Create app.py

app.py

@@ -0,0 +1,26 @@
+from diffusers import DiffusionPipeline
+import torch
+import PIL.Image
+import gradio as gr
+import random
+import numpy as np
+
+pipeline = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128")
+
+def predict(steps, seed):
+    generator = torch.manual_seed(seed)
+    for i in range(1,steps):
+        yield pipeline(generator=generator, num_inference_steps=i).images[0]
+
+random_seed = random.randint(0, 2147483647)
+gr.Interface(
+    predict,
+    inputs=[
+        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=[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.",
+).queue().launch()