Update main README (#120)

* Update README.md

* Update README.md

README.md

@@ -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