awesome-minimax-h3-integration README

The project's README from github.com/MiniMax-AI/awesome-minimax-h3-integration. As one markdown file: README.md.

MiniMax H3

MiniMax H3 Integrations

A community-maintained index of checkpoints, tools, and workflows for MiniMax H3, ordered by developer interest.

Official resources

Start here

This navigation guide is not a complete compatibility list.

Goal Start with
Run on your GPU Run locally — pick a stack from the VRAM table
Work with audio comfyui-minimax-h3-audio-T8 and audio VAE
Build in ComfyUI Official tutorial · Workflows & nodes
Write better prompts Prompting
Make it faster Speed
Fine-tune it Training & LoRAs
Serve it as an API Serving H3
Run on Apple Silicon antirez/h3.c

Run it locally

MiniMax-H3 generates video with native stereo audio from text, images, video, and audio inputs. It supports clips up to 2K and 15 seconds. Two base variants exist:

Checkpoints are the same size. FL2VA, trained only with keyframes, typically yields better raw output. Ref2VA accepts more reference material but has lower base quality; the Ref Patch can partially bridge this gap.

By VRAM and hardware

Find your GPU in the table, then use the notes to inform your configuration.

Situation Stack Why this combination
24 GB, first run pruned_int8_convrot DiT (19.53 GiB) + TE nvfp4_awq (14.61 GiB) + ComfyUI-MiniMaxH3-Easy Easy routes T2V, I2V, first/last-frame, and reference input through a single Media port. Sampling, LoRAs, and decoding remain outside the node for later modification.
24 GB, want speed The above + TE-Speed-MiniMaxH3 + Turbo v4_step600_ema at 6–8 steps Block-cache acceleration; v3.2 adapts to current ComfyUI's block prefetch and no longer patches core files. 6–8 steps reduce Turbo motion smear.
22 GB, Turing (sm_75) minimax-h3-turing — W4A8 mixed + Turbo 4-step field handbook 5.7 min/clip baseline; T8 BlockCache drafts −43%; PDD 8-step + T8 combo 210 s/clip. sm_75 has no FP8/BF16 tensor cores — quantization, launch flags and kernel support all differ from Ampere+
12–16 GB Pruned Q4_K_M GGUF (10.64 GiB) or pruned nvfp4 (11.67 GiB) + TE Q2_K (7.91 GiB) + fp8mix VAE pair GGUF offers the most size options, beneficial for tight memory. IQ1_S is smaller at 3.78 GiB, but quality noticeably drops.
8 GB DiffSynth-Studio NF4 path The project states 8 GB as its minimum for this path. Offloading performs most work here; expect slow performance, not just small memory footprint.
RTX 50-series / Blackwell NVIDIA Sol-Attn 1.14–1.44× faster than SageAttention with −37 % MLP peak VRAM, measured on a 5090. SM89–SM121, Triton 3.6.0. Also unlocks Blackwell-only hybrid-NVFP4 checkpoints.
Multi-shot / long video ComfyUI-H3-Motion-Context H3 generates in blocks up to 15 s. Motion-Context feeds the previous block's final frame and audio forward, preserving motion direction and speed.
Storyboard / timeline ComfyUI_MiniMaxH3_Director Five importable templates: t2v, fl2v, r2v, v2v, and rv2v.
Inpaint / local edit scraed/LanPaint v2.1.0 fixed H3 support. Training-free video and audio inpainting.
Apple Silicon antirez/h3.c (MIT, Metal-native) h3.c supports T2V/A, first-last-frame, and ordered Ref2VA references end-to-end, with M3 Max / M5 Max performance optimization ongoing.
One-command local open-video-ai/open-video "Ollama for video models" — install · pull · run.

Checkpoints

Source What it is Files Total
MiniMaxAI/MiniMax-H3 Original diffusers weights — transformer/ (FL2VA) and transformer_ref/ (Ref2VA) at 14 shards / 61.73 GiB each, plus text encoder, video VAE, audio VAE, and self-contained FL2VA/ and Ref2VA/ pipeline folders 280 464.2 GiB
Comfy-Org/MiniMax-H3 ComfyUI-repackaged single-file weights — 10 diffusion models, 3 text encoders, video + audio VAE 17 433.2 GiB
Variant Name Precision Size Download
FL2VA minimax_h3_fl2va ![bf16][badge-bf16] 61.73 GiB ![][gh-Comfy--Org]
FL2VA minimax_h3_fl2va ![int8][badge-int8] 31.70 GiB ![][gh-Comfy--Org]
FL2VA minimax_h3_fl2va_pruned ![bf16][badge-bf16] 37.46 GiB ![][gh-Comfy--Org]
FL2VA minimax_h3_fl2va_pruned ![fp8][badge-fp8] 19.52 GiB ![][gh-Comfy--Org]
FL2VA minimax_h3_fl2va_pruned ![int8][badge-int8] 19.53 GiB ![][gh-Comfy--Org]
Ref2VA minimax_h3_ref2va ![bf16][badge-bf16] 61.73 GiB ![][gh-Comfy--Org]
Ref2VA minimax_h3_ref2va ![int8][badge-int8] 31.70 GiB ![][gh-Comfy--Org]
Ref2VA minimax_h3_ref2va_pruned ![bf16][badge-bf16] 37.46 GiB ![][gh-Comfy--Org]
Ref2VA minimax_h3_ref2va_pruned ![fp8][badge-fp8] 19.52 GiB ![][gh-Comfy--Org]
Ref2VA minimax_h3_ref2va_pruned ![int8][badge-int8] 19.53 GiB ![][gh-Comfy--Org]

"Pruned" means AdaLN-pruned. These models are roughly 40% smaller, work only in ComfyUI, and underpin most of the consumer-GPU quants below. For a 24 GB card, pruned_int8_convrot (19.53 GiB) is the usual starting point.

