Create app.py

app.py

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+import gradio as gr
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128").to("cuda")
+
+def diffusion():
+    images = []
+    for i in range(3):
+        image = pipeline(num_inference_steps=25).images[0]
+        images.append(image)
+    return images
+
+demo = gr.Interface(
+    fn=diffusion,
+    inputs=None,
+    outputs=gr.Gallery(label="generated image", columns=3),
+    title="Unconditional image generation",
+    description="An unconditional diffusion model trained on a dataset of butterfly images."
+)
+
+demo.launch(debug=True)