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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}')
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()
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