Update app.py

app.py

@@ -5,7 +5,7 @@ import gradio as gr
 import random
 import numpy as np
 
-pipeline = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128")
+pipeline = DiffusionPipeline.from_pretrained("1aurent/ddpm-mnist")
 
 def predict(steps, seed):
     generator = torch.manual_seed(seed)
@@ -22,5 +22,5 @@ gr.Interface(
     outputs=gr.Image(shape=[128,128], type="pil", elem_id="output_image"),
     css="#output_image{width: 256px}",
     title="Unconditional butterflies",
-    description="A DDPM scheduler and UNet model trained (from this <a href=\"https://huggingface.co/anton-l/ddpm-butterflies-128\">checkpoint</a>) on a subset of the <a href=\"https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset\">Smithsonian Butterflies</a> dataset for unconditional image generation.",
+    description="A DDPM scheduler and UNet model trained on the MNIST dataset for unconditional image generation.",
 ).queue().launch()