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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import argparse | |
| import functools | |
| import os | |
| import subprocess | |
| import sys | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from huggingface_hub import hf_hub_download | |
| if os.environ.get('SYSTEM') == 'spaces': | |
| subprocess.call('git apply ../patch'.split(), cwd='stylegan2-pytorch') | |
| sys.path.insert(0, 'stylegan2-pytorch') | |
| from model import Generator | |
| TITLE = 'TADNE (This Anime Does Not Exist)' | |
| DESCRIPTION = '''The original TADNE site is https://thisanimedoesnotexist.ai/. | |
| The model used here is the one converted from the model provided in [this site](https://www.gwern.net/Faces) using [this repo](https://github.com/rosinality/stylegan2-pytorch). | |
| ''' | |
| SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/TADNE/resolve/main/samples' | |
| ARTICLE = f'''## Generated images | |
| - size: 512x512 | |
| - truncation: 0.7 | |
| - seed: 0-99 | |
|  | |
| ''' | |
| TOKEN = os.environ['TOKEN'] | |
| 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') | |
| parser.add_argument('--allow-screenshot', action='store_true') | |
| return parser.parse_args() | |
| def load_model(device: torch.device) -> nn.Module: | |
| model = Generator(512, 1024, 4, channel_multiplier=2) | |
| path = hf_hub_download('hysts/TADNE', | |
| 'models/aydao-anime-danbooru2019s-512-5268480.pt', | |
| use_auth_token=TOKEN) | |
| checkpoint = torch.load(path) | |
| model.load_state_dict(checkpoint['g_ema']) | |
| model.eval() | |
| model.to(device) | |
| model.latent_avg = checkpoint['latent_avg'].to(device) | |
| with torch.inference_mode(): | |
| z = torch.zeros((1, model.style_dim)).to(device) | |
| model([z], truncation=0.7, truncation_latent=model.latent_avg) | |
| return model | |
| 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, randomize_noise: bool, | |
| model: nn.Module, device: torch.device) -> np.ndarray: | |
| seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max)) | |
| z = generate_z(model.style_dim, seed, device) | |
| out, _ = model([z], | |
| truncation=truncation_psi, | |
| truncation_latent=model.latent_avg, | |
| randomize_noise=randomize_noise) | |
| out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) | |
| return out[0].cpu().numpy() | |
| def main(): | |
| gr.close_all() | |
| args = parse_args() | |
| device = torch.device(args.device) | |
| model = load_model(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.inputs.Checkbox(default=False, label='Randomize Noise'), | |
| ], | |
| gr.outputs.Image(type='numpy', label='Output'), | |
| title=TITLE, | |
| description=DESCRIPTION, | |
| article=ARTICLE, | |
| theme=args.theme, | |
| allow_screenshot=args.allow_screenshot, | |
| 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() | |