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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, 'StyleGAN-Human') |
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TITLE = 'StyleGAN-Human (Interpolation)' |
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DESCRIPTION = '''This is an unofficial demo for https://github.com/stylegan-human/StyleGAN-Human. |
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(Expected Execution Time on Hugging Face Spaces: 0.8s for one image) |
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Related App: [StyleGAN-Human](https://huggingface.co/spaces/hysts/StyleGAN-Human) |
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''' |
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ARTICLE = None |
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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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parser.add_argument('--allow-screenshot', action='store_true') |
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return parser.parse_args() |
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def load_model(file_name: str, device: torch.device) -> nn.Module: |
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path = hf_hub_download('hysts/StyleGAN-Human', |
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f'models/{file_name}', |
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use_auth_token=TOKEN) |
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with open(path, 'rb') as f: |
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model = pickle.load(f)['G_ema'] |
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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, model.z_dim)).to(device) |
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label = torch.zeros([1, model.c_dim], device=device) |
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model(z, label, force_fp32=True) |
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return model |
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def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor: |
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return torch.from_numpy(np.random.RandomState(seed).randn( |
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1, z_dim)).to(device).float() |
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@torch.inference_mode() |
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def generate_interpolated_images( |
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seed0: int, psi0: float, seed1: int, psi1: float, |
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num_intermediate: int, model: nn.Module, |
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device: torch.device) -> tuple[list[np.ndarray], np.ndarray]: |
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seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max)) |
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seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max)) |
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z0 = generate_z(model.z_dim, seed0, device) |
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z1 = generate_z(model.z_dim, seed1, device) |
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vec = z1 - z0 |
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dvec = vec / (num_intermediate + 1) |
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zs = [z0 + dvec * i for i in range(num_intermediate + 2)] |
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dpsi = (psi1 - psi0) / (num_intermediate + 1) |
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psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)] |
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label = torch.zeros([1, model.c_dim], device=device) |
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res = [] |
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for z, psi in zip(zs, psis): |
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out = model(z, label, truncation_psi=psi, force_fp32=True) |
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out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to( |
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torch.uint8) |
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out = out[0].cpu().numpy() |
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res.append(out) |
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concatenated = np.hstack(res) |
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return res, concatenated |
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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('stylegan_human_v2_1024.pkl', device) |
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func = functools.partial(generate_interpolated_images, |
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model=model, |
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device=device) |
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func = functools.update_wrapper(func, generate_interpolated_images) |
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gr.Interface( |
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func, |
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[ |
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gr.inputs.Number(default=0, label='Seed 1'), |
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gr.inputs.Slider( |
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0, 2, step=0.05, default=0.7, label='Truncation psi 1'), |
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gr.inputs.Number(default=1, label='Seed 2'), |
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gr.inputs.Slider( |
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0, 2, step=0.05, default=0.7, label='Truncation psi 2'), |
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gr.inputs.Slider(0, |
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21, |
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step=1, |
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default=7, |
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label='Number of Intermediate Frames'), |
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], |
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[ |
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gr.outputs.Carousel(gr.outputs.Image(type='numpy'), |
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label='Output Images'), |
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gr.outputs.Image(type='numpy', label='Concatenated'), |
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], |
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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_screenshot=args.allow_screenshot, |
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allow_flagging=args.allow_flagging, |
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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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