git as follows:
-
-```bash
-git clone https://github.com/YOUR-USERNAME/diffusers.git
-```
-
-**📋 Copy-paste the English version with a new language code**
-
-The documentation files are in one leading directory:
-
-- [`docs/source`](https://github.com/huggingface/diffusers/tree/main/docs/source): All the documentation materials are organized here by language.
-
-You'll only need to copy the files in the [`docs/source/en`](https://github.com/huggingface/diffusers/tree/main/docs/source/en) directory, so first navigate to your fork of the repo and run the following:
-
-```bash
-cd ~/path/to/diffusers/docs
-cp -r source/en source/LANG-ID
-```
-
-Here, `LANG-ID` should be one of the ISO 639-1 or ISO 639-2 language codes -- see [here](https://www.loc.gov/standards/iso639-2/php/code_list.php) for a handy table.
-
-**✍️ Start translating**
-
-The fun part comes - translating the text!
-
-The first thing we recommend is translating the part of the `_toctree.yml` file that corresponds to your doc chapter. This file is used to render the table of contents on the website. 
-
-> 🙋 If the `_toctree.yml` file doesn't yet exist for your language, you can create one by copy-pasting from the English version and deleting the sections unrelated to your chapter. Just make sure it exists in the `docs/source/LANG-ID/` directory!
-
-The fields you should add are `local` (with the name of the file containing the translation; e.g. `autoclass_tutorial`), and `title` (with the title of the doc in your language; e.g. `Load pretrained instances with an AutoClass`) -- as a reference, here is the `_toctree.yml` for [English](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml):
-
-```yaml
-- sections:
-  - local: pipeline_tutorial # Do not change this! Use the same name for your .md file
-    title: Pipelines for inference # Translate this!
-    ...
-  title: Tutorials # Translate this!
-```
-
-Once you have translated the `_toctree.yml` file, you can start translating the [MDX](https://mdxjs.com/) files associated with your docs chapter.
-
-> 🙋 If you'd like others to help you with the translation, you should [open an issue](https://github.com/huggingface/diffusers/issues) and tag @patrickvonplaten.

docs/source/_config.py

@@ -1,9 +0,0 @@
-# docstyle-ignore
-INSTALL_CONTENT = """
-# Diffusers installation
-! pip install diffusers transformers datasets accelerate
-# To install from source instead of the last release, comment the command above and uncomment the following one.
-# ! pip install git+https://github.com/huggingface/diffusers.git
-"""
-
-notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}]

docs/source/_toctree.yml

@@ -0,0 +1,94 @@
+- 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/configuration
+      title: "Configuring Pipelines, Models, and Schedulers"
+    - local: using-diffusers/custom_pipelines
+      title: "Loading and Creating Custom Pipelines"
+    title: "Loading"
+  - 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
+      title: "Create a custom pipeline"
+    title: "Pipelines for Inference"
+  title: "Using Diffusers"
+- sections:
+  - local: optimization/fp16
+    title: "Memory and Speed"
+  - local: optimization/onnx
+    title: "ONNX"
+  - local: optimization/open_vino
+    title: "Open Vino"
+  - 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: "Text Inversion"
+  - local: training/text2image
+    title: "Text-to-image"
+  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/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"
+    title: "Pipelines"
+  title: "API"

docs/source/api/configuration.mdx

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -12,14 +12,12 @@ specific language governing permissions and limitations under the License.
 
 # Configuration
 
-Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which conveniently takes care of storing all the parameters that are 
-passed to their respective `__init__` methods in a JSON-configuration file.
+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.
+
+TODO(PVP) - add example and better info here
 
 ## ConfigMixin
-
 [[autodoc]] ConfigMixin
-	- load_config
 	- from_config
 	- save_config
-	- to_json_file
-	- to_json_string

docs/source/api/diffusion_pipeline.mdx

@@ -0,0 +1,39 @@
+<!--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
+
+## ImagePipelineOutput
+By default diffusion pipelines return an object of class
+
+[[autodoc]] pipeline_utils.ImagePipelineOutput

docs/source/api/logging.mdx

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -61,7 +61,7 @@ verbose to the most verbose), those levels (with their corresponding int values
   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 is the default level used by the library.
+  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.
 

docs/source/api/models.mdx

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -13,7 +13,7 @@ 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}(x_{t-1}|x_{t})\\). 
+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
@@ -25,63 +25,27 @@ The models are built on the base class ['ModelMixin'] that is a `torch.nn.module
 ## UNet2DModel
 [[autodoc]] UNet2DModel
 
-## UNet1DOutput
-[[autodoc]] models.unet_1d.UNet1DOutput
-
-## UNet1DModel
-[[autodoc]] UNet1DModel
-
 ## UNet2DConditionOutput
 [[autodoc]] models.unet_2d_condition.UNet2DConditionOutput
 
 ## UNet2DConditionModel
 [[autodoc]] UNet2DConditionModel
 
-## UNet3DConditionOutput
-[[autodoc]] models.unet_3d_condition.UNet3DConditionOutput
-
-## UNet3DConditionModel
-[[autodoc]] UNet3DConditionModel
-
 ## DecoderOutput
 [[autodoc]] models.vae.DecoderOutput
 
 ## VQEncoderOutput
-[[autodoc]] models.vq_model.VQEncoderOutput
+[[autodoc]] models.vae.VQEncoderOutput
 
 ## VQModel
 [[autodoc]] VQModel
 
 ## AutoencoderKLOutput
-[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
+[[autodoc]] models.vae.AutoencoderKLOutput
 
 ## AutoencoderKL
 [[autodoc]] AutoencoderKL
 
-## Transformer2DModel
-[[autodoc]] Transformer2DModel
-
-## Transformer2DModelOutput
-[[autodoc]] models.transformer_2d.Transformer2DModelOutput
-
-## TransformerTemporalModel
-[[autodoc]] models.transformer_temporal.TransformerTemporalModel
-
-## Transformer2DModelOutput
-[[autodoc]] models.transformer_temporal.TransformerTemporalModelOutput
-
-## PriorTransformer
-[[autodoc]] models.prior_transformer.PriorTransformer
-
-## PriorTransformerOutput
-[[autodoc]] models.prior_transformer.PriorTransformerOutput
-
-## ControlNetOutput
-[[autodoc]] models.controlnet.ControlNetOutput
-
-## ControlNetModel
-[[autodoc]] ControlNetModel
-
 ## FlaxModelMixin
 [[autodoc]] FlaxModelMixin
 
@@ -99,9 +63,3 @@ The models are built on the base class ['ModelMixin'] that is a `torch.nn.module
 
 ## FlaxAutoencoderKL
 [[autodoc]] FlaxAutoencoderKL
-
-## FlaxControlNetOutput
-[[autodoc]] models.controlnet_flax.FlaxControlNetOutput
-
-## FlaxControlNetModel
-[[autodoc]] FlaxControlNetModel

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/ddim.mdx

@@ -1,15 +1,3 @@
-<!--Copyright 2023 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
@@ -20,8 +8,7 @@ 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/).
+The original codebase of this paper can be found [here](https://github.com/ermongroup/ddim).
 
 ## Available Pipelines:
 
@@ -32,5 +19,4 @@ For questions, feel free to contact the author on [tsong.me](https://tsong.me/).
 
 ## DDIMPipeline
 [[autodoc]] DDIMPipeline
-	- all
-	- __call__
+    - __call__

docs/source/api/pipelines/ddpm.mdx

@@ -1,15 +1,3 @@
-<!--Copyright 2023 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
@@ -33,5 +21,4 @@ The original codebase of this paper can be found [here](https://github.com/hojon
 
 # DDPMPipeline
 [[autodoc]] DDPMPipeline
-	- all
-	- __call__
+    - __call__

docs/source/api/pipelines/latent_diffusion.mdx

@@ -1,15 +1,3 @@
-<!--Copyright 2023 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
@@ -33,17 +21,10 @@ The original codebase can be found [here](https://github.com/CompVis/latent-diff
 | 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
-	- all
-	- __call__
-
-## LDMSuperResolutionPipeline
-[[autodoc]] LDMSuperResolutionPipeline
-	- all
-	- __call__
+[[autodoc]] pipelines.latent_diffusion.pipeline_latent_diffusion.LDMTextToImagePipeline
+    - __call__

docs/source/api/pipelines/latent_diffusion_uncond.mdx

@@ -1,15 +1,3 @@
-<!--Copyright 2023 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
@@ -38,5 +26,4 @@ The original codebase can be found [here](https://github.com/CompVis/latent-diff
 
 ## LDMPipeline
 [[autodoc]] LDMPipeline
-	- all
-	- __call__
+    - __call__

docs/source/api/pipelines/overview.mdx

@@ -0,0 +1,184 @@
+<!--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.* [LatentDiffusionPipeline](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
+|---|---|:---:|:---:|
+| [ddpm](./ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
+| [ddim](./ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional 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)
+| [latent_diffusion](./latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation | 
+| [latent_diffusion_uncond](./latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation | 
+| [pndm](./pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation | 
+| [score_sde_ve](./score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
+| [score_sde_vp](./score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
+| [stable_diffusion](./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](./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](./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](./stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional 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.* [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) 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.* [CompVis/stable-diffusion-v1-4/model_index.json](https://huggingface.co/CompVis/stable-diffusion-v1-4/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("CompVis/stable-diffusion-v1-4")
+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(
+    "CompVis/stable-diffusion-v1-4", 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
+from io import BytesIO
+
+import requests
+import PIL
+
+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))
+
+device = "cuda"
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
+).to(device)
+
+prompt = "a cat sitting on a bench"
+images = pipe(prompt=prompt, init_image=init_image, mask_image=mask_image, strength=0.75).images
+
+images[0].save("cat_on_bench.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/in_painting_with_stable_diffusion_using_diffusers.ipynb)

docs/source/api/pipelines/pndm.mdx

@@ -1,15 +1,3 @@
-<!--Copyright 2023 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
@@ -30,6 +18,6 @@ The original codebase can be found [here](https://github.com/luping-liu/PNDM).
 
 
 ## PNDMPipeline
-[[autodoc]] PNDMPipeline
-	- all
-	- __call__
+[[autodoc]] pipelines.pndm.pipeline_pndm.PNDMPipeline
+    - __call__
+

docs/source/api/pipelines/score_sde_ve.mdx

@@ -1,15 +1,3 @@
-<!--Copyright 2023 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
@@ -32,5 +20,5 @@ This pipeline implements the Variance Expanding (VE) variant of the method.
 
 ## ScoreSdeVePipeline
 [[autodoc]] ScoreSdeVePipeline
-	- all
-	- __call__
+    - __call__
+

docs/source/api/pipelines/stable_diffusion.mdx

@@ -0,0 +1,39 @@
+# 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
+
+## 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

@@ -1,15 +1,3 @@
-<!--Copyright 2023 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
@@ -32,5 +20,4 @@ This pipeline implements the Stochastic sampling tailored to the Variance-Expand
 
 ## KarrasVePipeline
 [[autodoc]] KarrasVePipeline
-	- all
-	- __call__
+    - __call__

docs/source/api/schedulers.mdx

@@ -0,0 +1,108 @@
+<!--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.
+-->
+
+# Schedulers
+
+Diffusers contains multiple pre-built schedule functions for the diffusion process.
+
+## What is a scheduler?
+
+The schedule functions, denoted *Schedulers* in the library take in the output of a trained model, a sample which the diffusion process is iterating on, and a timestep to return a denoised sample.
+
+- Schedulers define the methodology for iteratively adding noise to an image or for updating a sample based on model outputs.
+    - adding noise in different manners represent the algorithmic processes to train a diffusion model by adding noise to images.
+    - for inference, the scheduler defines how to update a sample based on an output from a pretrained model.
+- Schedulers are often defined by a *noise schedule* and an *update rule* to solve the differential equation solution.
+
+### Discrete versus continuous schedulers
+
+All schedulers take in a timestep to predict the updated version of the sample being diffused.
+The timesteps dictate where in the diffusion process the step is, where data is generated by iterating forward in time and inference is executed by propagating backwards through timesteps.
+Different algorithms use timesteps that both discrete (accepting `int` inputs), such as the [`DDPMScheduler`] or [`PNDMScheduler`], and continuous (accepting `float` inputs), such as the score-based schedulers [`ScoreSdeVeScheduler`] or [`ScoreSdeVpScheduler`].
+
+## Designing Re-usable schedulers
+
+The core design principle between the schedule functions is to be model, system, and framework independent.
+This allows for rapid experimentation and cleaner abstractions in the code, where the model prediction is separated from the sample update.
+To this end, the design of schedulers is such that:
+
+- Schedulers can be used interchangeably between diffusion models in inference to find the preferred trade-off between speed and generation quality.
+- Schedulers are currently by default in PyTorch, but are designed to be framework independent (partial Jax support currently exists).
+
+
+## API
+
+The core API for any new scheduler must follow a limited structure.
+- Schedulers should provide one or more `def step(...)` functions that should be called to update the generated sample iteratively.
+- Schedulers should provide a `set_timesteps(...)` method that configures the parameters of a schedule function for a specific inference task.
+- Schedulers should be framework-specific.
+
+The base class [`SchedulerMixin`] implements low level utilities used by multiple schedulers.
+
+### SchedulerMixin
+[[autodoc]] SchedulerMixin
+
+### SchedulerOutput
+The class [`SchedulerOutput`] contains the outputs from any schedulers `step(...)` call.
+
+[[autodoc]] schedulers.scheduling_utils.SchedulerOutput
+
+### Implemented Schedulers
+
+#### Denoising diffusion implicit models (DDIM)
+
+Original paper can be found here.
+
+[[autodoc]] DDIMScheduler
+
+#### Denoising diffusion probabilistic models (DDPM)
+
+Original paper can be found [here](https://arxiv.org/abs/2010.02502).
+
+[[autodoc]] DDPMScheduler
+
+#### Variance exploding, stochastic sampling from Karras et. al
+
+Original paper can be found [here](https://arxiv.org/abs/2006.11239).
+
+[[autodoc]] KarrasVeScheduler
+
+#### Linear multistep scheduler for discrete beta schedules
+
+Original implementation can be found [here](https://arxiv.org/abs/2206.00364).
+
+
+[[autodoc]] LMSDiscreteScheduler
+
+#### Pseudo numerical methods for diffusion models (PNDM)
+
+Original implementation can be found [here](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181).
+
+[[autodoc]] PNDMScheduler
+
+#### variance exploding stochastic differential equation (SDE) scheduler
+
+Original paper can be found [here](https://arxiv.org/abs/2011.13456).
+
+[[autodoc]] ScoreSdeVeScheduler
+
+#### variance preserving stochastic differential equation (SDE) scheduler
+
+Original paper can be found [here](https://arxiv.org/abs/2011.13456).
+
+<Tip warning={true}>
+
+Score SDE-VP is under construction.
+
+</Tip>
+
+[[autodoc]] schedulers.scheduling_sde_vp.ScoreSdeVpScheduler

docs/source/conceptual/contribution.mdx

@@ -0,0 +1,291 @@
+<!--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 🧨
+
+We ❤️ contributions from the open-source community! Everyone is welcome, and all types of participation –not just code– are valued and appreciated. Answering questions, helping others, reaching out and improving the documentation are all immensely valuable to the community, so don't be afraid and get involved if you're up for it!
+
+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".
+
+We encourage everyone to start by saying 👋 in our public Discord channel. We discuss the hottest trends about diffusion models, ask questions, show-off personal projects, help each other with contributions, or just hang out ☕. <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>
+
+Whichever way you choose to contribute, we strive to be part of an open, welcoming and kind community. Please, read our [code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md) and be mindful to respect it during your interactions.
+
+
+## Overview
+
+You can contribute in so many ways! Just to name a few:
+
+* 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).
+* [Contributing to the documentation](https://github.com/huggingface/diffusers/tree/main/docs/source).
+* Submitting issues related to bugs or desired new features.
+
+*All are equally valuable to the community.*
+
+### Browse GitHub issues for suggestions
+
+If you need inspiration, you can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library. There are a few filters that can be helpful:
+
+- 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 and getting started with the codebase.
+- See [New pipeline/model](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models or diffusion pipelines.
+- See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) to work on new samplers and schedulers.
+
+
+## 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#L212)):
+
+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 git@github.com:<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 `[pipeline_latent_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.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 or [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!
+
+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/
+```
+
+`unittest` is fully supported, here's how to run tests with it:
+
+```bash
+$ python -m unittest discover -s tests -t . -v
+$ python -m unittest discover -s examples -t examples -v
+```
+
+### 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
+```
+
+### 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).**

docs/source/conceptual/philosophy.mdx

@@ -0,0 +1,17 @@
+<!--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.
+-->
+
+# Philosophy
+
+- Readability and clarity are 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 use 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. This is one of the guiding goals even if the initial pipelines are devoted to vision tasks.
+- 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 implementations and can include components of other libraries, such as text encoders. Examples of diffusion pipelines are [Glide](https://github.com/openai/glide-text2im), [Latent Diffusion](https://github.com/CompVis/latent-diffusion) and [Stable Diffusion](https://github.com/compvis/stable-diffusion).

docs/source/conceptual/stable_diffusion.mdx

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -10,11 +10,8 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# VQDiffusionScheduler
+# Stable Diffusion
 
-## Overview
+Under construction 🚧
 
-Original paper can be found [here](https://arxiv.org/abs/2111.14822)
-
-## VQDiffusionScheduler
-[[autodoc]] VQDiffusionScheduler
+For now please visit this [very in-detail blog post](https://huggingface.co/blog/stable_diffusion)

docs/source/en/_toctree.yml

@@ -1,296 +0,0 @@
-- sections:
-  - local: index
-    title: 🧨 Diffusers
-  - local: quicktour
-    title: Quicktour
-  - local: stable_diffusion
-    title: Effective and efficient diffusion
-  - local: installation
-    title: Installation
-  title: Get started
-- sections:
-  - local: tutorials/tutorial_overview
-    title: Overview
-  - local: using-diffusers/write_own_pipeline
-    title: Understanding models and schedulers
-  - local: tutorials/basic_training
-    title: Train a diffusion model
-  title: Tutorials
-- sections:
-  - sections:
-    - local: using-diffusers/loading_overview
-      title: Overview
-    - local: using-diffusers/loading
-      title: Load pipelines, models, and schedulers
-    - local: using-diffusers/schedulers
-      title: Load and compare different schedulers
-    - local: using-diffusers/custom_pipeline_overview
-      title: Load community pipelines
-    - local: using-diffusers/using_safetensors
-      title: Load safetensors
-    - local: using-diffusers/other-formats
-      title: Load different Stable Diffusion formats
-    title: Loading & Hub
-  - sections:
-    - local: using-diffusers/pipeline_overview
-      title: Overview
-    - 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/depth2img
-      title: Text-guided depth-to-image
-    - local: using-diffusers/textual_inversion_inference
-      title: Textual inversion
-    - local: training/distributed_inference
-      title: Distributed inference with multiple GPUs
-    - local: using-diffusers/reusing_seeds
-      title: Improve image quality with deterministic generation
-    - local: using-diffusers/reproducibility
-      title: Create reproducible pipelines
-    - local: using-diffusers/custom_pipeline_examples
-      title: Community pipelines
-    - local: using-diffusers/contribute_pipeline
-      title: How to contribute a community pipeline
-    - local: using-diffusers/stable_diffusion_jax_how_to
-      title: Stable Diffusion in JAX/Flax
-    - local: using-diffusers/weighted_prompts
-      title: Weighting Prompts
-    title: Pipelines for Inference
-  - sections:
-    - local: training/overview
-      title: Overview
-    - local: training/create_dataset
-      title: Create a dataset for training
-    - local: training/adapt_a_model
-      title: Adapt a model to a new task
-    - local: 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
-    - local: training/lora
-      title: Low-Rank Adaptation of Large Language Models (LoRA)
-    - local: training/controlnet
-      title: ControlNet
-    - local: training/instructpix2pix
-      title: InstructPix2Pix Training
-    - local: training/custom_diffusion
-      title: Custom Diffusion
-    title: Training
-  - 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/opt_overview
-    title: Overview
-  - local: optimization/fp16
-    title: Memory and Speed
-  - local: optimization/torch2.0
-    title: Torch2.0 support
-  - local: optimization/xformers
-    title: xFormers
-  - local: optimization/onnx
-    title: ONNX
-  - local: optimization/open_vino
-    title: OpenVINO
-  - local: optimization/coreml
-    title: Core ML
-  - local: optimization/mps
-    title: MPS
-  - local: optimization/habana
-    title: Habana Gaudi
-  - local: optimization/tome
-    title: Token Merging
-  title: Optimization/Special Hardware
-- sections:
-  - local: conceptual/philosophy
-    title: Philosophy
-  - local: using-diffusers/controlling_generation
-    title: Controlled generation
-  - local: conceptual/contribution
-    title: How to contribute?
-  - local: conceptual/ethical_guidelines
-    title: Diffusers' Ethical Guidelines
-  - local: conceptual/evaluation
-    title: Evaluating Diffusion Models
-  title: Conceptual Guides
-- sections:
-  - sections:
-    - local: api/models
-      title: Models
-    - local: api/attnprocessor
-      title: Attention Processor
-    - local: api/diffusion_pipeline
-      title: Diffusion Pipeline
-    - local: api/logging
-      title: Logging
-    - local: api/configuration
-      title: Configuration
-    - local: api/outputs
-      title: Outputs
-    - local: api/loaders
-      title: Loaders
-    - local: api/utilities
-      title: Utilities
-    title: Main Classes
-  - sections:
-    - local: api/pipelines/overview
-      title: Overview
-    - local: api/pipelines/alt_diffusion
-      title: AltDiffusion
-    - local: api/pipelines/attend_and_excite
-      title: Attend and Excite
-    - local: api/pipelines/audio_diffusion
-      title: Audio Diffusion
-    - local: api/pipelines/audioldm
-      title: AudioLDM
-    - local: api/pipelines/controlnet
-      title: ControlNet
-    - local: api/pipelines/cycle_diffusion
-      title: Cycle Diffusion
-    - local: api/pipelines/dance_diffusion
-      title: Dance Diffusion
-    - local: api/pipelines/ddim
-      title: DDIM
-    - local: api/pipelines/ddpm
-      title: DDPM
-    - local: api/pipelines/diffedit
-      title: DiffEdit
-    - local: api/pipelines/dit
-      title: DiT
-    - local: api/pipelines/if
-      title: IF
-    - local: api/pipelines/pix2pix
-      title: InstructPix2Pix
-    - local: api/pipelines/kandinsky
-      title: Kandinsky
-    - local: api/pipelines/latent_diffusion
-      title: Latent Diffusion
-    - local: api/pipelines/panorama
-      title: MultiDiffusion Panorama
-    - local: api/pipelines/paint_by_example
-      title: PaintByExample
-    - local: api/pipelines/pix2pix_zero
-      title: Pix2Pix Zero
-    - local: api/pipelines/pndm
-      title: PNDM
-    - local: api/pipelines/repaint
-      title: RePaint
-    - local: api/pipelines/score_sde_ve
-      title: Score SDE VE
-    - local: api/pipelines/self_attention_guidance
-      title: Self-Attention Guidance
-    - local: api/pipelines/semantic_stable_diffusion
-      title: Semantic Guidance
-    - local: api/pipelines/spectrogram_diffusion
-      title: Spectrogram Diffusion
-    - sections:
-      - local: api/pipelines/stable_diffusion/overview
-        title: Overview
-      - local: api/pipelines/stable_diffusion/text2img
-        title: Text-to-Image
-      - local: api/pipelines/stable_diffusion/img2img
-        title: Image-to-Image
-      - local: api/pipelines/stable_diffusion/inpaint
-        title: Inpaint
-      - local: api/pipelines/stable_diffusion/depth2img
-        title: Depth-to-Image
-      - local: api/pipelines/stable_diffusion/image_variation
-        title: Image-Variation
-      - local: api/pipelines/stable_diffusion/stable_diffusion_safe
-        title: Safe Stable Diffusion
-      - local: api/pipelines/stable_diffusion/stable_diffusion_2
-        title: Stable Diffusion 2
-      - local: api/pipelines/stable_diffusion/latent_upscale
-        title: Stable-Diffusion-Latent-Upscaler
-      - local: api/pipelines/stable_diffusion/upscale
-        title: Super-Resolution
-      title: Stable Diffusion
-    - local: api/pipelines/stable_unclip
-      title: Stable unCLIP
-    - local: api/pipelines/stochastic_karras_ve
-      title: Stochastic Karras VE
-    - local: api/pipelines/model_editing
-      title: Text-to-Image Model Editing
-    - local: api/pipelines/text_to_video
-      title: Text-to-Video
-    - local: api/pipelines/text_to_video_zero
-      title: Text-to-Video Zero
-    - local: api/pipelines/unclip
-      title: UnCLIP
-    - local: api/pipelines/latent_diffusion_uncond
-      title: Unconditional Latent Diffusion
-    - local: api/pipelines/unidiffuser
-      title: UniDiffuser
-    - local: api/pipelines/versatile_diffusion
-      title: Versatile Diffusion
-    - local: api/pipelines/vq_diffusion
-      title: VQ Diffusion
-    title: Pipelines
-  - sections:
-    - local: api/schedulers/overview
-      title: Overview
-    - local: api/schedulers/ddim
-      title: DDIM
-    - local: api/schedulers/ddim_inverse
-      title: DDIMInverse
-    - local: api/schedulers/ddpm
-      title: DDPM
-    - local: api/schedulers/deis
-      title: DEIS
-    - local: api/schedulers/dpm_discrete
-      title: DPM Discrete Scheduler
-    - local: api/schedulers/dpm_discrete_ancestral
-      title: DPM Discrete Scheduler with ancestral sampling
-    - local: api/schedulers/dpm_sde
-      title: DPMSolverSDEScheduler
-    - local: api/schedulers/euler_ancestral
-      title: Euler Ancestral Scheduler
-    - local: api/schedulers/euler
-      title: Euler scheduler
-    - local: api/schedulers/heun
-      title: Heun Scheduler
-    - local: api/schedulers/multistep_dpm_solver_inverse
-      title: Inverse Multistep DPM-Solver
-    - local: api/schedulers/ipndm
-      title: IPNDM
-    - local: api/schedulers/lms_discrete
-      title: Linear Multistep
-    - local: api/schedulers/multistep_dpm_solver
-      title: Multistep DPM-Solver
-    - local: api/schedulers/pndm
-      title: PNDM
-    - local: api/schedulers/repaint
-      title: RePaint Scheduler
-    - local: api/schedulers/singlestep_dpm_solver
-      title: Singlestep DPM-Solver
-    - local: api/schedulers/stochastic_karras_ve
-      title: Stochastic Kerras VE
-    - local: api/schedulers/unipc
-      title: UniPCMultistepScheduler
-    - local: api/schedulers/score_sde_ve
-      title: VE-SDE
-    - local: api/schedulers/score_sde_vp
-      title: VP-SDE
-    - local: api/schedulers/vq_diffusion
-      title: VQDiffusionScheduler
-    title: Schedulers
-  - sections:
-    - local: api/experimental/rl
-      title: RL Planning
-    title: Experimental Features
-  title: API

docs/source/en/api/attnprocessor.mdx

@@ -1,42 +0,0 @@
-# Attention Processor
-
-An attention processor is a class for applying different types of attention mechanisms.
-
-## AttnProcessor
-[[autodoc]] models.attention_processor.AttnProcessor
-
-## AttnProcessor2_0
-[[autodoc]] models.attention_processor.AttnProcessor2_0
-
-## LoRAAttnProcessor
-[[autodoc]] models.attention_processor.LoRAAttnProcessor
-
-## LoRAAttnProcessor2_0
-[[autodoc]] models.attention_processor.LoRAAttnProcessor2_0
-
-## CustomDiffusionAttnProcessor
-[[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor
-
-## AttnAddedKVProcessor
-[[autodoc]] models.attention_processor.AttnAddedKVProcessor
-
-## AttnAddedKVProcessor2_0
-[[autodoc]] models.attention_processor.AttnAddedKVProcessor2_0
-
-## LoRAAttnAddedKVProcessor
-[[autodoc]] models.attention_processor.LoRAAttnAddedKVProcessor
-
-## XFormersAttnProcessor
-[[autodoc]] models.attention_processor.XFormersAttnProcessor
-
-## LoRAXFormersAttnProcessor
-[[autodoc]] models.attention_processor.LoRAXFormersAttnProcessor
-
-## CustomDiffusionXFormersAttnProcessor
-[[autodoc]] models.attention_processor.CustomDiffusionXFormersAttnProcessor
-
-## SlicedAttnProcessor
-[[autodoc]] models.attention_processor.SlicedAttnProcessor
-
-## SlicedAttnAddedKVProcessor
-[[autodoc]] models.attention_processor.SlicedAttnAddedKVProcessor

docs/source/en/api/diffusion_pipeline.mdx

@@ -1,36 +0,0 @@
-<!--Copyright 2023 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 use it for inference.
-
-<Tip>
-	
-You shouldn't use the [`DiffusionPipeline`] class for training or finetuning a diffusion model. Individual 
-components (for example, [`UNetModel`] and [`UNetConditionModel`]) of diffusion pipelines are usually trained individually, so we suggest directly working with instead.
-
-</Tip>
-
-The pipeline type (for example [`StableDiffusionPipeline`]) of any diffusion pipeline loaded with [`~DiffusionPipeline.from_pretrained`] is automatically 
-detected and pipeline components are loaded and passed to the `__init__` function of the pipeline.
-
-Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrained`].
-
-## DiffusionPipeline
-
-[[autodoc]] DiffusionPipeline
-	- all
-	- __call__
-	- device
-	- to
-	- components

docs/source/en/api/loaders.mdx

@@ -1,42 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Loaders
-
-There are many ways to train adapter neural networks for diffusion models, such as 
-- [Textual Inversion](./training/text_inversion.mdx)
-- [LoRA](https://github.com/cloneofsimo/lora)
-- [Hypernetworks](https://arxiv.org/abs/1609.09106)
-
-Such adapter neural networks often only consist of a fraction of the number of weights compared 
-to the pretrained model and as such are very portable. The Diffusers library offers an easy-to-use
-API to load such adapter neural networks via the [`loaders.py` module](https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders.py). 
-
-**Note**: This module is still highly experimental and prone to future changes.
-
-## LoaderMixins
-
-### UNet2DConditionLoadersMixin
-
-[[autodoc]] loaders.UNet2DConditionLoadersMixin
-
-### TextualInversionLoaderMixin
-
-[[autodoc]] loaders.TextualInversionLoaderMixin
-
-### LoraLoaderMixin
-
-[[autodoc]] loaders.LoraLoaderMixin
-
-### FromCkptMixin
-
-[[autodoc]] loaders.FromCkptMixin

docs/source/en/api/outputs.mdx

@@ -1,69 +0,0 @@
-<!--Copyright 2023 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 subclasses of [`~utils.BaseOutput`]. Those are
-data structures containing all the information returned by the model, but they can also be used as tuples or
-dictionaries.
-
-For example:
-
-```python
-from diffusers import DDIMPipeline
-
-pipeline = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
-outputs = pipeline()
-```
-
-The `outputs` object is a [`~pipelines.ImagePipelineOutput`] which means it has an image attribute.
-
-You can access each attribute as you normally would or with a keyword lookup, and if that attribute is not returned by the model, you will get `None`:
-
-```python
-outputs.images
-outputs["images"]
-```
-
-When considering the `outputs` object as a tuple, it only considers the attributes that don't have `None` values.
-For instance, retrieving an image by indexing into it returns the tuple `(outputs.images)`:
-
-```python
-outputs[:1]
-```
-
-<Tip>
-
-To check a specific pipeline or model output, refer to its corresponding API documentation.
-
-</Tip>
-
-## BaseOutput
-
-[[autodoc]] utils.BaseOutput
-    - to_tuple
-
-## ImagePipelineOutput
-
-[[autodoc]] pipelines.ImagePipelineOutput
-
-## FlaxImagePipelineOutput
-
-[[autodoc]] pipelines.pipeline_flax_utils.FlaxImagePipelineOutput
-
-## AudioPipelineOutput
-
-[[autodoc]] pipelines.AudioPipelineOutput
-
-## ImageTextPipelineOutput
-
-[[autodoc]] ImageTextPipelineOutput

docs/source/en/api/pipelines/alt_diffusion.mdx

@@ -1,83 +0,0 @@
-<!--Copyright 2023 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 exactly the same as [Stable Diffusion](./stable_diffusion/overview).
-
-- *Run AltDiffusion*
-
-AltDiffusion can be tested very easily with the [`AltDiffusionPipeline`], [`AltDiffusionImg2ImgPipeline`] and the `"BAAI/AltDiffusion-m9"` 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-m9")
->>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-
->>> # or
->>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("BAAI/AltDiffusion-m9", subfolder="scheduler")
->>> pipeline = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", scheduler=euler_scheduler)
-```
-
-
-- *How to convert 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-m9")
->>> 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
-	- all
-	- __call__
-
-## AltDiffusionPipeline
-[[autodoc]] AltDiffusionPipeline
-	- all
-	- __call__
-
-## AltDiffusionImg2ImgPipeline
-[[autodoc]] AltDiffusionImg2ImgPipeline
-	- all
-	- __call__

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

@@ -1,75 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Attend and Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
-
-## Overview
-
-Attend and Excite for Stable Diffusion was proposed in [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://attendandexcite.github.io/Attend-and-Excite/) and provides textual attention control over the image generation.
-
-The abstract of the paper is the following:
-
-*Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user's intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA's effectiveness on a variety of tasks and provide evidence for its versatility and flexibility.*
-
-Resources
-
-* [Project Page](https://attendandexcite.github.io/Attend-and-Excite/)
-* [Paper](https://arxiv.org/abs/2301.13826)
-* [Original Code](https://github.com/AttendAndExcite/Attend-and-Excite)
-* [Demo](https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite)
-
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab | Demo
-|---|---|:---:|:---:|
-| [pipeline_semantic_stable_diffusion_attend_and_excite.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_semantic_stable_diffusion_attend_and_excite) | *Text-to-Image Generation* | - | https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite
-
-
-### Usage example
-
-
-```python
-import torch
-from diffusers import StableDiffusionAttendAndExcitePipeline
-
-model_id = "CompVis/stable-diffusion-v1-4"
-pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-pipe = pipe.to("cuda")
-
-prompt = "a cat and a frog"
-
-# use get_indices function to find out indices of the tokens you want to alter
-pipe.get_indices(prompt)
-
-token_indices = [2, 5]
-seed = 6141
-generator = torch.Generator("cuda").manual_seed(seed)
-
-images = pipe(
-    prompt=prompt,
-    token_indices=token_indices,
-    guidance_scale=7.5,
-    generator=generator,
-    num_inference_steps=50,
-    max_iter_to_alter=25,
-).images
-
-image = images[0]
-image.save(f"../images/{prompt}_{seed}.png")
-```
-
-
-## StableDiffusionAttendAndExcitePipeline
-[[autodoc]] StableDiffusionAttendAndExcitePipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/audio_diffusion.mdx

@@ -1,98 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Audio Diffusion
-
-## Overview
-
-[Audio Diffusion](https://github.com/teticio/audio-diffusion) by Robert Dargavel Smith.
-
-Audio Diffusion leverages the recent advances in image generation using diffusion models by converting audio samples to
-and from mel spectrogram images.
-
-The original codebase of this implementation can be found [here](https://github.com/teticio/audio-diffusion), including
-training scripts and example notebooks.
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab
-|---|---|:---:|
-| [pipeline_audio_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py) | *Unconditional Audio Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/audio_diffusion_pipeline.ipynb) |
-
-
-## Examples:
-
-### Audio Diffusion
-
-```python
-import torch
-from IPython.display import Audio
-from diffusers import DiffusionPipeline
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device)
-
-output = pipe()
-display(output.images[0])
-display(Audio(output.audios[0], rate=mel.get_sample_rate()))
-```
-
-### Latent Audio Diffusion
-
-```python
-import torch
-from IPython.display import Audio
-from diffusers import DiffusionPipeline
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device)
-
-output = pipe()
-display(output.images[0])
-display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
-```
-
-### Audio Diffusion with DDIM (faster)
-
-```python
-import torch
-from IPython.display import Audio
-from diffusers import DiffusionPipeline
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256").to(device)
-
-output = pipe()
-display(output.images[0])
-display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
-```
-
-### Variations, in-painting, out-painting etc.
-
-```python
-output = pipe(
-    raw_audio=output.audios[0, 0],
-    start_step=int(pipe.get_default_steps() / 2),
-    mask_start_secs=1,
-    mask_end_secs=1,
-)
-display(output.images[0])
-display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
-```
-
-## AudioDiffusionPipeline
-[[autodoc]] AudioDiffusionPipeline
-	- all
-	- __call__
-
-## Mel
-[[autodoc]] Mel

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

@@ -1,84 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# AudioLDM
-
-## Overview
-
-AudioLDM was proposed in [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://arxiv.org/abs/2301.12503) by Haohe Liu et al.
-
-Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview), AudioLDM
-is a text-to-audio _latent diffusion model (LDM)_ that learns continuous audio representations from [CLAP](https://huggingface.co/docs/transformers/main/model_doc/clap)
-latents. AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional
-sound effects, human speech and music.
-
-This pipeline was contributed by [sanchit-gandhi](https://huggingface.co/sanchit-gandhi). The original codebase can be found [here](https://github.com/haoheliu/AudioLDM).
-
-## Text-to-Audio
-
-The [`AudioLDMPipeline`] can be used to load pre-trained weights from [cvssp/audioldm-s-full-v2](https://huggingface.co/cvssp/audioldm-s-full-v2) and generate text-conditional audio outputs:
-
-```python
-from diffusers import AudioLDMPipeline
-import torch
-import scipy
-
-repo_id = "cvssp/audioldm-s-full-v2"
-pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
-
-prompt = "Techno music with a strong, upbeat tempo and high melodic riffs"
-audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]
-
-# save the audio sample as a .wav file
-scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
-```
-
-### Tips
-
-Prompts:
-* Descriptive prompt inputs work best: you can use adjectives to describe the sound (e.g. "high quality" or "clear") and make the prompt context specific (e.g., "water stream in a forest" instead of "stream").
-* It's best to use general terms like 'cat' or 'dog' instead of specific names or abstract objects that the model may not be familiar with.
-
-Inference:
-* The _quality_ of the predicted audio sample can be controlled by the `num_inference_steps` argument: higher steps give higher quality audio at the expense of slower inference.
-* The _length_ of the predicted audio sample can be controlled by varying the `audio_length_in_s` argument.
-
-### How to load and use different schedulers
-
-The AudioLDM pipeline uses [`DDIMScheduler`] scheduler by default. But `diffusers` provides many other schedulers
-that can be used with the AudioLDM pipeline such as [`PNDMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`],
-[`EulerAncestralDiscreteScheduler`] etc. We recommend using the [`DPMSolverMultistepScheduler`] as it's currently the fastest
-scheduler there is.
-
-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
-[`DPMSolverMultistepScheduler`], you can do the following:
-
-```python
->>> from diffusers import AudioLDMPipeline, DPMSolverMultistepScheduler
->>> import torch
-
->>> pipeline = AudioLDMPipeline.from_pretrained("cvssp/audioldm-s-full-v2", torch_dtype=torch.float16)
->>> pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-
->>> # or
->>> dpm_scheduler = DPMSolverMultistepScheduler.from_pretrained("cvssp/audioldm-s-full-v2", subfolder="scheduler")
->>> pipeline = AudioLDMPipeline.from_pretrained(
-...     "cvssp/audioldm-s-full-v2", scheduler=dpm_scheduler, torch_dtype=torch.float16
-... )
-```
-
-## AudioLDMPipeline
-[[autodoc]] AudioLDMPipeline
-	- all
-	- __call__