Quantized Models

MiniMax provides original BF16 checkpoints. The files below are community conversions and repackaged variants, not official MiniMax releases. Confirm compatibility with your runtime's documentation before downloading.

Community FL2VA conversions
Pruned Precision Method Size Download
![bf16][badge-bf16] BF16 61.73 GiB ![][gh-Comfy--Org]
![bf16][badge-bf16] Hybrid (fl2va base + ref2va adaln b15-49) 20.97 GiB ![][gh-smhfacct]
![bf16][badge-bf16] Hybrid (fl2va base + ref2va adaln b20-49) 20.97 GiB ![][gh-smhfacct]
![bf16][badge-bf16] Hybrid (fl2va base + ref2va adaln b25-49) 20.97 GiB ![][gh-smhfacct]
![bf16][badge-bf16] Hybrid (fl2va base + ref2va adaln b30-49) 20.97 GiB ![][gh-smhfacct]
![int8][badge-int8] ConvRot 31.70 GiB ![][gh-Comfy--Org]
![int8][badge-int8] ConvRot Lean (HQ) 21.91 GiB ![][gh-DmitryDB]
![int8][badge-int8] ConvRot 20.94 GiB ![][gh-DmitryDB]
![int8][badge-int8] ConvRot Lite 20.33 GiB ![][gh-DmitryDB]
![fp8][badge-fp8] FP8 E4M3FN 43.78 GiB ![][gh-rzgar]
![mxfp8][badge-mxfp8] MXFP8 44.34 GiB ![][gh-rzgar]
![fp8][badge-fp8] FP8 + FP16 attention 26.70 GiB ![][gh-rzgar]
![nvfp4][badge-nvfp4] NVFP4 (HQ) 13.60 GiB ![][gh-DmitryDB]
![nvfp4][badge-nvfp4] NVFP4 10.86 GiB ![][gh-DmitryDB]
![nvfp4][badge-nvfp4] NVFP4 32.05 GiB ![][gh-rockerBOO]
![int4][badge-int4] NF4 (DiffSynth) 15.98 GiB ![][gh-DiffSynth-Studio]
OrbitQuant W4A4 17.03 GiB ![][gh-WaveCut]
![int8][badge-int8] ⚠️ DT-sQKV ConvRot 21.00 GiB ![][gh-DmitryDB]
![int8][badge-int8] ⚠️ DT-sQKV ConvRot Lean 27.99 GiB ![][gh-DmitryDB]
✓ ![bf16][badge-bf16] BF16 37.46 GiB ![][gh-Comfy--Org]
✓ ![fp8][badge-fp8] FP8 scaled 19.52 GiB ![][gh-Comfy--Org]
✓ ![int8][badge-int8] ConvRot 19.53 GiB ![][gh-Comfy--Org] ┊ ![][gh-Abiray]
✓ ![nvfp4][badge-nvfp4] NVFP4 18.69 GiB ![][gh-rockerBOO]
✓ ![nvfp4][badge-nvfp4] NVFP4 + ConvRot INT8 18.69 GiB ![][gh-rockerBOO]
✓ ![nvfp4][badge-nvfp4] NVFP4 11.67 GiB ![][gh-Abiray]
✓ ![int4][badge-int4] Mixed INT4/INT8 ConvRot 14.81 GiB ![][gh-Abiray] ┊ ![][gh-tsolful]
✓ ![int4][badge-int4] Mixed INT4/INT8 ConvRot Lean 17.27 GiB ![][gh-tsolful]
✓ ![int4][badge-int4] INT4 ConvRot 15.67 GiB ![][gh-rockerBOO]
✓ ![int4][badge-int4] Mixed INT4/INT8 ConvRot 18.92 GiB ![][gh-rockerBOO]
✓ ![int4][badge-int4] INT4 ConvRot 16.67 GiB ![][gh-Merserk]
✓ ![int4][badge-int4] INT4 ConvRot (pruned) 10.56 GiB ![][gh-Merserk]
✓ ![int4][badge-int4] W4A8 ConvRot 11.68 GiB ![][gh-AX1Y2JP] ┊ ![][gh-Kijai] ┊ ![][gh-Winnougan]

GGUF quants — see the GGUF section.

