Update README for 0.2.3 release (#225)

* Update README for 0.2.3 release:

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

README.md

@@ -39,9 +39,7 @@ In order to get started, we recommend taking a look at two notebooks:
 Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
 See the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information.
 
-**The Stable Diffusion weights are currently only available to universities, academics, research institutions and independent researchers. Please request access applying to <a href="https://stability.ai/academia-access-form" target="_blank">this</a> form**
-
-Please run `pip install diffusers transformers` for the example below to work, since the current `main` git branch is not compatible with the checkpoint yet. 
+You need to accept the model license before downloading or using the Stable Diffusion weights. Please, visit the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-3), read the license and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section](https://huggingface.co/docs/hub/security-tokens) of the documentation.
 
 ```py
 # make sure you're logged in with `huggingface-cli login`
@@ -55,10 +53,10 @@ lms = LMSDiscreteScheduler(
 )
 
 pipe = StableDiffusionPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-3-diffusers", 
+    "CompVis/stable-diffusion-v1-3", 
     scheduler=lms,
     use_auth_token=True
-)  
+) .to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
 with autocast("cuda"):