up

.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 +1,4 @@
 contact_links:
   - 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: General usage questions and community discussions

.github/workflows/build_documentation.yml

@@ -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

@@ -27,8 +27,9 @@ 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:
@@ -47,4 +48,3 @@ jobs:
         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 fast tests
 
 on:
   pull_request:
@@ -14,6 +14,7 @@ env:
   OMP_NUM_THREADS: 4
   MKL_NUM_THREADS: 4
   PYTEST_TIMEOUT: 60
+  MPS_TORCH_VERSION: 1.13.0
 
 jobs:
   run_fast_tests:
@@ -36,11 +37,6 @@ jobs:
             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_cpu
 
     name: ${{ matrix.config.name }}
 
@@ -62,10 +58,9 @@ jobs:
 
     - name: Install dependencies
       run: |
-        apt-get update && apt-get install libsndfile1-dev -y
         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
+        python -m pip install -U git+https://github.com/huggingface/transformers
 
     - name: Environment
       run: |
@@ -95,13 +90,6 @@ jobs:
           --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
@@ -138,7 +126,7 @@ jobs:
       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
+        ${CONDA_RUN} python -m pip install --pre torch==${MPS_TORCH_VERSION} --extra-index-url https://download.pytorch.org/whl/test/cpu
         ${CONDA_RUN} python -m pip install git+https://github.com/huggingface/accelerate
         ${CONDA_RUN} python -m pip install -U git+https://github.com/huggingface/transformers
 
@@ -149,9 +137,6 @@ jobs:
 
     - 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/
 

.github/workflows/push_tests.yml

@@ -1,4 +1,4 @@
-name: Slow tests on main
+name: Run all tests
 
 on:
   push:
@@ -10,7 +10,7 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
-  PYTEST_TIMEOUT: 600
+  PYTEST_TIMEOUT: 1000
   RUN_SLOW: yes
 
 jobs:
@@ -61,8 +61,8 @@ jobs:
     - 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
+        python -m pip install -U git+https://github.com/huggingface/transformers
 
     - name: Environment
       run: |

.github/workflows/push_tests_fast.yml

@@ -1,165 +0,0 @@
-name: Slow 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_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]
-        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 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
-
-  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 --extra-index-url https://download.pytorch.org/whl/cpu
-        ${CONDA_RUN} python -m pip install git+https://github.com/huggingface/accelerate
-        ${CONDA_RUN} python -m pip install -U git+https://github.com/huggingface/transformers
-
-    - 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

@@ -165,10 +165,4 @@ tags
 # DS_Store (MacOS)
 .DS_Store
 # RL pipelines may produce mp4 outputs
-*.mp4
-
-# dependencies
-/transformers
-
-# ruff
-.ruff_cache
+*.mp4

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

CONTRIBUTING.md

@@ -1,5 +1,5 @@
 <!---
-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.
@@ -177,7 +177,7 @@ Follow these steps to start contributing ([supported Python versions](https://gi
    $ make style
    ```
 
-   🧨 Diffusers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality
+   🧨 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

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
 

README.md

@@ -1,6 +1,6 @@
 <p align="center">
     <br>
-    <img src="./docs/source/en/imgs/diffusers_library.jpg" width="400"/>
+    <img src="https://github.com/huggingface/diffusers/raw/main/docs/source/imgs/diffusers_library.jpg" width="400"/>
     <br>
 <p>
 <p align="center">
@@ -29,13 +29,13 @@ More precisely, 🤗 Diffusers offers:
 
 ### For PyTorch
 
-**With `pip`** (official package)
+**With `pip`**
     
 ```bash
 pip install --upgrade diffusers[torch]
 ```
 
-**With `conda`** (maintained by the community)
+**With `conda`**
 
 ```sh
 conda install -c conda-forge diffusers
@@ -79,13 +79,19 @@ In order to get started, we recommend taking a look at two notebooks:
 Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [LAION](https://laion.ai/) and [RunwayML](https://runwayml.com/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 4GB VRAM.
 See the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information.
 
+You need to accept the model license before downloading or using the Stable Diffusion weights. Please, visit the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license carefully and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section](https://huggingface.co/docs/hub/security-tokens) of the documentation.
+
 
 ### Text-to-Image generation with Stable Diffusion
 
 First let's install
-
 ```bash
-pip install --upgrade diffusers transformers accelerate
+pip install --upgrade diffusers transformers scipy
+```
+
+Run this command to log in with your HF Hub token if you haven't before (you can skip this step if you prefer to run the model locally, follow [this](#running-the-model-locally) instead)
+```bash
+huggingface-cli login
 ```
 
 We recommend using the model in [half-precision (`fp16`)](https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/) as it gives almost always the same results as full
@@ -95,7 +101,7 @@ precision while being roughly twice as fast and requiring half the amount of GPU
 import torch
 from diffusers import StableDiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, revision="fp16")
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -103,16 +109,17 @@ image = pipe(prompt).images[0]
 ```
 
 #### Running the model locally
-
-You can also simply download the model folder and pass the path to the local folder to the `StableDiffusionPipeline`.
+If you don't want to login to Hugging Face, you can also simply download the model folder
+(after having [accepted the license](https://huggingface.co/runwayml/stable-diffusion-v1-5)) and pass
+the path to the local folder to the `StableDiffusionPipeline`.
 
 ```
 git lfs install
 git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
 ```
 
-Assuming the folder is stored locally under `./stable-diffusion-v1-5`, you can run stable diffusion
-as follows:
+Assuming the folder is stored locally under `./stable-diffusion-v1-5`, you can also run stable diffusion
+without requiring an authentication token:
 
 ```python
 pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
@@ -127,7 +134,11 @@ to using `fp16`.
 The following snippet should result in less than 4GB VRAM.
 
 ```python
-pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", 
+    revision="fp16", 
+    torch_dtype=torch.float16,
+)
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -153,6 +164,7 @@ If you want to run Stable Diffusion on CPU or you want to have maximum precision
 please run the model in the default *full-precision* setting:
 
 ```python
+# make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline
 
 pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
@@ -235,102 +247,6 @@ images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).
 images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
 ```
 
-Diffusers also has a Image-to-Image generation pipeline with Flax/Jax
-```python
-import jax
-import numpy as np
-import jax.numpy as jnp
-from flax.jax_utils import replicate
-from flax.training.common_utils import shard
-import requests
-from io import BytesIO
-from PIL import Image
-from diffusers import FlaxStableDiffusionImg2ImgPipeline
-
-def create_key(seed=0):
-    return jax.random.PRNGKey(seed)
-rng = create_key(0)
-
-url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-response = requests.get(url)
-init_img = Image.open(BytesIO(response.content)).convert("RGB")
-init_img = init_img.resize((768, 512))
-
-prompts = "A fantasy landscape, trending on artstation"
-
-pipeline, params = FlaxStableDiffusionImg2ImgPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", revision="flax",
-    dtype=jnp.bfloat16,
-)
-
-num_samples = jax.device_count()
-rng = jax.random.split(rng, jax.device_count())
-prompt_ids, processed_image = pipeline.prepare_inputs(prompt=[prompts]*num_samples, image = [init_img]*num_samples)
-p_params = replicate(params)
-prompt_ids = shard(prompt_ids)
-processed_image = shard(processed_image)
-
-output = pipeline(
-    prompt_ids=prompt_ids, 
-    image=processed_image, 
-    params=p_params, 
-    prng_seed=rng, 
-    strength=0.75, 
-    num_inference_steps=50, 
-    jit=True, 
-    height=512,
-    width=768).images
-
-output_images = pipeline.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
-```
-
-Diffusers also has a Text-guided inpainting pipeline with Flax/Jax
-
-```python
-import jax
-import numpy as np
-from flax.jax_utils import replicate
-from flax.training.common_utils import shard
-import PIL
-import requests
-from io import BytesIO
-
-
-from diffusers import FlaxStableDiffusionInpaintPipeline
-
-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))
-
-pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained("xvjiarui/stable-diffusion-2-inpainting")
-
-prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
-prng_seed = jax.random.PRNGKey(0)
-num_inference_steps = 50
-
-num_samples = jax.device_count()
-prompt = num_samples * [prompt]
-init_image = num_samples * [init_image]
-mask_image = num_samples * [mask_image]
-prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(prompt, init_image, mask_image)
-
-
-# shard inputs and rng
-params = replicate(params)
-prng_seed = jax.random.split(prng_seed, jax.device_count())
-prompt_ids = shard(prompt_ids)
-processed_masked_images = shard(processed_masked_images)
-processed_masks = shard(processed_masks)
-
-images = pipeline(prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True).images
-images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-```
-
 ### Image-to-Image text-guided generation with Stable Diffusion
 
 The `StableDiffusionImg2ImgPipeline` lets you pass a text prompt and an initial image to condition the generation of new images.
@@ -346,8 +262,11 @@ from diffusers import StableDiffusionImg2ImgPipeline
 # load the pipeline
 device = "cuda"
 model_id_or_path = "runwayml/stable-diffusion-v1-5"
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
-
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    model_id_or_path,
+    revision="fp16", 
+    torch_dtype=torch.float16,
+)
 # or download via git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
 # and pass `model_id_or_path="./stable-diffusion-v1-5"`.
 pipe = pipe.to(device)
@@ -361,7 +280,7 @@ init_image = init_image.resize((768, 512))
 
 prompt = "A fantasy landscape, trending on artstation"
 
-images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
+images = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5).images
 
 images[0].save("fantasy_landscape.png")
 ```
@@ -369,7 +288,10 @@ You can also run this example on colab [![Open In Colab](https://colab.research.
 
 ### In-painting using Stable Diffusion
 
-The `StableDiffusionInpaintPipeline` lets you edit specific parts of an image by providing a mask and a text prompt.
+The `StableDiffusionInpaintPipeline` lets you edit specific parts of an image by providing a mask and a text prompt. It uses a model optimized for this particular task, whose license you need to accept before use.
+
+Please, visit the [model card](https://huggingface.co/runwayml/stable-diffusion-inpainting), read the license carefully and tick the checkbox if you agree. Note that this is an additional license, you need to accept it even if you accepted the text-to-image Stable Diffusion license in the past. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section](https://huggingface.co/docs/hub/security-tokens) of the documentation.
+
 
 ```python
 import PIL
@@ -389,7 +311,11 @@ mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data
 init_image = download_image(img_url).resize((512, 512))
 mask_image = download_image(mask_url).resize((512, 512))
 
-pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16)
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    revision="fp16",
+    torch_dtype=torch.float16,
+)
 pipe = pipe.to("cuda")
 
 prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
@@ -398,8 +324,11 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
 
 ### Tweak prompts reusing seeds and latents
 
-You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked.
-Please have a look at [Reusing seeds for deterministic generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/reusing_seeds).
+You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. [This notebook](https://github.com/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) shows how to do it step by step. You can also run it in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb).
+
+
+For more details, check out [the Stable Diffusion notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb)
+and have a look into the [release notes](https://github.com/huggingface/diffusers/releases/tag/v0.2.0).
 
