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| # Utils Standards | |
| # Numerical Management | |
| import numpy as np | |
| # Pythorch | |
| import torch | |
| # A hugging face library | |
| from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN | |
| """Let's now set up the model functions""" | |
| def load_model(model_name = "ceyda/butterfly_cropped_uniq1K_512", model_version=None): | |
| # GAN set-up | |
| gan = LightweightGAN.from_pretrained(model_name, version=model_version) | |
| # GAN Inference Evaluation | |
| gan.eval() | |
| return gan | |
| # Let's set-up a second function for generation form | |
| def generate(gan, batch_size=1): | |
| with torch.no_grad(): | |
| # Cleaning process to properly fit it to the model | |
| ims = gan.G(torch.randn(batch_size, gan.latent_dim)).clamp_(0.0, 1.0) * 255 | |
| # | |
| ims = ims.permute(0, 2, 3, 1).detach().cpu().numpy().astype(np.uint8) | |
| return ims | |