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app.py

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+from diffusers import DDPMPipeline
+import torch
+import PIL.Image
+import gradio as gr
+import random
+import numpy as np
+
+ddpm_pipeline = DDPMPipeline.from_pretrained("nateraw/my-aurora")
+
+def predict(seed=42):
+    generator = torch.manual_seed(seed)
+    images = ddpm_pipeline(generator=generator)["sample"]
+    return images[0]
+
+random_seed = random.randint(0, 2147483647)
+gr.Interface(
+    predict,
+    inputs=[
+        gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
+    ],
+    outputs=gr.Image(shape=[32,32], type="pil", elem_id="output_image"),
+    title="Generate aurora with diffusers!",
+    description="This demo the <a href=\"https://huggingface.co/nateraw/my-aurora\">my-aurora</a> model to generate aurora created by <a href=\"https://huggingface.co/nateraw\">nateraw</a> using the <a href=\"https://github.com/osanseviero/diffuse-it\">Diffuse It! tool</a>. Inference might take around a minute.",
+).launch(debug=True, enable_queue=True)