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app.py

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+import gradio as gr
+from diffusers import DDPMPipeline
+
+model = 'alexktrs/CumulusCloudsGenerator'
+device='cpu'
+
+generator = DDPMPipeline.from_pretrained(model)
+generator.to(device)
+
+def generate(num_inference_steps):
+
+    image1 = generator(num_inference_steps=num_inference_steps).images[0]
+    image2 = generator(num_inference_steps=num_inference_steps).images[0]
+    
+    return [image1, image2]
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+                
+                # Generate Cumulus Clouds
+
+                """)
+    
+    gallery=gr.Gallery(type="pil")
+    slider=gr.Slider(label='Inference Steps', minimum=0, maximum=100)
+    
+    btn = gr.Button("Generate Clouds")
+    btn.click(fn=generate, inputs=slider, outputs=gallery)
+
+demo.launch()

requirements.txt

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+accelerate
+diffusers
+transformers