utils.py

import numpy as np
import torch
from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN

def load_model(model_name="ceyda/butterfly_cropped_uniq1K_512", model_version=None):
    gan=LightweightGAN.from_pretrained(model_name,version=model_version)
    gan.eval()
    return gan

def generate(gan,batch_size=1):
    with torch.no_grad():
        ims=gan.G(torch.randn(batch_size,gan.latent_dim)).clamp(0.0,1.0)*255
        ims=ims.permute(0,2,3,1).deatch().cpu().numpy().astype(np.uint8)
        return ims