Add files

.gitattributes

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+*.jpg filter=lfs diff=lfs merge=lfs -text
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text

.gitmodules

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+[submodule "MobileStyleGAN.pytorch"]
+	path = MobileStyleGAN.pytorch
+	url = https://github.com/bes-dev/MobileStyleGAN.pytorch

MobileStyleGAN.pytorch

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+Subproject commit a9776ff8f05a868b2d3b637bda14eca4c074d2a3

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+
+import gradio as gr
+import numpy as np
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+from model import Model
+
+ORIGINAL_REPO_URL = 'https://github.com/bes-dev/MobileStyleGAN.pytorch'
+TITLE = 'bes-dev/MobileStyleGAN.pytorch'
+DESCRIPTION = f'This is a demo for {ORIGINAL_REPO_URL}.'
+SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/MobileStyleGAN/resolve/main/samples'
+ARTICLE = f'''## Generated images
+### FFHQ
+- size: 1024x1024
+- seed: 0-99
+- truncation: 1.0
+![FFHQ]({SAMPLE_IMAGE_DIR}/ffhq.jpg)
+'''
+
+TOKEN = os.environ['TOKEN']
+
+
+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 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, generator: str,
+                   model: nn.Module, device: torch.device) -> np.ndarray:
+    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
+
+    z = generate_z(model.mapping_net.style_dim, seed, device)
+
+    out = model(z, truncation_psi=truncation_psi, generator=generator)
+    out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+    return out[0].cpu().numpy()
+
+
+def load_model(device: torch.device) -> nn.Module:
+    path = hf_hub_download('hysts/MobileStyleGAN',
+                           'models/mobilestylegan_ffhq_v2.pth',
+                           use_auth_token=TOKEN)
+    ckpt = torch.load(path)
+    model = Model()
+    model.load_state_dict(ckpt['state_dict'], strict=False)
+    model.eval()
+    model.to(device)
+    with torch.inference_mode():
+        z = torch.zeros((1, model.mapping_net.style_dim)).to(device)
+        model(z)
+    return model
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+    device = torch.device(args.device)
+
+    model = load_model(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=1.0, label='Truncation psi'),
+            gr.inputs.Radio(['student', 'teacher'],
+                            type='value',
+                            default='student',
+                            label='Generator'),
+        ],
+        gr.outputs.Image(type='numpy', label='Output'),
+        title=TITLE,
+        description=DESCRIPTION,
+        article=ARTICLE,
+        theme=args.theme,
+        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()

model.py

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+import sys
+
+import torch
+import torch.nn as nn
+
+sys.path.insert(0, 'MobileStyleGAN.pytorch')
+
+from core.models.mapping_network import MappingNetwork
+from core.models.mobile_synthesis_network import MobileSynthesisNetwork
+from core.models.synthesis_network import SynthesisNetwork
+
+
+class Model(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        # teacher model
+        mapping_net_params = {'style_dim': 512, 'n_layers': 8, 'lr_mlp': 0.01}
+        synthesis_net_params = {
+            'size': 1024,
+            'style_dim': 512,
+            'blur_kernel': [1, 3, 3, 1],
+            'channels': [512, 512, 512, 512, 512, 256, 128, 64, 32]
+        }
+        self.mapping_net = MappingNetwork(**mapping_net_params).eval()
+        self.synthesis_net = SynthesisNetwork(**synthesis_net_params).eval()
+        # student network
+        self.student = MobileSynthesisNetwork(
+            style_dim=self.mapping_net.style_dim,
+            channels=synthesis_net_params['channels'][:-1])
+
+        self.style_mean = nn.Parameter(torch.zeros((1, 512)),
+                                       requires_grad=False)
+
+    def forward(self,
+                var: torch.Tensor,
+                truncation_psi: float = 0.5,
+                generator: str = 'student') -> torch.Tensor:
+        style = self.mapping_net(var)
+        style = self.style_mean + truncation_psi * (style - self.style_mean)
+        if generator == 'student':
+            img = self.student(style)['img']
+        elif generator == 'teacher':
+            img = self.synthesis_net(style)['img']
+        else:
+            raise ValueError
+        return img

requirements.txt

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+numpy==1.22.3
+Pillow==9.0.1
+PyWavelets==1.2.0
+piq==0.6.0
+scipy==1.8.0
+torch==1.11.0
+torchvision==0.12.0
+git+https://github.com/fbcotter/pytorch_wavelets.git