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Update main README (#120)

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* Update README.md
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Omar Sanseviero
2022-07-21 16:43:47 +02:00
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@@ -34,6 +34,21 @@ In order to get started, we recommend taking a look at two notebooks:
- The [Training diffusers](https://colab.research.google.com/gist/anton-l/cde0c3643e991ad7dbc01939865acaf4/diffusers_training_example.ipynb) notebook, which summarizes diffuser model training methods. This notebook takes a step-by-step approach to training your
diffuser model on an image dataset, with explanatory graphics.
## Examples
If you want to run the code yourself 💻, you can try out:
- [Text-to-Image Latent Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256)
- [Unconditional Latent Diffusion](https://huggingface.co/CompVis/ldm-celebahq-256#)
- [Unconditional Diffusion with discrete scheduler](https://huggingface.co/google/ddpm-celebahq-256)
- [Unconditional Diffusion with continous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)
If you just want to play around with some web demos, you can try out the following 🚀 Spaces:
| Model | Hugging Face Spaces |
|-------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Text-to-Image Latent Diffusion | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CompVis/text2img-latent-diffusion) |
| Faces generator | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CompVis/celeba-latent-diffusion) |
| DDPM with different schedulers | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/fusing/celeba-diffusion) |
## Definitions
**Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image.
@@ -77,18 +92,7 @@ The class provides functionality to compute previous image according to alpha, b
pip install diffusers # should install diffusers 0.1.2
```
## Examples
If you want to run the code yourself 💻, you can try out:
- [Text-to-Image Latent Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256#usage)
- [Unconditional Latent Diffusion](https://huggingface.co/CompVis/ldm-celebahq-256#inference-with-an-unrolled-loop)
- [Unconditional Diffusion with discrete scheduler](https://huggingface.co/google/ddpm-celebahq-256)
- [Unconditional Diffusion with continous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)
If you just want to play around with some models, you can try out the following 🚀 spaces:
- [Text-to-Image Latent Diffusion](https://huggingface.co/spaces/CompVis/text2img-latent-diffusion)
- [Faces generator](https://huggingface.co/spaces/CompVis/celeba-latent-diffusion)
- [DDPM with different schedulers](https://huggingface.co/spaces/fusing/celeba-diffusion)
## In the works