[WIP] Don't merge / CPU offload example

changed named arg to self.tokenizer from `truncaton` to `truncation` according to Tokenizer docs (https://huggingface.co/docs/tokenizers/api/tokenizer#tokenizers.Tokenizer.truncation)

.github/workflows/build_documentation.yml

@@ -13,5 +13,6 @@ jobs:
     with:
       commit_sha: ${{ github.sha }}
       package: diffusers
+      languages: en ko
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}

.github/workflows/build_pr_documentation.yml

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

.github/workflows/nightly_tests.yml

@@ -62,6 +62,7 @@ jobs:
         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: |
@@ -134,6 +135,7 @@ jobs:
           ${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}
@@ -157,4 +159,4 @@ jobs:
         uses: actions/upload-artifact@v2
         with:
           name: torch_mps_test_reports
-          path: reports
+          path: reports

.github/workflows/pr_tests.yml

@@ -60,6 +60,7 @@ jobs:
         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: |
@@ -126,6 +127,7 @@ jobs:
         ${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

.github/workflows/push_tests.yml

@@ -62,6 +62,7 @@ jobs:
       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: |
@@ -130,6 +131,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install -e .[quality,test,training]
+        python -m pip install git+https://github.com/huggingface/accelerate
         python -m pip install -U git+https://github.com/huggingface/transformers
 
     - name: Environment
@@ -151,4 +153,4 @@ jobs:
       uses: actions/upload-artifact@v2
       with:
         name: examples_test_reports
-        path: reports
+        path: reports

Makefile

@@ -45,12 +45,14 @@ quality:
 	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
 

README.md

@@ -1,6 +1,6 @@
 <p align="center">
     <br>
-    <img src="https://github.com/huggingface/diffusers/raw/main/docs/source/imgs/diffusers_library.jpg" width="400"/>
+    <img src="./docs/source/en/imgs/diffusers_library.jpg" width="400"/>
     <br>
 <p>
 <p align="center">
@@ -235,6 +235,55 @@ 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:])))
+```
+
 ### 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.

docs/README.md

@@ -54,7 +54,7 @@ doc-builder preview {package_name} {path_to_docs}
 For example:
 
 ```bash
-doc-builder preview diffusers docs/source/
+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.
@@ -126,23 +126,28 @@ When adding a new pipeline:
     - 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. Usually as follows:
-
-```
-## XXXPipeline
-
-[[autodoc]] XXXPipeline
-```
-
-This will include every public method of the pipeline that is documented. You can specify which methods should be in the docs:
+- 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
@@ -155,9 +160,9 @@ adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`funct
 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: \[\`pipeline_utils.ImagePipelineOutput\`\]. This will be converted into a link with
-`pipeline_utils.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 ~: \[\`~pipeline_utils.ImagePipelineOutput\`\] will generate a link with `ImagePipelineOutput` in the description.
+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 \[~\`XXXClass.method\`\].
 

docs/TRANSLATING.md

@@ -0,0 +1,57 @@
+### 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/_toctree.yml

@@ -1,178 +0,0 @@
-- 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/depth2img
-      title: "Text-Guided Depth-to-Image"
-    - local: using-diffusers/reusing_seeds
-      title: "Reusing seeds for deterministic generation"
-    - 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/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 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/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/paint_by_example
-      title: "PaintByExample"
-    - 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/unclip
-      title: "UnCLIP"
-    - local: api/pipelines/versatile_diffusion
-      title: "Versatile Diffusion"
-    - local: api/pipelines/vq_diffusion
-      title: "VQ Diffusion"
-    - local: api/pipelines/repaint
-      title: "RePaint"
-    - local: api/pipelines/audio_diffusion
-      title: "Audio Diffusion"
-    title: "Pipelines"
-  - sections:
-    - local: api/schedulers/overview
-      title: "Overview"
-    - local: api/schedulers/ddim
-      title: "DDIM"
-    - local: api/schedulers/ddpm
-      title: "DDPM"
-    - local: api/schedulers/singlestep_dpm_solver
-      title: "Singlestep DPM-Solver"
-    - local: api/schedulers/multistep_dpm_solver
-      title: "Multistep DPM-Solver"
-    - local: api/schedulers/heun
-      title: "Heun Scheduler"
-    - 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/stochastic_karras_ve
-      title: "Stochastic Kerras VE"
-    - local: api/schedulers/lms_discrete
-      title: "Linear Multistep"
-    - local: api/schedulers/pndm
-      title: "PNDM"
-    - local: api/schedulers/score_sde_ve
-      title: "VE-SDE"
-    - local: api/schedulers/ipndm
-      title: "IPNDM"
-    - local: api/schedulers/score_sde_vp
-      title: "VP-SDE"
-    - local: api/schedulers/euler
-      title: "Euler scheduler"
-    - local: api/schedulers/euler_ancestral
-      title: "Euler Ancestral Scheduler"
-    - local: api/schedulers/vq_diffusion
-      title: "VQDiffusionScheduler"
-    - local: api/schedulers/repaint
-      title: "RePaint Scheduler"      
-    title: "Schedulers"
-  - sections:
-    - local: api/experimental/rl
-      title: "RL Planning"
-    title: "Experimental Features"
-  title: "API"

docs/source/en/_toctree.yml

@@ -0,0 +1,194 @@
+- sections:
+  - local: index
+    title: 🧨 Diffusers
+  - local: quicktour
+    title: Quicktour
+  - local: stable_diffusion
+    title: Stable Diffusion
+  - 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/depth2img
+      title: Text-Guided Depth-to-Image
+    - local: using-diffusers/reusing_seeds
+      title: Reusing seeds for deterministic generation
+    - 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/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 fine-tuning
+  title: Training
+- sections:
+  - local: conceptual/philosophy
+    title: Philosophy
+  - local: conceptual/contribution
+    title: How to contribute?
+  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
+    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/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
+    - 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
+      title: Stable Diffusion
+    - local: api/pipelines/stable_diffusion_2
+      title: Stable Diffusion 2
+    - 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/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/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/diffusion_pipeline.mdx

@@ -30,13 +30,17 @@ Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrain
 
 ## DiffusionPipeline
 [[autodoc]] DiffusionPipeline
-	- from_pretrained
-	- save_pretrained
-	- to
+	- all
+	- __call__
 	- device
-	- components
+	- to
 
 ## ImagePipelineOutput
 By default diffusion pipelines return an object of class
 
-[[autodoc]] pipeline_utils.ImagePipelineOutput
+[[autodoc]] pipelines.ImagePipelineOutput
+
+## AudioPipelineOutput
+By default diffusion pipelines return an object of class
+
+[[autodoc]] pipelines.AudioPipelineOutput

docs/source/en/api/models.mdx

@@ -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.vae.VQEncoderOutput
+[[autodoc]] models.vq_model.VQEncoderOutput
 
 ## VQModel
 [[autodoc]] VQModel
 
 ## AutoencoderKLOutput
-[[autodoc]] models.vae.AutoencoderKLOutput
+[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
 
 ## AutoencoderKL
 [[autodoc]] AutoencoderKL
@@ -56,7 +56,7 @@ The models are built on the base class ['ModelMixin'] that is a `torch.nn.module
 [[autodoc]] Transformer2DModel
 
 ## Transformer2DModelOutput
-[[autodoc]] models.attention.Transformer2DModelOutput
+[[autodoc]] models.transformer_2d.Transformer2DModelOutput
 
 ## PriorTransformer
 [[autodoc]] models.prior_transformer.PriorTransformer

docs/source/en/api/outputs.mdx

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

@@ -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).
+- AltDiffusion is conceptually exaclty the same as [Stable Diffusion](./api/pipelines/stable_diffusion/overview).
 
 - *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/en/api/pipelines/audio_diffusion.mdx

@@ -91,12 +91,8 @@ display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
 
 ## AudioDiffusionPipeline
 [[autodoc]] AudioDiffusionPipeline
-    - __call__
-    - encode
-    - slerp
-
+	- all
+	- __call__
 
 ## Mel
 [[autodoc]] Mel
-    - audio_slice_to_image
-    - image_to_audio

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

@@ -96,4 +96,5 @@ image.save("black_to_blue.png")
 
 ## CycleDiffusionPipeline
 [[autodoc]] CycleDiffusionPipeline
+	- all
 	- __call__

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

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

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

@@ -32,4 +32,5 @@ For questions, feel free to contact the author on [tsong.me](https://tsong.me/).
 
 ## DDIMPipeline
 [[autodoc]] DDIMPipeline
-    - __call__
+	- all
+	- __call__

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

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

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

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

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

@@ -38,4 +38,5 @@ The original codebase can be found [here](https://github.com/CompVis/latent-diff
 
 ## LDMPipeline
 [[autodoc]] LDMPipeline
-    - __call__
+	- all
+	- __call__

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

@@ -69,5 +69,6 @@ image
 ```
 
