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diffusers/docs/source/index.mdx
Robert Dargavel Smith 48d0123f0f add AudioDiffusionPipeline and LatentAudioDiffusionPipeline #1334 (#1426)
* add AudioDiffusionPipeline and LatentAudioDiffusionPipeline

* add docs to toc

* fix tests

* fix tests

* fix tests

* fix tests

* fix tests

* Update pr_tests.yml

Fix tests

* parent 499ff34b3e
author teticio <teticio@gmail.com> 1668765652 +0000
committer teticio <teticio@gmail.com> 1669041721 +0000

parent 499ff34b3e
author teticio <teticio@gmail.com> 1668765652 +0000
committer teticio <teticio@gmail.com> 1669041704 +0000

add colab notebook

[Flax] Fix loading scheduler from subfolder (#1319)

[FLAX] Fix loading scheduler from subfolder

Fix/Enable all schedulers for in-painting (#1331)

* inpaint fix k lms

* onnox as well

* up

Correct path to schedlure (#1322)

* [Examples] Correct path

* uP

Avoid nested fix-copies (#1332)

* Avoid nested `# Copied from` statements during `make fix-copies`

* style

Fix img2img speed with LMS-Discrete Scheduler (#896)

Casting `self.sigmas` into a different dtype (the one of original_samples) is not advisable. In my img2img pipeline this leads to a long running time in the  `integrate.quad` call later on- by long I mean more than 10x slower.

Co-authored-by: Anton Lozhkov <anton@huggingface.co>

Fix the order of casts for onnx inpainting (#1338)

Legacy Inpainting Pipeline for Onnx Models (#1237)

* Add legacy inpainting pipeline compatibility for onnx

* remove commented out line

* Add onnx legacy inpainting test

* Fix slow decorators

* pep8 styling

* isort styling

* dummy object

* ordering consistency

* style

* docstring styles

* Refactor common prompt encoding pattern

* Update tests to permanent repository home

* support all available schedulers until ONNX IO binding is available

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* updated styling from PR suggested feedback

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

Jax infer support negative prompt (#1337)

* support negative prompts in sd jax pipeline

* pass batched neg_prompt

* only encode when negative prompt is None

Co-authored-by: Juan Acevedo <jfacevedo@google.com>

Update README.md: Minor change to Imagic code snippet, missing dir error (#1347)

Minor change to Imagic Readme

Missing dir causes an error when running the example code.

make style

change the sample model (#1352)

* Update alt_diffusion.mdx

* Update alt_diffusion.mdx

Add bit diffusion [WIP] (#971)

* Create bit_diffusion.py

Bit diffusion based on the paper, arXiv:2208.04202, Chen2022AnalogBG

* adding bit diffusion to new branch

ran tests

* tests

* tests

* tests

* tests

* removed test folders + added to README

* Update README.md

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* move Mel to module in pipeline construction, make librosa optional

* fix imports

* fix copy & paste error in comment

* fix style

* add missing register_to_config

* fix class docstrings

* fix class docstrings

* tweak docstrings

* tweak docstrings

* update slow test

* put trailing commas back

* respect alphabetical order

* remove LatentAudioDiffusion, make vqvae optional

* move Mel from models back to pipelines :-)

* allow loading of pretrained audiodiffusion models

* fix tests

* fix dummies

* remove reference to latent_audio_diffusion in docs

* unused import

* inherit from SchedulerMixin to make loadable

* Apply suggestions from code review

* Apply suggestions from code review

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
2022-12-05 18:06:30 +01:00

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<p align="center">
<br>
<img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400"/>
<br>
</p>
# 🧨 Diffusers
🤗 Diffusers provides pretrained vision diffusion models, and serves as a modular toolbox for inference and training.
More precisely, 🤗 Diffusers offers:
- State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [**Using Diffusers**](./using-diffusers/conditional_image_generation)) or have a look at [**Pipelines**](#pipelines) to get an overview of all supported pipelines and their corresponding papers.
- Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference. For more information see [**Schedulers**](./api/schedulers).
- Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system. See [**Models**](./api/models) for more details
- Training examples to show how to train the most popular diffusion model tasks. For more information see [**Training**](./training/overview).
## 🧨 Diffusers Pipelines
The following table summarizes all officially supported pipelines, their corresponding paper, and if
available a colab notebook to directly try them out.
| Pipeline | Paper | Tasks | Colab
|---|---|:---:|:---:|
| [alt_diffusion](./api/pipelines/alt_diffusion) | [**AltDiffusion**](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation |
| [audio_diffusion](./api/pipelines/audio_diffusion) | [**Audio Diffusion**](https://github.com/teticio/audio-diffusion.git) | Unconditional Audio Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/audio_diffusion_pipeline.ipynb)
| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [**Cycle Diffusion**](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
| [dance_diffusion](./api/pipelines/dance_diffusion) | [**Dance Diffusion**](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
| [ddpm](./api/pipelines/ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
| [ddim](./api/pipelines/ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image |
| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation |
| [pndm](./api/pipelines/pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation |
| [score_sde_ve](./api/pipelines/score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
| [score_sde_vp](./api/pipelines/score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
| [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
| [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
| [stable_diffusion](./api/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [**Safe Stable Diffusion**](https://arxiv.org/abs/2211.05105) | Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/safe-latent-diffusion/blob/main/examples/Safe%20Latent%20Diffusion.ipynb)
| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation |
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation |
| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |
**Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers.