MiniMax H3 Integrations

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

Official resources

• MiniMax-H3 official model card · official repository
• Video Prompt Writing Guide — Base (FL2VA)
• Video Prompt Writing Guide — Reference (Ref2VA)
• ComfyUI day-0 blog post · tutorial

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:

• H3-Base-FL2VA (first-and-last-frame mode) — accepts zero, one, or two input images. Zero images for text-to-video; one image for first- or last-frame-to-video; two images for first-and-last-frame-to-video.
• H3-Base-Ref2VA (omni-reference mode) — accepts up to 9 images, 3 video clips (2–15 s each), and 3 audio clips, for a maximum of 12 files total.

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		61.73 GiB
FL2VA	minimax_h3_fl2va		31.70 GiB
FL2VA	minimax_h3_fl2va_pruned		37.46 GiB
FL2VA	minimax_h3_fl2va_pruned		19.52 GiB
FL2VA	minimax_h3_fl2va_pruned		19.53 GiB
Ref2VA	minimax_h3_ref2va		61.73 GiB
Ref2VA	minimax_h3_ref2va		31.70 GiB
Ref2VA	minimax_h3_ref2va_pruned		37.46 GiB
Ref2VA	minimax_h3_ref2va_pruned		19.52 GiB
Ref2VA	minimax_h3_ref2va_pruned		19.53 GiB

"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	61.73 GiB
		Hybrid (fl2va base + ref2va adaln b15-49)	20.97 GiB
		Hybrid (fl2va base + ref2va adaln b20-49)	20.97 GiB
		Hybrid (fl2va base + ref2va adaln b25-49)	20.97 GiB
		Hybrid (fl2va base + ref2va adaln b30-49)	20.97 GiB
		ConvRot	31.70 GiB
		ConvRot Lean (HQ)	21.91 GiB
		ConvRot	20.94 GiB
		ConvRot Lite	20.33 GiB
		FP8 E4M3FN	43.78 GiB
		MXFP8	44.34 GiB
		FP8 + FP16 attention	26.70 GiB
		NVFP4 (HQ)	13.60 GiB
		NVFP4	10.86 GiB
		NVFP4	32.05 GiB
		NF4 (DiffSynth)	15.98 GiB
		OrbitQuant W4A4	17.03 GiB
		⚠️ DT-sQKV ConvRot	21.00 GiB
		⚠️ DT-sQKV ConvRot Lean	27.99 GiB
✓		BF16	37.46 GiB
✓		FP8 scaled	19.52 GiB
✓		ConvRot	19.53 GiB	┊
✓		NVFP4	18.69 GiB
✓		NVFP4 + ConvRot INT8	18.69 GiB
✓		NVFP4	11.67 GiB
✓		Mixed INT4/INT8 ConvRot	14.81 GiB	┊
✓		Mixed INT4/INT8 ConvRot Lean	17.27 GiB
✓		INT4 ConvRot	15.67 GiB
✓		Mixed INT4/INT8 ConvRot	18.92 GiB
✓		INT4 ConvRot	16.67 GiB
✓		INT4 ConvRot (pruned)	10.56 GiB
✓		W4A8 ConvRot	11.68 GiB	┊ ┊

GGUF quants — see the GGUF section.

Community Ref2VA conversions

Pruned	Precision	Method	Size	Download
		BF16	61.73 GiB
		ConvRot ┊ (patchin HF 1.02)	31.70 GiB	┊
		ConvRot Lean (HQ)	21.91 GiB
		ConvRot	20.94 GiB
		ConvRot Lite	20.33 GiB
		NVFP4 (HQ)	13.60 GiB
		NVFP4	10.86 GiB
		NVFP4	32.05 GiB
		NVFP4 mixed	22.76 GiB
		NF4 (DiffSynth)	15.98 GiB
		OrbitQuant W4A4	17.03 GiB
		Hybrid NVFP4, FFN-only (Blackwell)	16.38 GiB
		Hybrid NVFP4, QKV+FFN (Blackwell)	14.03 GiB
		⚠️ DT-sQKV ConvRot	21.00 GiB
		⚠️ DT-sQKV ConvRot Lean	27.99 GiB
✓		BF16	37.46 GiB
✓		FP8 scaled	19.52 GiB
✓		ConvRot	19.53 GiB	┊
✓		NVFP4	18.69 GiB
✓		NVFP4 + ConvRot INT8	18.69 GiB
✓		NVFP4	11.67 GiB
✓		Mixed INT4/INT8 ConvRot	14.06 GiB	┊
✓		Mixed INT4/INT8 ConvRot Lean	17.18 GiB
✓		INT4 ConvRot	15.67 GiB
✓		W4A8 ConvRot	11.68 GiB	┊ ┊

GGUF quants — see the GGUF section.

