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Unverified Commit 21e61eb3 authored by YiYi Xu's avatar YiYi Xu Committed by GitHub
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Added a README page for docs and a "schedulers" page (#1710)


Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: default avatarPedro Cuenca <pedro@huggingface.co>
parent c891330f
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# Variance exploding, stochastic sampling from Karras et. al
## Overview
Original paper can be found [here](https://arxiv.org/abs/2206.00364).
## KarrasVeScheduler
[[autodoc]] KarrasVeScheduler
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# VQDiffusionScheduler
## Overview
Original paper can be found [here](https://arxiv.org/abs/2111.14822)
## VQDiffusionScheduler
[[autodoc]] VQDiffusionScheduler
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......@@ -23,7 +23,7 @@ specific language governing permissions and limitations under the License.
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).
- Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference. For more information see [**Schedulers**](./api/schedulers/overview).
- Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system. See [**Models**](./api/models) for more details
- Training examples to show how to train the most popular diffusion model tasks. For more information see [**Training**](./training/overview).
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......@@ -97,7 +97,7 @@ Running the pipeline is then identical to the code above as it's the same model
>>> image.save("image_of_squirrel_painting.png")
```
Diffusion systems can be used with multiple different [schedulers](./api/schedulers) each with their
Diffusion systems can be used with multiple different [schedulers](./api/schedulers/overview) each with their
pros and cons. By default, Stable Diffusion runs with [`PNDMScheduler`], but it's very simple to
use a different scheduler. *E.g.* if you would instead like to use the [`EulerDiscreteScheduler`] scheduler,
you could use it as follows:
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......@@ -13,7 +13,7 @@ specific language governing permissions and limitations under the License.
# Schedulers
Diffusion pipelines are inherently a collection of diffusion models and schedulers that are partly independent from each other. This means that one is able to switch out parts of the pipeline to better customize
a pipeline to one's use case. The best example of this are the [Schedulers](../api/schedulers.mdx).
a pipeline to one's use case. The best example of this are the [Schedulers](../api/schedulers/overview.mdx).
Whereas diffusion models usually simply define the forward pass from noise to a less noisy sample,
schedulers define the whole denoising process, *i.e.*:
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# Schedulers
For more information on the schedulers, please refer to the [docs](https://huggingface.co/docs/diffusers/api/schedulers).
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For more information on the schedulers, please refer to the [docs](https://huggingface.co/docs/diffusers/api/schedulers/overview).
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