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

@@ -1,363 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Text-to-Image Generation with ControlNet Conditioning
-
-## Overview
-
-[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
-
-Using the pretrained models we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details.
-
-The abstract of the paper is the following:
-
-*We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.*
-
-This model was contributed by the community contributor [takuma104](https://huggingface.co/takuma104) ❤️ .
-
-Resources:
-
-* [Paper](https://arxiv.org/abs/2302.05543)
-* [Original Code](https://github.com/lllyasviel/ControlNet)
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo
-|---|---|:---:|
-| [StableDiffusionControlNetPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/controlnet/pipeline_controlnet.py) | *Text-to-Image Generation with ControlNet Conditioning* | [Colab Example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/controlnet.ipynb)
-| [StableDiffusionControlNetImg2ImgPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py) | *Image-to-Image Generation with ControlNet Conditioning* | 
-| [StableDiffusionControlNetInpaintPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_controlnet_inpaint.py) | *Inpainting Generation with ControlNet Conditioning* | 
-
-## Usage example
-
-In the following we give a simple example of how to use a *ControlNet* checkpoint with Diffusers for inference.
-The inference pipeline is the same for all pipelines:
-
-* 1. Take an image and run it through a pre-conditioning processor.
-* 2. Run the pre-processed image through the [`StableDiffusionControlNetPipeline`].
-
-Let's have a look at a simple example using the [Canny Edge ControlNet](https://huggingface.co/lllyasviel/sd-controlnet-canny).
-
-```python
-from diffusers import StableDiffusionControlNetPipeline
-from diffusers.utils import load_image
-
-# Let's load the popular vermeer image
-image = load_image(
-    "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
-)
-```
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)
-
-Next, we process the image to get the canny image. This is step *1.* - running the pre-conditioning processor. The pre-conditioning processor is different for every ControlNet. Please see the model cards of the [official checkpoints](#controlnet-with-stable-diffusion-1.5) for more information about other models.
-
-First, we need to install opencv:
-
-```
-pip install opencv-contrib-python
-```
-
-Next, let's also install all required Hugging Face libraries:
-
-```
-pip install diffusers transformers git+https://github.com/huggingface/accelerate.git
-```
-
-Then we can retrieve the canny edges of the image.
-
-```python
-import cv2
-from PIL import Image
-import numpy as np
-
-image = np.array(image)
-
-low_threshold = 100
-high_threshold = 200
-
-image = cv2.Canny(image, low_threshold, high_threshold)
-image = image[:, :, None]
-image = np.concatenate([image, image, image], axis=2)
-canny_image = Image.fromarray(image)
-```
-
-Let's take a look at the processed image.
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png)
-
-Now, we load the official [Stable Diffusion 1.5 Model](runwayml/stable-diffusion-v1-5) as well as the ControlNet for canny edges.
-
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
-)
-```
-
-To speed-up things and reduce memory, let's enable model offloading and use the fast [`UniPCMultistepScheduler`].
-
-```py
-from diffusers import UniPCMultistepScheduler
-
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-
-# this command loads the individual model components on GPU on-demand.
-pipe.enable_model_cpu_offload()
-```
-
-Finally, we can run the pipeline:
-
-```py
-generator = torch.manual_seed(0)
-
-out_image = pipe(
-    "disco dancer with colorful lights", num_inference_steps=20, generator=generator, image=canny_image
-).images[0]
-```
-
-This should take only around 3-4 seconds on GPU (depending on hardware). The output image then looks as follows:
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_disco_dancing.png)
-
-
-**Note**: To see how to run all other ControlNet checkpoints, please have a look at [ControlNet with Stable Diffusion 1.5](#controlnet-with-stable-diffusion-1.5).
-
-<!-- TODO: add space -->
-
-## Combining multiple conditionings
-
-Multiple ControlNet conditionings can be combined for a single image generation. Pass a list of ControlNets to the pipeline's constructor and a corresponding list of conditionings to `__call__`.
-
-When combining conditionings, it is helpful to mask conditionings such that they do not overlap. In the example, we mask the middle of the canny map where the pose conditioning is located.
-
-It can also be helpful to vary the `controlnet_conditioning_scales` to emphasize one conditioning over the other.
-
-### Canny conditioning
-
-The original image:
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"/>
-
-Prepare the conditioning:
-
-```python 
-from diffusers.utils import load_image
-from PIL import Image
-import cv2
-import numpy as np
-from diffusers.utils import load_image
-
-canny_image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
-)
-canny_image = np.array(canny_image)
-
-low_threshold = 100
-high_threshold = 200
-
-canny_image = cv2.Canny(canny_image, low_threshold, high_threshold)
-
-# zero out middle columns of image where pose will be overlayed
-zero_start = canny_image.shape[1] // 4
-zero_end = zero_start + canny_image.shape[1] // 2
-canny_image[:, zero_start:zero_end] = 0
-
-canny_image = canny_image[:, :, None]
-canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
-canny_image = Image.fromarray(canny_image)
-```
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/landscape_canny_masked.png"/>
-
-### Openpose conditioning
-
-The original image:
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png" width=600/>
-
-Prepare the conditioning:
-
-```python
-from controlnet_aux import OpenposeDetector
-from diffusers.utils import load_image
-
-openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
-
-openpose_image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
-)
-openpose_image = openpose(openpose_image)
-```
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/person_pose.png" width=600/>
-
-### Running ControlNet with multiple conditionings
-
-```python
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-import torch
-
-controlnet = [
-    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16),
-    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16),
-]
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
-)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-
-pipe.enable_xformers_memory_efficient_attention()
-pipe.enable_model_cpu_offload()
-
-prompt = "a giant standing in a fantasy landscape, best quality"
-negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-
-generator = torch.Generator(device="cpu").manual_seed(1)
-
-images = [openpose_image, canny_image]
-
-image = pipe(
-    prompt,
-    images,
-    num_inference_steps=20,
-    generator=generator,
-    negative_prompt=negative_prompt,
-    controlnet_conditioning_scale=[1.0, 0.8],
-).images[0]
-
-image.save("./multi_controlnet_output.png")
-```
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/multi_controlnet_output.png" width=600/>
-
-### Guess Mode
-
-Guess Mode is [a ControlNet feature that was implemented](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) after the publication of [the paper](https://arxiv.org/abs/2302.05543). The description states:
-
->In this mode, the ControlNet encoder will try best to recognize the content of the input control map, like depth map, edge map, scribbles, etc, even if you remove all prompts.
-
-#### The core implementation:
-
-It adjusts the scale of the output residuals from ControlNet by a fixed ratio depending on the block depth. The shallowest DownBlock corresponds to `0.1`. As the blocks get deeper, the scale increases exponentially, and the scale for the output of the MidBlock becomes `1.0`. 
-
-Since the core implementation is just this, **it does not have any impact on prompt conditioning**. While it is common to use it without specifying any prompts, it is also possible to provide prompts if desired.
-
-#### Usage:
-
-Just specify `guess_mode=True` in the pipe() function. A `guidance_scale` between 3.0 and 5.0 is [recommended](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode).
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet).to(
-    "cuda"
-)
-image = pipe("", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]
-image.save("guess_mode_generated.png")
-```
-
-#### Output image comparison:
-Canny Control Example
-
-|no guess_mode with prompt|guess_mode without prompt|
-|---|---|
-|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"/></a>|
-
-
-## Available checkpoints
-
-ControlNet requires a *control image* in addition to the text-to-image *prompt*. 
-Each pretrained model is trained using a different conditioning method that requires different images for conditioning the generated outputs. For example, Canny edge conditioning requires the control image to be the output of a Canny filter, while depth conditioning requires the control image to be a depth map. See the overview and image examples below to know more.
-
-All checkpoints can be found under the authors' namespace [lllyasviel](https://huggingface.co/lllyasviel).
-
-**13.04.2024 Update**: The author has released improved controlnet checkpoints v1.1 - see [here](#controlnet-v1.1).
-
-### ControlNet v1.0
-
-| Model Name | Control Image Overview| Control Image Example | Generated Image Example |
-|---|---|---|---|
-|[lllyasviel/sd-controlnet-canny](https://huggingface.co/lllyasviel/sd-controlnet-canny)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_canny.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"/></a>|
-|[lllyasviel/sd-controlnet-depth](https://huggingface.co/lllyasviel/sd-controlnet-depth)<br/> *Trained with Midas depth estimation*  |A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_depth.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_depth.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"/></a>|
-|[lllyasviel/sd-controlnet-hed](https://huggingface.co/lllyasviel/sd-controlnet-hed)<br/> *Trained with HED edge detection (soft edge)*  |A monochrome image with white soft edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_hed.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_hed.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"/></a> |
-|[lllyasviel/sd-controlnet-mlsd](https://huggingface.co/lllyasviel/sd-controlnet-mlsd)<br/> *Trained with M-LSD line detection*  |A monochrome image composed only of white straight lines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_mlsd.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_mlsd.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"/></a>|
-|[lllyasviel/sd-controlnet-normal](https://huggingface.co/lllyasviel/sd-controlnet-normal)<br/> *Trained with normal map*  |A [normal mapped](https://en.wikipedia.org/wiki/Normal_mapping) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_normal.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_normal.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"/></a>|
-|[lllyasviel/sd-controlnet-openpose](https://huggingface.co/lllyasviel/sd-controlnet_openpose)<br/> *Trained with OpenPose bone image*  |A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_openpose.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_openpose.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"/></a>|
-|[lllyasviel/sd-controlnet-scribble](https://huggingface.co/lllyasviel/sd-controlnet_scribble)<br/> *Trained with human scribbles*  |A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_scribble.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_scribble.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"/></a> |
-|[lllyasviel/sd-controlnet-seg](https://huggingface.co/lllyasviel/sd-controlnet_seg)<br/>*Trained with semantic segmentation*  |An [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/)'s segmentation protocol image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_seg.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_seg.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"/></a> |
-
-### ControlNet v1.1
-
-| Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example |
-|---|---|---|---|---|
-|[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>|
-|[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>|
-
-## StableDiffusionControlNetPipeline
-[[autodoc]] StableDiffusionControlNetPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-
-## StableDiffusionControlNetImg2ImgPipeline
-[[autodoc]] StableDiffusionControlNetImg2ImgPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-
-## StableDiffusionControlNetInpaintPipeline
-[[autodoc]] StableDiffusionControlNetInpaintPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-
-## FlaxStableDiffusionControlNetPipeline
-[[autodoc]] FlaxStableDiffusionControlNetPipeline
-	- all
-	- __call__
-

docs/source/en/api/pipelines/cycle_diffusion.mdx

@@ -1,100 +0,0 @@
-<!--Copyright 2023 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,
-    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,
-    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
-	- all
-	- __call__

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

@@ -1,34 +0,0 @@
-<!--Copyright 2023 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
-	- all
-	- __call__

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

@@ -1,360 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Zero-shot Diffusion-based Semantic Image Editing with Mask Guidance
-
-## Overview
-
-[DiffEdit: Diffusion-based semantic image editing with mask guidance](https://arxiv.org/abs/2210.11427) by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord.
-
-The abstract of the paper is the following:
-
-*Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to edit an image based on a text query. Semantic image editing is an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image. Current editing methods based on diffusion models usually require to provide a mask, making the task much easier by treating it as a conditional inpainting task. In contrast, our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited, by contrasting predictions of a diffusion model conditioned on different text prompts. Moreover, we rely on latent inference to preserve content in those regions of interest and show excellent synergies with mask-based diffusion. DiffEdit achieves state-of-the-art editing performance on ImageNet. In addition, we evaluate semantic image editing in more challenging settings, using images from the COCO dataset as well as text-based generated images.*
-
-Resources:
-
-* [Paper](https://arxiv.org/abs/2210.11427).
-* [Blog Post with Demo](https://blog.problemsolversguild.com/technical/research/2022/11/02/DiffEdit-Implementation.html).
-* [Implementation on Github](https://github.com/Xiang-cd/DiffEdit-stable-diffusion/).
-
-## Tips 
-
-* The pipeline can generate masks that can be fed into other inpainting pipelines. Check out the code examples below to know more.
-* In order to generate an image using this pipeline, both an image mask (manually specified or generated using `generate_mask`)
-and a set of partially inverted latents (generated using `invert`) _must_ be provided as arguments when calling the pipeline to generate the final edited image.
-Refer to the code examples below for more details.
-* The function `generate_mask` exposes two prompt arguments, `source_prompt` and `target_prompt`,
-that let you control the locations of the semantic edits in the final image to be generated. Let's say,
-you wanted to translate from "cat" to "dog". In this case, the edit direction will be "cat -> dog". To reflect
-this in the generated mask, you simply have to set the embeddings related to the phrases including "cat" to
-`source_prompt_embeds` and "dog" to `target_prompt_embeds`. Refer to the code example below for more details.
-* When generating partially inverted latents using `invert`, assign a caption or text embedding describing the
-overall image to the `prompt` argument to help guide the inverse latent sampling process. In most cases, the
-source concept is sufficently descriptive to yield good results, but feel free to explore alternatives.
-Please refer to [this code example](#generating-image-captions-for-inversion) for more details.
-* When calling the pipeline to generate the final edited image, assign the source concept to `negative_prompt`
-and the target concept to `prompt`. Taking the above example, you simply have to set the embeddings related to
-the phrases including "cat" to `negative_prompt_embeds` and "dog" to `prompt_embeds`. Refer to the code example
-below for more details.
-* If you wanted to reverse the direction in the example above, i.e., "dog -> cat", then it's recommended to:
-    * Swap the `source_prompt` and `target_prompt` in the arguments to `generate_mask`.
-    * Change the input prompt for `invert` to include "dog".
-    * Swap the `prompt` and `negative_prompt` in the arguments to call the pipeline to generate the final edited image.
-* Note that the source and target prompts, or their corresponding embeddings, can also be automatically generated. Please, refer to [this discussion](#generating-source-and-target-embeddings) for more details.
-
-## Available Pipelines:
-
-| Pipeline | Tasks
-|---|---|
-| [StableDiffusionDiffEditPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_diffedit.py) | *Text-Based Image Editing*
-
-<!-- TODO: add Colab -->
-
-## Usage example
-
-### Based on an input image with a caption
-
-When the pipeline is conditioned on an input image, we first obtain partially inverted latents from the input image using a
-`DDIMInverseScheduler` with the help of a caption. Then we generate an editing mask to identify relevant regions in the image using the source and target prompts. Finally, 
-the inverted noise and generated mask is used to start the generation process. 
-
-First, let's load our pipeline: 
-
-```py
-import torch
-from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline
-
-sd_model_ckpt = "stabilityai/stable-diffusion-2-1"
-pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
-    sd_model_ckpt,
-    torch_dtype=torch.float16,
-    safety_checker=None,
-)
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
-pipeline.enable_model_cpu_offload()
-pipeline.enable_vae_slicing()
-generator = torch.manual_seed(0)
-```
-
-Then, we load an input image to edit using our method: 
-
-```py
-from diffusers.utils import load_image
-
-img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
-raw_image = load_image(img_url).convert("RGB").resize((768, 768))
-```
-
-Then, we employ the source and target prompts to generate the editing mask:
-
-```py
-# See the "Generating source and target embeddings" section below to
-# automate the generation of these captions with a pre-trained model like Flan-T5 as explained below.
-
-source_prompt = "a bowl of fruits"
-target_prompt = "a basket of fruits"
-mask_image = pipeline.generate_mask(
-    image=raw_image,
-    source_prompt=source_prompt,
-    target_prompt=target_prompt,
-    generator=generator,
-)
-```
-
-Then, we employ the caption and the input image to get the inverted latents: 
-
-```py 
-inv_latents = pipeline.invert(prompt=source_prompt, image=raw_image, generator=generator).latents
-```
-
-Now, generate the image with the inverted latents and semantically generated mask: 
-
-```py
-image = pipeline(
-    prompt=target_prompt,
-    mask_image=mask_image,
-    image_latents=inv_latents,
-    generator=generator,
-    negative_prompt=source_prompt,
-).images[0]
-image.save("edited_image.png")
-```
-
-## Generating image captions for inversion
-
-The authors originally used the source concept prompt as the caption for generating the partially inverted latents. However, we can also leverage open source and public image captioning models for the same purpose.
-Below, we provide an end-to-end example with the [BLIP](https://huggingface.co/docs/transformers/model_doc/blip) model
-for generating captions.
-
-First, let's load our automatic image captioning model:
-
-```py
-import torch
-from transformers import BlipForConditionalGeneration, BlipProcessor
-
-captioner_id = "Salesforce/blip-image-captioning-base"
-processor = BlipProcessor.from_pretrained(captioner_id)
-model = BlipForConditionalGeneration.from_pretrained(captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True)
-```
-
-Then, we define a utility to generate captions from an input image using the model:
-
-```py
-@torch.no_grad()
-def generate_caption(images, caption_generator, caption_processor):
-    text = "a photograph of"
-
-    inputs = caption_processor(images, text, return_tensors="pt").to(device="cuda", dtype=caption_generator.dtype)
-    caption_generator.to("cuda")
-    outputs = caption_generator.generate(**inputs, max_new_tokens=128)
-
-    # offload caption generator
-    caption_generator.to("cpu")
-
-    caption = caption_processor.batch_decode(outputs, skip_special_tokens=True)[0]
-    return caption
-```
-
-Then, we load an input image for conditioning and obtain a suitable caption for it: 
-
-```py
-from diffusers.utils import load_image
-
-img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
-raw_image = load_image(img_url).convert("RGB").resize((768, 768))
-caption = generate_caption(raw_image, model, processor)
-```
-
-Then, we employ the generated caption and the input image to get the inverted latents: 
-
-```py
-from diffusers import DDIMInverseScheduler, DDIMScheduler
-
-pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
-)
-pipeline = pipeline.to("cuda")
-pipeline.enable_model_cpu_offload()
-pipeline.enable_vae_slicing()
-
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
-
-generator = torch.manual_seed(0)
-inv_latents = pipeline.invert(prompt=caption, image=raw_image, generator=generator).latents
-```
-
-Now, generate the image with the inverted latents and semantically generated mask from our source and target prompts: 
-
-```py
-source_prompt = "a bowl of fruits"
-target_prompt = "a basket of fruits"
-
-mask_image = pipeline.generate_mask(
-    image=raw_image,
-    source_prompt=source_prompt,
-    target_prompt=target_prompt,
-    generator=generator,
-)
-
-image = pipeline(
-    prompt=target_prompt,
-    mask_image=mask_image,
-    image_latents=inv_latents,
-    generator=generator,
-    negative_prompt=source_prompt,
-).images[0]
-image.save("edited_image.png")
-```
-
-## Generating source and target embeddings 
-
-The authors originally required the user to manually provide the source and target prompts for discovering
-edit directions. However, we can also leverage open source and public models for the same purpose.
-Below, we provide an end-to-end example with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model
-for generating source an target embeddings.
-
-**1. Load the generation model**:
-
-```py
-import torch
-from transformers import AutoTokenizer, T5ForConditionalGeneration
-
-tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl")
-model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16)
-```
-
-**2. Construct a starting prompt**: 
-
-```py
-source_concept = "bowl"
-target_concept = "basket"
-
-source_text = f"Provide a caption for images containing a {source_concept}. "
-"The captions should be in English and should be no longer than 150 characters."
-
-target_text = f"Provide a caption for images containing a {target_concept}. "
-"The captions should be in English and should be no longer than 150 characters."
-```
-
-Here, we're interested in the "bowl -> basket" direction. 
-
-**3. Generate prompts**:
-
-We can use a utility like so for this purpose. 
-
-```py
-@torch.no_grad
-def generate_prompts(input_prompt):
-    input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to("cuda")
-
-    outputs = model.generate(
-        input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10
-    )
-    return tokenizer.batch_decode(outputs, skip_special_tokens=True)
-```
-
-And then we just call it to generate our prompts:
-
-```py
-source_prompts = generate_prompts(source_text)
-target_prompts = generate_prompts(target_text)
-```
-
-We encourage you to play around with the different parameters supported by the
-`generate()` method ([documentation](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_tf_utils.TFGenerationMixin.generate)) for the generation quality you are looking for.
-
-**4. Load the embedding model**: 
-
-Here, we need to use the same text encoder model used by the subsequent Stable Diffusion model.
-
-```py 
-from diffusers import StableDiffusionDiffEditPipeline 
-
-pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
-)
-pipeline = pipeline.to("cuda")
-pipeline.enable_model_cpu_offload()
-pipeline.enable_vae_slicing()
-
-generator = torch.manual_seed(0)
-```
-
-**5. Compute embeddings**:
-
-```py 
-import torch 
-
-@torch.no_grad()
-def embed_prompts(sentences, tokenizer, text_encoder, device="cuda"):
-    embeddings = []
-    for sent in sentences:
-        text_inputs = tokenizer(
-            sent,
-            padding="max_length",
-            max_length=tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
-        embeddings.append(prompt_embeds)
-    return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)
-
-source_embeddings = embed_prompts(source_prompts, pipeline.tokenizer, pipeline.text_encoder)
-target_embeddings = embed_prompts(target_captions, pipeline.tokenizer, pipeline.text_encoder)
-```
-
-And you're done! Now, you can use these embeddings directly while calling the pipeline: 
-
-```py
-from diffusers import DDIMInverseScheduler, DDIMScheduler
-from diffusers.utils import load_image
-
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
-
-img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
-raw_image = load_image(img_url).convert("RGB").resize((768, 768))
-
-
-mask_image = pipeline.generate_mask(
-    image=raw_image,
-    source_prompt_embeds=source_embeds,
-    target_prompt_embeds=target_embeds,
-    generator=generator,
-)
-
-inv_latents = pipeline.invert(
-    prompt_embeds=source_embeds,
-    image=raw_image,
-    generator=generator,
-).latents
-
-images = pipeline(
-    mask_image=mask_image,
-    image_latents=inv_latents,
-    prompt_embeds=target_embeddings,
-    negative_prompt_embeds=source_embeddings,
-    generator=generator,
-).images
-images[0].save("edited_image.png")
-```
-
-## StableDiffusionDiffEditPipeline
-[[autodoc]] StableDiffusionDiffEditPipeline
-    - all
-    - generate_mask
-    - invert
-	- __call__