 ## Fine-Tuning Stable Diffusion
 
@@ -467,12 +396,12 @@ image.save("ddpm_generated_image.png")
 - [Unconditional Diffusion with continuous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)
 
 **Other Image Notebooks**:
-* [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb),
-* [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/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb),
+* [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+* [tweak images via repeated Stable Diffusion seeds](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
 
 **Diffusers for Other Modalities**:
-* [Molecule conformation generation](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) ![Open In Colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb),
-* [Model-based reinforcement learning](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb),
+* [Molecule conformation generation](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+* [Model-based reinforcement learning](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
 
 ### Web Demos
 If you just want to play around with some web demos, you can try out the following 🚀 Spaces:

docker/diffusers-flax-cpu/Dockerfile

@@ -11,7 +11,6 @@ RUN apt update && \
                    git-lfs \
                    curl \
                    ca-certificates \
-                   libsndfile1-dev \
                    python3.8 \
                    python3-pip \
                    python3.8-venv && \
@@ -34,8 +33,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
-        Jinja2 \
-        librosa \
+        modelcards \
         numpy \
         scipy \
         tensorboard \

docker/diffusers-flax-tpu/Dockerfile

@@ -11,7 +11,6 @@ RUN apt update && \
                    git-lfs \
                    curl \
                    ca-certificates \
-                   libsndfile1-dev \
                    python3.8 \
                    python3-pip \
                    python3.8-venv && \
@@ -36,8 +35,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
-        Jinja2 \
-        librosa \        
+        modelcards \
         numpy \
         scipy \
         tensorboard \

docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -11,7 +11,6 @@ RUN apt update && \
                    git-lfs \
                    curl \
                    ca-certificates \
-                   libsndfile1-dev \
                    python3.8 \
                    python3-pip \
                    python3.8-venv && \
@@ -34,8 +33,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
-        Jinja2 \
-        librosa \
+        modelcards \
         numpy \
         scipy \
         tensorboard \

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -11,7 +11,6 @@ RUN apt update && \
                    git-lfs \
                    curl \
                    ca-certificates \
-                   libsndfile1-dev \
                    python3.8 \
                    python3-pip \
                    python3.8-venv && \
@@ -34,8 +33,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
-        Jinja2 \
-        librosa \
+        modelcards \
         numpy \
         scipy \
         tensorboard \

docker/diffusers-pytorch-cpu/Dockerfile

@@ -11,7 +11,6 @@ RUN apt update && \
                    git-lfs \
                    curl \
                    ca-certificates \
-                   libsndfile1-dev \
                    python3.8 \
                    python3-pip \
                    python3.8-venv && \
@@ -33,8 +32,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
-        Jinja2 \
-        librosa \
+        modelcards \
         numpy \
         scipy \
         tensorboard \

docker/diffusers-pytorch-cuda/Dockerfile

@@ -11,7 +11,6 @@ RUN apt update && \
                    git-lfs \
                    curl \
                    ca-certificates \
-                   libsndfile1-dev \
                    python3.8 \
                    python3-pip \
                    python3.8-venv && \
@@ -33,8 +32,7 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
         datasets \
         hf-doc-builder \
         huggingface-hub \
-        Jinja2 \
-        librosa \
+        modelcards \
         numpy \
         scipy \
         tensorboard \

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 +1,128 @@
+- sections:
+  - local: index
+    title: "🧨 Diffusers"
+  - local: quicktour
+    title: "Quicktour"
+  - local: installation
+    title: "Installation"
+  title: "Get started"
+- sections:
+  - sections:
+    - local: using-diffusers/loading
+      title: "Loading Pipelines, Models, and Schedulers"
+    - local: using-diffusers/schedulers
+      title: "Using different Schedulers"
+    - local: using-diffusers/configuration
+      title: "Configuring Pipelines, Models, and Schedulers"
+    - local: using-diffusers/custom_pipeline_overview
+      title: "Loading and Adding Custom Pipelines"
+    title: "Loading & Hub"
+  - sections:
+    - local: using-diffusers/unconditional_image_generation
+      title: "Unconditional Image Generation"
+    - local: using-diffusers/conditional_image_generation
+      title: "Text-to-Image Generation"
+    - local: using-diffusers/img2img
+      title: "Text-Guided Image-to-Image"
+    - local: using-diffusers/inpaint
+      title: "Text-Guided Image-Inpainting"
+    - local: using-diffusers/custom_pipeline_examples
+      title: "Community Pipelines"
+    - local: using-diffusers/contribute_pipeline
+      title: "How to contribute a Pipeline"
+    title: "Pipelines for Inference"
+  - sections:
+    - local: using-diffusers/rl
+      title: "Reinforcement Learning"
+    - local: using-diffusers/audio
+      title: "Audio"
+    - local: using-diffusers/other-modalities
+      title: "Other Modalities"
+    title: "Taking Diffusers Beyond Images"
+  title: "Using Diffusers"
+- sections:
+  - local: optimization/fp16
+    title: "Memory and Speed"
+  - local: optimization/onnx
+    title: "ONNX"
+  - local: optimization/open_vino
+    title: "OpenVINO"
+  - local: optimization/mps
+    title: "MPS"
+  title: "Optimization/Special Hardware"
+- sections:
+  - local: training/overview
+    title: "Overview"
+  - local: training/unconditional_training
+    title: "Unconditional Image Generation"
+  - local: training/text_inversion
+    title: "Textual Inversion"
+  - local: training/dreambooth
+    title: "Dreambooth"
+  - local: training/text2image
+    title: "Text-to-image fine-tuning"
+  title: "Training"
+- sections:
+  - local: conceptual/stable_diffusion
+    title: "Stable Diffusion"
+  - local: conceptual/philosophy
+    title: "Philosophy"
+  - local: conceptual/contribution
+    title: "How to contribute?"
+  title: "Conceptual Guides"
+- sections:
+  - sections:
+    - local: api/models
+      title: "Models"
+    - local: api/schedulers
+      title: "Schedulers"
+    - local: api/diffusion_pipeline
+      title: "Diffusion Pipeline"
+    - local: api/logging
+      title: "Logging"
+    - local: api/configuration
+      title: "Configuration"
+    - local: api/outputs
+      title: "Outputs"
+    title: "Main Classes"
+  - sections:
+    - local: api/pipelines/overview
+      title: "Overview"
+    - local: api/pipelines/alt_diffusion
+      title: "AltDiffusion"
+    - local: api/pipelines/cycle_diffusion
+      title: "Cycle Diffusion"
+    - local: api/pipelines/ddim
+      title: "DDIM"
+    - local: api/pipelines/ddpm
+      title: "DDPM"
+    - local: api/pipelines/latent_diffusion
+      title: "Latent Diffusion"
+    - local: api/pipelines/latent_diffusion_uncond
+      title: "Unconditional Latent Diffusion"
+    - local: api/pipelines/pndm
+      title: "PNDM"
+    - local: api/pipelines/score_sde_ve
+      title: "Score SDE VE"
+    - local: api/pipelines/stable_diffusion
+      title: "Stable Diffusion"
+    - local: api/pipelines/stable_diffusion_2
+      title: "Stable Diffusion 2"
+    - local: api/pipelines/stable_diffusion_safe
+      title: "Safe Stable Diffusion"
+    - local: api/pipelines/stochastic_karras_ve
+      title: "Stochastic Karras VE"
+    - local: api/pipelines/dance_diffusion
+      title: "Dance Diffusion"
+    - local: api/pipelines/versatile_diffusion
+      title: "Versatile Diffusion"
+    - local: api/pipelines/vq_diffusion
+      title: "VQ Diffusion"
+    - local: api/pipelines/repaint
+      title: "RePaint"
+    title: "Pipelines"
+  - sections:
+    - local: api/experimental/rl
+      title: "RL Planning"
+    title: "Experimental Features"
+  title: "API"

docs/source/api/configuration.mdx

@@ -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

docs/source/api/diffusion_pipeline.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
@@ -30,18 +30,13 @@ Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrain
 
 ## DiffusionPipeline
 [[autodoc]] DiffusionPipeline
-	- all
-	- __call__
-	- device
+	- from_pretrained
+	- save_pretrained
 	- to
+	- device
 	- components
 
 ## ImagePipelineOutput
 By default diffusion pipelines return an object of class
 
-[[autodoc]] pipelines.ImagePipelineOutput
-
-## AudioPipelineOutput
-By default diffusion pipelines return an object of class
-
-[[autodoc]] pipelines.AudioPipelineOutput
+[[autodoc]] pipeline_utils.ImagePipelineOutput

docs/source/api/experimental/rl.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

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

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
@@ -41,13 +41,13 @@ The models are built on the base class ['ModelMixin'] that is a `torch.nn.module
 [[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
@@ -56,19 +56,7 @@ The models are built on the base class ['ModelMixin'] that is a `torch.nn.module
 [[autodoc]] Transformer2DModel
 
 ## Transformer2DModelOutput
-[[autodoc]] models.transformer_2d.Transformer2DModelOutput
-
-## PriorTransformer
-[[autodoc]] models.prior_transformer.PriorTransformer
-
-## PriorTransformerOutput
-[[autodoc]] models.prior_transformer.PriorTransformerOutput
-
-## ControlNetOutput
-[[autodoc]] models.controlnet.ControlNetOutput
-
-## ControlNetModel
-[[autodoc]] ControlNetModel
+[[autodoc]] models.attention.Transformer2DModelOutput
 