 ## PaintByExamplePipeline
-[[autodoc]] pipelines.paint_by_example.pipeline_paint_by_example.PaintByExamplePipeline
-    - __call__
+[[autodoc]] PaintByExamplePipeline
+	- all
+	- __call__

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

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

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

@@ -72,6 +72,6 @@ inpainted_image = output.images[0]
 ```
 
 ## RePaintPipeline
-[[autodoc]] pipelines.repaint.pipeline_repaint.RePaintPipeline
-    - __call__
-
+[[autodoc]] RePaintPipeline
+	- all
+	- __call__

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

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

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

@@ -0,0 +1,33 @@
+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 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

@@ -0,0 +1,31 @@
+<!--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.
+-->
+
+# 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

@@ -0,0 +1,29 @@
+<!--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.
+-->
+
+# 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) 
+
+[[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

@@ -0,0 +1,33 @@
+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 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/overview.mdx

@@ -25,9 +25,14 @@ For more details about how Stable Diffusion works and how it differs from the ba
 
 | Pipeline | Tasks | Colab | Demo
 |---|---|:---:|:---:|
-| [pipeline_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) | [🤗 Stable Diffusion](https://huggingface.co/spaces/stabilityai/stable-diffusion)
-| [pipeline_stable_diffusion_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) | [🤗 Diffuse the Rest](https://huggingface.co/spaces/huggingface/diffuse-the-rest)
-| [pipeline_stable_diffusion_inpaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | **Experimental** – *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) | Coming soon
+| [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
+
+
 
 ## Tips
 
@@ -70,54 +75,3 @@ 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
-
-## StableDiffusionDepth2ImgPipeline
-[[autodoc]] StableDiffusionDepth2ImgPipeline
-	- __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/en/api/pipelines/stable_diffusion/text2img.mdx

@@ -0,0 +1,39 @@
+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 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*: [CampVis/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

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

@@ -0,0 +1,32 @@
+<!--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.
+-->
+
+# 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_diffusion_2.mdx

@@ -24,17 +24,20 @@ 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](./api/pipelines/stable_diffusion) so please refer to [this page](./api/pipelines/stable_diffusion) for API documentation.
+Note that the architecture is more or less identical to [Stable Diffusion 1](./stable_diffusion/overview) so please refer to [this page](./stable_diffusion/overview) for API documentation.
 
 - *Text-to-Image (512x512 resolution)*: [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) with [`StableDiffusionPipeline`]
 - *Text-to-Image (768x768 resolution)*: [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) with [`StableDiffusionPipeline`]
 - *Image Inpainting (512x512 resolution)*: [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) with [`StableDiffusionInpaintPipeline`]
-- *Image Upscaling (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) [`StableDiffusionUpscalePipeline`]
+- *Super-Resolution (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) [`StableDiffusionUpscalePipeline`]
 - *Depth-to-Image (512x512 resolution)*: [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth) with [`StableDiffusionDepth2ImagePipeline`]
 
 We recommend using the [`DPMSolverMultistepScheduler`] as it's currently the fastest scheduler there is.
 
-- *Text-to-Image (512x512 resolution)*:
+
+### Text-to-Image
+
+- *Text-to-Image (512x512 resolution)*: [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) with [`StableDiffusionPipeline`]
 
 ```python
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
@@ -51,7 +54,7 @@ image = pipe(prompt, num_inference_steps=25).images[0]
 image.save("astronaut.png")
 ```
 
-- *Text-to-Image (768x768 resolution)*:
+- *Text-to-Image (768x768 resolution)*: [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) with [`StableDiffusionPipeline`]
 
