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| from typing import List, Optional, Union | |
| import torch | |
| import torch.nn as nn | |
| from omegaconf import ListConfig | |
| from ...util import append_dims, instantiate_from_config | |
| from ...modules.autoencoding.lpips.loss.lpips import LPIPS | |
| class StandardDiffusionLoss(nn.Module): | |
| def __init__( | |
| self, | |
| sigma_sampler_config, | |
| type="l2", | |
| offset_noise_level=0.0, | |
| batch2model_keys: Optional[Union[str, List[str], ListConfig]] = None, | |
| ): | |
| super().__init__() | |
| assert type in ["l2", "l1", "lpips"] | |
| self.sigma_sampler = instantiate_from_config(sigma_sampler_config) | |
| self.type = type | |
| self.offset_noise_level = offset_noise_level | |
| if type == "lpips": | |
| self.lpips = LPIPS().eval() | |
| if not batch2model_keys: | |
| batch2model_keys = [] | |
| if isinstance(batch2model_keys, str): | |
| batch2model_keys = [batch2model_keys] | |
| self.batch2model_keys = set(batch2model_keys) | |
| def __call__(self, network, denoiser, conditioner, input, batch): | |
| cond = conditioner(batch) | |
| additional_model_inputs = { | |
| key: batch[key] for key in self.batch2model_keys.intersection(batch) | |
| } | |
| sigmas = self.sigma_sampler(input.shape[0]).to(input.device) | |
| noise = torch.randn_like(input) | |
| if self.offset_noise_level > 0.0: | |
| noise = noise + self.offset_noise_level * append_dims( | |
| torch.randn(input.shape[0], device=input.device), input.ndim | |
| ) | |
| noised_input = input + noise * append_dims(sigmas, input.ndim) | |
| model_output = denoiser( | |
| network, noised_input, sigmas, cond, **additional_model_inputs | |
| ) | |
| w = append_dims(denoiser.w(sigmas), input.ndim) | |
| return self.get_loss(model_output, input, w) | |
| def get_loss(self, model_output, target, w): | |
| if self.type == "l2": | |
| return torch.mean( | |
| (w * (model_output - target) ** 2).reshape(target.shape[0], -1), 1 | |
| ) | |
| elif self.type == "l1": | |
| return torch.mean( | |
| (w * (model_output - target).abs()).reshape(target.shape[0], -1), 1 | |
| ) | |
| elif self.type == "lpips": | |
| loss = self.lpips(model_output, target).reshape(-1) | |
| return loss | |