(self, latent, aug=True)
| 265 | return self.sd_vae.decode(latent) |
| 266 | |
| 267 | def decode(self, latent, aug=True): |
| 268 | origin_pixel = self.sd_vae.decode(latent).sample |
| 269 | origin_pixel = (origin_pixel * 0.5 + 0.5) |
| 270 | if not aug: |
| 271 | y = self.decoder(origin_pixel.to(self.dtype), latent.to(self.dtype)) |
| 272 | return origin_pixel, y |
| 273 | list_y = [] |
| 274 | for i in range(int(latent.shape[0])): |
| 275 | y = self.estimate_augmented(origin_pixel[i:i + 1].to(self.dtype), latent[i:i + 1].to(self.dtype)) |
| 276 | list_y.append(y) |
| 277 | y = torch.concat(list_y, dim=0) |
| 278 | return origin_pixel, y |
| 279 | |
| 280 | def encode(self, img_rgba, img_rgb, padded_img_rgb, use_offset=True): |
| 281 | a_bchw_01 = img_rgba[:, 3:, :, :] |
no test coverage detected