LDLMIn construction

Contextual evidence from Input projection

The denoiser owns the time embedding’s two affine layers, one rotary table for all blocks, an affine projection of the concatenated input, and the stack.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Input projection

The time embedding passes its MLP and a SiLU; the noised latents are concatenated with the previous clean estimate, zeros when there is none, and projected; the stack runs in bfloat16.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from LayerNorm

The denoiser’s and the token decoder’s LayerNorm computes in float32 and owns a gain and no bias.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from LayerNorm

PyTorch 2.4.1 functional LayerNorm defaults to epsilon 1e-5.

torch-layer-norm · pytorch/pytorch / torch/nn/functional.py
Pinned revision ee1b6804381c ↗ · lines 2561–2577

def layer_norm(
    input: Tensor,
    normalized_shape: List[int],
    weight: Optional[Tensor] = None,
    bias: Optional[Tensor] = None,
    eps: float = 1e-5,
) -> Tensor:
    r"""Apply Layer Normalization for last certain number of dimensions.

    See :class:`~torch.nn.LayerNorm` for details.
    """
    if has_torch_function_variadic(input, weight, bias):
        return handle_torch_function(
            layer_norm, (input, weight, bias), input, normalized_shape, weight=weight, bias=bias, eps=eps
        )
    return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)

Contextual evidence from Add attention update

The residual update is scale · dropout(x) added to the residual; modulation is x · (1 + scale) + shift.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Add attention update

A block normalizes, modulates by the time condition’s shift and scale, projects to queries, keys and values, rotates queries and keys, attends over all positions with softcap, and adds the projected output scaled by a gate; the MLP branch does the same with its own shift, scale and gate.

The implementation, pinned at revision e18c42acdfb7, not yet public