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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__init__.py	Pixal3D weight converter + corrected scope	2026-08-02 11:25:00 +10:00
convert.py	Pixal3D weight converter + corrected scope	2026-08-02 11:25:00 +10:00