File size: 3,877 Bytes
1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 0491d82 1dd3178 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
#!/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 = 'StyleGAN3 Anime Face Generation'
DESCRIPTION = 'Expected execution time on Hugging Face Spaces: 20s'
ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.stylegan3-anime-face-generation-exp001" alt="visitor badge"/></center>'
MODEL_REPO = 'hysts/stylegan3-anime-face-exp001-model'
MODEL_FILE_NAME = '006600.pkl'
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 make_transform(translate: tuple[float, float], angle: float) -> np.ndarray:
mat = np.eye(3)
sin = np.sin(angle / 360 * np.pi * 2)
cos = np.cos(angle / 360 * np.pi * 2)
mat[0][0] = cos
mat[0][1] = sin
mat[0][2] = translate[0]
mat[1][0] = -sin
mat[1][1] = cos
mat[1][2] = translate[1]
return mat
def generate_z(seed: int, device: torch.device) -> torch.Tensor:
return torch.from_numpy(np.random.RandomState(seed).randn(1,
512)).to(device)
@torch.inference_mode()
def generate_image(seed: int, truncation_psi: float, tx: float, ty: float,
angle: float, model: nn.Module,
device: torch.device) -> np.ndarray:
seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
z = generate_z(seed, device)
c = torch.zeros(0).to(device)
mat = make_transform((tx, ty), angle)
mat = np.linalg.inv(mat)
model.synthesis.input.transform.copy_(torch.from_numpy(mat))
out = model(z, c, 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(device: torch.device) -> nn.Module:
path = hf_hub_download(MODEL_REPO, MODEL_FILE_NAME, use_auth_token=TOKEN)
with open(path, 'rb') as f:
model = pickle.load(f)
model.eval()
model.to(device)
with torch.inference_mode():
z = torch.zeros((1, 512)).to(device)
c = torch.zeros(0).to(device)
model(z, c)
return model
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.Number(default=3407851645, label='Seed'),
gr.inputs.Slider(
0, 2, step=0.05, default=0.7, label='Truncation psi'),
gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate X'),
gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate Y'),
gr.inputs.Slider(-180, 180, step=5, default=0, label='Angle'),
],
gr.outputs.Image(type='numpy', label='Output'),
title=TITLE,
description=DESCRIPTION,
article=ARTICLE,
theme=args.theme,
allow_flagging='never',
live=args.live,
).launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
|