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from __future__ import annotations |
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import argparse |
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import functools |
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import os |
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import pickle |
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import sys |
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import gradio as gr |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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from huggingface_hub import hf_hub_download |
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sys.path.insert(0, 'stylegan3') |
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TITLE = 'StyleGAN3 Anime Face Generation' |
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DESCRIPTION = 'Expected execution time on Hugging Face Spaces: 20s' |
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ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.stylegan3-anime-face-generation-exp001" alt="visitor badge"/></center>' |
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MODEL_REPO = 'hysts/stylegan3-anime-face-exp001-model' |
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MODEL_FILE_NAME = '006600.pkl' |
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TOKEN = os.environ['TOKEN'] |
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def parse_args() -> argparse.Namespace: |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--device', type=str, default='cpu') |
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parser.add_argument('--theme', type=str) |
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parser.add_argument('--live', action='store_true') |
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parser.add_argument('--share', action='store_true') |
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parser.add_argument('--port', type=int) |
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parser.add_argument('--disable-queue', |
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dest='enable_queue', |
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action='store_false') |
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parser.add_argument('--allow-flagging', type=str, default='never') |
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return parser.parse_args() |
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def make_transform(translate: tuple[float, float], angle: float) -> np.ndarray: |
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mat = np.eye(3) |
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sin = np.sin(angle / 360 * np.pi * 2) |
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cos = np.cos(angle / 360 * np.pi * 2) |
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mat[0][0] = cos |
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mat[0][1] = sin |
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mat[0][2] = translate[0] |
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mat[1][0] = -sin |
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mat[1][1] = cos |
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mat[1][2] = translate[1] |
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return mat |
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def generate_z(seed: int, device: torch.device) -> torch.Tensor: |
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return torch.from_numpy(np.random.RandomState(seed).randn(1, |
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512)).to(device) |
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@torch.inference_mode() |
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def generate_image(seed: int, truncation_psi: float, tx: float, ty: float, |
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angle: float, model: nn.Module, |
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device: torch.device) -> np.ndarray: |
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seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max)) |
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z = generate_z(seed, device) |
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c = torch.zeros(0).to(device) |
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mat = make_transform((tx, ty), angle) |
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mat = np.linalg.inv(mat) |
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model.synthesis.input.transform.copy_(torch.from_numpy(mat)) |
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out = model(z, c, truncation_psi=truncation_psi) |
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out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) |
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return out[0].cpu().numpy() |
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def load_model(device: torch.device) -> nn.Module: |
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path = hf_hub_download(MODEL_REPO, MODEL_FILE_NAME, use_auth_token=TOKEN) |
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with open(path, 'rb') as f: |
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model = pickle.load(f) |
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model.eval() |
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model.to(device) |
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with torch.inference_mode(): |
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z = torch.zeros((1, 512)).to(device) |
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c = torch.zeros(0).to(device) |
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model(z, c) |
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return model |
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def main(): |
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args = parse_args() |
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device = torch.device(args.device) |
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model = load_model(device) |
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func = functools.partial(generate_image, model=model, device=device) |
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func = functools.update_wrapper(func, generate_image) |
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gr.Interface( |
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func, |
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[ |
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gr.inputs.Number(default=3407851645, label='Seed'), |
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gr.inputs.Slider( |
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0, 2, step=0.05, default=0.7, label='Truncation psi'), |
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gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate X'), |
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gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate Y'), |
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gr.inputs.Slider(-180, 180, step=5, default=0, label='Angle'), |
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], |
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gr.outputs.Image(type='numpy', label='Output'), |
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title=TITLE, |
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description=DESCRIPTION, |
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article=ARTICLE, |
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theme=args.theme, |
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allow_flagging='never', |
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live=args.live, |
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).launch( |
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enable_queue=args.enable_queue, |
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server_port=args.port, |
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share=args.share, |
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) |
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if __name__ == '__main__': |
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main() |
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