|
|
|
|
|
from __future__ import annotations |
|
|
|
import functools |
|
import os |
|
import pickle |
|
import sys |
|
|
|
sys.path.insert(0, 'stylegan3') |
|
|
|
import gradio as gr |
|
import numpy as np |
|
import PIL.Image |
|
import torch |
|
from huggingface_hub import hf_hub_download |
|
|
|
MODEL_REPO = 'hysts/stylegan3-anime-face-exp001-model' |
|
MODEL_FILE_NAME = '006600.pkl' |
|
TOKEN = os.environ['TOKEN'] |
|
|
|
DEFAULT_SEED = 3407851645 |
|
|
|
TITLE = 'StyleGAN3 Anime Face Generation' |
|
|
|
|
|
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, device): |
|
return torch.from_numpy(np.random.RandomState(seed).randn(1, |
|
512)).to(device) |
|
|
|
|
|
@torch.inference_mode() |
|
def generate_image(seed, truncation_psi, tx, ty, angle, model, device): |
|
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 PIL.Image.fromarray(out[0].cpu().numpy(), 'RGB') |
|
|
|
|
|
def load_model(device): |
|
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(): |
|
device = torch.device('cpu') |
|
|
|
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=DEFAULT_SEED, 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='pil', label='Output'), |
|
title=TITLE, |
|
enable_queue=True, |
|
allow_screenshot=False, |
|
allow_flagging=False, |
|
).launch() |
|
|
|
|
|
if __name__ == '__main__': |
|
main() |
|
|
