|
|
|
|
|
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() |
|
|
