Update app.py

app.py

@@ -1,11 +1,12 @@
-from diffusers import DDPMPipeline
+from diffusers import DiffusionPipeline
 import torch
 import PIL.Image
 import gradio as gr
 import random
 import numpy as np
 
-pipeline = DDPMPipeline.from_pretrained("johnowhitaker/ddpm-butterflies-32px")
+pipeline = DiffusionPipeline.from_pretrained("johnowhitaker/ddpm-butterflies-32px")
+pipeline.to('cuda')
 
 def predict(steps, seed):
     generator = torch.manual_seed(seed)
@@ -19,7 +20,7 @@ gr.Interface(
         gr.inputs.Slider(1, 100, label='Inference Steps', default=5, step=1),
         gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
     ],
-    outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"),
+    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 on a subset of the <a href=\"https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset\">Smithsonian Butterflies</a> dataset for unconditional image generation.",