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| import torch | |
| import sys | |
| sys.path.append(".") | |
| sys.path.append("..") | |
| from editings import ganspace, sefa | |
| from utils.common import tensor2im | |
| class LatentEditor(object): | |
| def __init__(self, stylegan_generator, is_cars=False): | |
| self.generator = stylegan_generator | |
| self.is_cars = is_cars # Since the cars StyleGAN output is 384x512, there is a need to crop the 512x512 output. | |
| def apply_ganspace(self, latent, ganspace_pca, edit_directions): | |
| edit_latents = ganspace.edit(latent, ganspace_pca, edit_directions) | |
| return self._latents_to_image(edit_latents) | |
| def apply_interfacegan(self, latent, direction, factor=1, factor_range=None): | |
| edit_latents = [] | |
| if factor_range is not None: # Apply a range of editing factors. for example, (-5, 5) | |
| for f in range(*factor_range): | |
| edit_latent = latent + f * direction | |
| edit_latents.append(edit_latent) | |
| edit_latents = torch.cat(edit_latents) | |
| else: | |
| edit_latents = latent + factor * direction | |
| return self._latents_to_image(edit_latents) | |
| def apply_sefa(self, latent, indices=[2, 3, 4, 5], **kwargs): | |
| edit_latents = sefa.edit(self.generator, latent, indices, **kwargs) | |
| return self._latents_to_image(edit_latents) | |
| # Currently, in order to apply StyleFlow editings, one should run inference, | |
| # save the latent codes and load them form the official StyleFlow repository. | |
| # def apply_styleflow(self): | |
| # pass | |
| def _latents_to_image(self, latents): | |
| with torch.no_grad(): | |
| images, _ = self.generator([latents], randomize_noise=False, input_is_latent=True) | |
| if self.is_cars: | |
| images = images[:, :, 64:448, :] # 512x512 -> 384x512 | |
| horizontal_concat_image = torch.cat(list(images), 2) | |
| final_image = tensor2im(horizontal_concat_image) | |
| return final_image | |