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

@@ -1,59 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Scalable Diffusion Models with Transformers (DiT)
-
-## Overview
-
-[Scalable Diffusion Models with Transformers](https://arxiv.org/abs/2212.09748) (DiT) by William Peebles and Saining Xie.
-
-The abstract of the paper is the following:
-
-*We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops -- through increased transformer depth/width or increased number of input tokens -- consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512x512 and 256x256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.*
-
-The original codebase of this paper can be found here: [facebookresearch/dit](https://github.com/facebookresearch/dit).
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab
-|---|---|:---:|
-| [pipeline_dit.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/dit/pipeline_dit.py) | *Conditional Image Generation* | - |
-
-
-## Usage example
-
-```python
-from diffusers import DiTPipeline, DPMSolverMultistepScheduler
-import torch
-
-pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
-
-# pick words from Imagenet class labels
-pipe.labels  # to print all available words
-
-# pick words that exist in ImageNet
-words = ["white shark", "umbrella"]
-
-class_ids = pipe.get_label_ids(words)
-
-generator = torch.manual_seed(33)
-output = pipe(class_labels=class_ids, num_inference_steps=25, generator=generator)
-
-image = output.images[0]  # label 'white shark'
-```
-
-## DiTPipeline
-[[autodoc]] DiTPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/if.mdx

@@ -1,523 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# IF
-
-## Overview
-
-DeepFloyd IF is a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding. 
-The model is a modular composed of a frozen text encoder and three cascaded pixel diffusion modules: 
-- Stage 1: a base model that generates 64x64 px image based on text prompt,
-- Stage 2: a 64x64 px => 256x256 px super-resolution model, and a
-- Stage 3: a 256x256 px => 1024x1024 px super-resolution model
-Stage 1 and Stage 2 utilize a frozen text encoder based on the T5 transformer to extract text embeddings, 
-which are then fed into a UNet architecture enhanced with cross-attention and attention pooling. 
-Stage 3 is [Stability's x4 Upscaling model](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler).
-The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID score of 6.66 on the COCO dataset. 
-Our work underscores the potential of larger UNet architectures in the first stage of cascaded diffusion models and depicts a promising future for text-to-image synthesis.
-
-## Usage
-
-Before you can use IF, you need to accept its usage conditions. To do so:
-1. Make sure to have a [Hugging Face account](https://huggingface.co/join) and be logged in
-2. Accept the license on the model card of [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0). Accepting the license on the stage I model card will auto accept for the other IF models.
-3. Make sure to login locally. Install `huggingface_hub`
-```sh
-pip install huggingface_hub --upgrade
-```
-
-run the login function in a Python shell
-
-```py
-from huggingface_hub import login
-
-login()
-```
-
-and enter your [Hugging Face Hub access token](https://huggingface.co/docs/hub/security-tokens#what-are-user-access-tokens).
-
-Next we install `diffusers` and dependencies:
-
-```sh
-pip install diffusers accelerate transformers safetensors
-```
-
-The following sections give more in-detail examples of how to use IF. Specifically:
-
-- [Text-to-Image Generation](#text-to-image-generation)
-- [Image-to-Image Generation](#text-guided-image-to-image-generation)
-- [Inpainting](#text-guided-inpainting-generation)
-- [Reusing model weights](#converting-between-different-pipelines)
-- [Speed optimization](#optimizing-for-speed)
-- [Memory optimization](#optimizing-for-memory)
-
-**Available checkpoints**
-- *Stage-1*
-  - [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)
-  - [DeepFloyd/IF-I-L-v1.0](https://huggingface.co/DeepFloyd/IF-I-L-v1.0)
-  - [DeepFloyd/IF-I-M-v1.0](https://huggingface.co/DeepFloyd/IF-I-M-v1.0)
-
-- *Stage-2*
-  - [DeepFloyd/IF-II-L-v1.0](https://huggingface.co/DeepFloyd/IF-II-L-v1.0)
-  - [DeepFloyd/IF-II-M-v1.0](https://huggingface.co/DeepFloyd/IF-II-M-v1.0)
-
-- *Stage-3*
-  - [stabilityai/stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler)
-
-**Demo**
-[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/DeepFloyd/IF)
-
-**Google Colab**
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/deepfloyd_if_free_tier_google_colab.ipynb)
-
-### Text-to-Image Generation
-
-By default diffusers makes use of [model cpu offloading](https://huggingface.co/docs/diffusers/optimization/fp16#model-offloading-for-fast-inference-and-memory-savings)
-to run the whole IF pipeline with as little as 14 GB of VRAM.
-
-```python
-from diffusers import DiffusionPipeline
-from diffusers.utils import pt_to_pil
-import torch
-
-# stage 1
-stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-stage_1.enable_model_cpu_offload()
-
-# stage 2
-stage_2 = DiffusionPipeline.from_pretrained(
-    "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
-)
-stage_2.enable_model_cpu_offload()
-
-# stage 3
-safety_modules = {
-    "feature_extractor": stage_1.feature_extractor,
-    "safety_checker": stage_1.safety_checker,
-    "watermarker": stage_1.watermarker,
-}
-stage_3 = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
-)
-stage_3.enable_model_cpu_offload()
-
-prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
-generator = torch.manual_seed(1)
-
-# text embeds
-prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
-
-# stage 1
-image = stage_1(
-    prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt"
-).images
-pt_to_pil(image)[0].save("./if_stage_I.png")
-
-# stage 2
-image = stage_2(
-    image=image,
-    prompt_embeds=prompt_embeds,
-    negative_prompt_embeds=negative_embeds,
-    generator=generator,
-    output_type="pt",
-).images
-pt_to_pil(image)[0].save("./if_stage_II.png")
-
-# stage 3
-image = stage_3(prompt=prompt, image=image, noise_level=100, generator=generator).images
-image[0].save("./if_stage_III.png")
-```
-
-### Text Guided Image-to-Image Generation
-
-The same IF model weights can be used for text-guided image-to-image translation or image variation.
-In this case just make sure to load the weights using the [`IFInpaintingPipeline`] and [`IFInpaintingSuperResolutionPipeline`] pipelines.
-
-**Note**: You can also directly move the weights of the text-to-image pipelines to the image-to-image pipelines
-without loading them twice by making use of the [`~DiffusionPipeline.components()`] function as explained [here](#converting-between-different-pipelines).
-
-```python
-from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
-from diffusers.utils import pt_to_pil
-
-import torch
-
-from PIL import Image
-import requests
-from io import BytesIO
-
-# download image
-url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-response = requests.get(url)
-original_image = Image.open(BytesIO(response.content)).convert("RGB")
-original_image = original_image.resize((768, 512))
-
-# stage 1
-stage_1 = IFImg2ImgPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-stage_1.enable_model_cpu_offload()
-
-# stage 2
-stage_2 = IFImg2ImgSuperResolutionPipeline.from_pretrained(
-    "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
-)
-stage_2.enable_model_cpu_offload()
-
-# stage 3
-safety_modules = {
-    "feature_extractor": stage_1.feature_extractor,
-    "safety_checker": stage_1.safety_checker,
-    "watermarker": stage_1.watermarker,
-}
-stage_3 = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
-)
-stage_3.enable_model_cpu_offload()
-
-prompt = "A fantasy landscape in style minecraft"
-generator = torch.manual_seed(1)
-
-# text embeds
-prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
-
-# stage 1
-image = stage_1(
-    image=original_image,
-    prompt_embeds=prompt_embeds,
-    negative_prompt_embeds=negative_embeds,
-    generator=generator,
-    output_type="pt",
-).images
-pt_to_pil(image)[0].save("./if_stage_I.png")
-
-# stage 2
-image = stage_2(
-    image=image,
-    original_image=original_image,
-    prompt_embeds=prompt_embeds,
-    negative_prompt_embeds=negative_embeds,
-    generator=generator,
-    output_type="pt",
-).images
-pt_to_pil(image)[0].save("./if_stage_II.png")
-
-# stage 3
-image = stage_3(prompt=prompt, image=image, generator=generator, noise_level=100).images
-image[0].save("./if_stage_III.png")
-```
-
-### Text Guided Inpainting Generation
-
-The same IF model weights can be used for text-guided image-to-image translation or image variation.
-In this case just make sure to load the weights using the [`IFInpaintingPipeline`] and [`IFInpaintingSuperResolutionPipeline`] pipelines.
-
-**Note**: You can also directly move the weights of the text-to-image pipelines to the image-to-image pipelines
-without loading them twice by making use of the [`~DiffusionPipeline.components()`] function as explained [here](#converting-between-different-pipelines).
-
-```python
-from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline
-from diffusers.utils import pt_to_pil
-import torch
-
-from PIL import Image
-import requests
-from io import BytesIO
-
-# download image
-url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png"
-response = requests.get(url)
-original_image = Image.open(BytesIO(response.content)).convert("RGB")
-original_image = original_image
-
-# download mask
-url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png"
-response = requests.get(url)
-mask_image = Image.open(BytesIO(response.content))
-mask_image = mask_image
-
-# stage 1
-stage_1 = IFInpaintingPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-stage_1.enable_model_cpu_offload()
-
-# stage 2
-stage_2 = IFInpaintingSuperResolutionPipeline.from_pretrained(
-    "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
-)
-stage_2.enable_model_cpu_offload()
-
-# stage 3
-safety_modules = {
-    "feature_extractor": stage_1.feature_extractor,
-    "safety_checker": stage_1.safety_checker,
-    "watermarker": stage_1.watermarker,
-}
-stage_3 = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
-)
-stage_3.enable_model_cpu_offload()
-
-prompt = "blue sunglasses"
-generator = torch.manual_seed(1)
-
-# text embeds
-prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
-
-# stage 1
-image = stage_1(
-    image=original_image,
-    mask_image=mask_image,
-    prompt_embeds=prompt_embeds,
-    negative_prompt_embeds=negative_embeds,
-    generator=generator,
-    output_type="pt",
-).images
-pt_to_pil(image)[0].save("./if_stage_I.png")
-
-# stage 2
-image = stage_2(
-    image=image,
-    original_image=original_image,
-    mask_image=mask_image,
-    prompt_embeds=prompt_embeds,
-    negative_prompt_embeds=negative_embeds,
-    generator=generator,
-    output_type="pt",
-).images
-pt_to_pil(image)[0].save("./if_stage_II.png")
-
-# stage 3
-image = stage_3(prompt=prompt, image=image, generator=generator, noise_level=100).images
-image[0].save("./if_stage_III.png")
-```
-
-### Converting between different pipelines
-
-In addition to being loaded with `from_pretrained`, Pipelines can also be loaded directly from each other.
-
-```python
-from diffusers import IFPipeline, IFSuperResolutionPipeline
-
-pipe_1 = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0")
-pipe_2 = IFSuperResolutionPipeline.from_pretrained("DeepFloyd/IF-II-L-v1.0")
-
-
-from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline
-
-pipe_1 = IFImg2ImgPipeline(**pipe_1.components)
-pipe_2 = IFImg2ImgSuperResolutionPipeline(**pipe_2.components)
-
-
-from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline
-
-pipe_1 = IFInpaintingPipeline(**pipe_1.components)
-pipe_2 = IFInpaintingSuperResolutionPipeline(**pipe_2.components)
-```
-
-### Optimizing for speed
-
-The simplest optimization to run IF faster is to move all model components to the GPU.
-
-```py
-pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-pipe.to("cuda")
-```
-
-You can also run the diffusion process for a shorter number of timesteps.
-
-This can either be done with the `num_inference_steps` argument
-
-```py
-pipe("<prompt>", num_inference_steps=30)
-```
-
-Or with the `timesteps` argument
-
-```py
-from diffusers.pipelines.deepfloyd_if import fast27_timesteps
-
-pipe("<prompt>", timesteps=fast27_timesteps)
-```
-
-When doing image variation or inpainting, you can also decrease the number of timesteps
-with the strength argument. The strength argument is the amount of noise to add to 
-the input image which also determines how many steps to run in the denoising process.
-A smaller number will vary the image less but run faster.
-
-```py
-pipe = IFImg2ImgPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-pipe.to("cuda")
-
-image = pipe(image=image, prompt="<prompt>", strength=0.3).images
-```
-
-You can also use [`torch.compile`](../../optimization/torch2.0). Note that we have not exhaustively tested `torch.compile`
-with IF and it might not give expected results.
-
-```py
-import torch
-
-pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-pipe.to("cuda")
-
-pipe.text_encoder = torch.compile(pipe.text_encoder)
-pipe.unet = torch.compile(pipe.unet)
-```
-
-### Optimizing for memory
-
-When optimizing for GPU memory, we can use the standard diffusers cpu offloading APIs.
-
-Either the model based CPU offloading,
-
-```py
-pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-pipe.enable_model_cpu_offload()
-```
-
-or the more aggressive layer based CPU offloading.
-
-```py
-pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-pipe.enable_sequential_cpu_offload()
-```
-
-Additionally, T5 can be loaded in 8bit precision
-
-```py
-from transformers import T5EncoderModel
-
-text_encoder = T5EncoderModel.from_pretrained(
-    "DeepFloyd/IF-I-XL-v1.0", subfolder="text_encoder", device_map="auto", load_in_8bit=True, variant="8bit"
-)
-
-from diffusers import DiffusionPipeline
-
-pipe = DiffusionPipeline.from_pretrained(
-    "DeepFloyd/IF-I-XL-v1.0",
-    text_encoder=text_encoder,  # pass the previously instantiated 8bit text encoder
-    unet=None,
-    device_map="auto",
-)
-
-prompt_embeds, negative_embeds = pipe.encode_prompt("<prompt>")
-```
-
-For CPU RAM constrained machines like google colab free tier where we can't load all 
-model components to the CPU at once, we can manually only load the pipeline with
-the text encoder or unet when the respective model components are needed.
-
-```py
-from diffusers import IFPipeline, IFSuperResolutionPipeline
-import torch
-import gc
-from transformers import T5EncoderModel
-from diffusers.utils import pt_to_pil
-
-text_encoder = T5EncoderModel.from_pretrained(
-    "DeepFloyd/IF-I-XL-v1.0", subfolder="text_encoder", device_map="auto", load_in_8bit=True, variant="8bit"
-)
-
-# text to image
-
-pipe = DiffusionPipeline.from_pretrained(
-    "DeepFloyd/IF-I-XL-v1.0",
-    text_encoder=text_encoder,  # pass the previously instantiated 8bit text encoder
-    unet=None,
-    device_map="auto",
-)
-
-prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
-prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
-
-# Remove the pipeline so we can re-load the pipeline with the unet
-del text_encoder
-del pipe
-gc.collect()
-torch.cuda.empty_cache()
-
-pipe = IFPipeline.from_pretrained(
-    "DeepFloyd/IF-I-XL-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16, device_map="auto"
-)
-
-generator = torch.Generator().manual_seed(0)
-image = pipe(
-    prompt_embeds=prompt_embeds,
-    negative_prompt_embeds=negative_embeds,
-    output_type="pt",
-    generator=generator,
-).images
-
-pt_to_pil(image)[0].save("./if_stage_I.png")
-
-# Remove the pipeline so we can load the super-resolution pipeline
-del pipe
-gc.collect()
-torch.cuda.empty_cache()
-
-# First super resolution
-
-pipe = IFSuperResolutionPipeline.from_pretrained(
-    "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16, device_map="auto"
-)
-
-generator = torch.Generator().manual_seed(0)
-image = pipe(
-    image=image,
-    prompt_embeds=prompt_embeds,
-    negative_prompt_embeds=negative_embeds,
-    output_type="pt",
-    generator=generator,
-).images
-
-pt_to_pil(image)[0].save("./if_stage_II.png")
-```
-
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab
-|---|---|:---:|
-| [pipeline_if.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py) | *Text-to-Image Generation* | - |
-| [pipeline_if_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py) | *Text-to-Image Generation* | - |
-| [pipeline_if_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py) | *Image-to-Image Generation* | - |
-| [pipeline_if_img2img_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py) | *Image-to-Image Generation* | - |
-| [pipeline_if_inpainting.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py) | *Image-to-Image Generation* | - |
-| [pipeline_if_inpainting_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py) | *Image-to-Image Generation* | - |
-
-## IFPipeline
-[[autodoc]] IFPipeline
-	- all
-	- __call__
-
-## IFSuperResolutionPipeline
-[[autodoc]] IFSuperResolutionPipeline
-	- all
-	- __call__
-
-## IFImg2ImgPipeline
-[[autodoc]] IFImg2ImgPipeline
-	- all
-	- __call__
-
-## IFImg2ImgSuperResolutionPipeline
-[[autodoc]] IFImg2ImgSuperResolutionPipeline
-	- all
-	- __call__
-
-## IFInpaintingPipeline
-[[autodoc]] IFInpaintingPipeline
-	- all
-	- __call__
-
-## IFInpaintingSuperResolutionPipeline
-[[autodoc]] IFInpaintingSuperResolutionPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/kandinsky.mdx