 ```python
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
@@ -68,7 +71,9 @@ image = pipe(prompt, guidance_scale=9, num_inference_steps=25).images[0]
 image.save("astronaut.png")
 ```
 
-- *Image Inpainting (512x512 resolution)*:
+### Image Inpainting
+
+- *Image Inpainting (512x512 resolution)*: [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) with [`StableDiffusionInpaintPipeline`]
 
 ```python
 import PIL
@@ -102,7 +107,10 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inferen
 image.save("yellow_cat.png")
 ```
 
-- *Image Upscaling (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) [`StableDiffusionUpscalePipeline`]
+### Super-Resolution
+
+- *Image Upscaling (x4 resolution resolution)*: [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) with [`StableDiffusionUpscalePipeline`]
+
 
 ```python
 import requests
@@ -126,16 +134,10 @@ 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`]
 
-**Installation**
-
-```bash
-!pip install -U git+https://github.com/huggingface/transformers.git
-!pip install diffusers[torch]
-```
-
-**Example**
 
 ```python
 import torch

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

@@ -28,7 +28,7 @@ The abstract of the paper is the following:
 
 ## Tips
 
-- Safe Stable Diffusion may also be used with weights of [Stable Diffusion](./api/pipelines/stable_diffusion).
+- Safe Stable Diffusion may also be used with weights of [Stable Diffusion](./api/pipelines/stable_diffusion/text2img).
 
 ### Run Safe Stable Diffusion
 
@@ -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/en/api/pipelines/stochastic_karras_ve.mdx

@@ -32,4 +32,5 @@ This pipeline implements the Stochastic sampling tailored to the Variance-Expand
 
 ## KarrasVePipeline
 [[autodoc]] KarrasVePipeline
-    - __call__
+	- all
+	- __call__

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

@@ -24,8 +24,14 @@ The unCLIP model in diffusers comes from kakaobrain's karlo and the original cod
 | 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]] pipelines.unclip.pipeline_unclip.UnCLIPPipeline
-    - __call__
+[[autodoc]] UnCLIPPipeline
+	- all
+	- __call__
+
+[[autodoc]] UnCLIPImageVariationPipeline
+	- all
+	- __call__

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

@@ -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),  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/overview),  but instead of providing just a image data stream conditioned on text, VersatileDiffusion provides both a image and text data stream and can be conditioned on both text and image.
 
 ### *Run VersatileDiffusion*
 
@@ -56,18 +56,15 @@ 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/en/api/pipelines/vq_diffusion.mdx

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

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

@@ -0,0 +1,22 @@
+<!--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.
+-->
+
+# 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/index.mdx

@@ -47,9 +47,9 @@ available a colab notebook to directly try them out.
 | [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](./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](./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](./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_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 |

docs/source/en/quicktour.mdx

@@ -22,7 +22,7 @@ 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)
+- [`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
 

docs/source/en/stable_diffusion.mdx

@@ -0,0 +1,333 @@
+<!--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.                                                                                                                                                                    
+-->                                                                                                                                                                                                                                           
+                                                                                                                                                                                                                                              
+# The Stable Diffusion Guide 🎨                                                                                                                                                                                                           
+<a target="_blank" href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_101_guide.ipynb">                                                                                                                                                                                                                                                                                                                                                            
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>                                                                                                                                                 
+</a>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
+
+## Intro                                                                                                                                                                                                                                                                                                                                                                                                                                                                                
+
+Stable Diffusion is a [Latent Diffusion model](https://github.com/CompVis/latent-diffusion) developed by researchers from the Machine Vision and Learning group at LMU Munich, *a.k.a* CompVis.                                                                                                                                                                                                                                                                                             
+Model checkpoints were publicly released at the end of August 2022 by a collaboration of Stability AI, CompVis, and Runway with support from EleutherAI and LAION. For more information, you can check out [the official blog post](https://stability.ai/blog/stable-diffusion-public-release).                                                                                                                                                                                                  
+                                                                                                                                                                                                                                              
+Since its public release the community has done an incredible job at working together to make the stable diffusion checkpoints **faster**, **more memory efficient**, and **more performant**.                                                                                                                                                                                                                                                                                              
+                                                                                                                                                                                                                                              
+🧨 Diffusers offers a simple API to run stable diffusion with all memory, computing, and quality improvements.                                                                                                                                   
+                                                                                                                                                                                                                                              
+This notebook walks you through the improvements one-by-one so you can best leverage [`StableDiffusionPipeline`] for **inference**.                                                                                                          
+                                                                                                                                                                                                                                              
+## Prompt Engineering 🎨                                                                                                                                                                                                                      
+                                                                                                                                                                                                                                              
+When running *Stable Diffusion* in inference, we usually want to generate a certain type, or style of image and then improve upon it. Improving upon a previously generated image means running inference over and over again with a different prompt and potentially a different seed until we are happy with our generation.                                                                                                                                                                 
+
+So to begin with, it is most important to speed up stable diffusion as much as possible to generate as many pictures as possible in a given amount of time.                                                                                   
+
+This can be done by both improving the **computational efficiency** (speed) and the **memory efficiency** (GPU RAM).                                                                                                                          
+
+Let's start by looking into computational efficiency first.                                                                                                                                                                               
+
+Throughout the notebook, we will focus on [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5):                                                                                                           
+
+``` python                                                                                                                                                                                                                                    
+model_id = "runwayml/stable-diffusion-v1-5"                                                                                                                                                                                                   
+```                                                                                                                                                                                                                                           
+
+Let's load the pipeline.                                                                                                                                                                                                                      
+
+## Speed Optimization                                                                                                                                                                                                                                                                                                                                                                                                                                                                       
+
+``` python                                                                                                                                                                                                                                    
+from diffusers import StableDiffusionPipeline                                                                                                                                                                                                 
+                                                                                                                                                                                                                                              
+pipe = StableDiffusionPipeline.from_pretrained(model_id)                                                                                                                                                                                      
+```                                                                                                                                                                                                                                           
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            
+We aim at generating a beautiful photograph of an *old warrior chief* and will later try to find the best prompt to generate such a photograph. For now, let's keep the prompt simple:                                                                                                                                                                                                                                                                                                      
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            
+``` python                                                                                                                                                                                                                                    
+prompt = "portrait photo of a old warrior chief"                                                                                                                                                                                                                                                                                                                                                                                                                                            
+```                                                                                                                                                                                                                                           
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            
+To begin with, we should make sure we run inference on GPU, so let's move the pipeline to GPU, just like you would with any PyTorch module.                                                                                                   
+                                                                                                                                                                                                                                              
+``` python                                                                                                                                                                                                                                    
+pipe = pipe.to("cuda")                                                                                                                                                                                                                                                                                                                                                                                                                                                                      
+```                                                                                                                                                                                                                                           
+                                                                                                                                                                                                                                              
+To generate an image, you should use the [~`StableDiffusionPipeline.__call__`] method.                                                                                                                                                        
