John e23233731b Pixal3D weight converter + corrected scope ...

Converter is light because Pixal3D ships safetensors with sibling .json configs, so
there is no architecture to infer and no pickle to unpack. Only rank-5 tensors are
rearranged; dtype is preserved (upcasting fp16->fp32 doubled 24GB for no benefit) and
the KRSC remap is verified a pure permutation of values.

Measured: the four flow models (~20GB) have ZERO 5-D tensors - pure transformers that
never touch sparse conv. shape_dec/tex_dec carry 40 KRSC kernels each, ss_dec 20 dense
Conv3d, and classify_5d separates them correctly on the real weights.

Corrects the earlier 'gap is two ops' claim: that was right about modules/sparse but
undercounted the model blocks. The configs show all four flow models need RoPE,
qk_rms_norm and AdaLN modulation, and the decoders need SparseConvNeXtBlock3d and
SparseResBlockC2S3d.

2026-08-02 11:25:00 +10:00

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