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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
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