Fast-generation fine-tunes and detail-oriented models often pull in different directions: merging them naively can preserve neither cleanly. This project’s central idea is to treat the merge as a signal-processing and geometry problem, aiming to retain the Turbo model’s motion behavior without letting its weight differences overwhelm the other fine-tune. The intended balance is a design goal, not an independently verified benchmark result.
Key Capabilities
- Combines Singularity V2 with an 8-step 10Eros-Max Turbo fine-tune, targeting both detailed appearance and fast, flexible motion.
- Applies randomized SVD to separate structured weight differences from diffuse residuals, then attenuates the residual adaptively. This is meant to reduce merge artifacts rather than simply average every parameter equally.
- Uses spherical interpolation for weight directions and log-space interpolation for magnitudes, followed by sign-conflict handling and row-norm re-anchoring. These steps aim to preserve useful structure and stabilize layer scales.
- Provides an FP16 checkpoint, with additional community test variants described for INT8 and other mix settings.
Who It’s For
A good fit for users already working with MiniMax H3 Ref2VA workflows who want to experiment with a merged checkpoint and can assess results on their own prompts. Look elsewhere if you need a controlled, independently benchmarked model or a license with uncomplicated regional permissions: the merge inherits terms from its source models, so check both before use.
How to Read the Merge
The method does not promise that two models’ strengths combine automatically. PRISM first reshapes the difference between their weights, then interpolates directions and magnitudes separately. That makes its value especially dependent on the compatibility of the source fine-tunes and on the intended generation workflow; sample videos and user testing should be treated as demonstrations, not proof of general superiority.