Community Ref2VA conversions
Pruned Precision Method Size Download
![bf16][badge-bf16] BF16 61.73 GiB ![][gh-Comfy--Org]
![int8][badge-int8] ConvRot ┊ (patchin HF 1.02) 31.70 GiB ![][gh-Comfy--Org] ┊ ![][gh-t8star]
![int8][badge-int8] ConvRot Lean (HQ) 21.91 GiB ![][gh-DmitryDB]
![int8][badge-int8] ConvRot 20.94 GiB ![][gh-DmitryDB]
![int8][badge-int8] ConvRot Lite 20.33 GiB ![][gh-DmitryDB]
![nvfp4][badge-nvfp4] NVFP4 (HQ) 13.60 GiB ![][gh-DmitryDB]
![nvfp4][badge-nvfp4] NVFP4 10.86 GiB ![][gh-DmitryDB]
![nvfp4][badge-nvfp4] NVFP4 32.05 GiB ![][gh-rockerBOO]
![nvfp4][badge-nvfp4] NVFP4 mixed 22.76 GiB ![][gh-Abiray]
![int4][badge-int4] NF4 (DiffSynth) 15.98 GiB ![][gh-DiffSynth-Studio]
OrbitQuant W4A4 17.03 GiB ![][gh-WaveCut]
![nvfp4][badge-nvfp4] Hybrid NVFP4, FFN-only (Blackwell) 16.38 GiB ![][gh-abakanai]
![nvfp4][badge-nvfp4] Hybrid NVFP4, QKV+FFN (Blackwell) 14.03 GiB ![][gh-abakanai]
![int8][badge-int8] ⚠️ DT-sQKV ConvRot 21.00 GiB ![][gh-DmitryDB]
![int8][badge-int8] ⚠️ DT-sQKV ConvRot Lean 27.99 GiB ![][gh-DmitryDB]
✓ ![bf16][badge-bf16] BF16 37.46 GiB ![][gh-Comfy--Org]
✓ ![fp8][badge-fp8] FP8 scaled 19.52 GiB ![][gh-Comfy--Org]
✓ ![int8][badge-int8] ConvRot 19.53 GiB ![][gh-Comfy--Org] ┊ ![][gh-Abiray]
✓ ![nvfp4][badge-nvfp4]

awesome-minimax-h3-integration (MiniMax-AI/awesome-minimax-h3-integration)

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Что это?Поддерживаемый сообществом индекс чекпоинтов, инструментов и воркфлоу для MiniMax H3 — модели генерации видео, создающей клипы с нативным стереозвуком из текстовых, графических, видео- и аудиовходов.
Это инструмент или просто список ссылок?Это курируемый справочный список (awesome-list), указывающий на официальные и общественные чекпоинты, квантизации, ноды ComfyUI и воркфлоу для MiniMax H3 — сам он ничего не запускает.
Как устроен список?Он начинается с таблицы по целям (запуск локально, работа со звуком, сборка в ComfyUI, написание промптов, ускорение, дообучение, выдача через API, запуск на Apple Silicon), затем идёт VRAM-таблица, сопоставляющая память GPU с рекомендуемым чекпоинтом и стеком текстовых энкодеров.
Сколько VRAM нужно для запуска MiniMax H3?От 8 ГБ (путь DiffSynth-Studio NF4 с агрессивной выгрузкой) до 24 ГБ+ для стандартного прунированного чекпоинта int8 ConvRot; VRAM-таблица в README сопоставляет конкретные классы GPU с комбинациями чекпоинт/текстовый энкодер.
Квантизированные чекпоинты — это официальные релизы MiniMax?Нет. Официальны только исходные веса BF16 diffusers от MiniMaxAI; все конверсии INT8/FP8/NVFP4/GGUF в списке — переупаковки сообщества, поэтому перед скачиванием стоит проверить совместимость с вашим рантаймом.
Это работает только на GPU NVIDIA или можно и на Mac?Большинство стеков нацелены на GPU NVIDIA от Turing (sm75) до Blackwell/серии RTX 50, но есть и нативный путь Metal для Apple Silicon через antirez/h3.c — оптимизация под M3/M5 Max ещё продолжается.
В чём разница между FL2VA и Ref2VA?FL2VA (режим первого и последнего кадра) принимает на вход ноль, одну или две картинки и обычно даёт лучшее исходное качество; Ref2VA (режим omni-reference) принимает до 9 изображений, 3 видеоклипа и 3 аудиоклипа для более богатого обусловливания, но с чуть меньшим базовым качеством, которое частично компенсирует Ref Patch.
Может ли MiniMax H3 генерировать звук вместе с видео?Да, он генерирует видео с нативным стереозвуком прямо из текстовых, графических, видео- и аудиовходов, а для этого есть отдельная нода comfyui-minimax-h3-audio-T8 плюс аудио-VAE.
Какой длины и разрешения получаются сгенерированные клипы?Клипы могут достигать разрешения 2K и длительности 15 секунд, а инструменты вроде ComfyUI-H3-Motion-Context позволяют сцеплять несколько блоков для более длинных многоракурсных видео.
Есть ли способ запустить MiniMax H3 локально одной командой?Да, open-video-ai/open-video описывается как 'Ollama для видеомоделей' — устанавливаете, подтягиваете модель и запускаете одной командой.
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CommitOct 17
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Commits · 90 Days