Add files

.gitmodules

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+[submodule "StyleSwin"]
+	path = StyleSwin
+	url = https://github.com/microsoft/StyleSwin

StyleSwin

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+Subproject commit 52c23dcfa39a5da75f02892cb775fe8f424be6ec

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import sys
+
+sys.path.insert(0, 'StyleSwin')
+
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn as nn
+from models.generator import Generator
+
+TOKEN = os.environ['TOKEN']
+
+MODEL_REPO = 'hysts/StyleSwin'
+MODEL_NAMES = [
+    'CelebAHQ_256',
+    'FFHQ_256',
+    'LSUNChurch_256',
+    'CelebAHQ_1024',
+    'FFHQ_1024',
+]
+
+
+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(model_name: str, device: torch.device) -> nn.Module:
+    size = int(model_name.split('_')[1])
+    channel_multiplier = 1 if size == 1024 else 2
+    model = Generator(size,
+                      style_dim=512,
+                      n_mlp=8,
+                      channel_multiplier=channel_multiplier)
+    ckpt_path = huggingface_hub.hf_hub_download(MODEL_REPO,
+                                                f'models/{model_name}.pt',
+                                                use_auth_token=TOKEN)
+    ckpt = torch.load(ckpt_path)
+    model.load_state_dict(ckpt['g_ema'])
+    model.to(device)
+    model.eval()
+    return model
+
+
+def generate_z(seed: int, device: torch.device) -> torch.Tensor:
+    return torch.from_numpy(np.random.RandomState(seed).randn(
+        1, 512)).to(device).float()
+
+
+def postprocess(tensors: torch.Tensor) -> torch.Tensor:
+    assert tensors.dim() == 4
+    tensors = tensors.cpu()
+    std = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None]
+    mean = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None]
+    tensors = tensors * std + mean
+    tensors = (tensors * 255).clamp(0, 255).to(torch.uint8)
+    return tensors
+
+
+@torch.inference_mode()
+def generate_image(model_name: str, seed: int, model_dict: dict,
+                   device: torch.device) -> PIL.Image.Image:
+    model = model_dict[model_name]
+    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
+    z = generate_z(seed, device)
+    out, _ = model(z)
+    out = postprocess(out)
+    out = out.numpy()[0].transpose(1, 2, 0)
+    return PIL.Image.fromarray(out, 'RGB')
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+    device = torch.device(args.device)
+
+    model_dict = {name: load_model(name, device) for name in MODEL_NAMES}
+
+    func = functools.partial(generate_image,
+                             model_dict=model_dict,
+                             device=device)
+    func = functools.update_wrapper(func, generate_image)
+
+    repo_url = 'https://github.com/microsoft/StyleSwin'
+    title = 'microsoft/StyleSwin'
+    description = f'A demo for {repo_url}'
+    article = None
+
+    gr.Interface(
+        func,
+        [
+            gr.inputs.Radio(MODEL_NAMES,
+                            type='value',
+                            default='FFHQ_256',
+                            label='model',
+                            optional=False),
+            gr.inputs.Slider(0, 2147483647, step=1, default=0, label='Seed'),
+        ],
+        gr.outputs.Image(type='pil', label='Output'),
+        theme=args.theme,
+        title=title,
+        description=description,
+        article=article,
+        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()

packages.txt

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+ninja-build

requirements.txt

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+numpy==1.22.3
+Pillow==9.0.1
+timm==0.5.4
+torch==1.11.0
+torchvision==0.12.0