69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
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
|