 ## FlaxModelMixin
 [[autodoc]] FlaxModelMixin

docs/source/api/outputs.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
@@ -25,7 +25,7 @@ pipeline = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32")
 outputs = pipeline()
 ```
 
-The `outputs` object is a [`~pipelines.ImagePipelineOutput`], as we can see in the
+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`:

docs/source/api/pipelines/alt_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
@@ -28,7 +28,7 @@ The abstract of the paper is the following:
 
 ## Tips
 
-- AltDiffusion is conceptually exaclty the same as [Stable Diffusion](./api/pipelines/stable_diffusion/overview).
+- AltDiffusion is conceptually exaclty the same as [Stable Diffusion](./api/pipelines/stable_diffusion).
 
 - *Run AltDiffusion*
 
@@ -69,15 +69,15 @@ If you want to use all possible use cases in a single `DiffusionPipeline` we rec
 
 ## AltDiffusionPipelineOutput
 [[autodoc]] pipelines.alt_diffusion.AltDiffusionPipelineOutput
-	- all
-	- __call__
 
 ## AltDiffusionPipeline
 [[autodoc]] AltDiffusionPipeline
-	- all
 	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
 
 ## AltDiffusionImg2ImgPipeline
 [[autodoc]] AltDiffusionImg2ImgPipeline
-	- all
 	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing

docs/source/api/pipelines/cycle_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
@@ -57,7 +57,7 @@ prompt = "An astronaut riding an elephant"
 image = pipe(
     prompt=prompt,
     source_prompt=source_prompt,
-    image=init_image,
+    init_image=init_image,
     num_inference_steps=100,
     eta=0.1,
     strength=0.8,
@@ -83,7 +83,7 @@ torch.manual_seed(0)
 image = pipe(
     prompt=prompt,
     source_prompt=source_prompt,
-    image=init_image,
+    init_image=init_image,
     num_inference_steps=100,
     eta=0.1,
     strength=0.85,
@@ -96,5 +96,4 @@ image.save("black_to_blue.png")
 
 ## CycleDiffusionPipeline
 [[autodoc]] CycleDiffusionPipeline
-	- all
 	- __call__

docs/source/api/pipelines/dance_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
@@ -30,5 +30,4 @@ The original codebase of this implementation can be found [here](https://github.
 
 ## DanceDiffusionPipeline
 [[autodoc]] DanceDiffusionPipeline
-	- all
-	- __call__
+    - __call__

docs/source/api/pipelines/ddim.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
@@ -32,5 +32,4 @@ 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

@@ -1,4 +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
@@ -33,5 +33,4 @@ 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

@@ -1,4 +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
@@ -40,10 +40,8 @@ The original codebase can be found [here](https://github.com/CompVis/latent-diff
 
 ## LDMTextToImagePipeline
 [[autodoc]] LDMTextToImagePipeline
-	- all
-	- __call__
+    - __call__
 
 ## LDMSuperResolutionPipeline
 [[autodoc]] LDMSuperResolutionPipeline
-	- all
-	- __call__
+    - __call__

docs/source/api/pipelines/latent_diffusion_uncond.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
@@ -38,5 +38,4 @@ 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

@@ -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
@@ -44,44 +44,29 @@ 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/stable_diffusion/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 |
-| [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 |
-| [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 | 
+| [alt_diffusion](./api/pipelines/alt_diffusion) | [**AltDiffusion**](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation | -
+| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [**Cycle Diffusion**](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
+| [dance_diffusion](./api/pipelines/dance_diffusion) | [**Dance Diffusion**](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
+| [ddpm](./api/pipelines/ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
+| [ddim](./api/pipelines/ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
+| [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation | 
+| [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image | 
+| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation | 
+| [pndm](./api/pipelines/pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation | 
+| [score_sde_ve](./api/pipelines/score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
+| [score_sde_vp](./api/pipelines/score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
+| [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
+| [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
+| [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
+| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation | 
+| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting | 
+| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
+| [stable_diffusion_safe](./api/pipelines/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)
+| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |
+| [versatile_diffusion](./api/pipelines/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](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation | 
+| [versatile_diffusion](./api/pipelines/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](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation | 
 
 
 **Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. 
@@ -151,9 +136,9 @@ from diffusers import StableDiffusionImg2ImgPipeline
 
 # load the pipeline
 device = "cuda"
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
-    device
-)
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16
+).to(device)
 
 # let's download an initial image
 url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
@@ -164,7 +149,7 @@ init_image = init_image.resize((768, 512))
 
 prompt = "A fantasy landscape, trending on artstation"
 
-images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
+images = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5).images
 
 images[0].save("fantasy_landscape.png")
 ```
@@ -201,6 +186,7 @@ mask_image = download_image(mask_url).resize((512, 512))
 
 pipe = StableDiffusionInpaintPipeline.from_pretrained(
     "runwayml/stable-diffusion-inpainting",
+    revision="fp16",
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")

docs/source/api/pipelines/pndm.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
@@ -30,6 +30,6 @@ 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/repaint.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
@@ -72,6 +72,6 @@ inpainted_image = output.images[0]
 ```
 
 ## RePaintPipeline
-[[autodoc]] RePaintPipeline
-	- all
-	- __call__
+[[autodoc]] pipelines.repaint.pipeline_repaint.RePaintPipeline
+    - __call__
+

docs/source/api/pipelines/score_sde_ve.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
@@ -32,5 +32,5 @@ This pipeline implements the Variance Expanding (VE) variant of the method.
 
 ## ScoreSdeVePipeline
 [[autodoc]] ScoreSdeVePipeline
-	- all
-	- __call__
+    - __call__
+

docs/source/api/pipelines/stable_diffusion.mdx

@@ -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
@@ -25,18 +25,9 @@ For more details about how Stable Diffusion works and how it differs from the ba
 
 | 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)
-
-
+| [pipeline_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) | [🤗 Stable Diffusion](https://huggingface.co/spaces/stabilityai/stable-diffusion)
+| [pipeline_stable_diffusion_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) | [🤗 Diffuse the Rest](https://huggingface.co/spaces/huggingface/diffuse-the-rest)
+| [pipeline_stable_diffusion_inpaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | **Experimental** – *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) | Coming soon
 
 ## Tips
 
@@ -79,3 +70,46 @@ If you want to use all possible use cases in a single `DiffusionPipeline` you ca
 
 ## StableDiffusionPipelineOutput
 [[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
+
+## StableDiffusionPipeline
+[[autodoc]] StableDiffusionPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+	- enable_vae_slicing
+	- disable_vae_slicing
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention
+
+## StableDiffusionImg2ImgPipeline
+[[autodoc]] StableDiffusionImg2ImgPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention
+
+## StableDiffusionInpaintPipeline
+[[autodoc]] StableDiffusionInpaintPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention
+
+## StableDiffusionImageVariationPipeline
+[[autodoc]] StableDiffusionImageVariationPipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention
+
+
+## StableDiffusionUpscalePipeline
+[[autodoc]] StableDiffusionUpscalePipeline
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention

docs/source/api/pipelines/stable_diffusion_2.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
@@ -24,20 +24,16 @@ For more details about how Stable Diffusion 2 works and how it differs from Stab
 
 ### 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.
+Note that the architecture is more or less identical to [Stable Diffusion 1](./api/pipelines/stable_diffusion) so please refer to [this page](./api/pipelines/stable_diffusion) 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`]
+- *Image Upscaling (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) [`StableDiffusionUpscalePipeline`]
 
 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`]
+- *Text-to-Image (512x512 resolution)*:
 
 ```python
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
@@ -54,7 +50,7 @@ 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`]
+- *Text-to-Image (768x768 resolution)*:
 
 ```python
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
@@ -71,9 +67,7 @@ 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`]
+- *Image Inpainting (512x512 resolution)*:
 
 ```python
 import PIL
@@ -107,10 +101,7 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inferen
 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`]
-
+- *Image Upscaling (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) [`StableDiffusionUpscalePipeline`]
 
 ```python
 import requests
@@ -121,7 +112,7 @@ import torch
 
 # load model and scheduler
 model_id = "stabilityai/stable-diffusion-x4-upscaler"
-pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16)
 pipeline = pipeline.to("cuda")
 
 # let's download an  image
@@ -134,31 +125,6 @@ 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.

docs/source/api/pipelines/stable_diffusion_safe.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
@@ -24,11 +24,11 @@ The abstract of the paper is the following:
 
 | 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)
+| [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) | -
 
 ## Tips
 
-- Safe Stable Diffusion may also be used with weights of [Stable Diffusion](./api/pipelines/stable_diffusion/text2img).
+- Safe Stable Diffusion may also be used with weights of [Stable Diffusion](./api/pipelines/stable_diffusion).
 
 ### Run Safe Stable Diffusion
 
@@ -58,7 +58,7 @@ You may use the 4 configurations defined in the [Safe Latent Diffusion paper](ht
 >>> out = pipeline(prompt=prompt, **SafetyConfig.MAX)
 ```
 
-The following configurations are available: `SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONG`, and `SafetyConfig.MAX`.
+The following configurations are available: `SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONg`, and `SafetyConfig.MAX`.
 
 ### How to load and use different schedulers.
 
@@ -81,10 +81,10 @@ To use a different scheduler, you can either change it via the [`ConfigMixin.fro
 
 ## StableDiffusionSafePipelineOutput
 [[autodoc]] pipelines.stable_diffusion_safe.StableDiffusionSafePipelineOutput
-	- all
-	- __call__
 
 ## StableDiffusionPipelineSafe
 [[autodoc]] StableDiffusionPipelineSafe
-	- all
 	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+

docs/source/api/pipelines/stochastic_karras_ve.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
@@ -32,5 +32,4 @@ This pipeline implements the Stochastic sampling tailored to the Variance-Expand
 
 ## KarrasVePipeline
 [[autodoc]] KarrasVePipeline
-	- all
-	- __call__
+    - __call__

docs/source/api/pipelines/versatile_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
@@ -20,7 +20,7 @@ The abstract of the paper is the following:
 
 ## Tips
 
-- VersatileDiffusion is conceptually very similar as [Stable Diffusion](./api/pipelines/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.
+- VersatileDiffusion is conceptually very similar as [Stable Diffusion](./api/pipelines/stable_diffusion),  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*
 
@@ -56,15 +56,18 @@ To use a different scheduler, you can either change it via the [`ConfigMixin.fro
 
 ## VersatileDiffusionTextToImagePipeline
 [[autodoc]] VersatileDiffusionTextToImagePipeline
-	- all
 	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
 
 ## VersatileDiffusionImageVariationPipeline
 [[autodoc]] VersatileDiffusionImageVariationPipeline
-	- all
 	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
 
 ## VersatileDiffusionDualGuidedPipeline
 [[autodoc]] VersatileDiffusionDualGuidedPipeline
-	- all
 	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing

docs/source/api/pipelines/vq_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
@@ -30,6 +30,5 @@ The original codebase can be found [here](https://github.com/microsoft/VQ-Diffus
 
 
 ## VQDiffusionPipeline
-[[autodoc]] VQDiffusionPipeline
-	- all
-	- __call__
+[[autodoc]] pipelines.vq_diffusion.pipeline_vq_diffusion.VQDiffusionPipeline
+    - __call__

docs/source/api/schedulers.mdx

@@ -0,0 +1,177 @@
+<!--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. That's why schedulers may also be called *Samplers* in other diffusion models implementations.
+
+- 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
+
+#### Multistep DPM-Solver
+
+Original paper can be found [here](https://arxiv.org/abs/2206.00927) and the [improved version](https://arxiv.org/abs/2211.01095). The original implementation can be found [here](https://github.com/LuChengTHU/dpm-solver).
+
+[[autodoc]] DPMSolverMultistepScheduler
+
+#### Heun scheduler inspired by Karras et. al paper
+
+Algorithm 1 of [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/)
+
+[[autodoc]] HeunDiscreteScheduler
+
+#### DPM Discrete Scheduler inspired by Karras et. al paper
+
+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/)
+
+[[autodoc]] KDPM2DiscreteScheduler
+
+#### DPM Discrete Scheduler with ancestral sampling inspired by Karras et. al paper
+
+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/)
+
+[[autodoc]] KDPM2AncestralDiscreteScheduler
+
+#### 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 (VE-SDE) scheduler
+
+Original paper can be found [here](https://arxiv.org/abs/2011.13456).
+
+[[autodoc]] ScoreSdeVeScheduler
+
+#### improved pseudo numerical methods for diffusion models (iPNDM)
+
+Original implementation can be found [here](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296).
+
+[[autodoc]] IPNDMScheduler
+
+#### variance preserving stochastic differential equation (VP-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
+
+#### Euler scheduler
+
+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.
+
+[[autodoc]] EulerDiscreteScheduler
+
+
+#### Euler Ancestral scheduler
+
+Ancestral sampling with Euler method steps. Based on the original (k-diffusion)[https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72] implementation by Katherine Crowson.
+Fast scheduler which often times generates good outputs with 20-30 steps.
+
+[[autodoc]] EulerAncestralDiscreteScheduler
+
+
+#### VQDiffusionScheduler
+
+Original paper can be found [here](https://arxiv.org/abs/2111.14822)
+
+[[autodoc]] VQDiffusionScheduler
+
+#### RePaint scheduler
+
+DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. 
+Intended for use with [`RePaintPipeline`].
+Based on the paper [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2201.09865) 
+and the original implementation by Andreas Lugmayr et al.: https://github.com/andreas128/RePaint
+
+[[autodoc]] RePaintScheduler

docs/source/conceptual/contribution.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
@@ -177,7 +177,7 @@ Follow these steps to start contributing ([supported Python versions](https://gi
    $ make style
    ```
 
