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| import torch | |
| import gradio as gr | |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel | |
| from PIL import Image | |
| # Load ControlNet model | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16) | |
| # Load Stable Diffusion Pipeline | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", | |
| controlnet=controlnet, | |
| torch_dtype=torch.float16 | |
| ).to("cuda") | |
| # Function to process images | |
| def remove_filter(image, prompt): | |
| output = pipe(prompt=prompt, image=image).images[0] | |
| return output | |
| # Gradio Interface | |
| iface = gr.Interface( | |
| fn=remove_filter, | |
| inputs=[ | |
| gr.Image(type="pil"), # Image input | |
| gr.Textbox(label="Custom Prompt", placeholder="Enter a custom prompt", value="restore the original colors and details, remove the filter") # Textbox for the prompt | |
| ], | |
| outputs=gr.Image(type="pil"), | |
| title="Filter Removal using ControlNet", | |
| description="Upload a filtered image and provide a prompt to restore the original image.", | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| iface.launch() | |