PulpCut/MiniMax-H3-Ref2VA-Turbo-INT8-ConvRot

MiniMax H3 Ref2VA Turbo · pruned INT8 ConvRot

What this repository is

An optimized MiniMax H3 Ref2VA package centered on the omni-reference diffusion transformer, which conditions generation on ordered reference images. It has the lightx2v turbo step-distillation merged into the weights and uses the same pruned INT8 ConvRot layout as the Comfy-Org release. The primary transformer is a drop-in replacement for minimax_h3_ref2va_pruned_int8_convrot.safetensors in any runtime that reads the optimized INT8 layout — including H3ddle, the open-source native macOS app it was built for.

The transformer is not a standalone model. It needs the rest of the optimized package (Qwen3-VL-32B INT8 text encoder, video/audio VAEs, tokenizer) from Comfy-Org/MiniMax-H3, and the FL2VA transformer alongside it if you also want prompt-only and keyframe generation.

H3ddle full input-major checkpoint

minimax_h3_ref2va_pruned_turbo_int8_convrot_input_major.safetensors is the H3ddle-optimized form of the primary transformer. All 200 quantized core projections across 50 blocks are stored [input, output] instead of [output, input]. No tensor is dequantized or numerically changed; scales, ConvRot metadata, reference conditioning, and non-core tensors are preserved.

This layout is selected by a versioned marker and validated against every projection shape before H3ddle runs it. It is not compatible with runtimes that assume the original output-major Comfy layout. The original and full input-major transformers remain in this repository for users and runtimes that want exact Ref2VA weights.

Measured performance versus the regular layout

On a 32 GiB M1 Pro, using the same prompt, reference, seed, 512x896 canvas, eight passes, 50 blocks, and output settings:

comparison	regular / baseline	optimized path	resulting gain
transformer layout, matched eight-pass run	541.2 s output-major	515.3 s input-major	4.8% (1.05x)
complete generation, matched layout A/B	631.4 s	595.9 s	5.6% (1.06x)
regular Ref2VA INT8 vs Turbo Ref2VA INT8, matched quality run	49.0 min / 20 passes	20.2 min / 8 passes	58.8% (2.43x)

The generated reference output remained good. Actual gains vary with canvas size, memory pressure, and Mac.

Compact H3ddle hybrid overlay

minimax_h3_ref2va_pruned_int8_convrot_hybrid_adaln_25_49.safetensors is a 43.55 MB optional overlay for H3ddle. It uses the complete FL2VA transformer as the base and replaces only Ref2VA's compact AdaLN weight and bias in blocks 25–49. The overlay is 99.79% smaller than carrying a second 20.97 GB transformer. Its bytes are identical whether extracted from the standard or Turbo Ref2VA checkpoint, so the same file supports both FL2VA variants.

This is a storage and download optimization, not a denoising-speed optimization. It intentionally produces a different sample than the full Ref2VA transformer. A matched 512-square, eight-pass test on an M1 Pro found good reference identity and image quality; transformer work remained the same within run-to-run system variation. Starting with H3ddle 0.7.4, both managed standard and Turbo + References packages download this overlay by default. The full Turbo checkpoints above remain available here for manual use and comparison, and the full standard checkpoint remains in Comfy-Org/MiniMax-H3.

Why this merge was made and republished

Every published turbo LoRA for MiniMax H3 targets the FL2VA transformer. Reference generation therefore had no fast path at all: on Apple silicon a short reference clip costs roughly fifty minutes at the base model's twenty denoising passes, with no distilled alternative to fall back on. Since the two transformers are structurally identical — 932 tensors, identical names and shapes — the FL2VA distillation can be merged into the Ref2VA weights, which is what this file is.

Does the distillation survive the transfer?