-   🧨 Diffusers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality
+   🧨 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

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
@@ -10,7 +10,6 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# 번역중
+# Stable Diffusion
 
-열심히 번역을 진행중입니다. 조금만 기다려주세요.
-감사합니다!
+Please visit this [very in-detail blog post](https://huggingface.co/blog/stable_diffusion) on Stable Diffusion!

docs/source/en/_toctree.yml

@@ -1,240 +0,0 @@
-- sections:
-  - local: index
-    title: 🧨 Diffusers
-  - local: quicktour
-    title: Quicktour
-  - local: stable_diffusion
-    title: Stable Diffusion
-  - local: installation
-    title: Installation
-  title: Get started
-- sections:
-  - local: tutorials/basic_training
-    title: Train a diffusion model
-  title: Tutorials
-- sections:
-  - sections:
-    - local: using-diffusers/loading
-      title: Loading Pipelines, Models, and Schedulers
-    - local: using-diffusers/schedulers
-      title: Using different Schedulers
-    - local: using-diffusers/configuration
-      title: Configuring Pipelines, Models, and Schedulers
-    - local: using-diffusers/custom_pipeline_overview
-      title: Loading and Adding Custom Pipelines
-    - local: using-diffusers/kerascv
-      title: Using KerasCV Stable Diffusion Checkpoints in Diffusers
-    title: Loading & Hub
-  - sections:
-    - local: using-diffusers/unconditional_image_generation
-      title: Unconditional Image Generation
-    - local: using-diffusers/conditional_image_generation
-      title: Text-to-Image Generation
-    - local: using-diffusers/img2img
-      title: Text-Guided Image-to-Image
-    - local: using-diffusers/inpaint
-      title: Text-Guided Image-Inpainting
-    - local: using-diffusers/depth2img
-      title: Text-Guided Depth-to-Image
-    - local: using-diffusers/controlling_generation
-      title: Controlling generation
-    - local: using-diffusers/reusing_seeds
-      title: Reusing seeds for deterministic generation
-    - local: using-diffusers/reproducibility
-      title: Reproducibility
-    - local: using-diffusers/custom_pipeline_examples
-      title: Community Pipelines
-    - local: using-diffusers/contribute_pipeline
-      title: How to contribute a Pipeline
-    - local: using-diffusers/using_safetensors
-      title: Using safetensors
-    - local: using-diffusers/weighted_prompts
-      title: Weighting Prompts
-    title: Pipelines for Inference
-  - sections:
-    - local: using-diffusers/rl
-      title: Reinforcement Learning
-    - local: using-diffusers/audio
-      title: Audio
-    - local: using-diffusers/other-modalities
-      title: Other Modalities
-    title: Taking Diffusers Beyond Images
-  title: Using Diffusers
-- sections:
-  - local: optimization/fp16
-    title: Memory and Speed
-  - local: optimization/torch2.0
-    title: Torch2.0 support
-  - local: optimization/xformers
-    title: xFormers
-  - local: optimization/onnx
-    title: ONNX
-  - local: optimization/open_vino
-    title: OpenVINO
-  - local: optimization/mps
-    title: MPS
-  - local: optimization/habana
-    title: Habana Gaudi
-  title: Optimization/Special Hardware
-- sections:
-  - local: training/overview
-    title: Overview
-  - local: training/unconditional_training
-    title: Unconditional Image Generation
-  - local: training/text_inversion
-    title: Textual Inversion
-  - local: training/dreambooth
-    title: DreamBooth
-  - local: training/text2image
-    title: Text-to-image
-  - local: training/lora
-    title: Low-Rank Adaptation of Large Language Models (LoRA)
-  title: Training
-- sections:
-  - local: conceptual/philosophy
-    title: Philosophy
-  - local: conceptual/contribution
-    title: How to contribute?
-  - local: conceptual/ethical_guidelines
-    title: Diffusers' Ethical Guidelines
-  title: Conceptual Guides
-- sections:
-  - sections:
-    - local: api/models
-      title: Models
-    - 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
-    title: Main Classes
-  - sections:
-    - local: api/pipelines/overview
-      title: Overview
-    - local: api/pipelines/alt_diffusion
-      title: AltDiffusion
-    - local: api/pipelines/audio_diffusion
-      title: Audio Diffusion
-    - 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/dit
-      title: DiT
-    - local: api/pipelines/latent_diffusion
-      title: Latent Diffusion
-    - local: api/pipelines/paint_by_example
-      title: PaintByExample
-    - local: api/pipelines/pndm
-      title: PNDM
-    - local: api/pipelines/repaint
-      title: RePaint
-    - local: api/pipelines/stable_diffusion_safe
-      title: Safe Stable Diffusion
-    - local: api/pipelines/score_sde_ve
-      title: Score SDE VE
-    - local: api/pipelines/semantic_stable_diffusion
-      title: Semantic Guidance
-    - 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/upscale
-        title: Super-Resolution
-      - local: api/pipelines/stable_diffusion/latent_upscale
-        title: Stable-Diffusion-Latent-Upscaler
-      - local: api/pipelines/stable_diffusion/pix2pix
-        title: InstructPix2Pix
-      - local: api/pipelines/stable_diffusion/attend_and_excite
-        title: Attend and Excite
-      - local: api/pipelines/stable_diffusion/pix2pix_zero
-        title: Pix2Pix Zero
-      - local: api/pipelines/stable_diffusion/self_attention_guidance
-        title: Self-Attention Guidance
-      - local: api/pipelines/stable_diffusion/panorama
-        title: MultiDiffusion Panorama
-      - local: api/pipelines/stable_diffusion/controlnet
-        title: Text-to-Image Generation with ControlNet Conditioning
-      title: Stable Diffusion
-    - local: api/pipelines/stable_diffusion_2
-      title: Stable Diffusion 2
-    - local: api/pipelines/stable_unclip
-      title: Stable unCLIP
-    - local: api/pipelines/stochastic_karras_ve
-      title: Stochastic Karras VE
-    - local: api/pipelines/unclip
-      title: UnCLIP
-    - local: api/pipelines/latent_diffusion_uncond
-      title: Unconditional Latent Diffusion
-    - 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/euler_ancestral
-      title: Euler Ancestral Scheduler
-    - local: api/schedulers/euler
-      title: Euler scheduler
-    - local: api/schedulers/heun
-      title: Heun Scheduler
-    - 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/loaders.mdx

@@ -1,30 +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

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/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/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/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) | *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](./api/pipelines/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/stable_diffusion/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/stable_diffusion/controlnet.mdx

@@ -1,167 +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 amazing 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/stable_diffusion/pipeline_stable_diffusion_controlnet.py) | *Text-to-Image Generation with ControlNet Conditioning* | [Colab Example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/controlnet.ipynb)
-
-## 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 -->
-
-## 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).
-
-### ControlNet with Stable Diffusion 1.5
-
-| 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> |
-
-## 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

docs/source/en/api/pipelines/stable_diffusion/depth2img.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.
--->
-
-# 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

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

@@ -1,32 +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.
--->
-
-# 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

docs/source/en/api/pipelines/stable_diffusion/inpaint.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.
--->
-
-# 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

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/panorama.mdx

@@ -1,58 +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")
-```
-
-## StableDiffusionPanoramaPipeline
-[[autodoc]] StableDiffusionPanoramaPipeline
-	- __call__
-	- all

docs/source/en/api/pipelines/stable_diffusion/pix2pix.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.
--->
-
-# 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

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

@@ -1,64 +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
-
-[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 been drawing much attention for their appreciable sample quality and diversity. Despite their remarkable performance, DDMs remain black boxes on which further study is necessary to take a profound step. Motivated by this, we delve into the design of conventional U-shaped diffusion models. More specifically, we investigate the self-attention modules within these models through carefully designed experiments and explore their characteristics. In addition, inspired by the studies that substantiate the effectiveness of the guidance schemes, we present plug-and-play diffusion guidance, namely Self-Attention Guidance (SAG), that can drastically boost the performance of existing diffusion models. Our method, SAG, extracts the intermediate attention map from a diffusion model at every iteration and selects tokens above a certain attention score for masking and blurring to obtain a partially blurred input. Subsequently, we measure the dissimilarity between the predicted noises obtained from feeding the blurred and original input to the diffusion model and leverage it as guidance. With this guidance, we observe apparent improvements in a wide range of diffusion models, e.g., ADM, IDDPM, and Stable Diffusion, and show that the results further improve by combining our method with the conventional guidance scheme. We provide extensive ablation studies to verify our choices.*
-
-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).
-* [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* | [Colab](https://colab.research.google.com/github/SusungHong/Self-Attention-Guidance/blob/main/SAG_Stable.ipynb) |
-
-## 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/stable_diffusion/text2img.mdx

@@ -1,41 +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

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

@@ -1,97 +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.
-
-## 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:
-
-TODO
-
-### Text-to-Image Generation
-
-```python
-import torch
-from diffusers import StableUnCLIPPipeline
-
-pipe = StableUnCLIPPipeline.from_pretrained(
-    "fusing/stable-unclip-2-1-l", torch_dtype=torch.float16
-)  # TODO update model path
-pipe = pipe.to("cuda")
-
-prompt = "a photo of an astronaut riding a horse on mars"
-images = pipe(prompt).images
-images[0].save("astronaut_horse.png")
-```
-
-
-### Text guided Image-to-Image Variation
-
-```python
-import requests
-import torch
-from PIL import Image
-from io import BytesIO
-
-from diffusers import StableUnCLIPImg2ImgPipeline
-
-pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
-    "fusing/stable-unclip-2-1-l-img2img", torch_dtype=torch.float16
-)  # TODO update model path
-pipe = pipe.to("cuda")
-
-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, init_image).images
-images[0].save("fantasy_landscape.png")
-```
-
-### 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/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/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/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

docs/source/en/api/schedulers/euler_ancestral.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 Ancestral scheduler
-
-## Overview
-
-Ancestral sampling with Euler method steps. Based on the original (k-diffusion)[https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72] implementation by Katherine Crowson.
-Fast scheduler which often times generates good outputs with 20-30 steps.
-
-## EulerAncestralDiscreteScheduler
-[[autodoc]] EulerAncestralDiscreteScheduler

docs/source/en/api/schedulers/heun.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.
--->
-
-# Heun scheduler inspired by Karras et. al paper
-
-## Overview
-
-Algorithm 1 of [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/)
-
-## HeunDiscreteScheduler
-[[autodoc]] HeunDiscreteScheduler

