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#!/usr/bin/env python
from __future__ import annotations
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"
MODEL_REPO = "hysts/stylegan3-anime-face-exp001-model"
MODEL_FILE_NAME = "006600.pkl"
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 load_model(device: torch.device) -> nn.Module:
path = hf_hub_download(MODEL_REPO, MODEL_FILE_NAME)
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
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = load_model(device)
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) -> 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()
demo = gr.Interface(
fn=generate_image,
inputs=[
gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.uint32).max, step=1, value=3407851645),
gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=0.7),
gr.Slider(label="Translate X", minimum=-1, maximum=1, step=0.05, value=0),
gr.Slider(label="Translate Y", minimum=-1, maximum=1, step=0.05, value=0),
gr.Slider(label="Angle", minimum=-180, maximum=180, step=5, value=0),
],
outputs=gr.Image(label="Output"),
title=TITLE,
css="style.css",
)
if __name__ == "__main__":
demo.queue(max_size=20, api_open=False).launch(show_api=False)
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