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| import spaces | |
| from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler | |
| from diffusers.utils import load_image | |
| from PIL import Image | |
| import torch | |
| import numpy as np | |
| import cv2 | |
| import gradio as gr | |
| from torchvision import transforms | |
| import fire | |
| import os | |
| controlnet = ControlNetModel.from_pretrained( | |
| "geyongtao/HumanWild", | |
| torch_dtype=torch.float16 | |
| ).to('cuda') | |
| vae = AutoencoderKL.from_pretrained( | |
| "madebyollin/sdxl-vae-fp16-fix", | |
| torch_dtype=torch.float16).to("cuda") | |
| pipe = StableDiffusionXLControlNetPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-base-1.0", | |
| controlnet=controlnet, | |
| vae=vae, | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| low_cpu_mem_usage=True, | |
| offload_state_dict=True, | |
| ).to('cuda') | |
| pipe.controlnet.to(memory_format=torch.channels_last) | |
| # pipe.enable_xformers_memory_efficient_attention() | |
| pipe.force_zeros_for_empty_prompt = False | |
| def resize_image(image): | |
| image = image.convert('RGB') | |
| current_size = image.size | |
| if current_size[0] > current_size[1]: | |
| center_cropped_image = transforms.functional.center_crop(image, (current_size[1], current_size[1])) | |
| else: | |
| center_cropped_image = transforms.functional.center_crop(image, (current_size[0], current_size[0])) | |
| resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024)) | |
| return resized_image | |
| def get_normal_map(image): | |
| image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda") | |
| with torch.no_grad(), torch.autocast("cuda"): | |
| depth_map = depth_estimator(image).predicted_depth | |
| image = transforms.functional.center_crop(image, min(image.shape[-2:])) | |
| depth_map = torch.nn.functional.interpolate( | |
| depth_map.unsqueeze(1), | |
| size=(1024, 1024), | |
| mode="bicubic", | |
| align_corners=False, | |
| ) | |
| depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True) | |
| depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True) | |
| depth_map = (depth_map - depth_min) / (depth_max - depth_min) | |
| image = torch.cat([depth_map] * 3, dim=1) | |
| image = image.permute(0, 2, 3, 1).cpu().numpy()[0] | |
| image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8)) | |
| return image | |
| def generate_(prompt, negative_prompt, normal_image, num_steps, controlnet_conditioning_scale, seed): | |
| generator = torch.Generator("cuda").manual_seed(seed) | |
| images = pipe( | |
| prompt, | |
| negative_prompt=negative_prompt, | |
| image=normal_image, | |
| num_inference_steps=num_steps, | |
| controlnet_conditioning_scale=float(controlnet_conditioning_scale), | |
| num_images_per_prompt=2, | |
| generator=generator, | |
| ).images | |
| return images | |
| def process(normal_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed): | |
| # resize input_image to 1024x1024 | |
| normal_image = resize_image(normal_image) | |
| # depth_image = get_depth_map(input_image) | |
| images = generate_(prompt, negative_prompt, normal_image, num_steps, controlnet_conditioning_scale, seed) | |
| return [images[0], images[1]] | |
| def run_demo(): | |
| _TITLE = '''3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models''' | |
| block = gr.Blocks().queue() | |
| with block: | |
| gr.Markdown("# 3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models ") | |
| gr.HTML(''' | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| This is a demo for Surface Normal ControlNet that using | |
| <a href="https://huggingface.co/geyongtao/HumanWild" target="_blank"> HumanWild model</a> pretrained weight. | |
| <a style="display:inline-block; margin-left: .5em" href='https://github.com/YongtaoGe/WildHuman/'><img src='https://img.shields.io/github/stars/YongtaoGe/WildHuman?style=social' /></a> | |
| </p> | |
| ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam | |
| example_folder = os.path.join(os.path.dirname(__file__), "./assets") | |
| example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)] | |
| gr.Examples( | |
| examples=example_fns, | |
| inputs=[input_image], | |
| cache_examples=False, | |
| label='Examples (click one of the images below to start)', | |
| examples_per_page=30 | |
| ) | |
| prompt = gr.Textbox(label="Prompt", value="a person, in the wild") | |
| negative_prompt = gr.Textbox(visible=False, 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=50, value=30, step=1) | |
| controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=1.0, value=0.95, 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) | |
| if __name__ == '__main__': | |
| fire.Fire(run_demo) |