Community multi-tier repack

DeepBeepMeep/MiniMax-H3 collects community files at several precisions. Check the model card for compatibility and licensing.

GGUF Quantized Models

GGUF files are community conversions, not part of the official MiniMax H3 release. Use a compatible loader and follow its documentation.

Community FL2VA GGUF conversions

Pruned	Quant	Size	Download
		17.42 GiB †
		14.50 GiB	┊ ┊
		14.50 GiB
		17.36 GiB	┊
		20.41 GiB
		18.50 GiB	┊ ┊
		18.49 GiB	┊
		21.21 GiB	┊
		24.17 GiB
		22.25 GiB	┊
		22.25 GiB	┊
		26.28 GiB	┊
		33.56 GiB	┊
✓		3.78 GiB — smallest DiT published
✓		4.22 GiB
✓		6.26 GiB	┊
✓		7.51 GiB
✓		8.16 GiB
✓		8.29 GiB
✓		8.90 GiB
✓		10.64 GiB	┊
✓		12.97 GiB
✓		15.45 GiB
✓		19.97 GiB
✓		20.10 GiB

† realrebelai ships Q2_K as a mixed-precision build, placing it above Q3_K_M. When comparing by size, prioritize the numerical value over the quant name.

Community Ref2VA GGUF conversions

Pruned	Quant	Size	Download
		14.50 GiB	┊ ┊
		14.50 GiB
		17.36 GiB	┊
		20.41 GiB
		18.49 GiB	┊ ┊
		18.49 GiB	┊
		21.21 GiB	┊
		24.17 GiB
		22.25 GiB	┊
		22.25 GiB	┊
		26.28 GiB	┊
		33.56 GiB	┊
✓		6.22 GiB
✓		8.12 GiB
✓		10.60 GiB
✓		10.60 GiB
✓		12.94 GiB
✓		14.00 GiB	‡
✓		15.42 GiB
✓		18.77 GiB	§
✓		19.94 GiB	┊

‡ molbal calls this one U16G — a hand-tuned mixed layout rather than a stock Q6_K. § molbal's Q8_CR is Q8_0 with ConvRot applied. Both are in the same repo as the plain quants; read the filename.

Fine-tuned checkpoint quants

DmitryDB also publishes stock-compatible quants of community fine-tunes of H3 — fine-tuned QKV weights in blocks 0–31 preserved alongside a tested quantization layout, no custom node or core patch required (the ConvRot / NVFP4 tiers match the base-model tiers exactly: 21.91 / 20.94 / 13.60 / 10.86 GiB, plus a ⚠️ DT-sQKV build at 21.00 GiB).

The fine-tunes themselves are third-party and are not itemised here; browse DmitryDB's model list directly for the current set.

Notes

• DmitryDB/MiniMax-H3-INT8-Lean-ConvRot and DmitryDB/MiniMax-H3-ComfyUI-Quants are the same repo — the author merged and rebranded. Likewise …-INT8-Lean-ConvRot-Dynamic-Time-Separate-QKV and …-DynTime-sQKV. Both names in each pair resolve to the same files, so don't download twice.
• t8star/minimax_h3_ref2va_patchin_hf102 is a weight modification, not a quant: +2 % on the 2×2 spatial high-frequency patch in the video-input projection. The author's own tests showed a weak HF-agent gain and did not confirm the "oily/waxy" look was removed. Treat as experimental.
• Winnougan/MiniMax-H3-INT4_Convrot_ComfyUI ships a matching quantized text encoder: qwen3vl_32b_minimax_h3-w4a8_convrot.safetensors.
• unsloth/MiniMax-H3-GGUF also carries Qwen3-VL text-encoder GGUFs: Q2_K_M 12.2 GiB and Q4_K_M 17.0 GiB.
• DmitryDB/MiniMax-H3-ComfyUI-Quants also carries VAE files: video VAE FP16 4.85 GiB, audio VAE FP32 577 MiB.
• DiffSynth-Studio/MiniMax-H3-NF4 bundles NF4 TE + video VAE + audio VAE. Requires DiffSynth-Studio; the project states a minimum of 8 GB VRAM on this path.
• WaveCut/MiniMax-H3-OrbitQuant-W4A4 bundles a quantized TE and FP32 VAE copies, and requires the ComfyUI-OrbitQuant node — the W4A4 path is not loadable without it. Workflow JSON.

Text encoders

MiniMax-H3 uses Qwen3-VL-32B for text and vision. On a 24 GB card, you will usually need to reduce the text encoder after reducing the DiT. The options are listed below.

Comfy-Org (official repackage)

Model	Precision	Size	Download
qwen3vl_32b_minimax_h3		47.97 GiB
qwen3vl_32b_minimax_h3		25.28 GiB
qwen3vl_32b_minimax_h3		14.61 GiB

The nvfp4_awq build at 14.61 GiB is the smallest official TE and the one to pair with a pruned INT8 DiT on a 24 GB card.

Community quantizations

Model	Precision	Size	Source
qwen3vl_32b_minimax_h3		13.58 GiB
qwen3vl_32b_minimax_h3		13.93 GiB
qwen3vl_32b_minimax_h3		25.28 GiB †
qwen3vl_32b_minimax_h3		15.35 GiB	— W4A8 ConvRot
qwen3vl_32b_minimax_h3		—	— W4A8 ConvRot
qwen3vl_32b_minimax_h3		12.2 GiB	— Q2_K_M GGUF
qwen3vl_32b_minimax_h3		17.0 GiB	— Q4_K_M GGUF
qwen3vl_32b_minimax_h3		7.91 GiB	— smallest TE published
qwen3vl_32b_minimax_h3		24.89 GiB	— quanto-INT8 ⚠️ no license

† Abiray's nvfp4_awq file is byte-for-byte the size of Comfy-Org's INT8 build, not of an NVFP4 one. Check the file before assuming it is a smaller download.

VAE and components

VAE (video & audio)

Both VAEs are required for every generation workflow — H3 decodes video and audio through separate autoencoders.

Component	Source	Precision	Size	Download
Video VAE	Comfy-Org		4.85 GiB
Audio VAE	Comfy-Org		577 MiB
Video VAE	dummy9996		2.60 GiB
Audio VAE	dummy9996		289 MiB
Video VAE	Kijai		2.95 GiB

The fp8mix video VAE (2.60 GiB) plus the bf16 audio VAE (289 MiB) save roughly 2.5 GiB over the official pair — worth taking on a 12–16 GB card, where the VAE competes with the DiT for the same headroom.

Tiny Autoencoder (TAE) — previews only

A quickly-trained 2D tiny VAE by Kijai. The author's own assessment: not a great outcome, but it still beats latent2rgb for previews. 9 MiB. Currently only usable through the ModelPreviewOverride node in ComfyUI-KJNodes.

Component	Size	Download
TAE (preview VAE)	9 MiB

Image VAE (single-frame)

An experimental image-specialised H3 VAE that decodes a single temporal latent (T=1) into one still. Merged checkpoint — no custom node needed.

⚠️ For image workflows only. The image-tuned decoder materially regresses multi-frame video reconstruction, so keep the original VAE loaded for video.

Component	Size	Download
Single-image VAE (step 1597)	4.85 GiB

Ref Patch — FL2VA that behaves more like Ref2VA

Diffs the 112 keys shared between the ref2va and fl2va weights and stores the differences as a single 148 MiB patch, letting the lighter FL2VA checkpoint partially mimic Ref2VA behaviour. Apache-2.0. Requires the ComfyUI-MiniMaxH3_Ref-Patch node.

