A 5M-parameter adapter injects cross-architecture semantics into MiniMax H3
NoMouse9610 · reddit · 2026-09-05
A developer transferred high-level semantic representations from SenseNova U1.5 into MiniMax H3's conditioning space, releasing weights, a ComfyUI node, and training scripts as the "Semantic Bridge" project.
- Direct weight grafting was impossible: zero shape matches between the two models.
- Screening 30 layer pairs found the best bridge, SenseNova L32 → H3 L49, via a 4096→128→5120 low-rank projector (validation cosine 0.9042).
- Since the initial bridge required running SenseNova at inference, he distilled it into a 5–6M-parameter student network that reconstructs the projected teacher representation from H3's own L49 conditioning—no diffusion weights modified, no donor model needed.
- Final V3 results: representation cosine 0.9959, all correction cosines above 0.98, and 0.999958 blended-conditioning cosine at alpha 0.10.
Targeted improvements include prompt adherence, composition, anatomy, materials/lighting, reflections, and text understanding.
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