+                                                                                                                                                                                                                                              
+To make sure we can reproduce more or less the same image in every call, let's make use of the generator. See the documentation on reproducibility [here](./conceptual/reproducibility) for more information.                                                                                                                                                                                                                                                                                   
+
+``` python                                                                                                                                                                                                                                    
+generator = torch.Generator("cuda").manual_seed(0)                                                                                                                                                                                            
+```                                                                                                                                                                                                                                           
+                                                                                                                                                                                                                                              
+Now, let's take a spin on it.                                                                                                                                                                                                                 
+                                                                                                                                                                                                                                              
+``` python                                                                                                                                                                                                                                    
+image = pipe(prompt, generator=generator).images[0]                                                                                                                                                                                           
+image                                                                                                                                                                                                                                         
+```                                                                                                                                                                                                                                           
+
+![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_1.png)                                                                                                                                  
+                                                                                                                                                                                                                                              
+Cool, this now took roughly 30 seconds on a T4 GPU (you might see faster inference if your allocated GPU is better than a T4).                                                                                                              
+                                                                                                                                                                                                                                              
+The default run we did above used full float32 precision and ran the default number of inference steps (50). The easiest speed-ups come from switching to float16 (or half) precision and simply running fewer inference steps. Let's load the model now in float16 instead.                                                                                                                                                                                                                            
+                                                                                                                                                                                                                                              
+``` python                                                                                                                                                                                                                                    
+import torch                                                                                                                                                                                                                                  
+
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)                                                                                                                                                           
+pipe = pipe.to("cuda")                                                                                                                                                                                                                        
+```                                                                                                                                                                                                                                           
+
+And we can again call the pipeline to generate an image.                                                                                                                                                                                      
+
+``` python                                                                                                                                                                                                                                    
+generator = torch.Generator("cuda").manual_seed(0)                                                                                                                                                                                            
+
+image = pipe(prompt, generator=generator).images[0]                                                                                                                                                                                           
+image                                                                                                                                                                                                                                         
+```                                                                                                                                                                                                                                           
+![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_2.png)                                                                                                                                  
+
+Cool, this is almost three times as fast for arguably the same image quality.                                                                                                                                                           
+                                                                                                                                                                                                                                              
+We strongly suggest always running your pipelines in float16 as so far we have very rarely seen degradations in quality because of it.                                                                                                         
+
+Next, let's see if we need to use 50 inference steps or whether we could use significantly fewer. The number of inference steps is associated with the denoising scheduler we use. Choosing a more efficient scheduler could help us decrease the number of steps.
+
+Let's have a look at all the schedulers the stable diffusion pipeline is compatible with.                                                                                                                                                        
+                                                                                                                                                                                                                                              
+``` python                                                                                                                                                                                                                                    
+pipe.scheduler.compatibles                                                                                                                                                                                                                    
+```                                                                                                                                                                                                                                           
+                                                                                                                                                                                                                                              
+```                                                                                                                                                                                                                                           
+    [diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler,                                                                                                                                                       
+     diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,                                                                                                                                                                       
+     diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler,                                                                                                                                                                     
+     diffusers.schedulers.scheduling_pndm.PNDMScheduler,                                                                                                                                                                                      
+     diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,                                                                                                                                                                   
+     diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler,                                                                                                                                                
+     diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,                                                                                                                                                         
+     diffusers.schedulers.scheduling_ddpm.DDPMScheduler,                                                                                                                                                                                      
+     diffusers.schedulers.scheduling_ddim.DDIMScheduler]                                                                                                                                                                                      
+```                                                                                                                                                                                                                                           
+
+Cool, that's a lot of schedulers.                                                                                                                                                                                                                                                                                                                                                                                                                                                           
+
+🧨 Diffusers is constantly adding a bunch of novel schedulers/samplers that can be used with Stable Diffusion. For more information, we recommend taking a look at the official documentation [here](https://huggingface.co/docs/diffusers/main/en/api/schedulers/overview).                                                                                                                                                                                                              
+                                                                                                                                                                                                                                              
+Alright, right now Stable Diffusion is using the `PNDMScheduler` which usually requires around 50 inference steps. However, other schedulers such as `DPMSolverMultistepScheduler` or `DPMSolverSinglestepScheduler` seem to get away with just 20 to 25 inference steps. Let's try them out.                                                                                                                                                                                               
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            
+You can set a new scheduler by making use of the [from_config](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) function.                                                                                                                                                                                                                                                                                                                 
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            
+``` python                                                                                                                                                                                                                                    
+from diffusers import DPMSolverMultistepScheduler                                                                                                                                                                                             
+                                                                                                                                                                                                                                              
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)                                                                                                                                                               
+```                                                                                                                                                                                                                                           
+                                                                                                                                                                                                                                              
+Now, let's try to reduce the number of inference steps to just 20.                                                                                                                                                                            
+
+``` python                                                                                                                                                                                                                                    
