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app.py

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+from diffusers import LatentDiffusionUncondPipeline
+import torch
+import PIL.Image
+import gradio as gr
+
+pipeline = LatentDiffusionUncondPipeline.from_pretrained("CompVis/latent-diffusion-celeba-256")
+
+
+def predict(seed):
+    torch.manual_seed(seed)
+    image = pipeline(generator=generator, num_inference_steps=1)["sample"]
+    image_processed = image.cpu().permute(0, 2, 3, 1)
+    image_processed = (image_processed + 1.0) * 127.5
+    image_processed = image_processed.clamp(0, 255).numpy().astype(np.uint8)
+    return PIL.Image.fromarray(image_processed[0])
+    
+gr.Interface(
+    predict,
+    inputs=[
+        gr.inputs.Slider(0, 1000, label='Seed', default=42),
+    ],
+    outputs="image",
+).launch()