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drozbay/MiniMax-H3-FastH3-Preview-LoRA
MiniMax-H3-FastH3-Preview-LoRA is a machine learning model from drozbay. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for minimax-h3. The card lists the license as other.
LoRA extraction of FastVideo-Minimax-FastH3-Preview-v0.2, the 4-step DMD2 distillation of MiniMax-H3 FL2VA, as the weight difference against the base Comfy-Org fl2va release. Apply to any fl2va base model to sample in…
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From the Hugging Face model README
LoRA extraction of FastVideo-Minimax-FastH3-Preview-v0.2, the 4-step DMD2 distillation of MiniMax-H3 FL2VA, as the weight difference against the base Comfy-Org fl2va release. Apply to any fl2va base model to sample in 4 steps instead of 50; guidance-distilled, so cfg 1.0.
| file | for | size |
|---|---|---|
loras/..._lora_pruned_rank128_fp16 | minimax_h3_fl2va_pruned_* bases (recommended) | 1.33 GB |
loras/..._lora_pruned_rank64_fp16 | same, smaller | 0.71 GB |
loras/..._lora_full_max256_avg253_fp16 | full (non-pruned) fl2va bases only | 5.06 GB |
The pruned and full variants are not interchangeable: the adaln projections differ between the two base layouts (see below).
Standard LoRA Loader (model only) at strength 1.0 on the matching fl2va base, then sample with
4 steps, euler, simple scheduler, cfg 1.0 (this reproduces the trained ladder
[999, 749, 500, 250] under the model's shift-12/3 schedule). Other step counts are
off-distribution per the FastVideo model card. fl2va / t2va only; there is no ref2va student.
Layer-by-layer SVD of the weight difference (student minus base), following the
KJNodes LoraExtractKJ recipe: rank 256 standard,
bias diffs included, no quantile clamp, fp16 output, with exact svd_linalg instead of
svd_lowrank. Lower ranks are truncations of the same SVD. Norm weights ship as .diff,
biases as .diff_b.
Two things specific to this model:
adaln_t_table (least squares over the 1025-point grid,
residual ~1e-5) and stores them as exact .diff keys, tiny at [out, 8].Measured on a live 4-step run (960x544, 73 frames, same seed/prompt, PSNR vs the actual FastH3 checkpoint; ceiling is the same student under a different quantization):
| run | PSNR |
|---|---|
| base, no LoRA | 14.6 dB |
| base + rank 64 | 17.1 dB |
| base + rank 128 | 17.6 dB |
| base + rank 256 | 17.7 dB |
| FastH3 itself, different quant (ceiling) | 19.9 dB |
Rank 128 is the sweet spot; rank 256 adds almost nothing at double the size. Visually all ranks land on FastH3's composition and sharpness.
Distributed under the MiniMax H3 Community License (see LICENSE), inherited from the base model. Review its territory and acceptable-use terms before use or redistribution.