+generator = torch.Generator("cuda").manual_seed(0)                                                                                                                                                                                            
+                                                                                                                                                                                                                                              
+image = pipe(prompt, generator=generator, num_inference_steps=20).images[0]                                                                                                                                                                   
+image                                                                                                                                                                                                                                         
+```                                                                                                                                                                                                                                           
+                                                                                                                                                                                                                                              
+![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_3.png)                                                                                                                                                                                                                                                                                                                                                                                
+                                                                                                                                                                                                                                              
+The image now does look a little different, but it's arguably still of equally high quality. We now cut inference time to just 4 seconds though 😍. 
+
+## Memory Optimization                                                                                                                                                                                                                        
+
+Less memory used in generation indirectly implies more speed, since we're often trying to maximize how many images we can generate per second. Usually, the more images per inference run, the more images per second too.                                                                                                                                              
+
+The easiest way to see how many images we can generate at once is to simply try it out, and see when we get a *"Out-of-memory (OOM)"* error.                                                                                                          
+
+We can run batched inference by simply passing a list of prompts and generators. Let's define a quick function that generates a batch for us.                                                                                                
+
+``` python                                                                                                                                                                                                                                    
+def get_inputs(batch_size=1):                                                                                                                                                                                                                 
+  generator = [torch.Generator("cuda").manual_seed(i) for i in range(batch_size)]                                                                                                                                                             
+  prompts = batch_size * [prompt]                                                                                                                                                                                                             
+  num_inference_steps = 20                                                                                                                                                                                                                    
+
+  return {"prompt": prompts, "generator": generator, "num_inference_steps": num_inference_steps}                                                                                                                                              
+```                                                                                                                                                                                                                                           
+This function returns a list of prompts and a list of generators, so we can reuse the generator that produced a result we like.
+
+We also need a method that allows us to easily display a batch of images.                                                                                                                                                                     
+
+``` python                                                                                                                                                                                                                                    
+from PIL import Image                                                                                                                                                                                                                         
+
+def image_grid(imgs, rows=2, cols=2):                                                                                                                                                                                                         
+    w, h = imgs[0].size                                                                                                                                                                                                                       
+    grid = Image.new('RGB', size=(cols*w, rows*h))                                                                                                                                                                                            
+                                                                                                                                                                                                                                              
+    for i, img in enumerate(imgs):                                                                                                                                                                                                            
+        grid.paste(img, box=(i%cols*w, i//cols*h))                                                                                                                                                                                            
+    return grid                                                                                                                                                                                                                               
+```                                                                                                                                                                                                                                           
+
+Cool, let's see how much memory we can use starting with `batch_size=4`.                                                                                                                                                                      
+
+``` python                                                                                                                                                                                                                                    
+images = pipe(**get_inputs(batch_size=4)).images                                                                                                                                                                                              
+image_grid(images)                                                                                                                                                                                                                            
+```                                                                                                                                                                                                                                           
+
+![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_4.png)                                                                                                                                  
+
+Going over a batch_size of 4 will error out in this notebook (assuming we are running it on a T4 GPU). Also, we can see we only generate slightly more images per second (3.75s/image) compared to 4s/image previously.                                                                                                                                                                                                                                                                                                                   
+
+However, the community has found some nice tricks to improve the memory constraints further. After stable diffusion was released, the community found improvements within days and shared them freely over GitHub - open-source at its finest! I believe the original idea came from [this](https://github.com/basujindal/stable-diffusion/pull/117) GitHub thread.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
+
+By far most of the memory is taken up by the cross-attention layers. Instead of running this operation in batch, one can run it sequentially to save a significant amount of memory.                                                                                                                                                                                                                                                                                                         
+
+It can easily be enabled by calling `enable_attention_slicing` as is documented [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.enable_attention_slicing).                                                                                                                                                                                                                                                   
+
+``` python                                                                                                                                                                                                                                    
+pipe.enable_attention_slicing()                                                                                                                                                                                                               
+```                                                                                                                                                                                                                                           
+
+Great, now that attention slicing is enabled, let's try to double the batch size again, going for `batch_size=8`.                                                                                                                              
+
+``` python                                                                                                                                                                                                                                    
+images = pipe(**get_inputs(batch_size=8)).images                                                                                                                                                                                              
+image_grid(images, rows=2, cols=4)                                                                                                                                                                                                            
+```                                                                                                                                                                                                                                           
+
+![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_5.png)                                                                                                                                  
+
+Nice, it works. However, the speed gain is again not very big (it might however be much more significant on other GPUs).                                                                                                                      
+
+We're at roughly 3.5 seconds per image 🔥 which is probably the fastest we can be with a simple T4 without sacrificing quality.                                                                                                               
+
+Next, let's look into how to improve the quality!                                                                                                                                                                                             
+
+## Quality Improvements                                                                                                                                                                                                                       
+
+Now that our image generation pipeline is blazing fast, let's try to get maximum image quality.                                                                                                                                               
+
+First of all, image quality is extremely subjective, so it's difficult to make general claims here.                                                                                                                                           
+
+The most obvious step to take to improve quality is to use *better checkpoints*. Since the release of Stable Diffusion, many improved versions have been released, which are summarized here:                                                                                                                                                                                                                                                                                                
+
+-   *Official Release - 22 Aug 2022*: [Stable-Diffusion 1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4)                                                                                                                            
+-   *20 October 2022*: [Stable-Diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5)                                                                                                                                          
+-   *24 Nov 2022*: [Stable-Diffusion 2.0](https://huggingface.co/stabilityai/stable-diffusion-2-0)                                                                                                                                            
+-   *7 Dec 2022*: [Stable-Diffusion 2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1)                                                                                                                                             
+
+Newer versions don't necessarily mean better image quality with the same parameters. People mentioned that *2.0* is slightly worse than *1.5* for certain prompts, but given the right prompt engineering *2.0* and *2.1* seem to be better.                                                                                                                                                                                                                                                 
+
+Overall, we strongly recommend just trying the models out and reading up on advice online (e.g. it has been shown that using negative prompts is very important for 2.0 and 2.1 to get the highest possible quality. See for example [this nice blog post](https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/).                                                                                                                                                                   
