Update app.py

app.py

@@ -17,93 +17,22 @@ 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)
+TITLE = 'Time-TravelRephotography'
+DESCRIPTION = '''This is an unofficial demo for https://github.com/Time-Travel-Rephotography.
 '''
 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()
-
-
-@torch.inference_mode()
-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, path:str, device: torch.device) -> nn.Module:
-    path = hf_hub_download(f'{path}',
-                           f'{file_name}',
-                           use_auth_token=TOKEN)
-    with open(path, 'rb') as f:
-        model = torch.load(f)
-    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_e4e = load_model('e4e_ffhq_encode.pt',"feng2022/Time-TravelRephotography_e4e_ffhq_encode", 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()