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

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

def genera(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.unit8)
    return ims