Create app.py

app.py

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+import gradio as gr
+from diffusers import StableDiffusionInpaintPipeline
+import torch
+from PIL import Image
+import base64
+import io
+
+# Load the Qwen Inpainting model
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "InstantX/Qwen-Image-ControlNet-Inpainting",
+    torch_dtype=torch.float16
+).to("cuda")
+
+def edit_image(image, mask, prompt):
+    image = image.convert("RGB")
+    mask = mask.convert("L")  # Black = edit, White = keep
+    result = pipe(
+        prompt=prompt,
+        image=image,
+        mask_image=mask,
+        num_inference_steps=30
+    ).images[0]
+    return result
+
+# Gradio UI
+demo = gr.Interface(
+    fn=edit_image,
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Image(type="pil", label="Mask Image"),
+        gr.Textbox(label="Prompt")
+    ],
+    outputs=gr.Image(type="pil", label="Edited Image"),
+    title="Qwen Image Inpainting"
+)
+
+demo.launch()