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import gradio as gr |
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import os |
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from PIL import Image |
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import torch |
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from diffusers.utils import load_image, check_min_version |
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from controlnet_flux import FluxControlNetModel |
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from transformer_flux import FluxTransformer2DModel |
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from pipeline_flux_controlnet_inpaint import FluxControlNetInpaintingPipeline |
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import spaces |
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import huggingface_hub |
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huggingface_hub.login(os.getenv('HF_TOKEN_FLUX')) |
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check_min_version("0.30.2") |
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transformer = FluxTransformer2DModel.from_pretrained( |
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"black-forest-labs/FLUX.1-dev", subfolder='transformer', torch_dytpe=torch.bfloat16 |
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) |
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controlnet = FluxControlNetModel.from_pretrained("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", torch_dtype=torch.bfloat16) |
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pipe = FluxControlNetInpaintingPipeline.from_pretrained( |
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"black-forest-labs/FLUX.1-dev", |
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controlnet=controlnet, |
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transformer=transformer, |
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torch_dtype=torch.bfloat16 |
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).to("cuda") |
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pipe.transformer.to(torch.bfloat16) |
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pipe.controlnet.to(torch.bfloat16) |
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MARKDOWN = """ |
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# FLUX.1-dev-Inpainting-Model-Beta-GPU 🔥 |
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Model by alimama-creative |
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""" |
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@spaces.GPU() |
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def process(input_image_editor, |
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prompt, |
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negative_prompt, |
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controlnet_conditioning_scale, |
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guidance_scale, |
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seed, |
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num_inference_steps, |
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true_guidance_scale |
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): |
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image = input_image_editor['background'] |
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mask = input_image_editor['layers'][0] |
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size = (768, 768) |
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image_or = image.copy() |
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image = image.convert("RGB").resize(size) |
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mask = mask.convert("RGB").resize(size) |
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generator = torch.Generator(device="cuda").manual_seed(seed) |
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result = pipe( |
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prompt=prompt, |
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height=size[1], |
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width=size[0], |
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control_image=image, |
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control_mask=mask, |
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num_inference_steps=num_inference_steps, |
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generator=generator, |
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controlnet_conditioning_scale=controlnet_conditioning_scale, |
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guidance_scale=guidance_scale, |
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negative_prompt=negative_prompt, |
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true_guidance_scale=true_guidance_scale |
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).images[0] |
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return result.resize((image_or.size[:2])) |
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with gr.Blocks(css=".gradio-container {background-color: #d1d5db;}") as demo: |
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gr.Markdown(MARKDOWN) |
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with gr.Row(): |
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with gr.Column(): |
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input_image_editor_component = gr.ImageEditor( |
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label='Image', |
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type='pil', |
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sources=["upload", "webcam"], |
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image_mode='RGB', |
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layers=False, |
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brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed")) |
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prompt = gr.Textbox(lines=2, placeholder="Enter prompt here...") |
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negative_prompt = gr.Textbox(lines=2, placeholder="Enter negative_prompt here...") |
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controlnet_conditioning_scale = gr.Slider(minimum=0, step=0.01, maximum=1, value=0.9, label="controlnet_conditioning_scale") |
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guidance_scale = gr.Slider(minimum=1, step=0.5, maximum=10, value=3.5, label="Image to generate") |
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seed = gr.Slider(minimum=0, step=1, maximum=10000000, value=124, label="Seed Value") |
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num_inference_steps = gr.Slider(minimum=1, step=1, maximum=30, value=24, label="num_inference_steps") |
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true_guidance_scale = gr.Slider(minimum=1, step=1, maximum=10, value=3.5, label="true_guidance_scale") |
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submit_button_component = gr.Button( |
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value='Submit', variant='primary', scale=0) |
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with gr.Column(): |
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output_image_component = gr.Image( |
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type='pil', image_mode='RGB', label='Generated image', format="png") |
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submit_button_component.click( |
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fn=process, |
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inputs=[ |
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input_image_editor_component, |
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prompt, |
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negative_prompt, |
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controlnet_conditioning_scale, |
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guidance_scale, |
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seed, |
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num_inference_steps, |
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true_guidance_scale |
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], |
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outputs=[ |
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output_image_component, |
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] |
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) |
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demo.launch(debug=False, show_error=True,share=True) |
