Update app.py

app.py

@@ -3,8 +3,9 @@ import torch
 from PIL import Image
 from torchvision import transforms
 
-from diffusers import StableDiffusionImageVariationPipeline
+from diffusers import DiffusionPipeline
 
+pipeline = DiffusionPipeline.from_pretrained("fusing/karlo-image-variations-diffusers")
 def main(
     input_im,
     scale=3.0,
@@ -46,16 +47,6 @@ 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).
-
-![](https://raw.githubusercontent.com/justinpinkney/stable-diffusion/main/assets/im-vars-thin.jpg)
-
 """
 
 article = \
@@ -73,8 +64,8 @@ More details are on the [model card](https://huggingface.co/lambdalabs/sd-image-
 """
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = StableDiffusionImageVariationPipeline.from_pretrained(
-    "lambdalabs/sd-image-variations-diffusers",
+pipe = KarloImageVariationPipeline.from_pretrained(
+    "fusing/karlo-image-variations-diffusers",
     )
 pipe = pipe.to(device)