@@ -1,351 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Kandinsky
-
-## Overview
-
-Kandinsky 2.1 inherits best practices from [DALL-E 2](https://arxiv.org/abs/2204.06125) and [Latent Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/latent_diffusion), while introducing some new ideas.
-
-It uses [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for encoding images and text, and a diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach enhances the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.
-
-The Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov) and the original codebase can be found [here](https://github.com/ai-forever/Kandinsky-2)
-
-## Available Pipelines:
-
-| Pipeline | Tasks |
-|---|---|
-| [pipeline_kandinsky.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py) | *Text-to-Image Generation* |
-| [pipeline_kandinsky_inpaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py) | *Image-Guided Image Generation* |
-| [pipeline_kandinsky_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py) | *Image-Guided Image Generation* |
-
-## Usage example
-
-In the following, we will walk you through some examples of how to use the Kandinsky pipelines to create some visually aesthetic artwork.
-
-### Text-to-Image Generation
-
-For text-to-image generation, we need to use both [`KandinskyPriorPipeline`] and [`KandinskyPipeline`].
-The first step is to encode text prompts with CLIP and then diffuse the CLIP text embeddings to CLIP image embeddings,
-as first proposed in [DALL-E 2](https://cdn.openai.com/papers/dall-e-2.pdf).
-Let's throw a fun prompt at Kandinsky to see what it comes up with.
-
-```py
-prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
-```
-
-First, let's instantiate the prior pipeline and the text-to-image pipeline. Both 
-pipelines are diffusion models.
-
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-pipe_prior = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16)
-pipe_prior.to("cuda")
-
-t2i_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-t2i_pipe.to("cuda")
-```
-
-Now we pass the prompt through the prior to generate image embeddings. The prior
-returns both the image embeddings corresponding to the prompt and negative/unconditional image 
-embeddings corresponding to an empty string.
-
-```py
-generator = torch.Generator(device="cuda").manual_seed(12)
-image_embeds, negative_image_embeds = pipe_prior(prompt, generator=generator).to_tuple()
-```
-
-<Tip warning={true}>
-
-The text-to-image pipeline expects both `image_embeds`, `negative_image_embeds` and the original 
-`prompt` as the text-to-image pipeline uses another text encoder to better guide the second diffusion 
-process of `t2i_pipe`.
-
-By default, the prior returns unconditioned negative image embeddings corresponding to the negative prompt of `""`.
-For better results, you can also pass a `negative_prompt` to the prior. This will increase the effective batch size
-of the prior by a factor of 2.
-
-```py
-prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
-negative_prompt = "low quality, bad quality"
-
-image_embeds, negative_image_embeds = pipe_prior(prompt, negative_prompt, generator=generator).to_tuple()
-```
-
-</Tip>
-
-
-Next, we can pass the embeddings as well as the prompt to the text-to-image pipeline. Remember that 
-in case you are using a customized negative prompt, that you should pass this one also to the text-to-image pipelines
-with `negative_prompt=negative_prompt`:
-
-```py
-image = t2i_pipe(prompt, image_embeds=image_embeds, negative_image_embeds=negative_image_embeds).images[0]
-image.save("cheeseburger_monster.png")
-```
-
-One cheeseburger monster coming up! Enjoy! 
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/cheeseburger.png)
-
-The Kandinsky model works extremely well with creative prompts. Here is some of the amazing art that can be created using the exact same process but with different prompts.
-
-```python
-prompt = "bird eye view shot of a full body woman with cyan light orange magenta makeup, digital art, long braided hair her face separated by makeup in the style of yin Yang surrealism, symmetrical face, real image, contrasting tone, pastel gradient background"
-```
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/hair.png)
-
-```python
-prompt = "A car exploding into colorful dust"
-```
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/dusts.png)
-
-```python
-prompt = "editorial photography of an organic, almost liquid smoke style armchair"
-```
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/smokechair.png)
-
-```python
-prompt = "birds eye view of a quilted paper style alien planet landscape, vibrant colours, Cinematic lighting"
-```
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/alienplanet.png)
-
-
-### Text Guided Image-to-Image Generation
-
-The same Kandinsky model weights can be used for text-guided image-to-image translation. In this case, just make sure to load the weights using the [`KandinskyImg2ImgPipeline`] pipeline.
-
-**Note**: You can also directly move the weights of the text-to-image pipelines to the image-to-image pipelines
-without loading them twice by making use of the [`~DiffusionPipeline.components`] function as explained [here](#converting-between-different-pipelines).
-
-Let's download an image.
-
-```python
-from PIL import Image
-import requests
-from io import BytesIO
-
-# download image
-url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-response = requests.get(url)
-original_image = Image.open(BytesIO(response.content)).convert("RGB")
-original_image = original_image.resize((768, 512))
-```
-
-![img](https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg)
-
-```python
-import torch
-from diffusers import KandinskyImg2ImgPipeline, KandinskyPriorPipeline
-
-# create prior
-pipe_prior = KandinskyPriorPipeline.from_pretrained(
-    "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
-)
-pipe_prior.to("cuda")
-
-# create img2img pipeline
-pipe = KandinskyImg2ImgPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-pipe.to("cuda")
-
-prompt = "A fantasy landscape, Cinematic lighting"
-negative_prompt = "low quality, bad quality"
-
-generator = torch.Generator(device="cuda").manual_seed(30)
-image_embeds, negative_image_embeds = pipe_prior(prompt, negative_prompt, generator=generator).to_tuple()
-
-out = pipe(
-    prompt,
-    image=original_image,
-    image_embeds=image_embeds,
-    negative_image_embeds=negative_image_embeds,
-    height=768,
-    width=768,
-    strength=0.3,
-)
-
-out.images[0].save("fantasy_land.png")
-```
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/img2img_fantasyland.png)
-
-
-### Text Guided Inpainting Generation
-
-You can use [`KandinskyInpaintPipeline`] to edit images. In this example, we will add a hat to the portrait of a cat.
-
-```py
-from diffusers import KandinskyInpaintPipeline, KandinskyPriorPipeline
-from diffusers.utils import load_image
-import torch
-import numpy as np
-
-pipe_prior = KandinskyPriorPipeline.from_pretrained(
-    "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
-)
-pipe_prior.to("cuda")
-
-prompt = "a hat"
-prior_output = pipe_prior(prompt)
-
-pipe = KandinskyInpaintPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16)
-pipe.to("cuda")
-
-init_image = load_image(
-    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"
-)
-
-mask = np.ones((768, 768), dtype=np.float32)
-# Let's mask out an area above the cat's head
-mask[:250, 250:-250] = 0
-
-out = pipe(
-    prompt,
-    image=init_image,
-    mask_image=mask,
-    **prior_output,
-    height=768,
-    width=768,
-    num_inference_steps=150,
-)
-
-image = out.images[0]
-image.save("cat_with_hat.png")
-```
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/inpaint_cat_hat.png)
-
-### Interpolate 
-
-The [`KandinskyPriorPipeline`] also comes with a cool utility function that will allow you to interpolate the latent space of different images and texts super easily. Here is an example of how you can create an Impressionist-style portrait for your pet based on "The Starry Night". 
-
-Note that you can interpolate between texts and images - in the below example, we passed a text prompt "a cat" and two images to the `interplate` function, along with a `weights` variable containing the corresponding weights for each condition we interplate. 
-
-```python
-from diffusers import KandinskyPriorPipeline, KandinskyPipeline
-from diffusers.utils import load_image
-import PIL
-
-import torch
-
-pipe_prior = KandinskyPriorPipeline.from_pretrained(
-    "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
-)
-pipe_prior.to("cuda")
-
-img1 = load_image(
-    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"
-)
-
-img2 = load_image(
-    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/starry_night.jpeg"
-)
-
-# add all the conditions we want to interpolate, can be either text or image
-images_texts = ["a cat", img1, img2]
-
-# specify the weights for each condition in images_texts
-weights = [0.3, 0.3, 0.4]
-
-# We can leave the prompt empty
-prompt = ""
-prior_out = pipe_prior.interpolate(images_texts, weights)
-
-pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-pipe.to("cuda")
-
-image = pipe(prompt, **prior_out, height=768, width=768).images[0]
-
-image.save("starry_cat.png")
-```
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/starry_cat.png)
-
-
-## Optimization
-
-Running Kandinsky in inference requires running both a first prior pipeline: [`KandinskyPriorPipeline`]
-and a second image decoding pipeline which is one of [`KandinskyPipeline`], [`KandinskyImg2ImgPipeline`], or [`KandinskyInpaintPipeline`].
-
-The bulk of the computation time will always be the second image decoding pipeline, so when looking 
-into optimizing the model, one should look into the second image decoding pipeline.
-
-When running with PyTorch < 2.0, we strongly recommend making use of [`xformers`](https://github.com/facebookresearch/xformers)
-to speed-up the optimization. This can be done by simply running:
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-t2i_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-t2i_pipe.enable_xformers_memory_efficient_attention()
-```
-
-When running on PyTorch >= 2.0, PyTorch's SDPA attention will automatically be used. For more information on 
-PyTorch's SDPA, feel free to have a look at [this blog post](https://pytorch.org/blog/accelerated-diffusers-pt-20/).
-
-To have explicit control , you can also manually set the pipeline to use PyTorch's 2.0 efficient attention:
-
-```py
-from diffusers.models.attention_processor import AttnAddedKVProcessor2_0
-
-t2i_pipe.unet.set_attn_processor(AttnAddedKVProcessor2_0())
-```
-
-The slowest and most memory intense attention processor is the default `AttnAddedKVProcessor` processor.
-We do **not** recommend using it except for testing purposes or cases where very high determistic behaviour is desired. 
-You can set it with:
-
-```py
-from diffusers.models.attention_processor import AttnAddedKVProcessor
-
-t2i_pipe.unet.set_attn_processor(AttnAddedKVProcessor())
-```
-
-With PyTorch >= 2.0, you can also use Kandinsky with `torch.compile` which depending 
-on your hardware can signficantly speed-up your inference time once the model is compiled.
-To use Kandinsksy with `torch.compile`, you can do:
-
-```py
-t2i_pipe.unet.to(memory_format=torch.channels_last)
-t2i_pipe.unet = torch.compile(t2i_pipe.unet, mode="reduce-overhead", fullgraph=True)
-```
-
-After compilation you should see a very fast inference time. For more information,
-feel free to have a look at [Our PyTorch 2.0 benchmark](https://huggingface.co/docs/diffusers/main/en/optimization/torch2.0).
-
-
-
-
-
-## KandinskyPriorPipeline
-
-[[autodoc]] KandinskyPriorPipeline
-	- all
-	- __call__
-	- interpolate
-	
-## KandinskyPipeline
-
-[[autodoc]] KandinskyPipeline
-	- all
-	- __call__
-
-## KandinskyImg2ImgPipeline
-
-[[autodoc]] KandinskyImg2ImgPipeline
-	- all
-	- __call__
-
-## KandinskyInpaintPipeline
-
-[[autodoc]] KandinskyInpaintPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/model_editing.mdx

@@ -1,61 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Editing Implicit Assumptions in Text-to-Image Diffusion Models
-
-## Overview
-
-[Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://arxiv.org/abs/2303.08084) by Hadas Orgad, Bahjat Kawar, and Yonatan Belinkov.
-
-The abstract of the paper is the following:
-
-*Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be outdated, incorrect, or reflective of social biases present in the training data. Thus, there is a need to control these assumptions without requiring explicit user input or costly re-training. In this work, we aim to edit a given implicit assumption in a pre-trained diffusion model. Our Text-to-Image Model Editing method, TIME for short, receives a pair of inputs: a "source" under-specified prompt for which the model makes an implicit assumption (e.g., "a pack of roses"), and a "destination" prompt that describes the same setting, but with a specified desired attribute (e.g., "a pack of blue roses"). TIME then updates the model's cross-attention layers, as these layers assign visual meaning to textual tokens. We edit the projection matrices in these layers such that the source prompt is projected close to the destination prompt. Our method is highly efficient, as it modifies a mere 2.2% of the model's parameters in under one second. To evaluate model editing approaches, we introduce TIMED (TIME Dataset), containing 147 source and destination prompt pairs from various domains. Our experiments (using Stable Diffusion) show that TIME is successful in model editing, generalizes well for related prompts unseen during editing, and imposes minimal effect on unrelated generations.*
-
-Resources:
-
-* [Project Page](https://time-diffusion.github.io/).
-* [Paper](https://arxiv.org/abs/2303.08084).
-* [Original Code](https://github.com/bahjat-kawar/time-diffusion).
-* [Demo](https://huggingface.co/spaces/bahjat-kawar/time-diffusion).
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo
-|---|---|:---:|
-| [StableDiffusionModelEditingPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py) | *Text-to-Image Model Editing* | [🤗 Space](https://huggingface.co/spaces/bahjat-kawar/time-diffusion)) |
-
-This pipeline enables editing the diffusion model weights, such that its assumptions on a given concept are changed. The resulting change is expected to take effect in all prompt generations pertaining to the edited concept.
-
-## Usage example
-
-```python
-import torch
-from diffusers import StableDiffusionModelEditingPipeline
-
-model_ckpt = "CompVis/stable-diffusion-v1-4"
-pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt)
-
-pipe = pipe.to("cuda")
-
-source_prompt = "A pack of roses"
-destination_prompt = "A pack of blue roses"
-pipe.edit_model(source_prompt, destination_prompt)
-
-prompt = "A field of roses"
-image = pipe(prompt).images[0]
-image.save("field_of_roses.png")
-```
-
-## StableDiffusionModelEditingPipeline
-[[autodoc]] StableDiffusionModelEditingPipeline
-	- __call__
-	- all

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

@@ -1,115 +0,0 @@
-<!--Copyright 2023 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/v4.27.1/en/model_doc/clip#transformers.CLIPTextModel)
-- a scheduler component, [scheduler](./api/scheduler#pndm), 
-- a [CLIPImageProcessor](https://huggingface.co/docs/transformers/v4.27.1/en/model_doc/clip#transformers.CLIPImageProcessor),
-- 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](./alt_diffusion) | [**AltDiffusion**](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation | -
-| [audio_diffusion](./audio_diffusion) | [**Audio Diffusion**](https://github.com/teticio/audio_diffusion.git) | Unconditional Audio Generation |
-| [controlnet](./api/pipelines/controlnet) | [**ControlNet with Stable Diffusion**](https://arxiv.org/abs/2302.05543) | 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/controlnet.ipynb)
-| [cycle_diffusion](./cycle_diffusion) | [**Cycle Diffusion**](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
-| [dance_diffusion](./dance_diffusion) | [**Dance Diffusion**](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
-| [ddpm](./ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
-| [ddim](./ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
-| [if](./if) | [**IF**](https://github.com/deep-floyd/IF) | 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/deepfloyd_if_free_tier_google_colab.ipynb)
-| [if_img2img](./if) | [**IF**](https://github.com/deep-floyd/IF) | Image-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/deepfloyd_if_free_tier_google_colab.ipynb)
-| [if_inpainting](./if) | [**IF**](https://github.com/deep-floyd/IF) | Image-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/deepfloyd_if_free_tier_google_colab.ipynb)
-| [latent_diffusion](./latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation | 
-| [latent_diffusion](./latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image | 
-| [latent_diffusion_uncond](./latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation | 
-| [paint_by_example](./paint_by_example) | [**Paint by Example: Exemplar-based Image Editing with Diffusion Models**](https://arxiv.org/abs/2211.13227) | Image-Guided Image Inpainting | 
-| [pndm](./pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation | 
-| [score_sde_ve](./score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
-| [score_sde_vp](./score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
-| [semantic_stable_diffusion](./semantic_stable_diffusion) | [**SEGA: Instructing Diffusion using Semantic Dimensions**](https://arxiv.org/abs/2301.12247) | Text-to-Image Generation |
-| [stable_diffusion_text2img](./stable_diffusion/text2img) | [**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_img2img](./stable_diffusion/img2img) | [**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_inpaint](./stable_diffusion/inpaint) | [**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)
-| [stable_diffusion_panorama](./stable_diffusion/panorama) | [**MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation**](https://arxiv.org/abs/2302.08113) | Text-Guided Panorama View Generation |
-| [stable_diffusion_pix2pix](./stable_diffusion/pix2pix) | [**InstructPix2Pix: Learning to Follow Image Editing Instructions**](https://arxiv.org/abs/2211.09800) | Text-Based Image Editing |
-| [stable_diffusion_pix2pix_zero](./stable_diffusion/pix2pix_zero) | [**Zero-shot Image-to-Image Translation**](https://arxiv.org/abs/2302.03027) | Text-Based Image Editing |
-| [stable_diffusion_attend_and_excite](./stable_diffusion/attend_and_excite) | [**Attend and Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models**](https://arxiv.org/abs/2301.13826) | Text-to-Image Generation |
-| [stable_diffusion_self_attention_guidance](./stable_diffusion/self_attention_guidance) | [**Self-Attention Guidance**](https://arxiv.org/abs/2210.00939) | Text-to-Image Generation |
-| [stable_diffusion_image_variation](./stable_diffusion/image_variation) | [**Stable Diffusion Image Variations**](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) | Image-to-Image Generation |
-| [stable_diffusion_latent_upscale](./stable_diffusion/latent_upscale) | [**Stable Diffusion Latent Upscaler**](https://twitter.com/StabilityAI/status/1590531958815064065) | Text-Guided Super Resolution Image-to-Image |
-| [stable_diffusion_2](./stable_diffusion_2/) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation | 
-| [stable_diffusion_2](./stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting | 
-| [stable_diffusion_2](./stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Depth-to-Image Text-Guided Generation |
-| [stable_diffusion_2](./stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
-| [stable_diffusion_safe](./stable_diffusion_safe) | [**Safe Stable Diffusion**](https://arxiv.org/abs/2211.05105) | Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/safe-latent-diffusion/blob/main/examples/Safe%20Latent%20Diffusion.ipynb)
-| [stable_unclip](./stable_unclip) | **Stable unCLIP** | Text-to-Image Generation |
-| [stable_unclip](./stable_unclip) | **Stable unCLIP** | Image-to-Image Text-Guided Generation |
-| [stochastic_karras_ve](./stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |
-| [text_to_video_sd](./api/pipelines/text_to_video) | [Modelscope's Text-to-video-synthesis Model in Open Domain](https://modelscope.cn/models/damo/text-to-video-synthesis/summary) | Text-to-Video Generation |
-| [unclip](./unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125) | Text-to-Image Generation |
-| [versatile_diffusion](./versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation | 
-| [versatile_diffusion](./versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation | 
-| [versatile_diffusion](./versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation | 
-| [vq_diffusion](./vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation | 
-| [text_to_video_zero](./text_to_video_zero) | [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://arxiv.org/abs/2303.13439) | Text-to-Video 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).

docs/source/en/api/pipelines/paint_by_example.mdx

@@ -1,74 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# PaintByExample
-
-## Overview
-
-[Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://arxiv.org/abs/2211.13227) by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen.
-
-The abstract of the paper is the following:
-
-*Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive approach will cause obvious fusing artifacts. We carefully analyze it and propose an information bottleneck and strong augmentations to avoid the trivial solution of directly copying and pasting the exemplar image. Meanwhile, to ensure the controllability of the editing process, we design an arbitrary shape mask for the exemplar image and leverage the classifier-free guidance to increase the similarity to the exemplar image. The whole framework involves a single forward of the diffusion model without any iterative optimization. We demonstrate that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity.*
-
-The original codebase can be found [here](https://github.com/Fantasy-Studio/Paint-by-Example).
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab
-|---|---|:---:|
-| [pipeline_paint_by_example.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py) | *Image-Guided Image Painting* | - |
-
-## Tips
-
-- PaintByExample is supported by the official [Fantasy-Studio/Paint-by-Example](https://huggingface.co/Fantasy-Studio/Paint-by-Example) checkpoint. The checkpoint has been warm-started from the [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) and with the objective to inpaint partly masked images conditioned on example / reference images
-- To quickly demo *PaintByExample*, please have a look at [this demo](https://huggingface.co/spaces/Fantasy-Studio/Paint-by-Example)
-- You can run the following code snippet as an example:
-
-
-```python
-# !pip install diffusers transformers
-
-import PIL
-import requests
-import torch
-from io import BytesIO
-from diffusers import DiffusionPipeline
-
-
-def download_image(url):
-    response = requests.get(url)
-    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
-
-
-img_url = "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/image/example_1.png"
-mask_url = "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/mask/example_1.png"
-example_url = "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/reference/example_1.jpg"
-
-init_image = download_image(img_url).resize((512, 512))
-mask_image = download_image(mask_url).resize((512, 512))
-example_image = download_image(example_url).resize((512, 512))
-
-pipe = DiffusionPipeline.from_pretrained(
-    "Fantasy-Studio/Paint-by-Example",
-    torch_dtype=torch.float16,
-)
-pipe = pipe.to("cuda")
-
-image = pipe(image=init_image, mask_image=mask_image, example_image=example_image).images[0]
-image
-```
-
-## PaintByExamplePipeline
-[[autodoc]] PaintByExamplePipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/panorama.mdx

@@ -1,66 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
-
-## Overview
-
-[MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://arxiv.org/abs/2302.08113) by Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel.
-
-The abstract of the paper is the following:
-
-*Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.
-
-Resources:
-
-* [Project Page](https://multidiffusion.github.io/).
-* [Paper](https://arxiv.org/abs/2302.08113).
-* [Original Code](https://github.com/omerbt/MultiDiffusion).
-* [Demo](https://huggingface.co/spaces/weizmannscience/MultiDiffusion).
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo
-|---|---|:---:|
-| [StableDiffusionPanoramaPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py) | *Text-Guided Panorama View Generation* | [🤗 Space](https://huggingface.co/spaces/weizmannscience/MultiDiffusion)) |
-
-<!-- TODO: add Colab -->
-
-## Usage example
-
-```python
-import torch
-from diffusers import StableDiffusionPanoramaPipeline, DDIMScheduler
-
-model_ckpt = "stabilityai/stable-diffusion-2-base"
-scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
-pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, torch_dtype=torch.float16)
-
-pipe = pipe.to("cuda")
-
-prompt = "a photo of the dolomites"
-image = pipe(prompt).images[0]
-image.save("dolomites.png")
-```
-
-<Tip>
-
-While calling this pipeline, it's possible to specify the `view_batch_size` to have a >1 value. 
-For some GPUs with high performance, higher a `view_batch_size`, can speedup the generation
-and increase the VRAM usage.
-
-</Tip>
-
-## StableDiffusionPanoramaPipeline
-[[autodoc]] StableDiffusionPanoramaPipeline
-	- __call__
-	- all

docs/source/en/api/pipelines/pix2pix.mdx

@@ -1,73 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# InstructPix2Pix: Learning to Follow Image Editing Instructions
-
-## Overview
-
-[InstructPix2Pix: Learning to Follow Image Editing Instructions](https://arxiv.org/abs/2211.09800) by Tim Brooks, Aleksander Holynski and Alexei A. Efros.
-
-The abstract of the paper is the following:
-
-*We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models -- a language model (GPT-3) and a text-to-image model (Stable Diffusion) -- to generate a large dataset of image editing examples. Our conditional diffusion model, InstructPix2Pix, is trained on our generated data, and generalizes to real images and user-written instructions at inference time. Since it performs edits in the forward pass and does not require per example fine-tuning or inversion, our model edits images quickly, in a matter of seconds. We show compelling editing results for a diverse collection of input images and written instructions.*
-
-Resources:
-
-* [Project Page](https://www.timothybrooks.com/instruct-pix2pix).
-* [Paper](https://arxiv.org/abs/2211.09800).
-* [Original Code](https://github.com/timothybrooks/instruct-pix2pix).
-* [Demo](https://huggingface.co/spaces/timbrooks/instruct-pix2pix).
-
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo
-|---|---|:---:|
-| [StableDiffusionInstructPix2PixPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py) | *Text-Based Image Editing* | [🤗 Space](https://huggingface.co/spaces/timbrooks/instruct-pix2pix) |
-
-<!-- TODO: add Colab -->
-
-## Usage example
-
-```python
-import PIL
-import requests
-import torch
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-model_id = "timbrooks/instruct-pix2pix"
-pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-
-url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
-
-
-def download_image(url):
-    image = PIL.Image.open(requests.get(url, stream=True).raw)
-    image = PIL.ImageOps.exif_transpose(image)
-    image = image.convert("RGB")
-    return image
-
-
-image = download_image(url)
-
-prompt = "make the mountains snowy"
-images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images
-images[0].save("snowy_mountains.png")
-```
-
-## StableDiffusionInstructPix2PixPipeline
-[[autodoc]] StableDiffusionInstructPix2PixPipeline
-	- __call__
-	- all
-	- load_textual_inversion
-	- load_lora_weights
-	- save_lora_weights