Component	Size	Download
Ref Patch	148 MiB

Runtimes

Engine	⭐	H3 support
ComfyUI	127159	Native, day-0. INT8 is now in mainline (commit 1a510f04) — see Compatibility before reusing older INT8 quants.
modelscope/DiffSynth-Studio	12925	MiniMaxH3Pipeline in diffsynth.pipelines.minimax_h3_audio_video; docs at docs/en/Model_Details/MiniMax-H3.md, examples at examples/minimax_h3/. Ships NF4 quantized inference with an 8 GB VRAM floor.
ModelTC/LightX2V	2655	Full inference support: parallelism, quantized DiT, feature caching. Scripts at scripts/minimax_h3. Also the home of the Turbo 4-step / 768p LoRAs.
NVlabs/Sana Sol-Engine	8812	Hardware-specific full-stack runtimes (kernel optimization, Sol-Attn, approximate caching, memory-efficient decoding) on the sol-engine branch, Apache-2.0. Released full-opt configs for GB200, GB10, H100, A100, RTX 4090 and RTX 5090 with 3.55–4.52x speedup vs the matching baseline on the same hardware (e.g. 4.44x on a single RTX 4090, 768p@5s); launch via scripts/launch_config.py, local or Slurm.
MiniMax-AI/MiniMax-H3	5536	The official repository — reference implementation and prompt guides.
antirez/h3.c	1652	Apple Silicon native Metal engine, MIT, tutorial in the README. T2V/A, first-last-frame, and ordered Ref2VA references all working.
MiniMaxH3ComfyUI/MiniMax-H3-ComfyUI	101	Runs the 33B + Turbo LoRA locally with SGLang / vLLM / diffusers as selectable backends; T2V / I2V / R2V templates included.
unslothai/unsloth	70709	Lists MiniMax-H3 among the models it can run and train.

ComfyUI workflows & nodes

Conditioning & orchestration

Node	⭐	What it does
comfyui-minimax-h3-audio-T8	653	v1.17.0, 62 nodes across eight menus: Audio (stable), Audio Experimental (multi-rate), Still, Conditioning, Models, Long Video, Speech, Source AV. Baseline ComfyUI 0.31.0, commit cbbc9dab1, Python 3.10+.
ComfyUI_MiniMaxH3_Director	553	Provides five importable JSON templates: t2v, fl2v, r2v, v2v, and rv2v.
ComfyUI-H3-Motion-Context	491	Chains clips to maintain motion and sound continuity: clip A's last frames and audio inform clip B's start. Patches at runtime only; re-validates against current ComfyUI source on each start, refusing to run on mismatch — the safest patching approach in this list.
ComfyUI_MiniMaxH3_Director	359	The original Director.
ComfyUI-MiniMaxH3-Easy	332	Provides a compact workflow for T2V, I2V, first/last-frame, and reference video. Supports unified multi-media input with @ references and inline dialogue blocks.
ComfyUI-MiniMaxH3-Director	182	Offers a timeline editor: drag media onto tracks, trim on a ruler, assign one prompt per shot, with live sampling preview, retakes, and shot chaining. The compiled final prompt remains visible during editing.
ComfyUI-PainterNodes	178	MiniMaxRefToVideo2 node supports the official reference and dialogue format.
OpenH3-IR	19	The ComfyUI side of OpenH3-IR (see Prompting), using its Context-IR service from the same repo, on a model you already run. Drop your pictures, clips and sounds on one panel, name them, then mention them by name in the sentence. The nodes and the service are one tool listed twice, not two projects.

Upscaling, loading & repair

Node	⭐	What it does
scraed/LanPaint	1331	Performs training-free video and audio inpainting; H3 support was fixed in v2.1.0.
ComfyUI-MiniMaxH3_LatentUpscaler	191	Latent spatial upscaler for H3's NestedTensor AV latents (video [B,24,T,H/16,W/16] + audio [B,32,2,T_audio]), which stock LatentUpscaleBy cannot process. Re-noises video and audio for two-pass sampling and scales minimax_refs / minimax_keyframes conditioning. audio_denoise: 0 locks audio, 1 fully remixes, 0.25–0.5 for light remixing.
Comfyui_Minimax_h3_latent_Upscaler	151	Learned neural upscaler for H3's 24-channel latents — generate at low resolution, upscale the latent in place, then refine at target resolution, skipping the decode → pixel upscale → encode round-trip through the ~5B VAE and the ghosting that naive latent interpolation introduces. Continuous 1.0–4.0× scale. Checkpoints on HF (bf16/fp16 ~691 MB, fp32 ~1.38 GB, Apache-2.0) with I2V and R2V example workflows; trained on ~80k paired samples.
ComfyUI-INT8-Fast	286	Largely superseded as INT8 is now native in ComfyUI. Its remaining value is convert_comfy_quant.py; see Compatibility.

