Update app.py

app.py

@@ -46,11 +46,13 @@ def main(
 
 description = \
 """
+__Now using Image Variations v2!__
+
 Generate variations on an input image using a fine-tuned version of Stable Diffision.
 Trained by [Justin Pinkney](https://www.justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda](https://lambdalabs.com/)
 
 This version has been ported to 🤗 Diffusers library, see more details on how to use this version in the [Lambda Diffusers repo](https://github.com/LambdaLabsML/lambda-diffusers).
-__For the original training code see [this repo](https://github.com/justinpinkney/stable-diffusion).
+For the original training code see [this repo](https://github.com/justinpinkney/stable-diffusion).
 
 ![](https://raw.githubusercontent.com/justinpinkney/stable-diffusion/main/assets/im-vars-thin.jpg)
 
@@ -66,8 +68,8 @@ This creates images which have the same rough style and content, but different d
 This is a totally different approach to the img2img script of the original Stable Diffusion and gives very different results.
 
 The model was fine tuned on the [LAION aethetics v2 6+ dataset](https://laion.ai/blog/laion-aesthetics/) to accept the new conditioning.
-Training was done on 4xA6000 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud).
-More details on the method and training will come in a future blog post.
+Training was done on 8xA100 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud).
+More details are on the [model card](https://huggingface.co/lambdalabs/sd-image-variations-diffusers).
 """
 
 device = "cuda" if torch.cuda.is_available() else "cpu"