+
+Additionally, the community has started fine-tuning many of the above versions on certain styles with some of them having an extremely high quality and gaining a lot of traction.                                                                                                                                                                                                                                                                                                          
+
+We recommend having a look at all [diffusers checkpoints sorted by downloads and trying out the different checkpoints](https://huggingface.co/models?library=diffusers).                                                                                                                                                                                                                                                                                                               
+
+For the following, we will stick to v1.5 for simplicity.                                                                                                                                                                                      
+
+Next, we can also try to optimize single components of the pipeline, e.g. switching out the latent decoder. For more details on how the whole Stable Diffusion pipeline works, please have a look at [this blog post](https://huggingface.co/blog/stable_diffusion).                                                                                                                                                                                                                        
+
+Let's load [stabilityai's newest auto-decoder](https://huggingface.co/stabilityai/stable-diffusion-2-1).                                                                                                                                      
+
+``` python                                                                                                                                                                                                                                    
+from diffusers import AutoencoderKL                                                                                                                                                                                                           
+
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16).to("cuda")                                                                                                                                        
+```                                                                                                                                                                                                                                           
+
+Now we can set it to the vae of the pipeline to use it.                                                                                                                                                                                       
+
+``` python                                                                                                                                                                                                                                    
+pipe.vae = vae                                                                                                                                                                                                                                
+```                                                                                                                                                                                                                                           
+
+Let's run the same prompt as before to compare quality.                                                                                                                                                                                       
+
+``` python                                                                                                                                                                                                                                    
+images = pipe(**get_inputs(batch_size=8)).images                                                                                                                                                                                              
+image_grid(images, rows=2, cols=4)                                                                                                                                                                                                            
+```                                                                                                                                                                                                                                           
+
+![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_6.png)                                                                                                                                  
+
+Seems like the difference is only very minor, but the new generations are arguably a bit *sharper*.                                                                                                                                           
+
+Cool, finally, let's look a bit into prompt engineering.                                                                                                                                                                                      
+
+Our goal was to generate a photo of an old warrior chief. Let's now try to bring a bit more color into the photos and make the look more impressive.                                                                                          
+
+Originally our prompt was "*portrait photo of an old warrior chief*".                                                                                                                                                                          
+
+To improve the prompt, it often helps to add cues that could have been used online to save high-quality photos, as well as add more details.                                                                                         
+Essentially, when doing prompt engineering, one has to think:                                                                                                                                                                                 
+
+-   How was the photo or similar photos of the one I want probably stored on the internet?                                                                                                                                                    
+-   What additional detail can I give that steers the models into the style that I want?                                                                                                                                                      
+
+Cool, let's add more details.                                                                                                                                                                                                                 
+
+``` python                                                                                                                                                                                                                                    
+prompt += ", tribal panther make up, blue on red, side profile, looking away, serious eyes"                                                                                                                                                   
+```                                                                                                                                                                                                                                           
+
+and let's also add some cues that usually help to generate higher quality images.                                                                                                                                                             
+
+``` python                                                                                                                                                                                                                                    
+prompt += " 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta"                                                                                                                                         
+prompt                                                                                                                                                                                                                                        
+```                                                                                                                                                                                                                                                                                                                                                                                                                                                                           
+
+Cool, let's now try this prompt.                                                                                                                                                                                                              
+
+``` python                                                                                                                                                                                                                                    
+images = pipe(**get_inputs(batch_size=8)).images                                                                                                                                                                                              
+image_grid(images, rows=2, cols=4)                                                                                                                                                                                                            
+```                                                                                                                                                                                                                                           
+
+![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_7.png)                                                                                                                                  
+
+Pretty impressive! We got some very high-quality image generations there. The 2nd image is my personal favorite, so I'll re-use this seed and see whether I can tweak the prompts slightly by using "oldest warrior", "old", "", and "young" instead of "old".                                                                                                                                                                                                                    
+
+``` python                                                                                                                                                                                                                                    
+prompts = [                                                                                                                                                                                                                                   
+    "portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",                                                                                                                                                                                                                                                                   
+    "portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",                                                                                                                                                                                                                                                                        
+    "portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",                                                                                                                                                                                                                                                                            
+    "portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3  --beta --upbeta",                                                                                                                                                                                                                                                                      
+]                                                                                                                                                                                                                                             
+
+generator = [torch.Generator("cuda").manual_seed(1) for _ in range(len(prompts))]  # 1 because we want the 2nd image                                                                                                                          
+
+images = pipe(prompt=prompts, generator=generator, num_inference_steps=25).images                                                                                                                                                             
+image_grid(images)                                                                                                                                                                                                                            
+```                                                                                                                                                                                                                                           
+
+![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_8.png)                                                                                                                                  
+
+The first picture looks nice! The eye movement slightly changed and looks nice. This finished up our 101-guide on how to use Stable Diffusion 🤗.                                                                                              
+
+For more information on optimization or other guides, I recommend taking a look at the following:                                                                                                                                            
+
+-   [Blog post about Stable Diffusion](https://huggingface.co/blog/stable_diffusion): In-detail blog post explaining Stable Diffusion.                                                                                                        
+-   [FlashAttention](https://huggingface.co/docs/diffusers/optimization/xformers): XFormers flash attention can optimize your model even further with more speed and memory improvements.                                                                                                                                                                                                                                                                                                   
+-   [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth) - Quickly customize the model by fine-tuning it.                                                                                                                  
+-   [General info on Stable Diffusion](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/overview) - Info on other tasks that are powered by Stable Diffusion.

docs/source/en/training/dreambooth.mdx

@@ -283,3 +283,5 @@ image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
 
 image.save("dog-bucket.png")
 ```
+
+You may also run inference from [any of the saved training checkpoints](#performing-inference-using-a-saved-checkpoint).