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

@@ -1,20 +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.
--->
-
-# improved pseudo numerical methods for diffusion models (iPNDM)
-
-## Overview
-
-Original implementation can be found [here](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296).
-
-## IPNDMScheduler
-[[autodoc]] IPNDMScheduler

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

@@ -1,20 +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.
--->
-
-# Linear multistep scheduler for discrete beta schedules
-
-## Overview
-
-Original implementation can be found [here](https://arxiv.org/abs/2206.00364).
-
-## LMSDiscreteScheduler
-[[autodoc]] LMSDiscreteScheduler

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

@@ -1,20 +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.
--->
-
-# Multistep DPM-Solver
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2206.00927) and the [improved version](https://arxiv.org/abs/2211.01095). The original implementation can be found [here](https://github.com/LuChengTHU/dpm-solver).
-
-## DPMSolverMultistepScheduler
-[[autodoc]] DPMSolverMultistepScheduler

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

@@ -1,92 +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.
--->
-
-# 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. That's why schedulers may also be called *Samplers* in other diffusion models implementations.
-
-- 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 can be discrete (accepting `int` inputs), such as the [`DDPMScheduler`] or [`PNDMScheduler`], or 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).
-- Many diffusion pipelines, such as [`StableDiffusionPipeline`] and [`DiTPipeline`] can use any of [`KarrasDiffusionSchedulers`]
-
-## Schedulers Summary
-
-The following table summarizes all officially supported schedulers, their corresponding paper
-
-| Scheduler | Paper |
-|---|---|
-| [ddim](./ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) |
-| [ddim_inverse](./ddim_inverse) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) |
-| [ddpm](./ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) |
-| [deis](./deis) | [**DEISMultistepScheduler**](https://arxiv.org/abs/2204.13902) |
-| [singlestep_dpm_solver](./singlestep_dpm_solver) | [**Singlestep DPM-Solver**](https://arxiv.org/abs/2206.00927) |
-| [multistep_dpm_solver](./multistep_dpm_solver) | [**Multistep DPM-Solver**](https://arxiv.org/abs/2206.00927) |
-| [heun](./heun) | [**Heun scheduler inspired by Karras et. al paper**](https://arxiv.org/abs/2206.00364) |
-| [dpm_discrete](./dpm_discrete) | [**DPM Discrete Scheduler inspired by Karras et. al paper**](https://arxiv.org/abs/2206.00364) |
-| [dpm_discrete_ancestral](./dpm_discrete_ancestral) | [**DPM Discrete Scheduler with ancestral sampling inspired by Karras et. al paper**](https://arxiv.org/abs/2206.00364) |
-| [stochastic_karras_ve](./stochastic_karras_ve) | [**Variance exploding, stochastic sampling from Karras et. al**](https://arxiv.org/abs/2206.00364) |
-| [lms_discrete](./lms_discrete) | [**Linear multistep scheduler for discrete beta schedules**](https://arxiv.org/abs/2206.00364) |
-| [pndm](./pndm) | [**Pseudo numerical methods for diffusion models (PNDM)**](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181) |
-| [score_sde_ve](./score_sde_ve) | [**variance exploding stochastic differential equation (VE-SDE) scheduler**](https://arxiv.org/abs/2011.13456) |
-| [ipndm](./ipndm) | [**improved pseudo numerical methods for diffusion models (iPNDM)**](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296) |
-| [score_sde_vp](./score_sde_vp) | [**Variance preserving stochastic differential equation (VP-SDE) scheduler**](https://arxiv.org/abs/2011.13456) |
-| [euler](./euler) | [**Euler scheduler**](https://arxiv.org/abs/2206.00364) |
-| [euler_ancestral](./euler_ancestral) | [**Euler Ancestral scheduler**](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72) |
-| [vq_diffusion](./vq_diffusion) | [**VQDiffusionScheduler**](https://arxiv.org/abs/2111.14822) |
-| [unipc](./unipc) | [**UniPCMultistepScheduler**](https://arxiv.org/abs/2302.04867) |
-| [repaint](./repaint) | [**RePaint scheduler**](https://arxiv.org/abs/2201.09865) |
-
-## 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
-
-### KarrasDiffusionSchedulers
-
-`KarrasDiffusionSchedulers` encompasses the main generalization of schedulers in Diffusers. The schedulers in this class are distinguished, at a high level, by their noise sampling strategy; the type of network and scaling; and finally the training strategy or how the loss is weighed.
-
-The different schedulers, depending on the type of ODE solver, fall into the above taxonomy and provide a good abstraction for the design of the main schedulers implemented in Diffusers. The schedulers in this class are given below:
-
-[[autodoc]] schedulers.scheduling_utils.KarrasDiffusionSchedulers

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

@@ -1,20 +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.
--->
-
-# Pseudo numerical methods for diffusion models (PNDM)
-
-## Overview
-
-Original implementation can be found [here](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181).
-
-## PNDMScheduler
-[[autodoc]] PNDMScheduler

docs/source/en/api/schedulers/repaint.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.
--->
-
-# RePaint scheduler
-
-## Overview
-
-DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. 
-Intended for use with [`RePaintPipeline`].
-Based on the paper [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2201.09865) 
-and the original implementation by Andreas Lugmayr et al.: https://github.com/andreas128/RePaint
-
-## RePaintScheduler
-[[autodoc]] RePaintScheduler

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

@@ -1,20 +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.
--->
-
-# variance exploding stochastic differential equation (VE-SDE) scheduler
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2011.13456).
-
-## ScoreSdeVeScheduler
-[[autodoc]] ScoreSdeVeScheduler

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

@@ -1,26 +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.
--->
-
-# Variance preserving stochastic differential equation (VP-SDE) scheduler
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2011.13456).
-
-<Tip warning={true}>
-
-Score SDE-VP is under construction.
-
-</Tip>
-
-## ScoreSdeVpScheduler
-[[autodoc]] schedulers.scheduling_sde_vp.ScoreSdeVpScheduler

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

@@ -1,20 +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.
--->
-
-# Singlestep DPM-Solver
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2206.00927) and the [improved version](https://arxiv.org/abs/2211.01095). The original implementation can be found [here](https://github.com/LuChengTHU/dpm-solver).
-
-## DPMSolverSinglestepScheduler
-[[autodoc]] DPMSolverSinglestepScheduler

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

@@ -1,20 +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.
--->
-
-# Variance exploding, stochastic sampling from Karras et. al
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2206.00364).
-
-## KarrasVeScheduler
-[[autodoc]] KarrasVeScheduler

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

@@ -1,24 +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.
--->
-
-# UniPC
-
-## Overview
-
-UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.
-
-For more details about the method, please refer to the [[paper]](https://arxiv.org/abs/2302.04867) and the [[code]](https://github.com/wl-zhao/UniPC).
-
-Fast Sampling of Diffusion Models with Exponential Integrator.
-
-## UniPCMultistepScheduler
-[[autodoc]] UniPCMultistepScheduler

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

@@ -1,20 +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.
--->
-
-# VQDiffusionScheduler
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2111.14822)
-
-## VQDiffusionScheduler
-[[autodoc]] VQDiffusionScheduler

docs/source/en/conceptual/ethical_guidelines.mdx

@@ -1,49 +0,0 @@
-# 🧨 Diffusers’ Ethical Guidelines
-
-## Preamble
-
-[Diffusers](https://huggingface.co/docs/diffusers/index) provides pre-trained diffusion models and serves as a modular toolbox for inference and training. 
-
-Given its real case applications in the world and potential negative impacts on society, we think it is important to provide the project with ethical guidelines to guide the development, users’ contributions, and usage of the Diffusers library.
-
-The risks associated with using this technology are still being examined, but to name a few: copyrights issues for artists; deep-fake exploitation; sexual content generation in inappropriate contexts; non-consensual impersonation; harmful social biases perpetuating the oppression of marginalized groups.
-We will keep tracking risks and adapt the following guidelines based on the community's responsiveness and valuable feedback.
-
-
-## Scope
-
-The Diffusers community will apply the following ethical guidelines to the project’s development and help coordinate how the community will integrate the contributions, especially concerning sensitive topics related to ethical concerns.
-
-
-## Ethical guidelines
-
-The following ethical guidelines apply generally, but we will primarily implement them when dealing with ethically sensitive issues while making a technical choice. Furthermore, we commit to adapting those ethical principles over time following emerging harms related to the state of the art of the technology in question.
-
-- **Transparency**: we are committed to being transparent in managing PRs, explaining our choices to users, and making technical decisions.
-
-- **Consistency**: we are committed to guaranteeing our users the same level of attention in project management, keeping it technically stable and consistent.
-
-- **Simplicity**: with a desire to make it easy to use and exploit the Diffusers library, we are committed to keeping the project’s goals lean and coherent.
-
-- **Accessibility**: the Diffusers project helps lower the entry bar for contributors who can help run it even without technical expertise. Doing so makes research artifacts more accessible to the community.
-
-- **Reproducibility**: we aim to be transparent about the reproducibility of upstream code, models, and datasets when made available through the Diffusers library.
-
-- **Responsibility**: as a community and through teamwork, we hold a collective responsibility to our users by anticipating and mitigating this technology's potential risks and dangers.
-
-
-## Examples of implementations: Safety features and Mechanisms
-
-The team works daily to make the technical and non-technical tools available to deal with the potential ethical and social risks associated with diffusion technology. Moreover, the community's input is invaluable in ensuring these features' implementation and raising awareness with us. 
-
-- [**Community tab**](https://huggingface.co/docs/hub/repositories-pull-requests-discussions): it enables the community to discuss and better collaborate on a project.
-
-- **Bias exploration and evaluation**: the Hugging Face team provides a [space](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer) to demonstrate the biases in Stable Diffusion interactively. In this sense, we support and encourage bias explorers and evaluations.
-
-- **Encouraging safety in deployment**
-
-  - [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).
-
-- **Staged released on the Hub**: in particularly sensitive situations, access to some repositories should be restricted. This staged release is an intermediary step that allows the repository’s authors to have more control over its use.
-
-- **Licensing**: [OpenRAILs](https://huggingface.co/blog/open_rail), a new type of licensing, allow us to ensure free access while having a set of restrictions that ensure more responsible use. 

docs/source/en/conceptual/philosophy.mdx

@@ -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.

