diff --git a/README.md b/README.md index 38b9680c..acb464be 100644 --- a/README.md +++ b/README.md @@ -30,20 +30,32 @@ More precisely, 🤗 Diffusers offers: **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. *Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet -![model_diff_1_50](https://user-images.githubusercontent.com/23423619/171610307-dab0cd8b-75da-4d4e-9f5a-5922072e2bb5.png) - +

+ +
+ Figure from DDPM paper (https://arxiv.org/abs/2006.11239). +

+ **Schedulers**: Algorithm class for both **inference** and **training**. The class provides functionality to compute previous image according to alpha, beta schedule as well as predict noise for training. *Examples*: [DDPM](https://arxiv.org/abs/2006.11239), [DDIM](https://arxiv.org/abs/2010.02502), [PNDM](https://arxiv.org/abs/2202.09778), [DEIS](https://arxiv.org/abs/2204.13902) -![sampling](https://user-images.githubusercontent.com/23423619/171608981-3ad05953-a684-4c82-89f8-62a459147a07.png) -![training](https://user-images.githubusercontent.com/23423619/171608964-b3260cce-e6b4-4841-959d-7d8ba4b8d1b2.png) +

+ +
+ Sampling and training algorithms. Figure from DDPM paper (https://arxiv.org/abs/2006.11239). +

+ **Diffusion Pipeline**: End-to-end pipeline that includes multiple diffusion models, possible text encoders, ... *Examples*: GLIDE, Latent-Diffusion, Imagen, DALL-E 2 -![imagen](https://user-images.githubusercontent.com/23423619/171609001-c3f2c1c9-f597-4a16-9843-749bf3f9431c.png) - +

+ +
+ Figure from ImageGen (https://imagen.research.google/). +

+ ## Philosophy - Readability and clarity is prefered over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.