Templates & example workflows

Official ComfyUI templates (these ship with ComfyUI; links allow viewing the graph without launching the app):

• Text-to-Video (T2V) · Image-to-Video (I2V) · Reference-to-Video (R2V)

Community workflows:

• MiniMax-H3 FL2V GGUF workflow — Loads and runs the GGUF-quantized FL2VA model.
• joeygambino/MiniMax-H3-Multishot-Workflow — Strings several FL2VA/Ref2VA clips into one continuous sequence with matched audio handoffs. Apache-2.0 licensed.
• javawock7618/comfy-MiniMax-H3-workflows — Bundles the entire low-VRAM acceleration stack into one importable workflow.
• OrbitQuant T2VA · T2VA API form · Ref2VA API form — require ComfyUI-OrbitQuant.

Prompting

H3 prompts have a fixed three-part structure, inline <Picture X> / <Video X> / <Audio X> reference tags, and <d> for dialogue. Start with official guides, then use one prompt tool at a time. For reference audio, a clean, clearly spoken 10-second clip is picked up more reliably than a noisy one.

Read first: Base prompt guide · Reference-mode prompt guide

Tool	Why you'd pick it
ComfyUI-MiniMax-H3-Promptor	From v1.1.0, embeds <Picture X> directly into the narrative action line for "zero-hallucination inline annotation." Decouples visual analysis from text structuring, reducing API cost.
ComfyUI-MiniMax-H3-Guide	Zero dependencies. "Typed Plan v2" splits identity, keyframes, motion, edit source, voice, and score into explicit roles, compiles them into valid H3 prose, and routes to native nodes. Includes reusable image/audio reference sheets and a locked-frame Foley mode.
OpenH3-IR	Follows the Context-IR format and examples MiniMax published, rather than rewriting your prompt: it writes the document, then checks it and fixes what is wrong. Command line, HTTP, or its own ComfyUI nodes, on a model you already run. Apache-2.0.
comfyui-minimax-h3-prompt-enhancer-T8	Provides server-side prompt enhancement via doubao-seed-evolving.
awesome-minimax-h3-prompts	A prompt corpus with WebM examples and author attribution, categorized into story, action/fantasy, ad/product, music performance, and vlog.
minimax-h3-prompt-skill-T8	"Creative DNA" case library, installable as an agent skill, with an Electron desktop viewer.
MiniMax H3 1K prompt dataset	Curated 1K text-to-video prompts with author attribution: 3-field prompt-structure anatomy, 10 hand-picked reusable prompts, and an H3 vs. peer model comparison.

Agent skills

Skill packages that let a coding agent drive H3 without the ComfyUI canvas. They differ in how far they take you, and are listed here from the most complete: one carries a brief through to a finished file, one drives a local ComfyUI, and the rest write the prompt and hand it back for you to run.

Skill repo	Runs against	What it does
awesome-minimax-h3	Hosted API	Carries a brief through to a finished file against the hosted MiniMax API: submit, poll with backoff, resolve the returned file_id, then post-process with ffmpeg. Reports that the documented /v2/…/query polling form returns 404 and gives GET /v1/query/video_generation as the working call. Three SKILL.md packages — minimax-video for H3, plus Speech 2.8 and Music 3.0 skills that compose with it. Needs a MINIMAX_API_KEY, not local weights. MIT.
ComfyUI-Agent-Kit	Local ComfyUI	Drives a local ComfyUI end to end from Claude Code, Codex, Gemini CLI, or Qwen Code — workflow building, hardware-aware model selection, multi-shot video. Ships a standalone H3 skill among its 581 templates. Apache-2.0.
Minimax-H3-Prompt-AgentSkill	Prompt only	Builds a formatted H3 prompt from your media and idea, then hands it back for you to run. No license stated.
minimax-h3-opencode-skills	Prompt only	OpenCode skill suite covering directing, routing, multi-shot planning, prompt generation, and review. MIT.

Also relevant: ComfyUI-PainterNodes (MiniMaxRefToVideo2, with the official reference and dialogue format), and minimax-h3-prompt-skill-T8 in the table above, which ships its case library as an installable skill.

Speed

Two levers stack: Turbo LoRAs cut step count from ~20 to 4–8, and caching or kernel work makes each step cheaper. Check your PyTorch build first; an outdated CUDA build commonly causes slow generations.

Turbo (Acceleration LoRA)

Turbo LoRAs are community acceleration models. Start with ModelTC/Minimax-H3-Turbo and lightx2v/Minimax-h3-Turbo, then use the workflow instructions provided by the project you choose. At 4 steps the audio track can degrade along with fast motion; 6–8 steps helps both.

Community Turbo checkpoint reference

Variant	Steps	Base	Precision	Size	Download
fl2v v0.1	4	Full		1.29 GiB
fl2v v1.0 768p	4	Full		1.29 GiB
fl2v v1.0 768p · comfyui	4	Full		1.82 GiB
fl2v v1.0	8	Full		1.29 GiB
fl2v v1.0 · comfyui	8	Full		1.82 GiB
lightx2v v0.1	4	Full		1.82 GiB
lightx2v v0.1 · rank-21 resize	4	Full		300 MiB
fl2v	4	Full		744 MiB
fl2v ema	4	Full		744 MiB
fl2v ckpt500	4	Full		744 MiB
fl2v ema ckpt500	4	Full		744 MiB
fl2v ckpt850 ← best 4-step under motion	4	Full		744 MiB
fl2v ema ckpt850	4	Full		744 MiB
fl2v v4 step600	4	Full		744 MiB
fl2v v4 step600 ema ← recommended default	4	Full		744 MiB
fl2v pruned	4	Pruned		592 MiB
fl2v pruned ema	4	Pruned		592 MiB
fl2v pruned ckpt500	4	Pruned		592 MiB
fl2v pruned ema ckpt500	4	Pruned		592 MiB
fl2v pruned ckpt850	4	Pruned		592 MiB
fl2v pruned ema ckpt850	4	Pruned		592 MiB
fl2v pruned v4 step600	4	Pruned		592 MiB
fl2v pruned v4 step600 ema	4	Pruned		592 MiB
fl2v v1.0 768p · rank-21 resize	4	Pruned		298 MiB
fl2v v1.0 · rank-21 resize	8	Pruned		327 MiB
fl2v pruned ckpt500 V1	4	Pruned		592 MiB
fl2v pruned ckpt600 V4	4	Pruned		592 MiB
fl2v pruned ckpt600 ema V4	4	Pruned		592 MiB
fl2v pruned ckpt850 V1	4	Pruned		592 MiB
fl2v diffusers	4	Full		0.79 GiB
fl2v	4	Full		717 MiB
fl2v step 100	8 NFE	Full		738 MiB
fl2v step 200	8 NFE	Full		738 MiB
fl2v step 300	8 NFE	Full		738 MiB
fl2v 4-step · ConvRot · ⚠️ dual-clock sampler or 8–10 steps	4	Full		779.9 MiB
fl2v 4-step ema · ConvRot	4	Full		779.9 MiB
fl2v v4 step600 (T8-convert) · ConvRot	4	Full		779.9 MiB
lightx2v v0.1 · alpha8 T8-convert · ConvRot · ⚠️ dual-clock sampler or 8–10 steps	4	Full		1.82 GiB
fl2v v1.0 768p · ConvRot · needs ComfyUI-LoraInt8Loader	4	Full		991 MiB
fl2v v1.0 · ConvRot · needs ComfyUI-LoraInt8Loader	8	Full		991 MiB
lightx2v v0.1 · ConvRot · needs ComfyUI-LoraInt8Loader	4	Full		991 MiB
fl2v CMF	4	Full	Q4TP (CMF)	25.20 GiB
fl2v CMF · FL2VA	4	Full	Q4TP (CMF)	25.70 GiB
fl2v CMF · FL2VA (smaller)	4	Full	Q2TP (CMF)	20.12 GiB

Acceleration nodes

The figures below come from each project's own testing.

