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on
Zero
Running
on
Zero
| import gradio as gr | |
| import os | |
| from PIL import Image | |
| import torch | |
| from diffusers.utils import load_image, check_min_version | |
| from controlnet_flux import FluxControlNetModel | |
| from transformer_flux import FluxTransformer2DModel | |
| from pipeline_flux_controlnet_inpaint import FluxControlNetInpaintingPipeline | |
| import spaces | |
| check_min_version("0.30.2") | |
| # Build pipeline | |
| 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_dytpe=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) | |
| MARKDOWN = """ | |
| # FLUX.1-dev-Inpainting-Model-Alpha-GPU 🔥 | |
| """ | |
| def process(input_image_editor, | |
| prompt, | |
| negative_prompt, | |
| controlnet_conditioning_scale, | |
| guidance_scale, | |
| seed, | |
| num_inference_steps, | |
| true_guidance_scale | |
| ): | |
| image = input_image_editor['background'] | |
| mask = input_image_editor['layers'][0] | |
| size = (768, 768) | |
| image = image.convert("RGB").resize(size) | |
| mask = mask.convert("RGB").resize(size) | |
| generator = torch.Generator(device="cuda").manual_seed(seed) | |
| result = pipe( | |
| prompt=prompt, | |
| height=size[1], | |
| width=size[0], | |
| control_image=image, | |
| control_mask=mask, | |
| num_inference_steps=num_inference_steps, | |
| generator=generator, | |
| controlnet_conditioning_scale=controlnet_conditioning_scale, | |
| guidance_scale=guidance_scale, | |
| negative_prompt=negative_prompt, | |
| true_guidance_scale=true_guidance_scale | |
| ).images[0] | |
| return image | |
| with gr.Blocks() as demo: | |
| gr.Markdown(MARKDOWN) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image_editor_component = gr.ImageEditor( | |
| label='Image', | |
| type='pil', | |
| sources=["upload", "webcam"], | |
| image_mode='RGB', | |
| layers=False, | |
| brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed")) | |
| prompt = gr.Textbox(lines=2, placeholder="Enter prompt here..."), | |
| negative_prompt = gr.Textbox(lines=2, placeholder="Enter negative_prompt here...") | |
| controlnet_conditioning_scale = gr.Slider(minimum=0, step=0.01, maximum=1, value=0.9, label="controlnet_conditioning_scale") | |
| guidance_scale = gr.Slider(minimum=1, step=0.5, maximum=10, value=3.5, label="Image to generate"), | |
| seed = gr.Slider(minimum=0, step=1, maximum=10000000, value=124, label="Seed Value"), | |
| num_inference_steps = gr.Slider(minimum=1, step=1, maximum=30, value=124, label="num_inference_steps"), | |
| true_guidance_scale = gr.Slider(minimum=1, step=1, maximum=10, value=3.5, label="true_guidance_scale"), | |
| submit_button_component = gr.Button( | |
| value='Submit', variant='primary', scale=0) | |
| with gr.Column(): | |
| output_image_component = gr.Image( | |
| type='pil', image_mode='RGB', label='Generated image', format="png") | |
| submit_button_component.click( | |
| fn=process, | |
| inputs=[ | |
| input_image_editor_component, | |
| prompt, | |
| negative_prompt, | |
| controlnet_conditioning_scale, | |
| guidance_scale, | |
| seed, | |
| num_inference_steps, | |
| true_guidance_scale | |
| ], | |
| outputs=[ | |
| output_image_component, | |
| ] | |
| ) | |
| demo.launch(debug=False, show_error=True,share=True) |