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| import torch | |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel | |
| import gradio as gr | |
| from PIL import Image | |
| import numpy as np | |
| # Ensure we use GPU if available | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| def load_model(): | |
| """Load ControlNet Inpainting model.""" | |
| controlnet = ControlNetModel.from_pretrained( | |
| "lllyasviel/control_v11p_sd15_inpaint", | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32 | |
| ) | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", | |
| controlnet=controlnet, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32 | |
| ).to(device) | |
| return pipe | |
| pipe = load_model() | |
| def inpaint(image_editor_output, prompt): | |
| """Perform inpainting using the uploaded image and mask.""" | |
| print(f"image_editor_output:{image_editor_output}") | |
| if not isinstance(image_editor_output, dict) or "background" not in image_editor_output or "layers" not in image_editor_output: | |
| raise ValueError("Invalid input. Please upload an image and paint a mask.") | |
| # Extract the base image (background or composite) | |
| image = Image.fromarray(image_editor_output["composite"]).convert("RGB") # Ensure image is in RGB format | |
| # Extract the first layer (mask) and convert to grayscale | |
| mask_array = np.array(image_editor_output["layers"][0], dtype=np.uint8) | |
| mask = Image.fromarray(mask_array[:, :, 3]).convert("L") # Use the alpha channel | |
| # Resize mask to match image size if needed | |
| if mask.size != image.size: | |
| mask = mask.resize(image.size, Image.LANCZOS) | |
| # Perform inpainting | |
| result = pipe(prompt=prompt, image=image, control_image=mask).images[0] | |
| return result | |
| # Gradio UI with ImageEditor for inpainting | |
| demo = gr.Interface( | |
| fn=inpaint, | |
| inputs=[ | |
| gr.ImageEditor(label="Upload & Paint Image (White areas will be inpainted)"), | |
| gr.Textbox(label="Prompt for Inpainting"), | |
| ], | |
| outputs=gr.Image(type="pil", label="Generated Image"), | |
| title="ControlNet Inpainting", | |
| description="Upload an image, paint directly on it to define mask areas, and provide a text prompt.", | |
| ) | |
| demo.launch() | |