(self, x, condition, t, diffusion)
| 330 | # self.init_predictor = UNet(input_channels, output_channels, 2 * n_channels, ch_mults, n_blocks) |
| 331 | |
| 332 | def forward(self, x, condition, t, diffusion): |
| 333 | x_ = self.init_predictor(condition, t) |
| 334 | residual = x - x_ |
| 335 | noisy_image, noise_ref = diffusion.noisy_image(t, residual) |
| 336 | x__ = self.denoiser(torch.cat((noisy_image, x_.clone().detach()), dim=1), t) |
| 337 | return x_, x__, noisy_image, noise_ref |
| 338 | |
| 339 | |
| 340 | class EMA(): |
nothing calls this directly
no test coverage detected