| --- |
| license: other |
| license_name: minimax-h3-community-license-agreement |
| license_link: https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/LICENSE |
| base_model: |
| - MiniMaxAI/MiniMax-H3 |
| tags: |
| - comfyui |
| - diffusion-single-file |
| - nvfp4 |
| - blackwell |
| --- |
| |
| # MiniMax H3 FL2VA pruned, NVFP4 (Blackwell) |
|
|
| An NVFP4 quantisation of the MiniMax H3 pruned FL2VA diffusion model for |
| ComfyUI. Every one of the 200 block linears (qkv, out, fc1, fc2 in all 50 |
| blocks) is stored as fp4 e2m1 with fp8 e4m3 block scales of 16 and one fp32 |
| per-tensor scale; embeds, token refiner, norms and heads stay bf16. On |
| Blackwell (sm120) the GEMMs run natively on the fp4 tensor cores. |
|
|
| **Modification notice (required by the license): this repository contains a |
| modified version of MiniMax H3. The modification is post-training weight |
| quantisation of the block linears to NVFP4, performed 2026-08-19. Built from |
| `diffusion_models/minimax_h3_fl2va_pruned_bf16.safetensors` in |
| [Comfy-Org/MiniMax-H3](https://huggingface.co/Comfy-Org/MiniMax-H3), revision |
| `3f57e8291d2ef846f9a074b1b76d2767db434abe`.** |
| |
| ## Should you use this file? |
| |
| - **RTX 50-series / Blackwell + pytorch cu130+, current ComfyUI**: yes, if you |
| want the speed. Measured against the W4A8 file on the same seed and graph: |
| 0.84x wall at 29k tokens, 0.90x at 45k, on an RTX PRO 6000. Same file size |
| as W4A8 (12.5 GB, both 4.54 bits per parameter), so this is a speed play, |
| not a memory play. |
| - **Any other GPU**: no. ComfyUI falls back to dequantised matmuls, which is |
| slower than `int8_convrot`. Use Comfy-Org's `int8_convrot` file instead. |
| |
| Runs on stock ComfyUI, no custom nodes required: the file carries per-layer |
| `comfy_quant` metadata, the same mechanism as the NVFP4 text encoder that |
| Comfy-Org already ships. |
| |
| ## How to run it |
| |
| 1. Blackwell GPU (RTX 50 series or RTX PRO Blackwell), pytorch built for CUDA |
| 13.0 or newer, and a current ComfyUI updated together with its |
| comfy-kitchen dependency. |
| 2. Put the `.safetensors` in `models/diffusion_models/minimax_h3/` next to the |
| usual H3 stack from Comfy-Org (text encoder, video VAE, audio VAE). |
| 3. Use any H3 workflow and point `UNETLoader` at this file, `weight_dtype` |
| default. No custom nodes; the per-layer metadata does the rest. |
| 4. Check the load log for `Native ops: nvfp4`. If a render comes out slower |
| than the int8 file you are on the dequant fallback (wrong pytorch or a |
| stale comfy-kitchen). |
| 5. The lightx2v turbo LoRA stacks cleanly and keeps the speed: measured |
| 12-step turbo, 210 s vs 239 s on the W4A8 file (0.88x), same graph and |
| seed. |
| |
| ## What it costs in quality |
| |
| Median weight error is 9.4 percent relative rms vs bf16 (the W4A8 file |
| carries 7.3, int8_convrot 1.0). In practice a same-seed render is a clean |
| sibling take: same scene and words, slightly different delivery. Side-by-side |
| pages with clips, pixel, flow and audio rulers, and a synced A/B player: |
| https://matlowai.github.io/ComfyUI-MAINodes/a6-review/ (the gold cards are |
| this checkpoint). Note that an int8 control lands in the same |
| distance-from-reference band, so within this model family that distance |
| measures which take you got, not how good it is. Judge with your eyes on your |
| own content. |
| |
| ## Rebuild it yourself |
| |
| The 30-second builder script (quantises the Comfy-Org bf16 file with comfy's |
| own TensorCoreNVFP4Layout) ships in |
| [ComfyUI-MAINodes](https://github.com/matlowai/ComfyUI-MAINodes) as |
| `tools/build_nvfp4_checkpoint.py`. The `.census.json` beside the weights holds |
| the per-layer weight error of this exact build. |
| |
| ## License |
| |
| MiniMax H3 Community License Agreement (see LICENSE and NOTICE in this |
| repository, and the license link above). The license carries territory |
| restrictions and other conditions; read it before using or redistributing. |
| Powered by MiniMax H3. |
| |
