Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from diffusers import StableDiffusionInpaintPipeline, StableDiffusionUpscalePipeline
+import numpy as np
+
+def process_image(image, prompt, mode, scale_factor=2):
+    if mode == "upscale":
+        # Upscale pipeline
+        pipeline = StableDiffusionUpscalePipeline.from_pretrained(
+            "stabilityai/stable-diffusion-x4-upscaler"
+        )
+        pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
+        
+        # Process image
+        upscaled_image = pipeline(
+            prompt=prompt,
+            image=image,
+            noise_level=20,
+            num_inference_steps=20
+        ).images[0]
+        
+        return upscaled_image
+    
+    elif mode == "inpaint":
+        # Inpainting pipeline
+        pipeline = StableDiffusionInpaintPipeline.from_pretrained(
+            "runwayml/stable-diffusion-inpainting"
+        )
+        pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
+        
+        # Create mask for extending the image
+        width, height = image.size
+        mask = Image.new('RGB', (width, height), 'white')
+        
+        # Process image
+        result = pipeline(
+            prompt=prompt,
+            image=image,
+            mask_image=mask,
+            num_inference_steps=20
+        ).images[0]
+        
+        return result
+
+# Gradio Interface
+def create_interface():
+    with gr.Blocks(title="AI Image Enhancement") as interface:
+        gr.Markdown("# AI Image Enhancement Studio")
+        gr.Markdown("Enhance, upscale, and recreate images using AI")
+        
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(type="pil", label="Upload Image")
+                prompt = gr.Textbox(label="Prompt", placeholder="Describe the desired enhancement...")
+                mode = gr.Radio(
+                    choices=["upscale", "inpaint"],
+                    label="Processing Mode",
+                    value="upscale"
+                )
+                scale_factor = gr.Slider(
+                    minimum=2,
+                    maximum=8,
+                    step=2,
+                    label="Upscale Factor",
+                    value=2
+                )
+                process_btn = gr.Button("Process Image")
+            
+            with gr.Column():
+                output_image = gr.Image(type="pil", label="Enhanced Result")
+        
+        process_btn.click(
+            fn=process_image,
+            inputs=[input_image, prompt, mode, scale_factor],
+            outputs=output_image
+        )
+    
+    return interface
+
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch()