Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,467 Bytes
cfb7702 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from einops import rearrange, repeat
from ...modules.autoencoding.lpips.loss.lpips import LPIPS
from ...modules.encoders.modules import GeneralConditioner
from ...util import append_dims, instantiate_from_config
from .denoiser import Denoiser
class StandardDiffusionLoss(nn.Module):
def __init__(
self,
sigma_sampler_config: dict,
loss_weighting_config: dict,
loss_type: str = "l2",
offset_noise_level: float = 0.0,
batch2model_keys: Optional[Union[str, List[str]]] = None,
):
super().__init__()
assert loss_type in ["l2", "l1", "lpips"]
self.sigma_sampler = instantiate_from_config(sigma_sampler_config)
self.loss_weighting = instantiate_from_config(loss_weighting_config)
self.loss_type = loss_type
self.offset_noise_level = offset_noise_level
if loss_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 get_noised_input(
self, sigmas_bc: torch.Tensor, noise: torch.Tensor, input: torch.Tensor
) -> torch.Tensor:
noised_input = input + noise * sigmas_bc
return noised_input
def forward(
self,
network: nn.Module,
denoiser: Denoiser,
conditioner: GeneralConditioner,
input: torch.Tensor,
batch: Dict,
return_model_output: bool = False,
) -> torch.Tensor:
cond = conditioner(batch)
# for video diffusion
if "num_video_frames" in batch:
num_frames = batch["num_video_frames"]
for k in ["crossattn", "concat"]:
cond[k] = repeat(cond[k], "b ... -> b t ...", t=num_frames)
cond[k] = rearrange(cond[k], "b t ... -> (b t) ...", t=num_frames)
return self._forward(network, denoiser, cond, input, batch, return_model_output)
def _forward(
self,
network: nn.Module,
denoiser: Denoiser,
cond: Dict,
input: torch.Tensor,
batch: Dict,
return_model_output: bool = False,
) -> Tuple[torch.Tensor, Dict]:
additional_model_inputs = {
key: batch[key] for key in self.batch2model_keys.intersection(batch)
}
sigmas = self.sigma_sampler(input.shape[0]).to(input)
noise = torch.randn_like(input)
if self.offset_noise_level > 0.0:
offset_shape = (
(input.shape[0], 1, input.shape[2])
if self.n_frames is not None
else (input.shape[0], input.shape[1])
)
noise = noise + self.offset_noise_level * append_dims(
torch.randn(offset_shape, device=input.device),
input.ndim,
)
sigmas_bc = append_dims(sigmas, input.ndim)
noised_input = self.get_noised_input(sigmas_bc, noise, input)
model_output = denoiser(
network, noised_input, sigmas, cond, **additional_model_inputs
)
w = append_dims(self.loss_weighting(sigmas), input.ndim)
if not return_model_output:
return self.get_loss(model_output, input, w)
else:
return self.get_loss(model_output, input, w), model_output
def get_loss(self, model_output, target, w):
if self.loss_type == "l2":
return torch.mean(
(w * (model_output - target) ** 2).reshape(target.shape[0], -1), 1
)
elif self.loss_type == "l1":
return torch.mean(
(w * (model_output - target).abs()).reshape(target.shape[0], -1), 1
)
elif self.loss_type == "lpips":
loss = self.lpips(model_output, target).reshape(-1)
return loss
else:
raise NotImplementedError(f"Unknown loss type {self.loss_type}")
class StandardDiffusionLossWithPixelNeRFLoss(StandardDiffusionLoss):
def __init__(
self,
sigma_sampler_config: Dict,
loss_weighting_config: Dict,
loss_type: str = "l2",
offset_noise_level: float = 0,
batch2model_keys: str | List[str] | None = None,
pixelnerf_loss_weight: float = 1.0,
pixelnerf_loss_type: str = "l2",
):
super().__init__(
sigma_sampler_config,
loss_weighting_config,
loss_type,
offset_noise_level,
batch2model_keys,
)
self.pixelnerf_loss_weight = pixelnerf_loss_weight
self.pixelnerf_loss_type = pixelnerf_loss_type
def get_pixelnerf_loss(self, model_output, target):
if self.pixelnerf_loss_type == "l2":
return torch.mean(
((model_output - target) ** 2).reshape(target.shape[0], -1), 1
)
elif self.pixelnerf_loss_type == "l1":
return torch.mean(
((model_output - target).abs()).reshape(target.shape[0], -1), 1
)
elif self.pixelnerf_loss_type == "lpips":
loss = self.lpips(model_output, target).reshape(-1)
return loss
else:
raise NotImplementedError(f"Unknown loss type {self.loss_type}")
def forward(
self,
network: nn.Module,
denoiser: Denoiser,
conditioner: GeneralConditioner,
input: torch.Tensor,
batch: Dict,
return_model_output: bool = False,
) -> torch.Tensor:
cond = conditioner(batch)
return self._forward(network, denoiser, cond, input, batch, return_model_output)
def _forward(
self,
network: nn.Module,
denoiser: Denoiser,
cond: Dict,
input: torch.Tensor,
batch: Dict,
return_model_output: bool = False,
) -> Tuple[torch.Tensor | Dict]:
loss = super()._forward(
network, denoiser, cond, input, batch, return_model_output
)
pixelnerf_loss = self.get_pixelnerf_loss(
cond["rgb"], batch["pixelnerf_input"]["rgb"]
)
if not return_model_output:
return loss + self.pixelnerf_loss_weight * pixelnerf_loss
else:
return loss[0] + self.pixelnerf_loss_weight * pixelnerf_loss, loss[1]
|