docs/source/en/api/pipelines/pix2pix_zero.mdx

@@ -1,291 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Zero-shot Image-to-Image Translation
-
-## Overview
-
-[Zero-shot Image-to-Image Translation](https://arxiv.org/abs/2302.03027).
-
-The abstract of the paper is the following:
-
-*Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse and high-quality images. However, it is still challenging to directly apply these models for editing real images for two reasons. First, it is hard for users to come up with a perfect text prompt that accurately describes every visual detail in the input image. Second, while existing models can introduce desirable changes in certain regions, they often dramatically alter the input content and introduce unexpected changes in unwanted regions. In this work, we propose pix2pix-zero, an image-to-image translation method that can preserve the content of the original image without manual prompting. We first automatically discover editing directions that reflect desired edits in the text embedding space. To preserve the general content structure after editing, we further propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process. In addition, our method does not need additional training for these edits and can directly use the existing pre-trained text-to-image diffusion model. We conduct extensive experiments and show that our method outperforms existing and concurrent works for both real and synthetic image editing.*
-
-Resources:
-
-* [Project Page](https://pix2pixzero.github.io/).
-* [Paper](https://arxiv.org/abs/2302.03027).
-* [Original Code](https://github.com/pix2pixzero/pix2pix-zero).
-* [Demo](https://huggingface.co/spaces/pix2pix-zero-library/pix2pix-zero-demo).
-
-## Tips 
-
-* The pipeline can be conditioned on real input images. Check out the code examples below to know more.
-* The pipeline exposes two arguments namely `source_embeds` and `target_embeds`
-that let you control the direction of the semantic edits in the final image to be generated. Let's say,
-you wanted to translate from "cat" to "dog". In this case, the edit direction will be "cat -> dog". To reflect
-this in the pipeline, you simply have to set the embeddings related to the phrases including "cat" to
-`source_embeds` and "dog" to `target_embeds`. Refer to the code example below for more details.
-* When you're using this pipeline from a prompt, specify the _source_ concept in the prompt. Taking
-the above example, a valid input prompt would be: "a high resolution painting of a **cat** in the style of van gough".
-* If you wanted to reverse the direction in the example above, i.e., "dog -> cat", then it's recommended to:
-    * Swap the `source_embeds` and `target_embeds`.
-    * Change the input prompt to include "dog".  
-* To learn more about how the source and target embeddings are generated, refer to the [original 
-paper](https://arxiv.org/abs/2302.03027). Below, we also provide some directions on how to generate the embeddings.
-* Note that the quality of the outputs generated with this pipeline is dependent on how good the `source_embeds` and `target_embeds` are. Please, refer to [this discussion](#generating-source-and-target-embeddings) for some suggestions on the topic.
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo
-|---|---|:---:|
-| [StableDiffusionPix2PixZeroPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py) | *Text-Based Image Editing* | [🤗 Space](https://huggingface.co/spaces/pix2pix-zero-library/pix2pix-zero-demo) |
-
-<!-- TODO: add Colab -->
-
-## Usage example
-
-### Based on an image generated with the input prompt
-
-```python
-import requests
-import torch
-
-from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline
-
-
-def download(embedding_url, local_filepath):
-    r = requests.get(embedding_url)
-    with open(local_filepath, "wb") as f:
-        f.write(r.content)
-
-
-model_ckpt = "CompVis/stable-diffusion-v1-4"
-pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
-    model_ckpt, conditions_input_image=False, torch_dtype=torch.float16
-)
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-pipeline.to("cuda")
-
-prompt = "a high resolution painting of a cat in the style of van gogh"
-src_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/cat.pt"
-target_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/dog.pt"
-
-for url in [src_embs_url, target_embs_url]:
-    download(url, url.split("/")[-1])
-
-src_embeds = torch.load(src_embs_url.split("/")[-1])
-target_embeds = torch.load(target_embs_url.split("/")[-1])
-
-images = pipeline(
-    prompt,
-    source_embeds=src_embeds,
-    target_embeds=target_embeds,
-    num_inference_steps=50,
-    cross_attention_guidance_amount=0.15,
-).images
-images[0].save("edited_image_dog.png")
-```
-
-### Based on an input image
-
-When the pipeline is conditioned on an input image, we first obtain an inverted
-noise from it using a `DDIMInverseScheduler` with the help of a generated caption. Then 
-the inverted noise is used to start the generation process. 
-
-First, let's load our pipeline: 
-
-```py
-import torch
-from transformers import BlipForConditionalGeneration, BlipProcessor
-from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline
-
-captioner_id = "Salesforce/blip-image-captioning-base"
-processor = BlipProcessor.from_pretrained(captioner_id)
-model = BlipForConditionalGeneration.from_pretrained(captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True)
-
-sd_model_ckpt = "CompVis/stable-diffusion-v1-4"
-pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
-    sd_model_ckpt,
-    caption_generator=model,
-    caption_processor=processor,
-    torch_dtype=torch.float16,
-    safety_checker=None,
-)
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
-pipeline.enable_model_cpu_offload()
-```
-
-Then, we load an input image for conditioning and obtain a suitable caption for it: 
-
-```py
-import requests
-from PIL import Image
-
-img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png"
-raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512))
-caption = pipeline.generate_caption(raw_image)
-```
-
-Then we employ the generated caption and the input image to get the inverted noise: 
-
-```py 
-generator = torch.manual_seed(0)
-inv_latents = pipeline.invert(caption, image=raw_image, generator=generator).latents
-```
-
-Now, generate the image with edit directions: 
-
-```py
-# See the "Generating source and target embeddings" section below to
-# automate the generation of these captions with a pre-trained model like Flan-T5 as explained below.
-source_prompts = ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"]
-target_prompts = ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"]
-
-source_embeds = pipeline.get_embeds(source_prompts, batch_size=2)
-target_embeds = pipeline.get_embeds(target_prompts, batch_size=2)
-
-
-image = pipeline(
-    caption,
-    source_embeds=source_embeds,
-    target_embeds=target_embeds,
-    num_inference_steps=50,
-    cross_attention_guidance_amount=0.15,
-    generator=generator,
-    latents=inv_latents,
-    negative_prompt=caption,
-).images[0]
-image.save("edited_image.png")
-```
-
-## Generating source and target embeddings 
-
-The authors originally used the [GPT-3 API](https://openai.com/api/) to generate the source and target captions for discovering
-edit directions. However, we can also leverage open source and public models for the same purpose.
-Below, we provide an end-to-end example with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model
-for generating captions and [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for
-computing embeddings on the generated captions.  
-
-**1. Load the generation model**:
-
-```py
-import torch
-from transformers import AutoTokenizer, T5ForConditionalGeneration
-
-tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl")
-model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16)
-```
-
-**2. Construct a starting prompt**: 
-
-```py
-source_concept = "cat"
-target_concept = "dog"
-
-source_text = f"Provide a caption for images containing a {source_concept}. "
-"The captions should be in English and should be no longer than 150 characters."
-
-target_text = f"Provide a caption for images containing a {target_concept}. "
-"The captions should be in English and should be no longer than 150 characters."
-```
-
-Here, we're interested in the "cat -> dog" direction. 
-
-**3. Generate captions**:
-
-We can use a utility like so for this purpose. 
-
-```py
-def generate_captions(input_prompt):
-    input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to("cuda")
-
-    outputs = model.generate(
-        input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10
-    )
-    return tokenizer.batch_decode(outputs, skip_special_tokens=True)
-```
-
-And then we just call it to generate our captions:
-
-```py
-source_captions = generate_captions(source_text)
-target_captions = generate_captions(target_concept)
-```
-
-We encourage you to play around with the different parameters supported by the
-`generate()` method ([documentation](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_tf_utils.TFGenerationMixin.generate)) for the generation quality you are looking for.
-
-**4. Load the embedding model**: 
-
-Here, we need to use the same text encoder model used by the subsequent Stable Diffusion model.
-
-```py 
-from diffusers import StableDiffusionPix2PixZeroPipeline 
-
-pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
-)
-pipeline = pipeline.to("cuda")
-tokenizer = pipeline.tokenizer
-text_encoder = pipeline.text_encoder
-```
-
-**5. Compute embeddings**:
-
-```py 
-import torch 
-
-def embed_captions(sentences, tokenizer, text_encoder, device="cuda"):
-    with torch.no_grad():
-        embeddings = []
-        for sent in sentences:
-            text_inputs = tokenizer(
-                sent,
-                padding="max_length",
-                max_length=tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
-            embeddings.append(prompt_embeds)
-    return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)
-
-source_embeddings = embed_captions(source_captions, tokenizer, text_encoder)
-target_embeddings = embed_captions(target_captions, tokenizer, text_encoder)
-```
-
-And you're done! [Here](https://colab.research.google.com/drive/1tz2C1EdfZYAPlzXXbTnf-5PRBiR8_R1F?usp=sharing) is a Colab Notebook that you can use to interact with the entire process.
-
-Now, you can use these embeddings directly while calling the pipeline: 
-
-```py
-from diffusers import DDIMScheduler
-
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-
-images = pipeline(
-    prompt,
-    source_embeds=source_embeddings,
-    target_embeds=target_embeddings,
-    num_inference_steps=50,
-    cross_attention_guidance_amount=0.15,
-).images
-images[0].save("edited_image_dog.png")
-```
-
-## StableDiffusionPix2PixZeroPipeline
-[[autodoc]] StableDiffusionPix2PixZeroPipeline
-	- __call__
-	- all

docs/source/en/api/pipelines/repaint.mdx

@@ -1,77 +0,0 @@
-<!--Copyright 2023 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(
-    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]] RePaintPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/self_attention_guidance.mdx

@@ -1,65 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Self-Attention Guidance (SAG)
-
-## Overview
-
-[Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939) by Susung Hong et al.
-
-The abstract of the paper is the following:
-
-*Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of class- or text-conditional diffusion guidance methods, such as classifier and classifier-free guidance. In this paper, we present a more comprehensive perspective that goes beyond the traditional guidance methods. From this generalized perspective, we introduce novel condition- and training-free strategies to enhance the quality of generated images. As a simple solution, blur guidance improves the suitability of intermediate samples for their fine-scale information and structures, enabling diffusion models to generate higher quality samples with a moderate guidance scale. Improving upon this, Self-Attention Guidance (SAG) uses the intermediate self-attention maps of diffusion models to enhance their stability and efficacy. Specifically, SAG adversarially blurs only the regions that diffusion models attend to at each iteration and guides them accordingly. Our experimental results show that our SAG improves the performance of various diffusion models, including ADM, IDDPM, Stable Diffusion, and DiT. Moreover, combining SAG with conventional guidance methods leads to further improvement.*
-
-Resources:
-
-* [Project Page](https://ku-cvlab.github.io/Self-Attention-Guidance).
-* [Paper](https://arxiv.org/abs/2210.00939).
-* [Original Code](https://github.com/KU-CVLAB/Self-Attention-Guidance).
-* [Hugging Face Demo](https://huggingface.co/spaces/susunghong/Self-Attention-Guidance).
-* [Colab Demo](https://colab.research.google.com/github/SusungHong/Self-Attention-Guidance/blob/main/SAG_Stable.ipynb).
-
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo
-|---|---|:---:|
-| [StableDiffusionSAGPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py) | *Text-to-Image Generation* | [🤗 Space](https://huggingface.co/spaces/susunghong/Self-Attention-Guidance) |
-
-## Usage example
-
-```python
-import torch
-from diffusers import StableDiffusionSAGPipeline
-from accelerate.utils import set_seed
-
-pipe = StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
-
-seed = 8978
-prompt = "."
-guidance_scale = 7.5
-num_images_per_prompt = 1
-
-sag_scale = 1.0
-
-set_seed(seed)
-images = pipe(
-    prompt, num_images_per_prompt=num_images_per_prompt, guidance_scale=guidance_scale, sag_scale=sag_scale
-).images
-images[0].save("example.png")
-```
-
-## StableDiffusionSAGPipeline
-[[autodoc]] StableDiffusionSAGPipeline
-	- __call__
-	- all

docs/source/en/api/pipelines/semantic_stable_diffusion.mdx

@@ -1,79 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Semantic Guidance
-
-Semantic Guidance for Diffusion Models was proposed in [SEGA: Instructing Diffusion using Semantic Dimensions](https://arxiv.org/abs/2301.12247) and provides strong semantic control over the image generation.
-Small changes to the text prompt usually result in entirely different output images. However, with SEGA a variety of changes to the image are enabled that can be controlled easily and intuitively, and stay true to the original image composition.
-
-The abstract of the paper is the following:
-
-*Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user's intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA's effectiveness on a variety of tasks and provide evidence for its versatility and flexibility.*
-
-
-*Overview*:
-
-| Pipeline | Tasks | Colab | Demo
-|---|---|:---:|:---:|
-| [pipeline_semantic_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_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/ml-research/semantic-image-editing/blob/main/examples/SemanticGuidance.ipynb) | [Coming Soon](https://huggingface.co/AIML-TUDA)
-
-## Tips
-
-- The Semantic Guidance pipeline can be used with any [Stable Diffusion](./stable_diffusion/text2img) checkpoint.
-
-### Run Semantic Guidance
-
-The interface of [`SemanticStableDiffusionPipeline`] provides several additional parameters to influence the image generation.
-Exemplary usage may look like this:
-
-```python
-import torch
-from diffusers import SemanticStableDiffusionPipeline
-
-pipe = SemanticStableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
-
-out = pipe(
-    prompt="a photo of the face of a woman",
-    num_images_per_prompt=1,
-    guidance_scale=7,
-    editing_prompt=[
-        "smiling, smile",  # Concepts to apply
-        "glasses, wearing glasses",
-        "curls, wavy hair, curly hair",
-        "beard, full beard, mustache",
-    ],
-    reverse_editing_direction=[False, False, False, False],  # Direction of guidance i.e. increase all concepts
-    edit_warmup_steps=[10, 10, 10, 10],  # Warmup period for each concept
-    edit_guidance_scale=[4, 5, 5, 5.4],  # Guidance scale for each concept
-    edit_threshold=[
-        0.99,
-        0.975,
-        0.925,
-        0.96,
-    ],  # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
-    edit_momentum_scale=0.3,  # Momentum scale that will be added to the latent guidance
-    edit_mom_beta=0.6,  # Momentum beta
-    edit_weights=[1, 1, 1, 1, 1],  # Weights of the individual concepts against each other
-)
-```
-
-For more examples check the Colab notebook.
-
-## StableDiffusionSafePipelineOutput
-[[autodoc]] pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput
-	- all
-
-## SemanticStableDiffusionPipeline
-[[autodoc]] SemanticStableDiffusionPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/spectrogram_diffusion.mdx

@@ -1,54 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Multi-instrument Music Synthesis with Spectrogram Diffusion
-
-## Overview
-
-[Spectrogram Diffusion](https://arxiv.org/abs/2206.05408) by Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, and Jesse Engel.
-
-An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural synthesizers have exhibited a tradeoff between domain-specific models that offer detailed control of only specific instruments, or raw waveform models that can train on any music but with minimal control and slow generation. In this work, we focus on a middle ground of neural synthesizers that can generate audio from MIDI sequences with arbitrary combinations of instruments in realtime. This enables training on a wide range of transcription datasets with a single model, which in turn offers note-level control of composition and instrumentation across a wide range of instruments. We use a simple two-stage process: MIDI to spectrograms with an encoder-decoder Transformer, then spectrograms to audio with a generative adversarial network (GAN) spectrogram inverter. We compare training the decoder as an autoregressive model and as a Denoising Diffusion Probabilistic Model (DDPM) and find that the DDPM approach is superior both qualitatively and as measured by audio reconstruction and Fréchet distance metrics. Given the interactivity and generality of this approach, we find this to be a promising first step towards interactive and expressive neural synthesis for arbitrary combinations of instruments and notes.
-
-The original codebase of this implementation can be found at [magenta/music-spectrogram-diffusion](https://github.com/magenta/music-spectrogram-diffusion).
-
-## Model
-
-![img](https://storage.googleapis.com/music-synthesis-with-spectrogram-diffusion/architecture.png)
-
-As depicted above the model takes as input a MIDI file and tokenizes it into a sequence of 5 second intervals. Each tokenized interval then together with positional encodings is passed through the Note Encoder and its representation is concatenated with the previous window's generated spectrogram representation obtained via the Context Encoder. For the initial 5 second window this is set to zero. The resulting context is then used as conditioning to sample the denoised Spectrogram from the MIDI window and we concatenate this spectrogram to the final output as well as use it for the context of the next MIDI window. The process repeats till we have gone over all the MIDI inputs. Finally a MelGAN decoder converts the potentially long spectrogram to audio which is the final result of this pipeline.
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab
-|---|---|:---:|
-| [pipeline_spectrogram_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/spectrogram_diffusion/pipeline_spectrogram_diffusion.py) | *Unconditional Audio Generation* | - |
-
-
-## Example usage
-
-```python
-from diffusers import SpectrogramDiffusionPipeline, MidiProcessor
-
-pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
-pipe = pipe.to("cuda")
-processor = MidiProcessor()
-
-# Download MIDI from: wget http://www.piano-midi.de/midis/beethoven/beethoven_hammerklavier_2.mid
-output = pipe(processor("beethoven_hammerklavier_2.mid"))
-
-audio = output.audios[0]
-```
-
-## SpectrogramDiffusionPipeline
-[[autodoc]] SpectrogramDiffusionPipeline
-	- all
-	- __call__

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

@@ -1,36 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Depth-to-Image Generation
-
-## StableDiffusionDepth2ImgPipeline
-
-The depth-guided stable diffusion model was created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), and [LAION](https://laion.ai/), as part of Stable Diffusion 2.0. It uses [MiDas](https://github.com/isl-org/MiDaS) to infer depth based on an image.
-
-[`StableDiffusionDepth2ImgPipeline`] lets you pass a text prompt and an initial image to condition the generation of new images as well as a `depth_map` to preserve the images’ structure. 
-
-The original codebase can be found here: 
-- *Stable Diffusion v2*: [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion#depth-conditional-stable-diffusion)
-
-Available Checkpoints are:
-- *stable-diffusion-2-depth*: [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth)
-
-[[autodoc]] StableDiffusionDepth2ImgPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-	- load_lora_weights
-	- save_lora_weights

docs/source/en/api/pipelines/stable_diffusion/image_variation.mdx

@@ -1,31 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Image Variation
-
-## StableDiffusionImageVariationPipeline
-
-[`StableDiffusionImageVariationPipeline`] lets you generate variations from an input image using Stable Diffusion. It uses a fine-tuned version of Stable Diffusion model, trained by  [Justin Pinkney](https://www.justinpinkney.com/) (@Buntworthy) at [Lambda](https://lambdalabs.com/).
-
-The original codebase can be found here:
-[Stable Diffusion Image Variations](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations)
-
-Available Checkpoints are:
-- *sd-image-variations-diffusers*: [lambdalabs/sd-image-variations-diffusers](https://huggingface.co/lambdalabs/sd-image-variations-diffusers)
-
-[[autodoc]] StableDiffusionImageVariationPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention

docs/source/en/api/pipelines/stable_diffusion/img2img.mdx

@@ -1,40 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Image-to-Image Generation
-
-## StableDiffusionImg2ImgPipeline
-
-The Stable Diffusion model was created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [runway](https://github.com/runwayml), and [LAION](https://laion.ai/). The [`StableDiffusionImg2ImgPipeline`] lets you pass a text prompt and an initial image to condition the generation of new images using Stable Diffusion.
-
-The original codebase can be found here: [CampVis/stable-diffusion](https://github.com/CompVis/stable-diffusion/blob/main/scripts/img2img.py) 
-
-[`StableDiffusionImg2ImgPipeline`] is compatible with all Stable Diffusion checkpoints for [Text-to-Image](./text2img) 
-
-The pipeline uses the diffusion-denoising mechanism proposed by SDEdit ([SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://arxiv.org/abs/2108.01073)
-proposed by Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon).
-
-[[autodoc]] StableDiffusionImg2ImgPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-	- from_ckpt
-	- load_lora_weights
-	- save_lora_weights
-
-[[autodoc]] FlaxStableDiffusionImg2ImgPipeline
-	- all
-	- __call__

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

@@ -1,40 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Text-Guided Image Inpainting
-
-## StableDiffusionInpaintPipeline
-
-The Stable Diffusion model was created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [runway](https://github.com/runwayml), and [LAION](https://laion.ai/). The [`StableDiffusionInpaintPipeline`] lets you edit specific parts of an image by providing a mask and a text prompt using Stable Diffusion.
-
-The original codebase can be found here: 
-- *Stable Diffusion V1*: [CampVis/stable-diffusion](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion)
-- *Stable Diffusion V2*: [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion#image-inpainting-with-stable-diffusion)
-
-Available checkpoints are:
-- *stable-diffusion-inpainting (512x512 resolution)*: [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)
-- *stable-diffusion-2-inpainting (512x512 resolution)*: [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting)
-
-[[autodoc]] StableDiffusionInpaintPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-	- load_lora_weights
-	- save_lora_weights
-
-[[autodoc]] FlaxStableDiffusionInpaintPipeline
-	- all
-	- __call__

