An optimized MiniMax H3 FL2VA package centered on a diffusion transformer with the lightx2v turbo step-distillation merged into the weights, quantized in the same pruned INT8 ConvRot layout as the Comfy-Org release. The primary transformer is a drop-in replacement for minimax_h3_fl2va_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.

minimax_h3_fl2va_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, and all non-core tensors are copied byte-for-byte.

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 transformer remains in this repository for those runtimes; current H3ddle managed installs download only the full input-major checkpoint.

On a 32 GiB M1 Pro, using the same prompt, seed, 512x512 canvas, eight passes, 50 blocks, and output settings:

The generated images were identical. Actual gains vary with canvas size, memory pressure, and Mac. The external wall-time row comes from a reproducible three-pattern RTX 4070 benchmark that used the regular minimax_h3_fl2va_pruned_int8_convrot.safetensors as its 272.97-second baseline. Its two published eight-step Turbo means were 130.30 and 131.02 seconds; their 130.66-second mean is shown above. That is external NVIDIA/ComfyUI evidence, not a projected M1 time. The work-count row is exact, and the Turbo checkpoint is intended to reach roughly the regular model's 20-pass fidelity in 6–8 passes.

Step-distilled turbo checkpoints reach roughly 20-pass visual quality in 6–8 denoising passes, which is the difference between usable and unusable generation times on low-memory Apple-silicon machines. No hosted turbo variant existed in the INT8 ConvRot format that memory-constrained runtimes stream from disk, so we merged and requantized one. H3ddle's managed model downloads also require a pinned, hash-verified hosted artifact, which this repository provides.

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.

In our testing that gap did not produce a measurable prompt-adherence penalty. Every prompt-following miss we observed at 256²–512² with 6–8 passes — wrong subject species, illustration-style output, text-like artifacts — is reproduced by the unmodified base package at matched settings, seed, and canvas, so those are properties of the base model at low step counts rather than effects of the distillation. What the merge does change is fidelity: detail, fur, and lighting improve substantially at the same step count. We asked the turbo authors about a curve-compatible variant in ModelTC/Minimax-H3-Turbo#7.

Recommended settings: 6–8 denoising passes, euler sampling, all 50 blocks. A Beta(0.6, 0.6) sigma schedule is commonly paired with turbo checkpoints; we measured no consistent difference against the released linear grid on this package.

For each of the 200 quantized projections, the BF16 pruned 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 it regenerates the official file with all 3,046,400 scales identical and 1,682 of 19.27 billion int8 values differing (rounding ties).

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

H3ddle uses the input-major file as the FL2VA core of its "MiniMax H3 · Turbo + Hybrid References" managed model. The app verifies the SHA-256, adds the compact reference overlay, reuses shared package files via hardlinks, and defaults the model to 8 passes. Published by PulpCut, whose editor family shares the local-first media generation stack that H3ddle implements in the open.

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.

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.

Open an issue in the H3ddle repository.