Node	⭐	Mechanism & published parameters
ComfyUI-Spectrum-MiniMax-H3	493	Spectral feature forecasting — fits post-transformer features with Chebyshev ridge regression and extrapolates future steps, skipping selected transformer evaluations. Adaptive scheduling with native fallbacks. The author is explicit that this is an approximation: output is not bit-identical to native.
ComfyUI-SolAttn_triton	266	SolAttention Triton kernel — optimized attention for H3 and other Sol-Attn models.
TE-Speed-MiniMaxH3	2	Block-cache accelerator, by the original TE-Speed author. v3.2 targets current ComfyUI (block prefetch, no core patch), adds a 4/8-step LoRA mode with automatic strategy selection by step count, a long-video (>10 s) cache strategy, and chunked CPU residual transfer; bundles a TE-Speed-compatible fork of the Sol-Attn node. ⚠️ Ships a compiled nodes.pyd, no license stated.

Training & LoRAs

Training status. H3 includes weights and inference code, but no official trainer. The Hugging Face Diffusers integration is also inference-only. Everything below comes from the community.

Project	⭐	Notes
radixark/miles_diffusion	42	Flow-GRPO RL post-training against reward models. The H3 t2va recipe is verified on 2×H200.
IAmIronMan42/MiniMax-H3-FineTuning	487	The most complete trainer currently available. Supervised rectified-flow training on top of the official Diffusers implementation, with latent caching (prepare_cache.py, prepare_cache_pairs.py) and a FIXES.md documenting nine fixes the author needed to make it converge. Verified scale: LoRA on 8×A800, 2000 clips of ~30 s at 448×768, ~65k tokens per sequence, stereo audio inside the loss.
shootthesound/Fizgig	157	LoRA / LoKr training studio with a built-in "✨ MiniMax H3 Fast" preset (LoKr, 8 dim / alpha 16, 60 epochs). Also does profile / repair / extract.
inlineresearch/Inline-Studio	213	Node-canvas film tool that trains H3 LoRAs on a local GPU. States "MiniMax H3 (4-bit, video) ~20.6 GB".
ModelTC/LightX2V	2655	The training side of Turbo distillation. The DMD config is public at configs/minimax_h3/dmd.
unslothai/unsloth	70709	Lists MiniMax-H3 among the models it can run and train. Check the current Unsloth documentation for the H3 workflow that fits your setup.

Ready-made LoRAs

Acceleration LoRAs live in Turbo. This section covers everything else.

Styles

LoRA	Size	What it does
minimax-h3-turnaround	60 MiB each	Contact-Sheet diffusion — one reference image + one instruction produces five coherent, progressively rotated views of the same subject in a single pass, by using H3's timeline as a slot axis rather than as time. A character turnaround from one photo: ~10 s at 512², ~57 s at 1024². Three builds: 1024-cont/s600, 512/s1500, 512-instruct/s400.
Realism — People	125 MiB	Natural-looking people in everyday scenarios, trained by fal on diverse photo data. Works across T2V / I2V / R2V.
Looping Sketch Anime	569 MiB	Hand-drawn 2D outlines, flat colours, white outline, built to loop. Strength 0.75–1.25; pair with a Turbo LoRA if you want to push toward the high end.
Krea2 Style	1.03 GiB (r512) / 266 MiB (r128)	Experimental style shift toward Krea 2's aesthetics, extracted from a cross-model attn-graft (Krea2 attn K/V). Only effective for pure T2V. The author states r512 captures 52 % of the extraction target and r128 only 24 % (negligible) — prefer r512. MiniMax-H3 Community License.

