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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, 'StyleGAN-Human')
TITLE = 'StyleGAN-Human (Interpolation)'
DESCRIPTION = '''This is an unofficial demo for https://github.com/stylegan-human/StyleGAN-Human.
Expected execution time on Hugging Face Spaces: 0.8s for one image
Related App: [StyleGAN-Human](https://huggingface.co/spaces/hysts/StyleGAN-Human)
'''
ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.stylegan-human-interpolation" 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 load_model(file_name: str, device: torch.device) -> nn.Module:
path = hf_hub_download('hysts/StyleGAN-Human',
f'models/{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 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_interpolated_images(seed0: int, psi0: float, seed1: int,
psi1: float, num_intermediate: int,
model: nn.Module,
device: torch.device) -> list[np.ndarray]:
seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))
z0 = generate_z(model.z_dim, seed0, device)
z1 = generate_z(model.z_dim, seed1, device)
vec = z1 - z0
dvec = vec / (num_intermediate + 1)
zs = [z0 + dvec * i for i in range(num_intermediate + 2)]
dpsi = (psi1 - psi0) / (num_intermediate + 1)
psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)]
label = torch.zeros([1, model.c_dim], device=device)
res = []
for z, psi in zip(zs, psis):
out = model(z, label, truncation_psi=psi, force_fp32=True)
out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
torch.uint8)
out = out[0].cpu().numpy()
res.append(out)
return res
def main():
args = parse_args()
device = torch.device(args.device)
model = load_model('stylegan_human_v2_1024.pkl', device)
func = functools.partial(generate_interpolated_images,
model=model,
device=device)
func = functools.update_wrapper(func, generate_interpolated_images)
gr.Interface(
func,
[
gr.inputs.Number(default=0, label='Seed 1'),
gr.inputs.Slider(
0, 2, step=0.05, default=0.7, label='Truncation psi 1'),
gr.inputs.Number(default=1, label='Seed 2'),
gr.inputs.Slider(
0, 2, step=0.05, default=0.7, label='Truncation psi 2'),
gr.inputs.Slider(0,
21,
step=1,
default=7,
label='Number of Intermediate Frames'),
],
gr.Gallery(type='numpy', label='Output Images'),
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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