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

@@ -1,33 +0,0 @@
-<!--Copyright 2023 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 Latent Upscaler
-
-## StableDiffusionLatentUpscalePipeline
-
-The Stable Diffusion Latent Upscaler model was created by [Katherine Crowson](https://github.com/crowsonkb/k-diffusion) in collaboration with [Stability AI](https://stability.ai/). It can be used on top of any [`StableDiffusionUpscalePipeline`] checkpoint to enhance its output image resolution by a factor of 2.
-
-A notebook that demonstrates the original implementation can be found here: 
-- [Stable Diffusion Upscaler Demo](https://colab.research.google.com/drive/1o1qYJcFeywzCIdkfKJy7cTpgZTCM2EI4)
-
-Available Checkpoints are:
-- *stabilityai/latent-upscaler*: [stabilityai/sd-x2-latent-upscaler](https://huggingface.co/stabilityai/sd-x2-latent-upscaler)
-
-
-[[autodoc]] StableDiffusionLatentUpscalePipeline
-	- all
-	- __call__
-	- enable_sequential_cpu_offload
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention

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

@@ -1,83 +0,0 @@
-<!--Copyright 2023 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
-|---|---|:---:|:---:|
-| [StableDiffusionPipeline](./text2img) | *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)
-| [StableDiffusionImg2ImgPipeline](./img2img) | *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)
-| [StableDiffusionInpaintPipeline](./inpaint) | **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
-| [StableDiffusionDepth2ImgPipeline](./depth2img) | **Experimental** – *Depth-to-Image Text-Guided Generation * | | Coming soon
-| [StableDiffusionImageVariationPipeline](./image_variation) | **Experimental** – *Image Variation Generation * | | [🤗 Stable Diffusion Image Variations](https://huggingface.co/spaces/lambdalabs/stable-diffusion-image-variations)
-| [StableDiffusionUpscalePipeline](./upscale) | **Experimental** – *Text-Guided Image Super-Resolution * | | Coming soon
-| [StableDiffusionLatentUpscalePipeline](./latent_upscale) | **Experimental** – *Text-Guided Image Super-Resolution * | | Coming soon
-| [StableDiffusionInstructPix2PixPipeline](./pix2pix) | **Experimental** – *Text-Based Image Editing * | | [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://huggingface.co/spaces/timbrooks/instruct-pix2pix)
-| [StableDiffusionAttendAndExcitePipeline](./attend_and_excite) | **Experimental** – *Text-to-Image Generation * | | [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite)
-| [StableDiffusionPix2PixZeroPipeline](./pix2pix_zero) | **Experimental** – *Text-Based Image Editing * | | [Zero-shot Image-to-Image Translation](https://arxiv.org/abs/2302.03027)
-| [StableDiffusionModelEditingPipeline](./model_editing) | **Experimental** – *Text-to-Image Model Editing * | | [Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://arxiv.org/abs/2303.08084)
-| [StableDiffusionDiffEditPipeline](./diffedit) | **Experimental** – *Text-Based Image Editing * | | [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://arxiv.org/abs/2210.11427)
-
-
-
-## 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 convert 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

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

@@ -1,176 +0,0 @@
-<!--Copyright 2023 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 2
-
-Stable Diffusion 2 is a text-to-image _latent diffusion_ model built upon the work of [Stable Diffusion 1](https://stability.ai/blog/stable-diffusion-public-release). 
-The project to train Stable Diffusion 2 was led by Robin Rombach and Katherine Crowson from [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/).
-
-*The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text encoder (OpenCLIP), developed by LAION with support from Stability AI, which greatly improves the quality of the generated images compared to earlier V1 releases. The text-to-image models in this release can generate images with default resolutions of both 512x512 pixels and 768x768 pixels. 
-These models are trained on an aesthetic subset of the [LAION-5B dataset](https://laion.ai/blog/laion-5b/) created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using [LAION’s NSFW filter](https://openreview.net/forum?id=M3Y74vmsMcY).*
-
-For more details about how Stable Diffusion 2 works and how it differs from Stable Diffusion 1, please refer to the official [launch announcement post](https://stability.ai/blog/stable-diffusion-v2-release).
-
-## Tips
-
-### Available checkpoints:
-
-Note that the architecture is more or less identical to [Stable Diffusion 1](./stable_diffusion/overview) so please refer to [this page](./stable_diffusion/overview) for API documentation.
-
-- *Text-to-Image (512x512 resolution)*: [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) with [`StableDiffusionPipeline`]
-- *Text-to-Image (768x768 resolution)*: [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) with [`StableDiffusionPipeline`]
-- *Image Inpainting (512x512 resolution)*: [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) with [`StableDiffusionInpaintPipeline`]
-- *Super-Resolution (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) [`StableDiffusionUpscalePipeline`]
-- *Depth-to-Image (512x512 resolution)*: [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth) with [`StableDiffusionDepth2ImagePipeline`]
-
-We recommend using the [`DPMSolverMultistepScheduler`] as it's currently the fastest scheduler there is.
-
-
-### Text-to-Image
-
-- *Text-to-Image (512x512 resolution)*: [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) with [`StableDiffusionPipeline`]
-
-```python
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-import torch
-
-repo_id = "stabilityai/stable-diffusion-2-base"
-pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
-
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
-
-prompt = "High quality photo of an astronaut riding a horse in space"
-image = pipe(prompt, num_inference_steps=25).images[0]
-image.save("astronaut.png")
-```
-
-- *Text-to-Image (768x768 resolution)*: [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) with [`StableDiffusionPipeline`]
-
-```python
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-import torch
-
-repo_id = "stabilityai/stable-diffusion-2"
-pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
-
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
-
-prompt = "High quality photo of an astronaut riding a horse in space"
-image = pipe(prompt, guidance_scale=9, num_inference_steps=25).images[0]
-image.save("astronaut.png")
-```
-
-### Image Inpainting
-
-- *Image Inpainting (512x512 resolution)*: [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) with [`StableDiffusionInpaintPipeline`]
-
-```python
-import PIL
-import requests
-import torch
-from io import BytesIO
-
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-
-
-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))
-
-repo_id = "stabilityai/stable-diffusion-2-inpainting"
-pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
-
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-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, num_inference_steps=25).images[0]
-
-image.save("yellow_cat.png")
-```
-
-### Super-Resolution
-
-- *Image Upscaling (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) with [`StableDiffusionUpscalePipeline`]
-
-
-```python
-import requests
-from PIL import Image
-from io import BytesIO
-from diffusers import StableDiffusionUpscalePipeline
-import torch
-
-# load model and scheduler
-model_id = "stabilityai/stable-diffusion-x4-upscaler"
-pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipeline = pipeline.to("cuda")
-
-# let's download an  image
-url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
-response = requests.get(url)
-low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
-low_res_img = low_res_img.resize((128, 128))
-prompt = "a white cat"
-upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
-upscaled_image.save("upsampled_cat.png")
-```
-
-### Depth-to-Image
-
-- *Depth-Guided Text-to-Image*: [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth) [`StableDiffusionDepth2ImagePipeline`]
-
-
-```python
-import torch
-import requests
-from PIL import Image
-
-from diffusers import StableDiffusionDepth2ImgPipeline
-
-pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-depth",
-    torch_dtype=torch.float16,
-).to("cuda")
-
-
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-init_image = Image.open(requests.get(url, stream=True).raw)
-prompt = "two tigers"
-n_propmt = "bad, deformed, ugly, bad anotomy"
-image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]
-```
-
-### How to load and use different schedulers.
-
-The stable diffusion pipeline uses [`DDIMScheduler`] scheduler by default. But `diffusers` provides many other schedulers that can be used with the stable 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 StableDiffusionPipeline, EulerDiscreteScheduler
-
->>> pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
->>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-
->>> # or
->>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2", subfolder="scheduler")
->>> pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=euler_scheduler)
-```

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.mdx

@@ -1,90 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Safe Stable Diffusion
-
-Safe Stable Diffusion was proposed in [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105) and mitigates the well known issue that models like Stable Diffusion that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. For instance Stable Diffusion may unexpectedly generate nudity, violence, images depicting self-harm, or otherwise offensive content.
-Safe Stable Diffusion is an extension to the Stable Diffusion that drastically reduces content like this.
-
-The abstract of the paper is the following:
-
-*Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.*
-
-
-*Overview*:
-
-| Pipeline | Tasks | Colab | Demo
-|---|---|:---:|:---:|
-| [pipeline_stable_diffusion_safe.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py) | *Text-to-Image Generation* |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/safe-latent-diffusion/blob/main/examples/Safe%20Latent%20Diffusion.ipynb) | [![Huggingface Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/AIML-TUDA/unsafe-vs-safe-stable-diffusion)
-
-## Tips
-
-- Safe Stable Diffusion may also be used with weights of [Stable Diffusion](./stable_diffusion/text2img).
-
-### Run Safe Stable Diffusion
-
-Safe Stable Diffusion can be tested very easily with the [`StableDiffusionPipelineSafe`], and the `"AIML-TUDA/stable-diffusion-safe"` checkpoint exactly in the same way it is shown in the [Conditional Image Generation Guide](../../using-diffusers/conditional_image_generation).
-
-### Interacting with the Safety Concept
-
-To check and edit the currently used safety concept, use the `safety_concept` property of [`StableDiffusionPipelineSafe`]:
-```python
->>> from diffusers import StableDiffusionPipelineSafe
-
->>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe")
->>> pipeline.safety_concept
-```
-For each image generation the active concept is also contained in [`StableDiffusionSafePipelineOutput`].
-
-### Using pre-defined safety configurations
-
-You may use the 4 configurations defined in the [Safe Latent Diffusion paper](https://arxiv.org/abs/2211.05105) as follows:
-
-```python
->>> from diffusers import StableDiffusionPipelineSafe
->>> from diffusers.pipelines.stable_diffusion_safe import SafetyConfig
-
->>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe")
->>> prompt = "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c. leyendecker"
->>> out = pipeline(prompt=prompt, **SafetyConfig.MAX)
-```
-
-The following configurations are available: `SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONG`, and `SafetyConfig.MAX`.
-
-### How to load and use different schedulers
-
-The safe 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 StableDiffusionPipelineSafe, EulerDiscreteScheduler
-
->>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe")
->>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-
->>> # or
->>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("AIML-TUDA/stable-diffusion-safe", subfolder="scheduler")
->>> pipeline = StableDiffusionPipelineSafe.from_pretrained(
-...     "AIML-TUDA/stable-diffusion-safe", scheduler=euler_scheduler
-... )
-```
-
-
-## StableDiffusionSafePipelineOutput
-[[autodoc]] pipelines.stable_diffusion_safe.StableDiffusionSafePipelineOutput
-	- all
-	- __call__
-
-## StableDiffusionPipelineSafe
-[[autodoc]] StableDiffusionPipelineSafe
-	- all
-	- __call__

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

@@ -1,49 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Text-to-Image Generation
-
-## StableDiffusionPipeline
-
-The Stable Diffusion model was created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [runway](https://github.com/runwayml), and [LAION](https://laion.ai/). The [`StableDiffusionPipeline`] is capable of generating photo-realistic images given any text input using Stable Diffusion.
-
-The original codebase can be found here: 
-- *Stable Diffusion V1*: [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion)
-- *Stable Diffusion v2*: [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion)
-
-Available Checkpoints are:
-- *stable-diffusion-v1-4 (512x512 resolution)* [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
-- *stable-diffusion-v1-5 (512x512 resolution)* [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
-- *stable-diffusion-2-base (512x512 resolution)*: [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base)
-- *stable-diffusion-2 (768x768 resolution)*: [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 
-- *stable-diffusion-2-1-base (512x512 resolution)* [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)
-- *stable-diffusion-2-1 (768x768 resolution)*: [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1)
-
-[[autodoc]] StableDiffusionPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- enable_vae_tiling
-	- disable_vae_tiling
-	- load_textual_inversion
-	- from_ckpt
-	- load_lora_weights
-	- save_lora_weights
-
-[[autodoc]] FlaxStableDiffusionPipeline
-	- all
-	- __call__

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

@@ -1,32 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Super-Resolution
-
-## StableDiffusionUpscalePipeline
-
-The upscaler diffusion model was created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), and [LAION](https://laion.ai/), as part of Stable Diffusion 2.0. [`StableDiffusionUpscalePipeline`] can be used to enhance the resolution of input images by a factor of 4.
-
-The original codebase can be found here: 
-- *Stable Diffusion v2*: [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion#image-upscaling-with-stable-diffusion)
-
-Available Checkpoints are:
-- *stabilityai/stable-diffusion-x4-upscaler (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler)
-
-
-[[autodoc]] StableDiffusionUpscalePipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention

docs/source/en/api/pipelines/stable_unclip.mdx

@@ -1,175 +0,0 @@
-<!--Copyright 2023 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 unCLIP
-
-Stable unCLIP checkpoints are finetuned from [stable diffusion 2.1](./stable_diffusion_2) checkpoints to condition on CLIP image embeddings.
-Stable unCLIP also still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used
-for text guided image variation. When combined with an unCLIP prior, it can also be used for full text to image generation.
-
-To know more about the unCLIP process, check out the following paper:
-
-[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125) by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen.
-
-## Tips
-
-Stable unCLIP takes a `noise_level` as input during inference. `noise_level` determines how much noise is added 
-to the image embeddings. A higher `noise_level` increases variation in the final un-noised images. By default, 
-we do not add any additional noise to the image embeddings i.e. `noise_level = 0`.
-
-### Available checkpoints:
-
-* Image variation
-	* [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip)
-	* [stabilityai/stable-diffusion-2-1-unclip-small](https://hf.co/stabilityai/stable-diffusion-2-1-unclip-small)
-* Text-to-image 
-	* [stabilityai/stable-diffusion-2-1-unclip-small](https://hf.co/stabilityai/stable-diffusion-2-1-unclip-small)
-
-### Text-to-Image Generation
-Stable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain's open source DALL-E 2 replication [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha)
-
-```python
-import torch
-from diffusers import UnCLIPScheduler, DDPMScheduler, StableUnCLIPPipeline
-from diffusers.models import PriorTransformer
-from transformers import CLIPTokenizer, CLIPTextModelWithProjection
-
-prior_model_id = "kakaobrain/karlo-v1-alpha"
-data_type = torch.float16
-prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type)
-
-prior_text_model_id = "openai/clip-vit-large-patch14"
-prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id)
-prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type)
-prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler")
-prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
-
-stable_unclip_model_id = "stabilityai/stable-diffusion-2-1-unclip-small"
-
-pipe = StableUnCLIPPipeline.from_pretrained(
-    stable_unclip_model_id,
-    torch_dtype=data_type,
-    variant="fp16",
-    prior_tokenizer=prior_tokenizer,
-    prior_text_encoder=prior_text_model,
-    prior=prior,
-    prior_scheduler=prior_scheduler,
-)
-
-pipe = pipe.to("cuda")
-wave_prompt = "dramatic wave, the Oceans roar, Strong wave spiral across the oceans as the waves unfurl into roaring crests; perfect wave form; perfect wave shape; dramatic wave shape; wave shape unbelievable; wave; wave shape spectacular"
-
-images = pipe(prompt=wave_prompt).images
-images[0].save("waves.png")
-```
-<Tip warning={true}>
-
-For text-to-image we use `stabilityai/stable-diffusion-2-1-unclip-small` as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip) was trained on OpenCLIP ViT-H, so we don't recommend its use. 
-
-</Tip>
-
-### Text guided Image-to-Image Variation
-
-```python
-from diffusers import StableUnCLIPImg2ImgPipeline
-from diffusers.utils import load_image
-import torch
-
-pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
-)
-pipe = pipe.to("cuda")
-
-url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
-init_image = load_image(url)
-
-images = pipe(init_image).images
-images[0].save("variation_image.png")
-```
-
-Optionally, you can also pass a prompt to `pipe` such as:
-
-```python 
-prompt = "A fantasy landscape, trending on artstation"
-
-images = pipe(init_image, prompt=prompt).images
-images[0].save("variation_image_two.png")
-```
-
-### Memory optimization
-
-If you are short on GPU memory, you can enable smart CPU offloading so that models that are not needed
-immediately for a computation can be offloaded to CPU:
-
-```python 
-from diffusers import StableUnCLIPImg2ImgPipeline
-from diffusers.utils import load_image
-import torch
-
-pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
-)
-# Offload to CPU.
-pipe.enable_model_cpu_offload()
-
-url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
-init_image = load_image(url)
-
-images = pipe(init_image).images
-images[0]
-```
-
-Further memory optimizations are possible by enabling VAE slicing on the pipeline: 
-
-```python 
-from diffusers import StableUnCLIPImg2ImgPipeline
-from diffusers.utils import load_image
-import torch
-
-pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
-)
-pipe.enable_model_cpu_offload()
-pipe.enable_vae_slicing()
-
-url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
-init_image = load_image(url)
-
-images = pipe(init_image).images
-images[0]
-```
-
-### StableUnCLIPPipeline
-
-[[autodoc]] StableUnCLIPPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-
-
-### StableUnCLIPImg2ImgPipeline
-
-[[autodoc]] StableUnCLIPImg2ImgPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-    

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

@@ -1,130 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-<Tip warning={true}>
-
-This pipeline is for research purposes only. 
-
-</Tip>
-
-# Text-to-video synthesis
-
-## Overview
-
-[VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation](https://arxiv.org/abs/2303.08320) by Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liang Wang, Yujun Shen, Deli Zhao, Jingren Zhou, Tieniu Tan.
-
-The abstract of the paper is the following:
-
-*A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generation is still challenging due to high-dimensional data spaces. Previous methods usually adopt a standard diffusion process, where frames in the same video clip are destroyed with independent noises, ignoring the content redundancy and temporal correlation. This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. The denoising pipeline employs two jointly-learned networks to match the noise decomposition accordingly. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation. We further show that our decomposed formulation can benefit from pre-trained image diffusion models and well-support text-conditioned video creation.*
-
-Resources:
-
-* [Website](https://modelscope.cn/models/damo/text-to-video-synthesis/summary)
-* [GitHub repository](https://github.com/modelscope/modelscope/)
-* [🤗 Spaces](https://huggingface.co/spaces/damo-vilab/modelscope-text-to-video-synthesis)
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo
-|---|---|:---:|
-| [TextToVideoSDPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py) | *Text-to-Video Generation* | [🤗 Spaces](https://huggingface.co/spaces/damo-vilab/modelscope-text-to-video-synthesis)
-
-## Usage example 
-
-Let's start by generating a short video with the default length of 16 frames (2s at 8 fps):
-
-```python 
-import torch
-from diffusers import DiffusionPipeline
-from diffusers.utils import export_to_video
-
-pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
-pipe = pipe.to("cuda")
-
-prompt = "Spiderman is surfing"
-video_frames = pipe(prompt).frames
-video_path = export_to_video(video_frames)
-video_path
-```
-
-Diffusers supports different optimization techniques to improve the latency
-and memory footprint of a pipeline. Since videos are often more memory-heavy than images,
-we can enable CPU offloading and VAE slicing to keep the memory footprint at bay.
-
-Let's generate a video of 8 seconds (64 frames) on the same GPU using CPU offloading and VAE slicing:
-
-```python
-import torch
-from diffusers import DiffusionPipeline
-from diffusers.utils import export_to_video
-
-pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
-pipe.enable_model_cpu_offload()
-
-# memory optimization
-pipe.enable_vae_slicing()
-
-prompt = "Darth Vader surfing a wave"
-video_frames = pipe(prompt, num_frames=64).frames
-video_path = export_to_video(video_frames)
-video_path
-```
-
-It just takes **7 GBs of GPU memory** to generate the 64 video frames using PyTorch 2.0, "fp16" precision and the techniques mentioned above.
-
-We can also use a different scheduler easily, using the same method we'd use for Stable Diffusion:
-
-```python
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from diffusers.utils import export_to_video
-
-pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-
-prompt = "Spiderman is surfing"
-video_frames = pipe(prompt, num_inference_steps=25).frames
-video_path = export_to_video(video_frames)
-video_path
-```
-
-Here are some sample outputs: 
-
-<table>
-    <tr>
-        <td><center>
-        An astronaut riding a horse.
-        <br>
-        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astr.gif"
-            alt="An astronaut riding a horse."
-            style="width: 300px;" />
-        </center></td>
-        <td ><center>
-        Darth vader surfing in waves.
-        <br>
-        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vader.gif"
-            alt="Darth vader surfing in waves."
-            style="width: 300px;" />
-        </center></td>
-    </tr>
-</table>
-
-## Available checkpoints 
-
-* [damo-vilab/text-to-video-ms-1.7b](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b/)
-* [damo-vilab/text-to-video-ms-1.7b-legacy](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b-legacy)
-
-## TextToVideoSDPipeline
-[[autodoc]] TextToVideoSDPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/text_to_video_zero.mdx