docs/source/en/index.mdx

@@ -1,76 +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.
--->
-
-<p align="center">
-    <br>
-    <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400"/>
-    <br>
-</p>
-
-# 🧨 Diffusers
-
-🤗 Diffusers provides pretrained vision and audio diffusion models, and serves as a modular toolbox for inference and training.
-
-More precisely, 🤗 Diffusers offers:
-
-- State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [**Using Diffusers**](./using-diffusers/conditional_image_generation)) or have a look at [**Pipelines**](#pipelines) to get an overview of all supported pipelines and their corresponding papers.
-- Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference. For more information see [**Schedulers**](./api/schedulers/overview).
-- Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system. See [**Models**](./api/models) for more details 
-- Training examples to show how to train the most popular diffusion model tasks. For more information see [**Training**](./training/overview).
-
-## 🧨 Diffusers Pipelines
-
-The following table summarizes all officially supported pipelines, their corresponding paper, and if 
-available a colab notebook to directly try them out.
-
-| Pipeline | Paper | Tasks | Colab
-|---|---|:---:|:---:|
-| [alt_diffusion](./api/pipelines/alt_diffusion) | [**AltDiffusion**](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation |
-| [audio_diffusion](./api/pipelines/audio_diffusion) | [**Audio Diffusion**](https://github.com/teticio/audio-diffusion.git) | 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)
-| [controlnet](./api/pipelines/stable_diffusion/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/github/huggingface/notebooks/blob/main/diffusers/controlnet.ipynb)
-| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [**Cycle Diffusion**](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
-| [dance_diffusion](./api/pipelines/dance_diffusion) | [**Dance Diffusion**](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
-| [ddpm](./api/pipelines/ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
-| [ddim](./api/pipelines/ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
-| [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation | 
-| [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image | 
-| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation | 
-| [paint_by_example](./api/pipelines/paint_by_example) | [**Paint by Example: Exemplar-based Image Editing with Diffusion Models**](https://arxiv.org/abs/2211.13227) | Image-Guided Image Inpainting | 
-| [pndm](./api/pipelines/pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation | 
-| [score_sde_ve](./api/pipelines/score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
-| [score_sde_vp](./api/pipelines/score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
-| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [**Semantic Guidance**](https://arxiv.org/abs/2301.12247) | Text-Guided 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)
-| [stable_diffusion_text2img](./api/pipelines/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](./api/pipelines/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](./api/pipelines/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](./api/pipelines/stable_diffusion/panorama) | [**MultiDiffusion**](https://multidiffusion.github.io/) | Text-to-Panorama Generation |
-| [stable_diffusion_pix2pix](./api/pipelines/stable_diffusion/pix2pix) | [**InstructPix2Pix**](https://github.com/timothybrooks/instruct-pix2pix) | Text-Guided Image Editing| 
-| [stable_diffusion_pix2pix_zero](./api/pipelines/stable_diffusion/pix2pix_zero) | [**Zero-shot Image-to-Image Translation**](https://pix2pixzero.github.io/) | Text-Guided Image Editing | 
-| [stable_diffusion_attend_and_excite](./api/pipelines/stable_diffusion/attend_and_excite) | [**Attend and Excite for Stable Diffusion**](https://attendandexcite.github.io/Attend-and-Excite/) | Text-to-Image Generation | 
-| [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [**Self-Attention Guidance**](https://ku-cvlab.github.io/Self-Attention-Guidance) | 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](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |
-| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting | 
-| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Depth-Conditional Stable Diffusion**](https://github.com/Stability-AI/stablediffusion#depth-conditional-stable-diffusion) | Depth-to-Image Generation | 
-| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
-| [stable_diffusion_safe](./api/pipelines/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](./api/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
-| [unclip](./api/pipelines/unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125) | Text-to-Image Generation |
-| [versatile_diffusion](./api/pipelines/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](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation | 
-| [versatile_diffusion](./api/pipelines/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](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation | 
-
-**Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. 

docs/source/en/optimization/habana.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.
--->
-
-# How to use Stable Diffusion on Habana Gaudi
-
-🤗 Diffusers is compatible with Habana Gaudi through 🤗 [Optimum Habana](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion).
-
-## Requirements
-
-- Optimum Habana 1.3 or later, [here](https://huggingface.co/docs/optimum/habana/installation) is how to install it.
-- SynapseAI 1.7.
-
-
-## Inference Pipeline
-
-To generate images with Stable Diffusion 1 and 2 on Gaudi, you need to instantiate two instances:
-- A pipeline with [`GaudiStableDiffusionPipeline`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline). This pipeline supports *text-to-image generation*.
-- A scheduler with [`GaudiDDIMScheduler`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline#optimum.habana.diffusers.GaudiDDIMScheduler). This scheduler has been optimized for Habana Gaudi.
-
-When initializing the pipeline, you have to specify `use_habana=True` to deploy it on HPUs.
-Furthermore, in order to get the fastest possible generations you should enable **HPU graphs** with `use_hpu_graphs=True`.
-Finally, you will need to specify a [Gaudi configuration](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config) which can be downloaded from the [Hugging Face Hub](https://huggingface.co/Habana).
-
-```python
-from optimum.habana import GaudiConfig
-from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline
-
-model_name = "stabilityai/stable-diffusion-2-base"
-scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")
-pipeline = GaudiStableDiffusionPipeline.from_pretrained(
-    model_name,
-    scheduler=scheduler,
-    use_habana=True,
-    use_hpu_graphs=True,
-    gaudi_config="Habana/stable-diffusion",
-)
-```
-
-You can then call the pipeline to generate images by batches from one or several prompts:
-```python
-outputs = pipeline(
-    prompt=[
-        "High quality photo of an astronaut riding a horse in space",
-        "Face of a yellow cat, high resolution, sitting on a park bench",
-    ],
-    num_images_per_prompt=10,
-    batch_size=4,
-)
-```
-
-For more information, check out Optimum Habana's [documentation](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion) and the [example](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion) provided in the official Github repository.
-
-
-## Benchmark
-
-Here are the latencies for Habana Gaudi 1 and Gaudi 2 with the [Habana/stable-diffusion](https://huggingface.co/Habana/stable-diffusion) Gaudi configuration (mixed precision bf16/fp32):
-
-|         | Latency | Batch size |
-| ------- |:-------:|:----------:|
-| Gaudi 1 | 4.37s   | 4/8        |
-| Gaudi 2 | 1.19s   | 4/8        |

