Denoiser

Pinned implementation and paper LDLM @ e18c42a, not yet public · arXiv:2605.07933v1 · OpenWebText · 1024 · checkpoint, tokenizer and saved statistics not verified · How to read

Sampler › Denoiser

Time projection

×

learned

An affine layer turns the time condition into a shift and a scale of 768 features for the output.

mout=[ bo  so ]=atWo+co{\htmlData{light=final-projection}{m_{\mathrm{out}}}}=[\,b_o\;s_o\,]={\htmlData{light=condition time-embedding}{a_t}}W_{o}+c_{o}
in
ata_t [B batch, 768 features]
out
moutm_{\mathrm{out}} [B batch, 1 every position, 1536 two vectors of 768]
parameters
1,181,184
Parameters

1,181,184 parameters owned by this component

Exact per-instance accounting
ComponentCalculationParameters
weights + bias768×1,536+1,536768 \times 1{,}536 + 1{,}5361,181,184
Per-instance total1,181,184

Across 1024 shared calls: 1,181,184×1=1,181,1841{,}181{,}184 \times 1 = 1{,}181{,}184 parameters.

Derived from the pinned implementation’s constructors and configuration. The checkpoint’s tensors have not been counted. 306,011,162 of these parameters are trained; frozen GPT-2 and the decoder’s mask vector, 124,442,112, are not.

A 768 × 1,536 matrix and a bias of 1,536, both zero at initialization.

Sources1

Direct evidence

The output layer: gain-only LayerNorm, a shift and a scale from the time condition, and an affine projection initialized at zero.

The implementation, pinned at revision e18c42acdfb7, not yet public