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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import argparse | |
| import functools | |
| import os | |
| import pickle | |
| import sys | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import torch_utils | |
| import torch.nn as nn | |
| from huggingface_hub import hf_hub_download | |
| sys.path.insert(0, 'StyleGAN-Human') | |
| TITLE = 'StyleGAN-Human' | |
| DESCRIPTION = '''This is an unofficial demo for https://github.com/stylegan-human/StyleGAN-Human. | |
| Expected execution time on Hugging Face Spaces: 0.8s | |
| Related App: [StyleGAN-Human (Interpolation)](https://huggingface.co/spaces/hysts/StyleGAN-Human-Interpolation) | |
| ''' | |
| ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.stylegan-human" alt="visitor badge"/></center>' | |
| TOKEN = "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" | |
| def parse_args() -> argparse.Namespace: | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--device', type=str, default='cpu') | |
| parser.add_argument('--theme', type=str) | |
| parser.add_argument('--live', action='store_true') | |
| parser.add_argument('--share', action='store_true') | |
| parser.add_argument('--port', type=int) | |
| parser.add_argument('--disable-queue', | |
| dest='enable_queue', | |
| action='store_false') | |
| parser.add_argument('--allow-flagging', type=str, default='never') | |
| return parser.parse_args() | |
| def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor: | |
| return torch.from_numpy(np.random.RandomState(seed).randn( | |
| 1, z_dim)).to(device).float() | |
| def generate_image(seed: int, truncation_psi: float, model: nn.Module, | |
| device: torch.device) -> np.ndarray: | |
| seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max)) | |
| z = generate_z(model.z_dim, seed, device) | |
| label = torch.zeros([1, model.c_dim], device=device) | |
| out = model(z, label, truncation_psi=truncation_psi, force_fp32=True) | |
| out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) | |
| return out[0].cpu().numpy() | |
| def load_model(file_name: str, device: torch.device) -> nn.Module: | |
| path = hf_hub_download('hysts/StyleGAN-Human', | |
| f'models/{file_name}', | |
| use_auth_token=TOKEN) | |
| with open(path, 'rb') as f: | |
| model = pickle.load(f)['G_ema'] | |
| model.eval() | |
| model.to(device) | |
| with torch.inference_mode(): | |
| z = torch.zeros((1, model.z_dim)).to(device) | |
| label = torch.zeros([1, model.c_dim], device=device) | |
| model(z, label, force_fp32=True) | |
| return model | |
| def main(): | |
| args = parse_args() | |
| device = torch.device(args.device) | |
| model = load_model('stylegan_human_v2_1024.pkl', device) | |
| func = functools.partial(generate_image, model=model, device=device) | |
| func = functools.update_wrapper(func, generate_image) | |
| gr.Interface( | |
| func, | |
| [ | |
| gr.inputs.Number(default=0, label='Seed'), | |
| gr.inputs.Slider( | |
| 0, 2, step=0.05, default=0.7, label='Truncation psi'), | |
| ], | |
| gr.outputs.Image(type='numpy', label='Output'), | |
| title=TITLE, | |
| description=DESCRIPTION, | |
| article=ARTICLE, | |
| theme=args.theme, | |
| allow_flagging=args.allow_flagging, | |
| live=args.live, | |
| ).launch( | |
| enable_queue=args.enable_queue, | |
| server_port=args.port, | |
| share=args.share, | |
| ) | |
| if __name__ == '__main__': | |
| main() | |