app.py

#!/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, "StyleGAN-Human")

TITLE = "StyleGAN-Human"
DESCRIPTION = "https://github.com/stylegan-human/StyleGAN-Human"


def load_model(file_name: str, device: torch.device) -> nn.Module:
    path = hf_hub_download("public-data/StyleGAN-Human", f"models/{file_name}")
    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


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = load_model("stylegan_human_v2_1024.pkl", device)


def generate_z(z_dim: int, seed: int) -> torch.Tensor:
    return torch.from_numpy(np.random.RandomState(seed).randn(1, z_dim)).float()


@torch.inference_mode()
def generate_image(seed: int, truncation_psi: float) -> np.ndarray:
    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))

    z = generate_z(model.z_dim, seed)
    z = z.to(device)
    label = torch.zeros([1, model.c_dim], device=device)

    out = model(z, label, truncation_psi=truncation_psi, force_fp32=True)
    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=100000, step=1, value=0),
        gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=0.7),
    ],
    outputs=gr.Image(label="Output"),
    title=TITLE,
    description=DESCRIPTION,
)


if __name__ == "__main__":
    demo.queue(max_size=10).launch()