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| import spaces | |
| import cv2 | |
| import gradio as gr | |
| import torch | |
| from diffusers import ( | |
| AutoencoderKL, | |
| EulerAncestralDiscreteScheduler, | |
| ) | |
| from diffusers.utils import load_image | |
| from replace_bg.model.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline | |
| from replace_bg.model.controlnet import ControlNetModel | |
| from replace_bg.utilities import resize_image, remove_bg_from_image, paste_fg_over_image, get_control_image_tensor | |
| controlnet = ControlNetModel.from_pretrained("briaai/BRIA-2.3-ControlNet-BG-Gen", torch_dtype=torch.float16) | |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
| pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet, torch_dtype=torch.float16, vae=vae).to('cuda:0') | |
| pipe.scheduler = EulerAncestralDiscreteScheduler( | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| beta_schedule="scaled_linear", | |
| num_train_timesteps=1000, | |
| steps_offset=1 | |
| ) | |
| def generate_(prompt, negative_prompt, control_tensor, num_steps, controlnet_conditioning_scale, seed): | |
| generator = torch.Generator("cuda").manual_seed(seed) | |
| gen_img = pipe( | |
| negative_prompt=negative_prompt, | |
| prompt=prompt, | |
| controlnet_conditioning_scale=controlnet_conditioning_scale, | |
| num_inference_steps=num_steps, | |
| image = control_tensor, | |
| generator=generator | |
| ).images[0] | |
| result_image = paste_fg_over_image(gen_img, image, mask) | |
| return result_image | |
| def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed): | |
| # resize input_image to 1024x1024 | |
| input_image = resize_image(input_image) | |
| image = resize_image(image) | |
| mask = remove_bg_from_image(image_path) | |
| control_tensor = get_control_image_tensor(pipe.vae, image, mask) | |
| images = generate_(prompt, negative_prompt, control_tensor, num_steps, controlnet_conditioning_scale, seed) | |
| return [depth_image, images[0]] | |
| block = gr.Blocks().queue() | |
| with block: | |
| gr.Markdown("## BRIA Generate Background") | |
| gr.HTML(''' | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| This is a demo for ControlNet Depth that using | |
| <a href="briaai/BRIA-2.3-ControlNet-BG-Gen" target="_blank">BRIA 2.3 text-to-image model</a> as backbone. | |
| Trained on licensed data, BRIA 2.3 provide full legal liability coverage for copyright and privacy infringement. | |
| </p> | |
| ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam | |
| prompt = gr.Textbox(label="Prompt") | |
| negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers") | |
| num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1) | |
| controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05) | |
| seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,) | |
| run_button = gr.Button(value="Run") | |
| with gr.Column(): | |
| result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto') | |
| ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed] | |
| run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) | |
| block.launch(debug = True) |