Update

README.md

@@ -4,6 +4,7 @@ emoji: 📚
 colorFrom: red
 colorTo: indigo
 sdk: gradio
+sdk_version: 3.0.2
 app_file: app.py
 pinned: false
 ---

app.py

@@ -2,26 +2,42 @@
 
 from __future__ import annotations
 
+import argparse
 import functools
 import os
 import pickle
 import sys
 
-sys.path.insert(0, 'stylegan3')
-
 import gradio as gr
 import numpy as np
-import PIL.Image
 import torch
+import torch.nn as nn
 from huggingface_hub import hf_hub_download
 
+sys.path.insert(0, 'stylegan3')
+
+TITLE = 'StyleGAN3 Anime Face Generation'
+DESCRIPTION = 'Expected execution time on Hugging Face Spaces: 20s'
+ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.stylegan3-anime-face-generation-exp001" alt="visitor badge"/></center>'
+
 MODEL_REPO = 'hysts/stylegan3-anime-face-exp001-model'
 MODEL_FILE_NAME = '006600.pkl'
+
 TOKEN = os.environ['TOKEN']
 
-DEFAULT_SEED = 3407851645
 
-TITLE = 'StyleGAN3 Anime Face Generation'
+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 make_transform(translate: tuple[float, float], angle: float) -> np.ndarray:
@@ -37,13 +53,15 @@ def make_transform(translate: tuple[float, float], angle: float) -> np.ndarray:
     return mat
 
 
-def generate_z(seed, device):
+def generate_z(seed: int, device: torch.device) -> torch.Tensor:
     return torch.from_numpy(np.random.RandomState(seed).randn(1,
                                                               512)).to(device)
 
 
 @torch.inference_mode()
-def generate_image(seed, truncation_psi, tx, ty, angle, model, device):
+def generate_image(seed: int, truncation_psi: float, tx: float, ty: float,
+                   angle: float, model: nn.Module,
+                   device: torch.device) -> np.ndarray:
     seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
     z = generate_z(seed, device)
     c = torch.zeros(0).to(device)
@@ -54,10 +72,10 @@ def generate_image(seed, truncation_psi, tx, ty, angle, model, device):
 
     out = model(z, c, truncation_psi=truncation_psi)
     out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-    return PIL.Image.fromarray(out[0].cpu().numpy(), 'RGB')
+    return out[0].cpu().numpy()
 
 
-def load_model(device):
+def load_model(device: torch.device) -> nn.Module:
     path = hf_hub_download(MODEL_REPO, MODEL_FILE_NAME, use_auth_token=TOKEN)
     with open(path, 'rb') as f:
         model = pickle.load(f)
@@ -71,7 +89,8 @@ def load_model(device):
 
 
 def main():
-    device = torch.device('cpu')
+    args = parse_args()
+    device = torch.device(args.device)
 
     model = load_model(device)
     func = functools.partial(generate_image, model=model, device=device)
@@ -80,19 +99,25 @@ def main():
     gr.Interface(
         func,
         [
-            gr.inputs.Number(default=DEFAULT_SEED, label='Seed'),
+            gr.inputs.Number(default=3407851645, label='Seed'),
             gr.inputs.Slider(
                 0, 2, step=0.05, default=0.7, label='Truncation psi'),
             gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate X'),
             gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate Y'),
             gr.inputs.Slider(-180, 180, step=5, default=0, label='Angle'),
         ],
-        gr.outputs.Image(type='pil', label='Output'),
+        gr.outputs.Image(type='numpy', label='Output'),
         title=TITLE,
-        enable_queue=True,
-        allow_screenshot=False,
-        allow_flagging=False,
-    ).launch()
+        description=DESCRIPTION,
+        article=ARTICLE,
+        theme=args.theme,
+        allow_flagging='never',
+        live=args.live,
+    ).launch(
+        enable_queue=args.enable_queue,
+        server_port=args.port,
+        share=args.share,
+    )
 
 
 if __name__ == '__main__':