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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, 'stylegan3')
TITLE = 'Self-Distilled StyleGAN'
DESCRIPTION = '''This is an unofficial demo for models provided in https://github.com/self-distilled-stylegan/self-distilled-internet-photos.
Expected execution time on Hugging Face Spaces: 2s
'''
SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/samples'
ARTICLE = f'''## Generated images
- truncation: 0.7
### Dogs
- size: 1024x1024
- seed: 0-99
### Elephants
- size: 512x512
- seed: 0-99
### Horses
- size: 256x256
- seed: 0-99
### Bicycles
- size: 256x256
- seed: 0-99
### Lions
- size: 512x512
- seed: 0-99
### Giraffes
- size: 512x512
- seed: 0-99
### Parrots
- size: 512x512
- seed: 0-99
<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.self-distilled-stylegan" alt="visitor badge"/></center>
'''
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')
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(model_name: str, seed: int, truncation_psi: float,
model_dict: dict[str, nn.Module],
device: torch.device) -> np.ndarray:
model = model_dict[model_name]
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)
out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
return out[0].cpu().numpy()
def load_model(model_name: str, device: torch.device) -> nn.Module:
path = hf_hub_download('hysts/Self-Distilled-StyleGAN',
f'models/{model_name}_pytorch.pkl',
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)
return model
def main():
args = parse_args()
device = torch.device(args.device)
model_names = [
'dogs_1024',
'elephants_512',
'horses_256',
'bicycles_256',
'lions_512',
'giraffes_512',
'parrots_512',
]
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)
gr.Interface(
func,
[
gr.inputs.Radio(
model_names, type='value', default='dogs_1024', label='Model'),
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,
article=ARTICLE,
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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