Create app.py

app.py

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+import gradio as gr
+from diffusers import StableDiffusionUpscalePipeline
+import torch
+from PIL import Image
+
+# Load model
+model_id = "Kim2091/UltraSharpV2"
+pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+
+def enhance_image(input_image):
+    # input_image is a PIL.Image
+    result = pipe(input_image).images[0]
+    return result
+
+title = "UltraSharpV2 Image Enhancer"
+description = "Upload an image and UltraSharpV2 will enhance it (faces, objects, text, general quality)."
+
+demo = gr.Interface(
+    fn=enhance_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title=title,
+    description=description,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()