The adapter was distilled against FL2VA, and reference-identity transfer is exactly the capability step distillation is most likely to damage, so this was measured rather than assumed. Matched control on an M1 Pro — same prompt, same reference image, same seed, 448², adapter as the only variable:

comparison	regular / baseline	optimized path	resulting gain
passes	20	8	60% fewer
wall time	49.0 min	20.2 min	58.8% (2.43x)
identity vs reference (SSIM)	0.426	0.476	identity retained

2.4x faster with identity transfer intact. The reference subject's markings, build, and setting carry through; detail in fur and whiskers is slightly sharper than the base at its full pass count. The SSIM difference is modest and comes from a single prompt, so treat it as evidence that identity transfer is not degraded rather than proof that it improves.

Recommended settings: 8 denoising passes, euler sampling, all 50 blocks.

Known behavior and limitations

The pruned ComfyUI conversion of the turbo LoRA drops all 51 AdaLN adapter pairs (the source targets AdaLN input dimension 2688, while pruned "compact-curve" models use dimension 8), and its own metadata warns that four-step distillation behaviour may therefore be degraded. We use 8 passes rather than 4 for that reason. The same caveat applies to our FL2VA turbo file; see ModelTC/Minimax-H3-Turbo#7.

How the merge/quantization is done (high level)

For each of the 208 quantized projections, the BF16 pruned Ref2VA base weight is merged with strength × B·A (rank-64, strength 1.0, ema_pruned variant), rotated by the grouped 256-wide Hadamard transform used by the ConvRot runtime kernels, and requantized with symmetric per-row absmax INT8 scales. Token-refiner adapters merge losslessly in BF16. All other tensors are copied byte-identical from the official INT8 file. The pipeline reproduces the official quantizer exactly: run at strength 0 against the Ref2VA base it regenerates the official file with all 3,046,400 scales identical and 1,711 of 19.27 billion int8 values differing (rounding ties).

Source and attribution

• Original model: MiniMaxAI/MiniMax-H3
• Pruned INT8 ConvRot base + shared package files: Comfy-Org/MiniMax-H3
• Turbo distillation LoRA: ModelTC/Minimax-H3-Turbo (lightx2v team)
• Pruned ComfyUI LoRA conversion: drbaph/MiniMax-H3-Turbo-Lora-ComfyUI

Licensing

Derivative of MiniMax H3 weights; the MiniMax H3 Community License Agreement applies. By downloading you agree to its terms.

What these files are used for in H3ddle

H3ddle installs it in both managed reference-capable packages: the app verifies the SHA-256 below and reuses the overlay and other shared package files it already has via hardlinks. The Turbo package defaults to 8 passes; standard keeps its regular generation profile. Published by PulpCut, whose editor family shares the local-first media generation stack that H3ddle implements in the open.

Safety and intended use

Intended for local, personal video generation. The merge changes speed characteristics, not the base model's content behavior; all usage restrictions of the MiniMax H3 Community License apply unchanged.

File inventory

File	Bytes	SHA-256
minimax_h3_ref2va_pruned_turbo_int8_convrot.safetensors	20,970,379,854	e64cef63bc2785bcd72e6103c52aa78c6cd2c4f9870a7ce79675083fd65cf2e7
minimax_h3_ref2va_pruned_turbo_int8_convrot_input_major.safetensors	20,970,380,012	5ca6696fe1cd9a8f254594ac67ee541f151b2377735dea3557364bd868270463
minimax_h3_ref2va_pruned_int8_convrot_hybrid_adaln_25_49.safetensors	43,551,180	c3d80a9a2d17a30caf83e933262473cbf0b1ba7de4d29556646e9a92ab5f17aa

Reproducibility references

The conversion is a single dependency-free Python script, Scripts/convert-turbo-package.py in the H3ddle repository, including the strength-0 self-check used to validate the pipeline against the official file.

The full optimized layout is reproducible with Scripts/repack-h3-input-major.py.

The compact overlay is reproducible with Scripts/build-h3-hybrid-adaln.py.

Contact

Open an issue in the H3ddle repository.