docs/source/ko/_toctree.yml

@@ -0,0 +1,193 @@
+- sections:
+  - local: index
+    title: "🧨 Diffusers"
+  - local: quicktour
+    title: "훑어보기"
+  - local: installation
+    title: "설치"
+  title: "시작하기"
+- sections:
+  - sections:
+    - local: in_translation
+      title: "Loading Pipelines, Models, and Schedulers"
+    - local: in_translation
+      title: "Using different Schedulers"
+    - local: in_translation
+      title: "Configuring Pipelines, Models, and Schedulers"
+    - local: in_translation
+      title: "Loading and Adding Custom Pipelines"
+    title: "불러오기 & 허브 (번역 예정)"
+  - sections:
+    - local: in_translation
+      title: "Unconditional Image Generation"
+    - local: in_translation
+      title: "Text-to-Image Generation"
+    - local: in_translation
+      title: "Text-Guided Image-to-Image"
+    - local: in_translation
+      title: "Text-Guided Image-Inpainting"
+    - local: in_translation
+      title: "Text-Guided Depth-to-Image"
+    - local: in_translation
+      title: "Reusing seeds for deterministic generation"
+    - local: in_translation
+      title: "Community Pipelines"
+    - local: in_translation
+      title: "How to contribute a Pipeline"
+    title: "추론을 위한 파이프라인 (번역 예정)"
+  - sections:
+    - local: in_translation
+      title: "Reinforcement Learning"
+    - local: in_translation
+      title: "Audio"
+    - local: in_translation
+      title: "Other Modalities"
+    title: "Taking Diffusers Beyond Images"
+  title: "Diffusers 사용법 (번역 예정)"
+- sections:
+  - local: in_translation
+    title: "Memory and Speed"
+  - local: in_translation
+    title: "xFormers"
+  - local: in_translation
+    title: "ONNX"
+  - local: in_translation
+    title: "OpenVINO"
+  - local: in_translation
+    title: "MPS"
+  - local: in_translation
+    title: "Habana Gaudi"
+  title: "최적화/특수 하드웨어 (번역 예정)"
+- sections:
+  - local: in_translation
+    title: "Overview"
+  - local: in_translation
+    title: "Unconditional Image Generation"
+  - local: in_translation
+    title: "Textual Inversion"
+  - local: in_translation
+    title: "Dreambooth"
+  - local: in_translation
+    title: "Text-to-image fine-tuning"
+  title: "학습 (번역 예정)"
+- sections:
+  - local: in_translation
+    title: "Stable Diffusion"
+  - local: in_translation
+    title: "Philosophy"
+  - local: in_translation
+    title: "How to contribute?"
+  title: "개념 설명 (번역 예정)"
+- sections:
+  - sections:
+    - local: in_translation
+      title: "Models"
+    - local: in_translation
+      title: "Diffusion Pipeline"
+    - local: in_translation
+      title: "Logging"
+    - local: in_translation
+      title: "Configuration"
+    - local: in_translation
+      title: "Outputs"
+    title: "Main Classes"
+    
+  - sections:
+    - local: in_translation
+      title: "Overview"
+    - local: in_translation
+      title: "AltDiffusion"
+    - local: in_translation
+      title: "Cycle Diffusion"
+    - local: in_translation
+      title: "DDIM"
+    - local: in_translation
+      title: "DDPM"
+    - local: in_translation
+      title: "Latent Diffusion"
+    - local: in_translation
+      title: "Unconditional Latent Diffusion"
+    - local: in_translation
+      title: "PaintByExample"
+    - local: in_translation
+      title: "PNDM"
+    - local: in_translation
+      title: "Score SDE VE"
+    - sections:
+      - local: in_translation
+        title: "Overview"
+      - local: in_translation
+        title: "Text-to-Image"
+      - local: in_translation
+        title: "Image-to-Image"
+      - local: in_translation
+        title: "Inpaint"
+      - local: in_translation
+        title: "Depth-to-Image"
+      - local: in_translation
+        title: "Image-Variation"  
+      - local: in_translation
+        title: "Super-Resolution"  
+      title: "Stable Diffusion"
+    - local: in_translation
+      title: "Stable Diffusion 2"
+    - local: in_translation
+      title: "Safe Stable Diffusion"
+    - local: in_translation
+      title: "Stochastic Karras VE"
+    - local: in_translation
+      title: "Dance Diffusion"
+    - local: in_translation
+      title: "UnCLIP"
+    - local: in_translation
+      title: "Versatile Diffusion"
+    - local: in_translation
+      title: "VQ Diffusion"
+    - local: in_translation
+      title: "RePaint"
+    - local: in_translation
+      title: "Audio Diffusion"
+    title: "파이프라인 (번역 예정)"
+  - sections:
+    - local: in_translation
+      title: "Overview"
+    - local: in_translation
+      title: "DDIM"
+    - local: in_translation
+      title: "DDPM"
+    - local: in_translation
+      title: "Singlestep DPM-Solver"
+    - local: in_translation
+      title: "Multistep DPM-Solver"
+    - local: in_translation
+      title: "Heun Scheduler"
+    - local: in_translation
+      title: "DPM Discrete Scheduler"
+    - local: in_translation
+      title: "DPM Discrete Scheduler with ancestral sampling"
+    - local: in_translation
+      title: "Stochastic Kerras VE"
+    - local: in_translation
+      title: "Linear Multistep"
+    - local: in_translation
+      title: "PNDM"
+    - local: in_translation
+      title: "VE-SDE"
+    - local: in_translation
+      title: "IPNDM"
+    - local: in_translation
+      title: "VP-SDE"
+    - local: in_translation
+      title: "Euler scheduler"
+    - local: in_translation
+      title: "Euler Ancestral Scheduler"
+    - local: in_translation
+      title: "VQDiffusionScheduler"
+    - local: in_translation
+      title: "RePaint Scheduler"      
+    title: "스케줄러 (번역 예정)"
+  - sections:
+    - local: in_translation
+      title: "RL Planning"
+    title: "Experimental Features"
+  title: "API (번역 예정)"

docs/source/ko/in_translation.mdx

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

docs/source/ko/index.mdx

@@ -0,0 +1,63 @@
+<!--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.
+-->
+
+<p align="center">
+    <br>
+    <img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400"/>
+    <br>
+</p>
+
+# 🧨 Diffusers
+
+🤗 Diffusers는 사전학습된 비전 및 오디오 확산 모델을 제공하고, 추론 및 학습을 위한 모듈식 도구 상자 역할을 합니다.
+
+보다 정확하게, 🤗 Diffusers는 다음을 제공합니다:
+
+- 단 몇 줄의 코드로 추론을 실행할 수 있는 최신 확산 파이프라인을 제공합니다. ([**Using Diffusers**](./using-diffusers/conditional_image_generation)를 살펴보세요) 지원되는 모든 파이프라인과 해당 논문에 대한 개요를 보려면 [**Pipelines**](#pipelines)을 살펴보세요.
+- 추론에서 속도 vs 품질의 절충을 위해 상호교환적으로 사용할 수 있는 다양한 노이즈 스케줄러를 제공합니다. 자세한 내용은 [**Schedulers**](./api/schedulers/overview)를 참고하세요.
+- UNet과 같은 여러 유형의 모델을 end-to-end 확산 시스템의 구성 요소로 사용할 수 있습니다. 자세한 내용은 [**Models**](./api/models)을 참고하세요.
+- 가장 인기있는 확산 모델 테스크를 학습하는 방법을 보여주는 예제들을 제공합니다. 자세한 내용은 [**Training**](./training/overview)를 참고하세요.
+
+## 🧨 Diffusers 파이프라인
+
+다음 표에는 공시적으로 지원되는 모든 파이프라인, 관련 논문, 직접 사용해 볼 수 있는 Colab 노트북(사용 가능한 경우)이 요약되어 있습니다.
+
+| 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)
+| [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 | 
+| [stable_diffusion](./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](./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](./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_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 | 
+| [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 | 
+
+**참고**: 파이프라인은 해당 문서에 설명된 대로 확산 시스템을 사용한 방법에 대한 간단한 예입니다.

docs/source/ko/installation.mdx

@@ -0,0 +1,142 @@
+<!--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.
+-->
+
+# 설치
+
+사용하시는 라이브러리에 맞는 🤗 Diffusers를 설치하세요.
+
+🤗 Diffusers는 Python 3.7+, PyTorch 1.7.0+ 및 flax에서 테스트되었습니다. 사용중인 딥러닝 라이브러리에 대한 아래의 설치 안내를 따르세요.
+
+- [PyTorch 설치 안내](https://pytorch.org/get-started/locally/)
+- [Flax 설치 안내](https://flax.readthedocs.io/en/latest/)
+
+## pip를 이용한 설치
+
+[가상 환경](https://docs.python.org/3/library/venv.html)에 🤗 Diffusers를 설치해야 합니다.
+Python 가상 환경에 익숙하지 않은 경우 [가상환경 pip 설치 가이드](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)를 살펴보세요.
+가상 환경을 사용하면 서로 다른 프로젝트를 더 쉽게 관리하고, 종속성간의 호환성 문제를 피할 수 있습니다.
+
+프로젝트 디렉토리에 가상 환경을 생성하는 것으로 시작하세요:
+
+```bash
+python -m venv .env
+```
+
+그리고 가상 환경을 활성화합니다:
+
+```bash
+source .env/bin/activate
+```
+
+이제 다음의 명령어로 🤗 Diffusers를 설치할 준비가 되었습니다:
+
+**PyTorch의 경우**
+
+```bash
+pip install diffusers["torch"]
+```
+
+**Flax의 경우**
+
+```bash
+pip install diffusers["flax"]
+```
+
+## 소스로부터 설치
+
+소스에서 `diffusers`를 설치하기 전에, `torch` 및 `accelerate`이 설치되어 있는지 확인하세요.
+
+`torch` 설치에 대해서는 [torch docs](https://pytorch.org/get-started/locally/#start-locally)를 참고하세요.
+
+다음과 같이 `accelerate`을 설치하세요.
+
+```bash
+pip install accelerate
+```
+
+다음 명령어를 사용하여 소스에서 🤗 Diffusers를 설치하세요:
+
+```bash
+pip install git+https://github.com/huggingface/diffusers
+```
+
+이 명령어는 최신 `stable` 버전이 아닌 최첨단 `main` 버전을 설치합니다.
+`main` 버전은 최신 개발 정보를 최신 상태로 유지하는 데 유용합니다.
+예를 들어 마지막 공식 릴리즈 이후 버그가 수정되었지만, 새 릴리즈가 아직 출시되지 않은 경우입니다.
+그러나 이는 `main` 버전이 항상 안정적이지 않을 수 있음을 의미합니다.
+우리는 `main` 버전이 지속적으로 작동하도록 노력하고 있으며, 대부분의 문제는 보통 몇 시간 또는 하루 안에 해결됩니다.
+문제가 발생하면 더 빨리 해결할 수 있도록 [Issue](https://github.com/huggingface/transformers/issues)를 열어주세요!
+
+
+## 편집가능한 설치
+
+다음을 수행하려면 편집가능한 설치가 필요합니다:
+
+* 소스 코드의 `main` 버전을 사용
+* 🤗 Diffusers에 기여 (코드의 변경 사항을 테스트하기 위해 필요)
+
+저장소를 복제하고 다음 명령어를 사용하여 🤗 Diffusers를 설치합니다:
+
+```bash
+git clone https://github.com/huggingface/diffusers.git
+cd diffusers
+```
+
+**PyTorch의 경우**
+
+```
+pip install -e ".[torch]"
+```
+
+**Flax의 경우**
+
+```
+pip install -e ".[flax]"
+```
+
+이러한 명령어들은 저장소를 복제한 폴더와 Python 라이브러리 경로를 연결합니다.
+Python은 이제 일반 라이브러리 경로에 더하여 복제한 폴더 내부를 살펴봅니다.
+예를들어 Python 패키지가 `~/anaconda3/envs/main/lib/python3.7/site-packages/`에 설치되어 있는 경우 Python은 복제한 폴더인 `~/diffusers/`도 검색합니다.
+
+<Tip warning={true}>
+
+라이브러리를 계속 사용하려면 `diffusers` 폴더를 유지해야 합니다.
+
+</Tip>
+
+이제 다음 명령어를 사용하여 최신 버전의 🤗 Diffusers로 쉽게 업데이트할 수 있습니다:
+
+```bash
+cd ~/diffusers/
+git pull
+```
+
+이렇게 하면, 다음에 실행할 때 Python 환경이 🤗 Diffusers의 `main` 버전을 찾게 됩니다.
+
+## 텔레메트리 로깅에 대한 알림
+
+우리 라이브러리는 `from_pretrained()` 요청 중에 텔레메트리 정보를 원격으로 수집합니다.
+이 데이터에는 Diffusers 및 PyTorch/Flax의 버전, 요청된 모델 또는 파이프라인 클래스, 그리고 허브에서 호스팅되는 경우 사전학습된 체크포인트에 대한 경로를 포함합니다.
+이 사용 데이터는 문제를 디버깅하고 새로운 기능의 우선순위를 지정하는데 도움이 됩니다.
+텔레메트리는 HuggingFace 허브에서 모델과 파이프라인을 불러올 때만 전송되며, 로컬 사용 중에는 수집되지 않습니다.
+
+우리는 추가 정보를 공유하지 않기를 원하는 사람이 있다는 것을 이해하고 개인 정보를 존중하므로, 터미널에서 `DISABLE_TELEMETRY` 환경 변수를 설정하여 텔레메트리 수집을 비활성화할 수 있습니다.
+
+Linux/MacOS에서:
+```bash
+export DISABLE_TELEMETRY=YES
+```
+
+Windows에서:
+```bash
+set DISABLE_TELEMETRY=YES
+```