docs/source/en/optimization/torch2.0.mdx

@@ -1,208 +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.
--->
-
-# Accelerated PyTorch 2.0 support in Diffusers
-
-Starting from version `0.13.0`, Diffusers supports the latest optimization from the upcoming [PyTorch 2.0](https://pytorch.org/get-started/pytorch-2.0/) release. These include:
-1. Support for accelerated transformers implementation with memory-efficient attention – no extra dependencies required.
-2. [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) support for extra performance boost when individual models are compiled.
-
-
-## Installation
-To benefit from the accelerated transformers implementation and `torch.compile`, we will need to install the nightly version of PyTorch, as the stable version is yet to be released. The first step is to install CUDA 11.7 or CUDA 11.8, 
-as PyTorch 2.0 does not support the previous versions. Once CUDA is installed, torch nightly can be installed using:
-
-```bash
-pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu117
-```
-
-## Using accelerated transformers and torch.compile.
-
-
-1. **Accelerated Transformers implementation**
-
-   PyTorch 2.0 includes an optimized and memory-efficient attention implementation through the [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention) function, which automatically enables several optimizations depending on the inputs and the GPU type. This is similar to the `memory_efficient_attention` from [xFormers](https://github.com/facebookresearch/xformers), but built natively into PyTorch. 
-
-   These optimizations will be enabled by default in Diffusers if PyTorch 2.0 is installed and if `torch.nn.functional.scaled_dot_product_attention` is available. To use it, just install `torch 2.0` as suggested above and simply use the pipeline. For example:
-
-    ```Python
-    import torch
-    from diffusers import StableDiffusionPipeline
-
-    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-    pipe = pipe.to("cuda")
-
-    prompt = "a photo of an astronaut riding a horse on mars"
-    image = pipe(prompt).images[0]
-    ```
-
-    If you want to enable it explicitly (which is not required), you can do so as shown below.
-
-    ```Python
-    import torch
-    from diffusers import StableDiffusionPipeline
-    from diffusers.models.cross_attention import AttnProcessor2_0
-
-    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
-    pipe.unet.set_attn_processor(AttnProcessor2_0())
-
-    prompt = "a photo of an astronaut riding a horse on mars"
-    image = pipe(prompt).images[0]
-    ```
-
-    This should be as fast and memory efficient as `xFormers`. More details [in our benchmark](#benchmark).
-
-
-2. **torch.compile**
-
-    To get an additional speedup, we can use the new `torch.compile` feature. To do so, we simply wrap our `unet` with `torch.compile`. For more information and different options, refer to the 
-    [torch compile docs](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html).
-
-    ```python
-    import torch
-    from diffusers import StableDiffusionPipeline
-
-    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
-        "cuda"
-    )
-    pipe.unet = torch.compile(pipe.unet)
-
-    batch_size = 10
-    prompt = "A photo of an astronaut riding a horse on marse."
-    images = pipe(prompt, num_inference_steps=steps, num_images_per_prompt=batch_size).images
-    ```
-
-    Depending on the type of GPU, `compile()` can yield between 2-9% of _additional speed-up_ over the accelerated transformer optimizations. Note, however, that compilation is able to squeeze more performance improvements in more recent GPU architectures such as Ampere (A100, 3090), Ada (4090) and Hopper (H100).
-    
-    Compilation takes some time to complete, so it is best suited for situations where you need to prepare your pipeline once and then perform the same type of inference operations multiple times.
-
-
-## Benchmark
-
-We conducted a simple benchmark on different GPUs to compare vanilla attention, xFormers, `torch.nn.functional.scaled_dot_product_attention` and `torch.compile+torch.nn.functional.scaled_dot_product_attention`.
-For the benchmark we used the the [stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) model with 50 steps. The `xFormers` benchmark is done using the `torch==1.13.1` version, while the accelerated transformers optimizations are tested using nightly versions of PyTorch 2.0. The tables below summarize the results we got.
-
-The `Speed over xformers` columns denote the speed-up gained over `xFormers` using the `torch.compile+torch.nn.functional.scaled_dot_product_attention`.
-
-
-### FP16 benchmark
-
-The table below shows the benchmark results for inference using `fp16`. As we can see, `torch.nn.functional.scaled_dot_product_attention` is as fast as `xFormers` (sometimes slightly faster/slower) on all the GPUs we tested.
-And using `torch.compile` gives further speed-up of up of 10% over `xFormers`, but it's mostly noticeable on the A100 GPU.
-
-___The time reported is in seconds.___
-
-| GPU | Batch Size | Vanilla Attention | xFormers | PyTorch2.0 SDPA | SDPA + torch.compile | Speed over xformers (%) |
-| --- | --- | --- | --- | --- | --- | --- |
-| A100 | 10 | 12.02 | 8.7 | 8.79 | 7.89 | 9.31 |
-| A100 | 16 | 18.95 | 13.57 | 13.67 | 12.25 | 9.73 |
-| A100 | 32 (1) | OOM | 26.56 | 26.68 | 24.08 | 9.34 |
-| A100 | 64 | | 52.51 | 53.03 | 47.81 | 8.95 |
-| | | | | | | |
-| A10 | 4 | 13.94 | 9.81 | 10.01 | 9.35 | 4.69 |
-| A10 | 8 | 27.09 | 19 | 19.53 | 18.33 | 3.53 |
-| A10 | 10 | 33.69 | 23.53 | 24.19 | 22.52 | 4.29 |
-| A10 | 16 | OOM | 37.55 | 38.31 | 36.81 | 1.97 |
-| A10 | 32 (1) | | 77.19 | 78.43 | 76.64 | 0.71 |
-| A10 | 64 (1) | | 173.59 | 158.99 | 155.14 | 10.63 |
-| | | | | | | |
-| T4 | 4 | 38.81 | 30.09 | 29.74 | 27.55 | 8.44 |
-| T4 | 8 | OOM | 55.71 | 55.99 | 53.85 | 3.34 |
-| T4 | 10 | OOM | 68.96 | 69.86 | 65.35 | 5.23 |
-| T4 | 16 | OOM | 111.47 | 113.26  | 106.93  | 4.07 |
-| | | | | | | |
-| V100 | 4 | 9.84 | 8.16 | 8.09 | 7.65 | 6.25 |
-| V100 | 8 | OOM | 15.62 | 15.44 | 14.59 | 6.59 |
-| V100 | 10 | OOM | 19.52 | 19.28 | 18.18 | 6.86 |
-| V100 | 16 | OOM | 30.29 | 29.84 | 28.22 | 6.83 |
-| | | | | | | |
-| 3090 | 4 | 10.04 | 7.82 | 7.89 | 7.47 | 4.48 |
-| 3090 | 8 | 19.27 | 14.97 | 15.04 | 14.22 | 5.01 |
-| 3090 | 10| 24.08 | 18.7 | 18.7 | 17.69 | 5.40 |
-| 3090 | 16 | OOM | 29.06 | 29.06 | 28.2 | 2.96 |
-| 3090 | 32 (1) | | 58.05 | 58 | 54.88 | 5.46 |
-| 3090 | 64 (1) | | 126.54 | 126.03 | 117.33 | 7.28 |
-| | | | | | | |
-| 3090 Ti | 4 | 9.07 | 7.14 | 7.15 | 6.81 | 4.62 |
-| 3090 Ti | 8 | 17.51 | 13.65 | 13.72 | 12.99 | 4.84 |
-| 3090 Ti | 10 (2) | 21.79 | 16.85 | 16.93 | 16.02 | 4.93 |
-| 3090 Ti | 16 | OOM | 26.1 | 26.28 | 25.46 | 2.45 |
-| 3090 Ti | 32 (1) | | 51.78 | 52.04 | 49.15 | 5.08 |
-| 3090 Ti | 64 (1) | | 112.02 | 112.33 | 103.91 | 7.24 |
-| | | | | | | |
-| 4090 | 4 | 10.48 | 8.37 | 8.32 | 8.01 | 4.30 |
-| 4090 | 8 | 14.33 | 10.22 | 10.42 | 9.78 | 4.31 |
-| 4090 | 16 | | 17.07 | 17.46 | 17.15 | -0.47 |
-| 4090 | 32 (1) | | 39.03 | 39.86 | 37.97 | 2.72 |
-| 4090 | 64 (1) | | 77.29 | 79.44 | 77.67 | -0.49 |
-
-
-				
-### FP32 benchmark
-
-The table below shows the benchmark results for inference using `fp32`. In this case, `torch.nn.functional.scaled_dot_product_attention` is faster than `xFormers` on all the GPUs we tested.
-
-Using `torch.compile` in addition to the accelerated transformers implementation can yield up to 19% performance improvement over `xFormers` in Ampere and Ada cards, and up to 20% (Ampere) or 28% (Ada) over vanilla attention.
-
-| GPU | Batch Size | Vanilla Attention | xFormers | PyTorch2.0 SDPA | SDPA + torch.compile | Speed over xformers (%) | Speed over vanilla (%) |
-| --- | --- | --- | --- | --- | --- | --- | --- |
-| A100 | 4 | 16.56 | 12.42 | 12.2 | 11.84 | 4.67 | 28.50 |
-| A100 | 10 | OOM | 29.93 | 29.44 | 28.5 | 4.78 | |
-| A100 | 16 | | 47.08 | 46.27 | 44.8 | 4.84 | |
-| A100 | 32 | | 92.89 | 91.34 | 88.35 | 4.89 | |
-| A100 | 64 | | 185.3 | 182.71 | 176.48 | 4.76 | |
-| | | | | | | |
-| A10 | 1 | 10.59 | 8.81 | 7.51 | 7.35 | 16.57 | 30.59 |
-| A10 | 4 | 34.77 | 27.63 | 22.77 | 22.07 | 20.12 | 36.53 |
-| A10 | 8 | | 56.19 | 43.53 | 43.86 | 21.94 | |
-| A10 | 16 | | 116.49 | 88.56 | 86.64 | 25.62 | |
-| A10 | 32 | | 221.95 | 175.74 | 168.18 | 24.23 | |
-| A10 | 48 | | 333.23 | 264.84 | | 20.52 | |
-| | | | | | | |
-| T4 | 1 | 28.2 | 24.49 | 23.93 | 23.56 | 3.80 | 16.45 |
-| T4 | 2 | 52.77 | 45.7 | 45.88 | 45.06 | 1.40 | 14.61 |
-| T4 | 4 | OOM | 85.72 | 85.78 | 84.48 | 1.45 | |
-| T4 | 8 | | 149.64 | 150.75 | 148.4 | 0.83 | |
-| | | | | | | |
-| V100 | 1 | 7.4 | 6.84 | 6.8 | 6.66 | 2.63 | 10.00 |
-| V100 | 2 | 13.85 | 12.81 | 12.66 | 12.35 | 3.59 | 10.83 |
-| V100 | 4 | OOM | 25.73 | 25.31 | 24.78 | 3.69 | |
-| V100 | 8 | | 43.95 | 43.37 | 42.25 | 3.87 | |
-| V100 | 16 | | 84.99 | 84.73 | 82.55 | 2.87 | |
-| | | | | | | |
-| 3090 | 1 | 7.09 | 6.78 | 6.11 | 6.03 | 11.06 | 14.95 |
-| 3090 | 4 | 22.69 | 21.45 | 18.67 | 18.09 | 15.66 | 20.27 |
-| 3090 | 8 | | 42.59 | 36.75 | 35.59 | 16.44 | |
-| 3090 | 16 | | 85.35 | 72.37 | 70.25 | 17.69 | |
-| 3090 | 32 (1) | | 162.05 | 138.99 | 134.53 | 16.98 | |
-| 3090 | 48 | | 241.91 | 207.75 | | 14.12 | |
-| | | | | | | |
-| 3090 Ti | 1 | 6.45 | 6.19 | 5.64 | 5.49 | 11.31 | 14.88 |
-| 3090 Ti | 4 | 20.32 | 19.31 | 16.9 | 16.37 | 15.23 | 19.44 |
-| 3090 Ti | 8 (2) | | 37.93 | 33.05 | 31.99 | 15.66 | |
-| 3090 Ti | 16 | | 75.37 | 65.25 | 64.32 | 14.66 | |
-| 3090 Ti | 32 (1) | | 142.55 | 124.44 | 120.74 | 15.30 | |
-| 3090 Ti | 48 | | 213.19 | 186.55 | | 12.50 | |
-| | | | | | | |
-| 4090 | 1 | 5.54 | 4.99 | 4.51 | 4.44 | 11.02 | 19.86 |
-| 4090 | 4 | 13.67 | 11.4 | 10.3 | 9.84 | 13.68 | 28.02 |
-| 4090 | 8 | | 19.79 | 17.13 | 16.19 | 18.19 | |
-| 4090 | 16 | | 38.62 | 33.14 | 32.31 | 16.34 | |
-| 4090 | 32 (1) | | 76.57 | 65.96 | 62.05 | 18.96 | |
-| 4090 | 48 | | 114.44 | 98.78 | | 13.68 | |
-
-
-
-(1) Batch Size >= 32 requires enable_vae_slicing() because of https://github.com/pytorch/pytorch/issues/81665																										
-This is required for PyTorch 1.13.1, and also for PyTorch 2.0 and batch size of 64
-
-For more details about how this benchmark was run, please refer to [this PR](https://github.com/huggingface/diffusers/pull/2303).	

docs/source/en/optimization/xformers.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.
--->
-
-# Installing xFormers
-
-We recommend the use of [xFormers](https://github.com/facebookresearch/xformers) for both inference and training. In our tests, the optimizations performed in the attention blocks allow for both faster speed and reduced memory consumption.
-
-Starting from version `0.0.16` of xFormers, released on January 2023, installation can be easily performed using pre-built pip wheels:
-
-```bash
-pip install xformers
-```
-
-<Tip>
-
-The xFormers PIP package requires the latest version of PyTorch (1.13.1 as of xFormers 0.0.16). If you need to use a previous version of PyTorch, then we recommend you install xFormers from source using [the project instructions](https://github.com/facebookresearch/xformers#installing-xformers).
-
-</Tip>
-
-After xFormers is installed, you can use `enable_xformers_memory_efficient_attention()` for faster inference and reduced memory consumption, as discussed [here](fp16#memory-efficient-attention).
-
-<Tip warning={true}>
-
-According to [this issue](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212), xFormers `v0.0.16` cannot be used for training (fine-tune or Dreambooth) in some GPUs. If you observe that problem, please install a development version as indicated in that comment.
-
-</Tip>

