NCSNConfig¶

class lucid.models.NCSNConfig(in_channels: int = 3, nf: int = 128, num_classes: int = 10, dilations: Sequence[int] = (1, 2, 4, 8), scale_by_sigma: bool = True, sigmas: lucid._tensor.tensor.Tensor | Sequence[float] | None = None)¶

NCSNConfig stores the RefineNet width, conditional noise schedule settings, and optional initial sigma buffer used by lucid.models.NCSN.

Class Signature¶

@dataclass
class NCSNConfig:
    in_channels: int = 3
    nf: int = 128
    num_classes: int = 10
    dilations: Sequence[int] = (1, 2, 4, 8)
    scale_by_sigma: bool = True
    sigmas: Tensor | Sequence[float] | None = None

Parameters¶

in_channels (int): Number of image channels.
nf (int): Base feature width of the RefineNet-style backbone.
num_classes (int): Number of noise levels and conditional normalization labels.
dilations (Sequence[int]): Four dilation values used by the RCU stages.
scale_by_sigma (bool): Whether the output score is divided by the selected sigma.
sigmas (Tensor | Sequence[float] | None): Optional sigma schedule to preload into the registered sigmas buffer.

Validation¶

in_channels, nf, and num_classes must be greater than 0.
dilations must contain exactly four positive integers.
scale_by_sigma must be a boolean.
sigmas, when provided, must be 1D, positive, and have length num_classes.

Usage¶

import lucid.models as models

config = models.NCSNConfig(
    in_channels=3,
    nf=128,
    num_classes=10,
    sigmas=models.NCSN.make_sigmas(50.0, 0.01, 10),
)
model = models.NCSN(config)