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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 sys |
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if os.environ.get('SYSTEM') == 'spaces': |
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os.system("sed -i '14,21d' StyleSwin/op/fused_act.py") |
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os.system("sed -i '12,19d' StyleSwin/op/upfirdn2d.py") |
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sys.path.insert(0, 'StyleSwin') |
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import gradio as gr |
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import huggingface_hub |
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import numpy as np |
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import PIL.Image |
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import torch |
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import torch.nn as nn |
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from models.generator import Generator |
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REPO_URL = 'https://github.com/microsoft/StyleSwin' |
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TITLE = 'microsoft/StyleSwin' |
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DESCRIPTION = f'A demo for {REPO_URL}' |
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SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/StyleSwin/resolve/main/samples' |
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ARTICLE = f'''## Generated images |
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### CelebA-HQ |
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- size: 1024x1024 |
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- seed: 0-99 |
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### FFHQ |
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- size: 1024x1024 |
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- seed: 0-99 |
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### LSUN Church |
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- size: 256x256 |
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- seed: 0-99 |
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''' |
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TOKEN = os.environ['TOKEN'] |
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MODEL_REPO = 'hysts/StyleSwin' |
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MODEL_NAMES = [ |
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'CelebAHQ_256', |
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'FFHQ_256', |
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'LSUNChurch_256', |
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'CelebAHQ_1024', |
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'FFHQ_1024', |
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] |
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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(model_name: str, device: torch.device) -> nn.Module: |
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size = int(model_name.split('_')[1]) |
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channel_multiplier = 1 if size == 1024 else 2 |
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model = Generator(size, |
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style_dim=512, |
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n_mlp=8, |
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channel_multiplier=channel_multiplier) |
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ckpt_path = huggingface_hub.hf_hub_download(MODEL_REPO, |
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f'models/{model_name}.pt', |
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use_auth_token=TOKEN) |
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ckpt = torch.load(ckpt_path) |
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model.load_state_dict(ckpt['g_ema']) |
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model.to(device) |
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model.eval() |
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return model |
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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( |
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1, 512)).to(device).float() |
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def postprocess(tensors: torch.Tensor) -> torch.Tensor: |
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assert tensors.dim() == 4 |
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tensors = tensors.cpu() |
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std = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None] |
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mean = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None] |
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tensors = tensors * std + mean |
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tensors = (tensors * 255).clamp(0, 255).to(torch.uint8) |
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return tensors |
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@torch.inference_mode() |
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def generate_image(model_name: str, seed: int, model_dict: dict, |
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device: torch.device) -> PIL.Image.Image: |
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model = model_dict[model_name] |
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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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out, _ = model(z) |
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out = postprocess(out) |
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out = out.numpy()[0].transpose(1, 2, 0) |
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return PIL.Image.fromarray(out, 'RGB') |
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def main(): |
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gr.close_all() |
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args = parse_args() |
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device = torch.device(args.device) |
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model_dict = {name: load_model(name, device) for name in MODEL_NAMES} |
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func = functools.partial(generate_image, |
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model_dict=model_dict, |
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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.Radio(MODEL_NAMES, |
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type='value', |
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default='FFHQ_256', |
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label='Model', |
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optional=False), |
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gr.inputs.Slider(0, 2147483647, step=1, default=0, label='Seed'), |
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], |
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gr.outputs.Image(type='pil', 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_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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