| | 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 |
| | import huggingface_hub |
| | huggingface_hub.login(os.getenv('HF_TOKEN_FLUX')) |
| |
|
| | check_min_version("0.30.2") |
| | transformer = FluxTransformer2DModel.from_pretrained( |
| | "black-forest-labs/FLUX.1-dev", subfolder='transformer', torch_dytpe=torch.bfloat16 |
| | ) |
| |
|
| |
|
| | |
| | controlnet = FluxControlNetModel.from_pretrained("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", 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) |
| |
|
| |
|
| | MARKDOWN = """ |
| | # FLUX.1-dev-Inpainting-Model-Beta-GPU 🔥 |
| | Model by alimama-creative |
| | """ |
| |
|
| | @spaces.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_or = image.copy() |
| | |
| | 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 result.resize((image_or.size[:2])) |
| |
|
| | 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=24, 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) |
