import numpy as np
import torch
from huggan.pytorch.lightweight_gan.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):
  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