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| import gradio as gr | |
| import torch | |
| from diffusers.utils import load_image | |
| from controlnet_flux import FluxControlNetModel | |
| from transformer_flux import FluxTransformer2DModel | |
| from pipeline_flux_controlnet_inpaint import FluxControlNetInpaintingPipeline | |
| from PIL import Image, ImageDraw | |
| # Load models | |
| controlnet = FluxControlNetModel.from_pretrained("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", torch_dtype=torch.bfloat16) | |
| transformer = FluxTransformer2DModel.from_pretrained( | |
| "black-forest-labs/FLUX.1-dev", subfolder='transformer', torch_dtype=torch.bfloat16 | |
| ) | |
| pipe = FluxControlNetInpaintingPipeline.from_pretrained( | |
| "black-forest-labs/FLUX.1-dev", | |
| controlnet=controlnet, | |
| transformer=transformer, | |
| torch_dtype=torch.bfloat16 | |
| ).to("cuda") | |
| pipe.transformer.to(torch.bfloat16) | |
| pipe.controlnet.to(torch.bfloat16) | |
| def prepare_image_and_mask(image, width, height, overlap_percentage): | |
| # Resize the input image to fit within the target size | |
| image.thumbnail((width, height), Image.LANCZOS) | |
| # Create a new white background image of the target size | |
| background = Image.new('RGB', (width, height), (255, 255, 255)) | |
| # Paste the resized image onto the background | |
| offset = ((width - image.width) // 2, (height - image.height) // 2) | |
| background.paste(image, offset) | |
| # Create a mask | |
| mask = Image.new('L', (width, height), 255) | |
| draw = ImageDraw.Draw(mask) | |
| # Calculate the overlap area | |
| overlap_x = int(image.width * overlap_percentage / 100) | |
| overlap_y = int(image.height * overlap_percentage / 100) | |
| # Draw the mask (black area is where we want to inpaint) | |
| draw.rectangle([ | |
| (offset[0] + overlap_x, offset[1] + overlap_y), | |
| (offset[0] + image.width - overlap_x, offset[1] + image.height - overlap_y) | |
| ], fill=0) | |
| return background, mask | |
| def inpaint(image, prompt, width, height, overlap_percentage, num_inference_steps, guidance_scale): | |
| # Prepare image and mask | |
| image, mask = prepare_image_and_mask(image, width, height, overlap_percentage) | |
| # Set up generator for reproducibility | |
| generator = torch.Generator(device="cuda").manual_seed(42) | |
| # Run inpainting | |
| result = pipe( | |
| prompt=prompt, | |
| height=height, | |
| width=width, | |
| control_image=image, | |
| control_mask=mask, | |
| num_inference_steps=num_inference_steps, | |
| generator=generator, | |
| controlnet_conditioning_scale=0.9, | |
| guidance_scale=guidance_scale, | |
| negative_prompt="", | |
| true_guidance_scale=guidance_scale | |
| ).images[0] | |
| return result | |
| # Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# FLUX Outpainting Demo") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(type="pil", label="Input Image") | |
| prompt_input = gr.Textbox(label="Prompt") | |
| width_slider = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=768) | |
| height_slider = gr.Slider(label="Height", minimum=256, maximum=1024, step=64, value=768) | |
| overlap_slider = gr.Slider(label="Overlap Percentage", minimum=0, maximum=50, step=1, value=10) | |
| steps_slider = gr.Slider(label="Inference Steps", minimum=1, maximum=100, step=1, value=28) | |
| guidance_slider = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=10.0, step=0.1, value=3.5) | |
| run_button = gr.Button("Generate") | |
| with gr.Column(): | |
| output_image = gr.Image(label="Output Image") | |
| run_button.click( | |
| fn=inpaint, | |
| inputs=[input_image, prompt_input, width_slider, height_slider, overlap_slider, steps_slider, guidance_slider], | |
| outputs=output_image | |
| ) | |
| demo.launch() |