docs/source/en/quicktour.mdx

@@ -1,313 +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.
--->
-
-[[open-in-colab]]
-
-# Quicktour
-
-Diffusion models are trained to denoise random Gaussian noise step-by-step to generate a sample of interest, such as an image or audio. This has sparked a tremendous amount of interest in generative AI, and you have probably seen examples of diffusion generated images on the internet. 🧨 Diffusers is a library aimed at making diffusion models widely accessible to everyone.
-
-Whether you're a developer or an everyday user, this quicktour will introduce you to 🧨 Diffusers and help you get up and generating quickly! There are three main components of the library to know about:
-
-* The [`DiffusionPipeline`] is a high-level end-to-end class designed to rapidly generate samples from pretrained diffusion models for inference.
-* Popular pretrained [model](./api/models) architectures and modules that can be used as building blocks for creating diffusion systems.
-* Many different [schedulers](./api/schedulers/overview) - algorithms that control how noise is added for training, and how to generate denoised images during inference.
-
-The quicktour will show you how to use the [`DiffusionPipeline`] for inference, and then walk you through how to combine a model and scheduler to replicate what's happening inside the [`DiffusionPipeline`].
-
-<Tip>
-
-The quicktour is a simplified version of the introductory 🧨 Diffusers [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) to help you get started quickly. If you want to learn more about 🧨 Diffusers goal, design philosophy, and additional details about it's core API, check out the notebook!
-
-</Tip>
-
-Before you begin, make sure you have all the necessary libraries installed:
-
-```bash
-pip install --upgrade diffusers accelerate transformers
-```
-
-- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index) speeds up model loading for inference and training.
-- [🤗 Transformers](https://huggingface.co/docs/transformers/index) is required to run the most popular diffusion models, such as [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview).
-
-## DiffusionPipeline
-
-The [`DiffusionPipeline`] is the easiest way to use a pretrained diffusion system for inference. It is an end-to-end system containing the model and the scheduler. You can use the [`DiffusionPipeline`] out-of-the-box for many tasks. Take a look at the table below for some supported tasks, and for a complete list of supported tasks, check out the [🧨 Diffusers Summary](./api/pipelines/overview#diffusers-summary) table.
-
-| **Task**                     | **Description**                                                                                              | **Pipeline**
-|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|
-| Unconditional Image Generation          | generate an image from Gaussian noise | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |
-| Text-Guided Image Generation | generate an image given a text prompt | [conditional_image_generation](./using-diffusers/conditional_image_generation) |
-| Text-Guided Image-to-Image Translation     | adapt an image guided by a text prompt | [img2img](./using-diffusers/img2img) |
-| Text-Guided Image-Inpainting          | fill the masked part of an image given the image, the mask and a text prompt | [inpaint](./using-diffusers/inpaint) |
-| Text-Guided Depth-to-Image Translation | adapt parts of an image guided by a text prompt while preserving structure via depth estimation | [depth2img](./using-diffusers/depth2img) |
-
-Start by creating an instance of a [`DiffusionPipeline`] and specify which pipeline checkpoint you would like to download.
-You can use the [`DiffusionPipeline`] for any [checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads) stored on the Hugging Face Hub.
-In this quicktour, you'll load the [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint for text-to-image generation.
-
-<Tip warning={true}>
-
-For [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) models, please carefully read the [license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) first before running the model. 🧨 Diffusers implements a [`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) to prevent offensive or harmful content, but the model's improved image generation capabilities can still produce potentially harmful content.
-
-</Tip>
-
-Load the model with the [`~DiffusionPipeline.from_pretrained`] method:
-
-```python
->>> from diffusers import DiffusionPipeline
-
->>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-```
-
-The [`DiffusionPipeline`] downloads and caches all modeling, tokenization, and scheduling components. You'll see that the Stable Diffusion pipeline is composed of the [`UNet2DConditionModel`] and [`PNDMScheduler`] among other things:
-
-```py
->>> pipeline
-StableDiffusionPipeline {
-  "_class_name": "StableDiffusionPipeline",
-  "_diffusers_version": "0.13.1",
-  ...,
-  "scheduler": [
-    "diffusers",
-    "PNDMScheduler"
-  ],
-  ...,
-  "unet": [
-    "diffusers",
-    "UNet2DConditionModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKL"
-  ]
-}
-```
-
-We strongly recommend running the pipeline on a GPU because the model consists of roughly 1.4 billion parameters.
-You can move the generator object to a GPU, just like you would in PyTorch:
-
-```python
->>> pipeline.to("cuda")
-```
-
-Now you can pass a text prompt to the `pipeline` to generate an image, and then access the denoised image. By default, the image output is wrapped in a [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) object.
-
-```python
->>> image = pipeline("An image of a squirrel in Picasso style").images[0]
->>> image
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/image_of_squirrel_painting.png"/>
-</div>
-
-Save the image by calling `save`:
-
-```python
->>> image.save("image_of_squirrel_painting.png")
-```
-
-### Local pipeline
-
-You can also use the pipeline locally. The only difference is you need to download the weights first:
-
-```
-git lfs install
-git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
-```
-
-Then load the saved weights into the pipeline:
-
-```python
->>> pipeline = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
-```
-
-Now you can run the pipeline as you would in the section above.
-
-### Swapping schedulers
-
-Different schedulers come with different denoising speeds and quality trade-offs. The best way to find out which one works best for you is to try them out! One of the main features of 🧨 Diffusers is to allow you to easily switch between schedulers. For example, to replace the default [`PNDMScheduler`] with the [`EulerDiscreteScheduler`], load it with the [`~diffusers.ConfigMixin.from_config`] method:
-
-```py
->>> from diffusers import EulerDiscreteScheduler
-
->>> pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
->>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-```
-
-Try generating an image with the new scheduler and see if you notice a difference!
-
-In the next section, you'll take a closer look at the components - the model and scheduler - that make up the [`DiffusionPipeline`] and learn how to use these components to generate an image of a cat.
-
-## Models
-
-Most models take a noisy sample, and at each timestep it predicts the *noise residual* (other models learn to predict the previous sample directly or the velocity or [`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)), the difference between a less noisy image and the input image. You can mix and match models to create other diffusion systems.
-
-Models are initiated with the [`~ModelMixin.from_pretrained`] method which also locally caches the model weights so it is faster the next time you load the model. For the quicktour, you'll load the [`UNet2DModel`], a basic unconditional image generation model with a checkpoint trained on cat images:
-
-```py
->>> from diffusers import UNet2DModel
-
->>> repo_id = "google/ddpm-cat-256"
->>> model = UNet2DModel.from_pretrained(repo_id)
-```
-
-To access the model parameters, call `model.config`:
-
-```py
->>> model.config
-```
-
-The model configuration is a 🧊 frozen 🧊 dictionary, which means those parameters can't be changed after the model is created. This is intentional and ensures that the parameters used to define the model architecture at the start remain the same, while other parameters can still be adjusted during inference.
-
-Some of the most important parameters are:
-
-* `sample_size`: the height and width dimension of the input sample.
-* `in_channels`: the number of input channels of the input sample.
-* `down_block_types` and `up_block_types`: the type of down- and upsampling blocks used to create the UNet architecture.
-* `block_out_channels`: the number of output channels of the downsampling blocks; also used in reverse order for the number of input channels of the upsampling blocks.
-* `layers_per_block`: the number of ResNet blocks present in each UNet block.
-
-To use the model for inference, create the image shape with random Gaussian noise. It should have a `batch` axis because the model can receive multiple random noises, a `channel` axis corresponding to the number of input channels, and a `sample_size` axis for the height and width of the image:
-
-```py
->>> import torch
-
->>> torch.manual_seed(0)
-
->>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
->>> noisy_sample.shape
-torch.Size([1, 3, 256, 256])
-```
-
-For inference, pass the noisy image to the model and a `timestep`. The `timestep` indicates how noisy the input image is, with more noise at the beginning and less at the end. This helps the model determine its position in the diffusion process, whether it is closer to the start or the end. Use the `sample` method to get the model output:
-
-```py
->>> with torch.no_grad():
-...     noisy_residual = model(sample=noisy_sample, timestep=2).sample
-```
-
-To generate actual examples though, you'll need a scheduler to guide the denoising process. In the next section, you'll learn how to couple a model with a scheduler.
-
-## Schedulers
-
-Schedulers manage going from a noisy sample to a less noisy sample given the model output - in this case, it is the `noisy_residual`.
-
-<Tip>
-
-🧨 Diffusers is a toolbox for building diffusion systems. While the [`DiffusionPipeline`] is a convenient way to get started with a pre-built diffusion system, you can also choose your own the model and scheduler components separately to build a custom diffusion system.
-
-</Tip>
-
-For the quicktour, you'll instantiate the [`DDPMScheduler`] with it's [`~diffusers.ConfigMixin.from_config`] method:
-
-```py
->>> from diffusers import DDPMScheduler
-
->>> scheduler = DDPMScheduler.from_config(repo_id)
->>> scheduler
-DDPMScheduler {
-  "_class_name": "DDPMScheduler",
-  "_diffusers_version": "0.13.1",
-  "beta_end": 0.02,
-  "beta_schedule": "linear",
-  "beta_start": 0.0001,
-  "clip_sample": true,
-  "clip_sample_range": 1.0,
-  "num_train_timesteps": 1000,
-  "prediction_type": "epsilon",
-  "trained_betas": null,
-  "variance_type": "fixed_small"
-}
-```
-
-<Tip>
-
-💡 Notice how the scheduler is instantiated from a configuration. Unlike a model, a scheduler does not have trainable weights and is parameter-free!
-
-</Tip>
-
-Some of the most important parameters are:
-
-* `num_train_timesteps`: the length of the denoising process or in other words, the number of timesteps required to process random Gaussian noise into a data sample.
-* `beta_schedule`: the type of noise schedule to use for inference and training.
-* `beta_start` and `beta_end`: the start and end noise values for the noise schedule.
-
-To predict a slightly less noisy image, pass the following to the scheduler's [`~diffusers.DDPMScheduler.step`] method: model output, `timestep`, and current `sample`.
-
-```py
->>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample
->>> less_noisy_sample.shape
-```
-
-The `less_noisy_sample` can be passed to the next `timestep` where it'll get even less noisier! Let's bring it all together now and visualize the entire denoising process. 
-
-First, create a function that postprocesses and displays the denoised image as a `PIL.Image`:
-
-```py
->>> import PIL.Image
->>> import numpy as np
-
-
->>> def display_sample(sample, i):
-...     image_processed = sample.cpu().permute(0, 2, 3, 1)
-...     image_processed = (image_processed + 1.0) * 127.5
-...     image_processed = image_processed.numpy().astype(np.uint8)
-
-...     image_pil = PIL.Image.fromarray(image_processed[0])
-...     display(f"Image at step {i}")
-...     display(image_pil)
-```
-
-To speed up the denoising process, move the input and model to a GPU:
-
-```py
->>> model.to("cuda")
->>> noisy_sample = noisy_sample.to("cuda")
-```
-
-Now create a denoising loop that predicts the residual of the less noisy sample, and computes the less noisy sample with the scheduler:
-
-```py
->>> import tqdm
-
->>> sample = noisy_sample
-
->>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
-...     # 1. predict noise residual
-...     with torch.no_grad():
-...         residual = model(sample, t).sample
-
-...     # 2. compute less noisy image and set x_t -> x_t-1
-...     sample = scheduler.step(residual, t, sample).prev_sample
-
-...     # 3. optionally look at image
-...     if (i + 1) % 50 == 0:
-...         display_sample(sample, i + 1)
-```
-
-Sit back and watch as a cat is generated from nothing but noise! 😻
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/diffusion-quicktour.png"/>
-</div>
-
-## Next steps
-
-Hopefully you generated some cool images with 🧨 Diffusers in this quicktour! For your next steps, you can:
-
-* Train or finetune a model to generate your own images in the [training](./tutorials/basic_training) tutorial.
-* See example official and community [training or finetuning scripts](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples) for a variety of use cases.
-* Learn more about loading, accessing, changing and comparing schedulers in the [Using different Schedulers](./using-diffusers/schedulers) guide.
-* Explore prompt engineering, speed and memory optimizations, and tips and tricks for generating higher quality images with the [Stable Diffusion](./stable_diffusion) guide.
-* Dive deeper into speeding up 🧨 Diffusers with guides on [optimized PyTorch on a GPU](./optimization/fp16), and inference guides for running [Stable Diffusion on Apple Silicon (M1/M2)](./optimization/mps) and [ONNX Runtime](./optimization/onnx).