@@ -1,240 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Zero-Shot Text-to-Video Generation 
-
-## Overview
-
-
-[Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://arxiv.org/abs/2303.13439) by
-Levon Khachatryan,
-Andranik Movsisyan,
-Vahram Tadevosyan,
-Roberto Henschel,
-[Zhangyang Wang](https://www.ece.utexas.edu/people/faculty/atlas-wang), Shant Navasardyan, [Humphrey Shi](https://www.humphreyshi.com).
-
-Our method Text2Video-Zero enables zero-shot video generation using either
-1. A textual prompt, or
-2. A prompt combined with guidance from poses or edges, or 
-3. Video Instruct-Pix2Pix, i.e., instruction-guided video editing.
-
-Results are temporally consistent and follow closely the guidance and textual prompts.
-
-![teaser-img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2v_zero_teaser.png)
-
-The abstract of the paper is the following:
-
-*Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain.
-Our key modifications include (i) enriching the latent codes of the generated frames with motion dynamics to keep the global scene and the background time consistent; and (ii) reprogramming frame-level self-attention using a new cross-frame attention of each frame on the first frame, to preserve the context, appearance, and identity of the foreground object.
-Experiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation. Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instruct-Pix2Pix, i.e., instruction-guided video editing.
-As experiments show, our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.*
-
-
-
-Resources:
-
-* [Project Page](https://text2video-zero.github.io/)
-* [Paper](https://arxiv.org/abs/2303.13439)
-* [Original Code](https://github.com/Picsart-AI-Research/Text2Video-Zero)
-
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo
-|---|---|:---:|
-| [TextToVideoZeroPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py) | *Zero-shot Text-to-Video Generation* | [🤗 Space](https://huggingface.co/spaces/PAIR/Text2Video-Zero)
-
-
-## Usage example
-
-### Text-To-Video
-
-To generate a video from prompt, run the following python command
-```python
-import torch
-import imageio
-from diffusers import TextToVideoZeroPipeline
-
-model_id = "runwayml/stable-diffusion-v1-5"
-pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-
-prompt = "A panda is playing guitar on times square"
-result = pipe(prompt=prompt).images
-result = [(r * 255).astype("uint8") for r in result]
-imageio.mimsave("video.mp4", result, fps=4)
-```
-You can change these parameters in the pipeline call:
-* Motion field strength (see the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1):
-    * `motion_field_strength_x` and `motion_field_strength_y`. Default: `motion_field_strength_x=12`, `motion_field_strength_y=12`
-* `T` and `T'` (see the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1)
-    * `t0` and `t1` in the range `{0, ..., num_inference_steps}`. Default: `t0=45`, `t1=48`
-* Video length:
-    * `video_length`, the number of frames video_length to be generated. Default: `video_length=8`
-
-
-### Text-To-Video with Pose Control
-To generate a video from prompt with additional pose control
-
-1. Download a demo video
-
-    ```python
-    from huggingface_hub import hf_hub_download
-
-    filename = "__assets__/poses_skeleton_gifs/dance1_corr.mp4"
-    repo_id = "PAIR/Text2Video-Zero"
-    video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
-    ```
-
-
-2. Read video containing extracted pose images
-    ```python
-    from PIL import Image
-    import imageio
-
-    reader = imageio.get_reader(video_path, "ffmpeg")
-    frame_count = 8
-    pose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
-    ```
-    To extract pose from actual video, read [ControlNet documentation](./stable_diffusion/controlnet).
-
-3. Run `StableDiffusionControlNetPipeline` with our custom attention processor
-
-    ```python
-    import torch
-    from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-    from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
-
-    model_id = "runwayml/stable-diffusion-v1-5"
-    controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16)
-    pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        model_id, controlnet=controlnet, torch_dtype=torch.float16
-    ).to("cuda")
-
-    # Set the attention processor
-    pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
-    pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
-
-    # fix latents for all frames
-    latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)
-
-    prompt = "Darth Vader dancing in a desert"
-    result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images
-    imageio.mimsave("video.mp4", result, fps=4)
-    ```
-
-
-### Text-To-Video with Edge Control
-
-To generate a video from prompt with additional pose control,
-follow the steps described above for pose-guided generation using [Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny).
-
-
-### Video Instruct-Pix2Pix
-
-To perform text-guided video editing (with [InstructPix2Pix](./stable_diffusion/pix2pix)):
-
-1. Download a demo video
-
-    ```python
-    from huggingface_hub import hf_hub_download
-
-    filename = "__assets__/pix2pix video/camel.mp4"
-    repo_id = "PAIR/Text2Video-Zero"
-    video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
-    ```
-
-2. Read video from path
-    ```python
-    from PIL import Image
-    import imageio
-
-    reader = imageio.get_reader(video_path, "ffmpeg")
-    frame_count = 8
-    video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
-    ```
-
-3. Run `StableDiffusionInstructPix2PixPipeline` with our custom attention processor
-    ```python
-    import torch
-    from diffusers import StableDiffusionInstructPix2PixPipeline
-    from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
-
-    model_id = "timbrooks/instruct-pix2pix"
-    pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-    pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=3))
-
-    prompt = "make it Van Gogh Starry Night style"
-    result = pipe(prompt=[prompt] * len(video), image=video).images
-    imageio.mimsave("edited_video.mp4", result, fps=4)
-    ```
-
-
-### DreamBooth specialization 
-
-Methods **Text-To-Video**, **Text-To-Video with Pose Control** and **Text-To-Video with Edge Control**
-can run with custom [DreamBooth](../training/dreambooth) models, as shown below for
-[Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny) and
-[Avatar style DreamBooth](https://huggingface.co/PAIR/text2video-zero-controlnet-canny-avatar) model
-
-1. Download a demo video
-
-    ```python
-    from huggingface_hub import hf_hub_download
-
-    filename = "__assets__/canny_videos_mp4/girl_turning.mp4"
-    repo_id = "PAIR/Text2Video-Zero"
-    video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
-    ```
-
-2. Read video from path
-    ```python
-    from PIL import Image
-    import imageio
-
-    reader = imageio.get_reader(video_path, "ffmpeg")
-    frame_count = 8
-    video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
-    ```
-
-3. Run `StableDiffusionControlNetPipeline` with custom trained DreamBooth model
-    ```python
-    import torch
-    from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-    from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor
-
-    # set model id to custom model
-    model_id = "PAIR/text2video-zero-controlnet-canny-avatar"
-    controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
-    pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        model_id, controlnet=controlnet, torch_dtype=torch.float16
-    ).to("cuda")
-
-    # Set the attention processor
-    pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
-    pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
-
-    # fix latents for all frames
-    latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)
-
-    prompt = "oil painting of a beautiful girl avatar style"
-    result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images
-    imageio.mimsave("video.mp4", result, fps=4)
-    ```
-
-You can filter out some available DreamBooth-trained models with [this link](https://huggingface.co/models?search=dreambooth).
-
-
-
-## TextToVideoZeroPipeline
-[[autodoc]] TextToVideoZeroPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/unclip.mdx

@@ -1,37 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# unCLIP
-
-## Overview
-
-[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125) by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen
-
-The abstract of the paper is the following:
-
-Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.
-
-The unCLIP model in diffusers comes from kakaobrain's karlo and the original codebase can be found [here](https://github.com/kakaobrain/karlo). Additionally, lucidrains has a DALL-E 2 recreation [here](https://github.com/lucidrains/DALLE2-pytorch).
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Colab
-|---|---|:---:|
-| [pipeline_unclip.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/unclip/pipeline_unclip.py) | *Text-to-Image Generation* | - |
-| [pipeline_unclip_image_variation.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py) | *Image-Guided Image Generation* | - |
-
-
-## UnCLIPPipeline
-[[autodoc]] UnCLIPPipeline
-	- all
-	- __call__
-
-[[autodoc]] UnCLIPImageVariationPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/unidiffuser.mdx

@@ -1,204 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# UniDiffuser
-
-The UniDiffuser model was proposed in [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://arxiv.org/abs/2303.06555) by Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu.
-
-The abstract of the [paper](https://arxiv.org/abs/2303.06555) is the following:
-
-*This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning diffusion models for marginal, conditional, and joint distributions can be unified as predicting the noise in the perturbed data, where the perturbation levels (i.e. timesteps) can be different for different modalities. Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model -- perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality. UniDiffuser is parameterized by a transformer for diffusion models to handle input types of different modalities. Implemented on large-scale paired image-text data, UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead. In particular, UniDiffuser is able to produce perceptually realistic samples in all tasks and its quantitative results (e.g., the FID and CLIP score) are not only superior to existing general-purpose models but also comparable to the bespoken models (e.g., Stable Diffusion and DALL-E 2) in representative tasks (e.g., text-to-image generation).*
-
-Resources:
-
-* [Paper](https://arxiv.org/abs/2303.06555).
-* [Original Code](https://github.com/thu-ml/unidiffuser).
-
-Available Checkpoints are:
-- *UniDiffuser-v0 (512x512 resolution)* [thu-ml/unidiffuser-v0](https://huggingface.co/thu-ml/unidiffuser-v0)
-- *UniDiffuser-v1 (512x512 resolution)* [thu-ml/unidiffuser-v1](https://huggingface.co/thu-ml/unidiffuser-v1)
-
-This pipeline was contributed by our community member [dg845](https://github.com/dg845).
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo | Colab |
-|:---:|:---:|:---:|:---:|
-| [UniDiffuserPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_unidiffuser.py) | *Joint Image-Text Gen*, *Text-to-Image*, *Image-to-Text*,<br> *Image Gen*, *Text Gen*, *Image Variation*, *Text Variation* | [🤗 Spaces](https://huggingface.co/spaces/thu-ml/unidiffuser) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/unidiffuser.ipynb) |
-
-## Usage Examples
-
-Because the UniDiffuser model is trained to model the joint distribution of (image, text) pairs, it is capable of performing a diverse range of generation tasks.
-
-### Unconditional Image and Text Generation
-
-Unconditional generation (where we start from only latents sampled from a standard Gaussian prior) from a [`UniDiffuserPipeline`] will produce a (image, text) pair:
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Unconditional image and text generation. The generation task is automatically inferred.
-sample = pipe(num_inference_steps=20, guidance_scale=8.0)
-image = sample.images[0]
-text = sample.text[0]
-image.save("unidiffuser_joint_sample_image.png")
-print(text)
-```
-
-This is also called "joint" generation in the UniDiffusers paper, since we are sampling from the joint image-text distribution.
-
-Note that the generation task is inferred from the inputs used when calling the pipeline.
-It is also possible to manually specify the unconditional generation task ("mode") manually with [`UniDiffuserPipeline.set_joint_mode`]:
-
-```python
-# Equivalent to the above.
-pipe.set_joint_mode()
-sample = pipe(num_inference_steps=20, guidance_scale=8.0)
-```
-
-When the mode is set manually, subsequent calls to the pipeline will use the set mode without attempting the infer the mode.
-You can reset the mode with [`UniDiffuserPipeline.reset_mode`], after which the pipeline will once again infer the mode.
-
-You can also generate only an image or only text (which the UniDiffuser paper calls "marginal" generation since we sample from the marginal distribution of images and text, respectively):
-
-```python
-# Unlike other generation tasks, image-only and text-only generation don't use classifier-free guidance
-# Image-only generation
-pipe.set_image_mode()
-sample_image = pipe(num_inference_steps=20).images[0]
-# Text-only generation
-pipe.set_text_mode()
-sample_text = pipe(num_inference_steps=20).text[0]
-```
-
-### Text-to-Image Generation
-
-UniDiffuser is also capable of sampling from conditional distributions; that is, the distribution of images conditioned on a text prompt or the distribution of texts conditioned on an image.
-Here is an example of sampling from the conditional image distribution (text-to-image generation or text-conditioned image generation):
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Text-to-image generation
-prompt = "an elephant under the sea"
-
-sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)
-t2i_image = sample.images[0]
-t2i_image.save("unidiffuser_text2img_sample_image.png")
-```
-
-The `text2img` mode requires that either an input `prompt` or `prompt_embeds` be supplied. You can set the `text2img` mode manually with [`UniDiffuserPipeline.set_text_to_image_mode`].
-
-### Image-to-Text Generation
-
-Similarly, UniDiffuser can also produce text samples given an image (image-to-text or image-conditioned text generation):
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-from diffusers.utils import load_image
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Image-to-text generation
-image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg"
-init_image = load_image(image_url).resize((512, 512))
-
-sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)
-i2t_text = sample.text[0]
-print(i2t_text)
-```
-
-The `img2text` mode requires that an input `image` be supplied. You can set the `img2text` mode manually with [`UniDiffuserPipeline.set_image_to_text_mode`].
-
-### Image Variation
-
-The UniDiffuser authors suggest performing image variation through a "round-trip" generation method, where given an input image, we first perform an image-to-text generation, and the perform a text-to-image generation on the outputs of the first generation.
-This produces a new image which is semantically similar to the input image:
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-from diffusers.utils import load_image
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Image variation can be performed with a image-to-text generation followed by a text-to-image generation:
-# 1. Image-to-text generation
-image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg"
-init_image = load_image(image_url).resize((512, 512))
-
-sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)
-i2t_text = sample.text[0]
-print(i2t_text)
-
-# 2. Text-to-image generation
-sample = pipe(prompt=i2t_text, num_inference_steps=20, guidance_scale=8.0)
-final_image = sample.images[0]
-final_image.save("unidiffuser_image_variation_sample.png")
-```
-
-### Text Variation
-
-
-Similarly, text variation can be performed on an input prompt with a text-to-image generation followed by a image-to-text generation:
-
-```python
-import torch
-
-from diffusers import UniDiffuserPipeline
-
-device = "cuda"
-model_id_or_path = "thu-ml/unidiffuser-v1"
-pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-pipe.to(device)
-
-# Text variation can be performed with a text-to-image generation followed by a image-to-text generation:
-# 1. Text-to-image generation
-prompt = "an elephant under the sea"
-
-sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)
-t2i_image = sample.images[0]
-t2i_image.save("unidiffuser_text2img_sample_image.png")
-
-# 2. Image-to-text generation
-sample = pipe(image=t2i_image, num_inference_steps=20, guidance_scale=8.0)
-final_prompt = sample.text[0]
-print(final_prompt)
-```
-
-## UniDiffuserPipeline
-[[autodoc]] UniDiffuserPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/versatile_diffusion.mdx

@@ -1,70 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# VersatileDiffusion
-
-VersatileDiffusion was proposed in [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) by Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi .
-
-The abstract of the paper is the following:
-
-*The recent advances in diffusion models have set an impressive milestone in many generation tasks. Trending works such as DALL-E2, Imagen, and Stable Diffusion have attracted great interest in academia and industry. Despite the rapid landscape changes, recent new approaches focus on extensions and performance rather than capacity, thus requiring separate models for separate tasks. In this work, we expand the existing single-flow diffusion pipeline into a multi-flow network, dubbed Versatile Diffusion (VD), that handles text-to-image, image-to-text, image-variation, and text-variation in one unified model. Moreover, we generalize VD to a unified multi-flow multimodal diffusion framework with grouped layers, swappable streams, and other propositions that can process modalities beyond images and text. Through our experiments, we demonstrate that VD and its underlying framework have the following merits: a) VD handles all subtasks with competitive quality; b) VD initiates novel extensions and applications such as disentanglement of style and semantic, image-text dual-guided generation, etc.; c) Through these experiments and applications, VD provides more semantic insights of the generated outputs.*
-
-## Tips
-
-- VersatileDiffusion is conceptually very similar as [Stable Diffusion](./stable_diffusion/overview),  but instead of providing just a image data stream conditioned on text, VersatileDiffusion provides both a image and text data stream and can be conditioned on both text and image.
-
-### *Run VersatileDiffusion*
-
-You can both load the memory intensive "all-in-one" [`VersatileDiffusionPipeline`] that can run all tasks 
-with the same class as shown in [`VersatileDiffusionPipeline.text_to_image`], [`VersatileDiffusionPipeline.image_variation`], and [`VersatileDiffusionPipeline.dual_guided`]
-
-**or**
-
-You can run the individual pipelines which are much more memory efficient:
-
-- *Text-to-Image*: [`VersatileDiffusionTextToImagePipeline.__call__`]
-- *Image Variation*: [`VersatileDiffusionImageVariationPipeline.__call__`]
-- *Dual Text and Image Guided Generation*: [`VersatileDiffusionDualGuidedPipeline.__call__`]
-
-### *How to load and use different schedulers.*
-
-The versatile diffusion pipelines 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 VersatileDiffusionPipeline, EulerDiscreteScheduler
-
->>> pipeline = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion")
->>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-
->>> # or
->>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("shi-labs/versatile-diffusion", subfolder="scheduler")
->>> pipeline = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", scheduler=euler_scheduler)
-```
-
-## VersatileDiffusionPipeline
-[[autodoc]] VersatileDiffusionPipeline
-
-## VersatileDiffusionTextToImagePipeline
-[[autodoc]] VersatileDiffusionTextToImagePipeline
-	- all
-	- __call__
-
-## VersatileDiffusionImageVariationPipeline
-[[autodoc]] VersatileDiffusionImageVariationPipeline
-	- all
-	- __call__
-
-## VersatileDiffusionDualGuidedPipeline
-[[autodoc]] VersatileDiffusionDualGuidedPipeline
-	- all
-	- __call__

docs/source/en/api/pipelines/vq_diffusion.mdx

@@ -1,35 +0,0 @@
-<!--Copyright 2023 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]] VQDiffusionPipeline
-	- all
-	- __call__

docs/source/en/api/schedulers/ddim.mdx

@@ -1,27 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Denoising Diffusion Implicit Models (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/).
-
-## DDIMScheduler
-[[autodoc]] DDIMScheduler

docs/source/en/api/schedulers/ddim_inverse.mdx

@@ -1,21 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Inverse Denoising Diffusion Implicit Models (DDIMInverse)
-
-## Overview
-
-This scheduler is the inverted scheduler of [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.
-The implementation is mostly based on the DDIM inversion definition of [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://arxiv.org/pdf/2211.09794.pdf)
-
-## DDIMInverseScheduler
-[[autodoc]] DDIMInverseScheduler

docs/source/en/api/schedulers/ddpm.mdx

@@ -1,27 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Denoising Diffusion Probabilistic Models (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 paper can be found [here](https://arxiv.org/abs/2010.02502).
-
-## DDPMScheduler
-[[autodoc]] DDPMScheduler

docs/source/en/api/schedulers/deis.mdx

@@ -1,22 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# DEIS
-
-Fast Sampling of Diffusion Models with Exponential Integrator.
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2204.13902). The original implementation can be found [here](https://github.com/qsh-zh/deis).
-
-## DEISMultistepScheduler
-[[autodoc]] DEISMultistepScheduler

docs/source/en/api/schedulers/dpm_discrete.mdx

@@ -1,22 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# DPM Discrete Scheduler inspired by Karras et. al paper
-
-## Overview
-
-Inspired by [Karras et. al](https://arxiv.org/abs/2206.00364). Scheduler ported from @crowsonkb's https://github.com/crowsonkb/k-diffusion library:
-
-All credit for making this scheduler work goes to [Katherine Crowson](https://github.com/crowsonkb/)
-
-## KDPM2DiscreteScheduler
-[[autodoc]] KDPM2DiscreteScheduler

docs/source/en/api/schedulers/dpm_discrete_ancestral.mdx

@@ -1,22 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# DPM Discrete Scheduler with ancestral sampling inspired by Karras et. al paper
-
-## Overview
-
-Inspired by [Karras et. al](https://arxiv.org/abs/2206.00364). Scheduler ported from @crowsonkb's https://github.com/crowsonkb/k-diffusion library:
-
-All credit for making this scheduler work goes to [Katherine Crowson](https://github.com/crowsonkb/)
-
-## KDPM2AncestralDiscreteScheduler
-[[autodoc]] KDPM2AncestralDiscreteScheduler

docs/source/en/api/schedulers/dpm_sde.mdx

@@ -1,23 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# DPM Stochastic Scheduler inspired by Karras et. al paper
-
-## Overview
-
-Inspired by Stochastic Sampler from [Karras et. al](https://arxiv.org/abs/2206.00364).
-Scheduler ported from @crowsonkb's https://github.com/crowsonkb/k-diffusion library:
-
-All credit for making this scheduler work goes to [Katherine Crowson](https://github.com/crowsonkb/)
-
-## DPMSolverSDEScheduler
-[[autodoc]] DPMSolverSDEScheduler

docs/source/en/api/schedulers/euler.mdx

@@ -1,21 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Euler scheduler
-
-## Overview
-
-Euler scheduler (Algorithm 2) from the paper [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) by Karras et al. (2022). Based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by Katherine Crowson.
-Fast scheduler which often times generates good outputs with 20-30 steps.
-
-## EulerDiscreteScheduler
-[[autodoc]] EulerDiscreteScheduler