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#!/usr/bin/env python
from __future__ import annotations
import argparse
import functools
import os
import subprocess
import sys
import gradio as gr
import numpy as np
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
#if os.environ.get('SYSTEM') == 'spaces':
# subprocess.call('git apply ../patch'.split(), cwd='stylegan2-pytorch')
sys.path.insert(0, 'stylegan2-pytorch')
from model import Generator
TITLE = 'TADNE (This Anime Does Not Exist)'
DESCRIPTION = '''The original TADNE site is https://thisanimedoesnotexist.ai/.
The model used here is the one converted from the model provided in [this site](https://www.gwern.net/Faces) using [this repo](https://github.com/rosinality/stylegan2-pytorch).
Expected execution time on Hugging Face Spaces: 4s
Based off of [hysts/TADNE](https://huggingface.co/spaces/hysts/TADNE)
'''
SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/TADNE/resolve/main/samples'
ARTICLE = f'''## Generated images
- size: 512x512
- truncation: 0.7
- seed: 0-99
'''
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(device: torch.device) -> nn.Module:
model = Generator(512, 1024, 4, channel_multiplier=2)
path = hf_hub_download('hysts/TADNE',
'models/aydao-anime-danbooru2019s-512-5268480.pt',
use_auth_token=TOKEN)
checkpoint = torch.load(path)
model.load_state_dict(checkpoint['g_ema'])
model.eval()
model.to(device)
model.latent_avg = checkpoint['latent_avg'].to(device)
with torch.inference_mode():
z = torch.zeros((1, model.style_dim)).to(device)
model([z], truncation=0.7, truncation_latent=model.latent_avg)
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_image(seed: str, truncation_psi: float, randomize_noise: bool,
model: nn.Module, device: torch.device) -> np.ndarray:
seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
arr=bytearray.fromhex(seed[2:])
z = np.random.RandomState(arr).randn(n_sample, inputSize).astype("float32")
#z = generate_z(model.style_dim, seed, device)
out, _ = model([z],
truncation=truncation_psi,
truncation_latent=model.latent_avg,
randomize_noise=randomize_noise)
out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
return out[0].cpu().numpy()
def main():
args = parse_args()
device = torch.device(args.device)
model = load_model(device)
func = functools.partial(generate_image, model=model, device=device)
func = functools.update_wrapper(func, generate_image)
gr.Interface(
func,
[
gr.inputs.String(default="0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984", label='Seed'),
gr.inputs.Slider(
0, 2, step=0.05, default=0.7, label='Truncation psi'),
gr.inputs.Checkbox(default=False, label='Randomize Noise'),
],
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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