improve readme

README.md

@@ -1,23 +1,57 @@
-# Diffusers
+<p align="center">
+    <br>
+    <img src="https://raw.githubusercontent.com/huggingface/diffusers/master/docs/source/imgs/diffusers_library.jpg" width="400"/>
+    <br>
+<p>
+<p align="center">
+    <a href="https://github.com/huggingface/diffusers/blob/master/LICENSE">
+        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue">
+    </a>
+    <a href="https://github.com/huggingface/diffusers/releases">
+        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/datasets.svg">
+    </a>
+    <a href="CODE_OF_CONDUCT.md">
+        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg">
+    </a>
+    <a href="https://zenodo.org/badge/latestdoi/250213286"><img src="https://zenodo.org/badge/250213286.svg" alt="DOI"></a>
+</p>
+
+🤗 Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves
+as a modular toolbox for inference and training of diffusion models.
+
+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 [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)).
+- Various noise schedulers that can be used interchangeably for the prefered speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)).
+- Multiple types of diffusion models, such as UNet, that can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)).
+- Training examples to show how to train the most popular diffusion models (see [examples](https://github.com/huggingface/diffusers/tree/main/examples))
 
 ## Definitions
 
-**Models**: Single neural network that models p_θ(x_t-1|x_t) and is trained to “denoise” to image
-*Examples: UNet, Conditioned UNet, 3D UNet, Transformer UNet*
+**Models**: Neural network that models **p_θ(x_t-1|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)
 
-**Schedulers**: Algorithm to compute previous image according to alpha, beta schedule and to sample noise. Should be used for both *training* and *inference*.
-*Example: Gaussian DDPM, DDIM, PMLS, DEIN*
+**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)
 
-**Diffusion Pipeline**: End-to-end pipeline that includes multiple diffusion models, possible text encoders, CLIP
-*Example: GLIDE,CompVis/Latent-Diffusion, Imagen, DALL-E*
+**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)
 
+
+## Philosophy
+
+- Readability and clarity is prefered over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code desgin. *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.
+- Diffusers is **modality independent** and focusses on providing pretrained models and tools to build systems that generate **continous outputs**, *e.g.* vision and audio.
+- Diffusion models and schedulers are provided as consise, elementary building blocks whereas diffusion pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box, should stay as close as possible to their original implementation and can include components of other library, such as text-encoders. Examples for diffusion pipelines are [Glide](https://github.com/openai/glide-text2im) and [Latent Diffusion](https://github.com/CompVis/latent-diffusion).
+
 ## Quickstart
 
 ```