Add contributions to README and re-order a bit (open-mmlab#316)

* up

* thanks Clau

* finish

* finish

* up

Author: patrickvonplaten

README.md

@@ -25,6 +25,32 @@ More precisely, 🤗 Diffusers offers:
 - Multiple types of models, such as UNet, 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 model tasks (see [examples](https://github.com/huggingface/diffusers/tree/main/examples), *e.g.* [unconditional-image-generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)).
 
+## Installation
+
+**With `pip`**
+    
+```bash
+pip install --upgrade diffusers  # should install diffusers 0.2.4
+```
+
+**With `conda`**
+
+```sh
+conda install -c conda-forge diffusers
+```
+
+## Contributing
+
+We ❤️  contributions from the open-source community! 
+If you want to contribute to this library, please check out (or at least pretend to) our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md).
+You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library.
+- See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute
+- See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines
+- See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)
+
+Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or
+just hang out ☕.
+
 ## Quickstart
 
 In order to get started, we recommend taking a look at two notebooks:
@@ -310,20 +336,6 @@ The class provides functionality to compute previous image according to alpha, b
 - Diffusers is **modality independent** and focuses 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 concise, elementary building blocks. In contrast, 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 another 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).
 
-## Installation
-
-**With `pip`**
-    
-```bash
-pip install --upgrade diffusers  # should install diffusers 0.2.4
-```
-
-**With `conda`**
-
-```sh
-conda install -c conda-forge diffusers
-```
-
 ## In the works
 
 For the first release, 🤗 Diffusers focuses on text-to-image diffusion techniques. However, diffusers can be used for much more than that! Over the upcoming releases, we'll be focusing on: