MiniMax-H3-10Eros-Max-Quants

10Eros Max MiniMax-H3 ComfyUI Quants

Stock-ComfyUI quantized FL2VA checkpoints for the 10Eros_Max fine-tune of MiniMaxAI/MiniMax-H3.

10Eros_Max retains the full MiniMax-H3 architecture and all 50 transformer blocks. Its fine-tuned weights are confined to the fused QKV projections in blocks 0–31. These files preserve those fine-tuned QKV weights while retaining the tested quantization layouts and all remaining tensors from the corresponding MiniMax-H3 reference quants.

No custom node or ComfyUI core patch is required. These are community conversions, not official MiniMax or ComfyOrg releases.

Download

Checkpoint size is not full-workflow peak VRAM. Resolution, frame count, attention backend, text encoder, VAEs, and ComfyUI offload settings also affect memory use. The memory classes above follow the identical tensor layouts of the corresponding MiniMax-H3 reference quants; a full prompt-to-decoded-video VRAM measurement has not yet been performed for 10Eros_Max.

What was preserved

All four checkpoints:

• retain all 50 MiniMax-H3 transformer blocks;
• use the stock-ComfyUI fused qkv_proj = cat(Q,K,V) representation;
• preserve the 10Eros_Max fine-tuned fused-QKV weights in blocks 0–31;
• preserve the original/reference QKV weights in blocks 32–49;
• use the rank-16 FP32, 4,097-point AdaLN time table;
• retain 51 independent FP32 AdaLN projections;
• preserve norms, conditioning projections, patch projections, output heads, and other small or sensitive tensors in the same representation as their corresponding reference checkpoint.

The time table does not remove timestep conditioning. It interpolates a compact representation of the original measured time curve.

Quantization layouts

INT8 weights use tensorwise INT8 ConvRot/Hadamard rotation with group size 256, per-row FP32 scales, and deterministic scale search.

The compact NVFP4 profile uses block-scaled NVFP4 for every main and token-refiner linear matrix. The NVFP4-HQ profile follows the same sensitive layer policy as INT8-ConvRot: 170 main matrices use NVFP4, while 30 main matrices and all eight token-refiner matrices remain BF16. Both NVFP4 profiles are intended primarily for Blackwell hardware.

The experimental W4, W4-Offload, and mixed W8/W4 layouts are intentionally not published for 10Eros_Max. Their base-model checkpoints failed perceptual video tests even though their containers and tensor layouts were structurally valid.

Conversion method

The complete 10Eros_Max BF16 checkpoint was compared tensor-by-tensor with the local original MiniMax-H3 FL2VA source:

• all 535 expected tensors and all 50 blocks were present;
• shapes and dtypes matched the stock full-width architecture;
• no NaN or Inf values were found;
• 503 tensors were bit-identical to the original source;
• the intentional fine-tune consisted of 32 modified fused-QKV tensors in blocks 0–31.

Only those 32 QKV tensors were requantized for each runtime format. The INT8 QKV payload was produced once and reused for both INT8 profiles; the NVFP4 QKV payload was likewise reused for both NVFP4 profiles. Each final checkpoint was assembled from its corresponding tested reference checkpoint by replacing only the QKV weight, scale, and quantization-configuration byte ranges for blocks 0–31.

This avoids requantizing or rewriting the semantic content of unchanged layers.

Validation

All four published files passed an independent byte-range audit:

1. file size, safetensors header, key inventory, shapes, and dtypes match the corresponding reference checkpoint;
2. every byte outside the permitted QKV ranges remains identical to the reference checkpoint;
3. every replaced QKV/scale/config tensor matches the separately generated 10Eros_Max quantized payload;
4. the replacement weights differ from the base reference as expected;
5. the local INT8 and NVFP4 quantizers reproduced the reference quantized tensors bit-for-bit on an unchanged control block.

No prompt-to-decoded-video perceptual A/B result is claimed yet. The validation above proves structural and conversion correctness, not subjective fine-tune quality.

Installation

Place one selected checkpoint in:

ComfyUI/models/diffusion_models/

Then refresh the model list or restart ComfyUI and load it through the standard MiniMax-H3 FL2VA workflow.

A complete workflow also requires the MiniMax-H3 Qwen3-VL text encoder and the video/audio VAEs. They are not duplicated here. Shared components and additional base-model quant profiles are documented in MiniMax-H3-ComfyUI-Quants.

License and attribution

Use is subject to the included MiniMax-H3 Community License Agreement. The base model is by MiniMax. The BF16 fine-tune was supplied under the name 10Eros_Max; this repository provides quantized conversions and does not claim authorship of the fine-tune itself.

ComfyUI and its quantization runtimes are separate upstream projects. This repository is not endorsed by MiniMax or ComfyOrg.