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Don't add tiny bit of random noise when VAE encoding. (#8705)

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Shouldn't change outputs but might make things a tiny bit more
deterministic.
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comfyanonymous 2025-06-27 11:14:56 -07:00 committed by GitHub
parent bd951a714f
commit 9093301a49
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1 changed files with 2 additions and 6 deletions
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comfy/ldm/models/autoencoder.py
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@ -11,7 +11,7 @@ from comfy.ldm.modules.ema import LitEma
import comfy.ops import comfy.ops
class DiagonalGaussianRegularizer(torch.nn.Module): class DiagonalGaussianRegularizer(torch.nn.Module):
def __init__(self, sample: bool = True): def __init__(self, sample: bool = False):
super().__init__() super().__init__()
self.sample = sample self.sample = sample
@ -19,16 +19,12 @@ class DiagonalGaussianRegularizer(torch.nn.Module):
yield from () yield from ()
def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
log = dict()
posterior = DiagonalGaussianDistribution(z) posterior = DiagonalGaussianDistribution(z)
if self.sample: if self.sample:
z = posterior.sample() z = posterior.sample()
else: else:
z = posterior.mode() z = posterior.mode()
kl_loss = posterior.kl() return z, None
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
log["kl_loss"] = kl_loss
return z, log
class AbstractAutoencoder(torch.nn.Module): class AbstractAutoencoder(torch.nn.Module):