MiniMax H3 SparseRef15 Hybrid – FL2VA × Ref2VA INT8 ConvRot
A custom MiniMax H3 FL2VA / Ref2VA hybrid designed to balance FL2VA image quality and motion freedom with lightweight Ref2VA reference consistency.
Unlike conventional H3 hybrid models that replace one continuous range of later transformer blocks with Ref2VA blocks, SparseRef15 distributes Ref2VA AdaLN layers sparsely across the DiT.
Base:
minimax_h3_fl2va_pruned_int8_convrot
Reference overlay:
minimax_h3_ref2va_pruned_int8_convrot
Ref2VA AdaLN blocks:
1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43
Only 15 of the 50 transformer blocks use Ref2VA AdaLN conditioning.
The remaining blocks, including final_layer.adaln_proj, remain FL2VA-based.
The idea is to distribute relatively light Ref2VA influence across early, middle, and later parts of the network instead of concentrating it in a single block range.
Intended characteristics:
- Better reference retention than pure FL2VA
- More motion and prompt freedom than strongly Ref2VA-weighted hybrids
- Reduced tendency toward overly rigid reference following
- Good balance between character/reference consistency and FL2VA visual quality
- Especially interesting for chained or multi-stage video workflows
IMPORTANT:
No Lightning / Turbo / acceleration LoRA is merged into this checkpoint.
You can freely use your preferred MiniMax H3 acceleration LoRA separately.
The use of ModelSamplingMiniMaxH3 is not recommended.
Recommended acceleration LoRA:
MiniMax H3 FL2V LightX2V Turbo 4-step v0.1
LightX2V v0.1 is a good starting point when stability, image quality, and chained video consistency are more important than maximum motion intensity.
Other acceleration LoRAs, including DARE-TIES based merges or stronger Turbo LoRAs, may also work well if more aggressive motion is desired.
Recommended starting points:
Stable / chained video:
- LightX2V Turbo 4-step v0.1
- Euler
- simple scheduler
- Video Shift around 12
- Ref video around 12–24 frames, adjusted depending on desired reference strength
Higher visual impact / more dynamic results:
- ER SDE
- beta scheduler
- Video Shift around 16~28
ER SDE + beta is also highly recommended and can produce particularly clean and visually rich results.
For long chained workflows, Euler + simple may still be the safer starting point when maximum continuity is the priority.
The model retains the original pruned INT8 ConvRot format.
No additional FP8 conversion or re-quantization was applied.
This is an experimental custom hybrid, not an officially trained MiniMax model.
Results will vary depending on prompt, reference images/videos, reference length, sampler, scheduler, Video Shift, acceleration LoRA, and workflow design.
