Upload app.py

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import pickle
+import sys
+
+import gradio as gr
+import numpy as np
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+sys.path.insert(0, 'gen_ability_icon')
+
+TITLE = 'gen_ability_icon'
+DESCRIPTION = '''creates circular magic ability icons from stylegan2ada model trained on synthetic dataset .
+more information here :  https://github.com/CorvaeOboro/gen_ability_icon.
+'''
+
+
+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, device: torch.device) -> nn.Module:
+    path = hf_hub_download(f'CorvaeOboro/gen_ability_icon',
+                           f'{file_name}',
+                           use_auth_token=TOKEN)
+    with open(path, 'rb') as f:
+        model = pickle.load(f)['G_ema']
+    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 = load_model('gen_ability_icon_stylegan2ada_20220801.pkl', 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,
+        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()