Utility

LoRA	Size	What it does
RAVEN Streaming (4-NFE preview)	4.71 GiB	Turns H3 into a causal streaming generator — video is produced chunk by chunk, each extrapolated from prior content instead of denoised as one bidirectional clip (r=128, 4 NFE, 192 frames at 768×1376/24 fps, sink=2 / window=2). Preview release by the RAVEN team (Imperial College); the authors state texture detail is still limited and real-time playback needs further inference acceleration. Runs through the RAVEN code repo, not ComfyUI. MiniMax-H3 Community License.
Prompt Rewriter	3.48 GiB	A Qwen3.6-27B fine-tune that rewrites a short prompt into H3's expected three-part structure. This is a language-model LoRA — it does not load into the DiT.
Prompt Rewriter 8B	2.60 GiB	The multimodal sibling of the Prompt Rewriter above: a Qwen3-VL-8B-Instruct LoRA that also sees your keyframes — covers T2VA, I2VA (first frame), L2VA (last frame), and FL2VA rewriting with an integrated shot timeline and synchronized physical/ambient sound and music guidance. Ref2VA is not supported. Also a language-model LoRA — it does not load into the DiT.

Serving H3

These projects are the main open-source options for serving H3 outside a ComfyUI workflow. SGLang and vLLM-Omni both ship official, hardware-verified H3 recipes; pick by ecosystem and by which published GPU profile matches your hardware.

Project	Where it fits
SGLang	First-class H3 serving. SGLang Diffusion ships a native H3 pipeline with an official cookbook: sglang serve --model-variant fl2va|ref2va covers t2va, fl2va, and ref2va (V2V is a ref2va use case), with every published recipe verified on real hardware — 4×H200/H100, B200/B300 (online quantization), 2×RTX 5090 layerwise offload, single RTX 4090 24 GB (kitchen_int8), and day-0 AMD MI355X/MI300X via ROCm + AITER. Also the selectable backend in MiniMax-H3-ComfyUI.
vLLM-Omni	First-class H3 serving on the vLLM stack. Since v0.26.0 it ships an official MiniMax-H3 recipe: OpenAI-compatible /v1/videos serving of T2VA, FL2VA, and Ref2VA from one diffusion stage that loads both DiTs and shares the TE and VAEs. Published profiles from 2× RTX 4090/5090 (CPU offload, ≥200 GiB host RAM) up to 4× B300/GB200, plus AMD ROCm.
EzgiTastan/minimax-h3-gb300-ebpf	Reproducible GB300 benchmarking and observability companion for SGLang H3 serving: end-to-end stage timing, Ulysses scaling, concurrency, host-side eBPF CUDA tracing, Grace–GPU PMU traffic, energy, and Nsight comparison. Includes retained measurements, figure renderers, and the full methodology and results.
vLLM	The underlying engine vLLM-Omni extends — vLLM alone serves autoregressive models, not H3's DiT. Deploy H3 through vLLM-Omni; keep plain vLLM for the text-model side of your stack (e.g. a prompt-rewriter endpoint).

Compatibility & Licensing

ComfyUI INT8

ComfyUI includes native INT8 support. Older I8Fast files use different tensor names; use convert_comfy_quant.py or download a native-format quant.

ComfyUI patches

Some community tools modify or patch ComfyUI. Check the project's documentation and keep your ComfyUI version compatible.

Type	Project	Notes
Runtime patch	DmitryDB/MiniMax-H3-DynTime-sQKV	Required for its DT-sQKV files.
Runtime patch	ComfyUI-H3-Motion-Context	Checks its ComfyUI assumptions at startup.

Reported environments

• comfyui-minimax-h3-audio-T8: ComfyUI 0.31.0, commit cbbc9dab1, Python 3.10+.
• minimax-h3-turing: RTX 2080Ti 22G (Turing sm_75), ComfyUI 0.33.1 → master 345c919, PyTorch 2.9.1+cu130; W4A8 mixed + fl2v Turbo 4-step; T8 BlockCache −43% (drafts, non-reproducible), PDD 8-step + T8 210 s/clip
• ComfyUI_MiniMaxH3_Director: RTX 4090 48 GB, ComfyUI 0.30.0, PyTorch 2.11.0, CUDA 12.8, Ref2VA INT8.

Licenses

License	Where
Apache-2.0	ModelTC/Minimax-H3-Turbo and the Turbo LoRA line · Ref Patch
MIT	antirez/h3.c
No license stated	DeepBeepMeep/MiniMax-H3

For other projects, check the repository or model card.

Acknowledgements

This index is only possible because other people made the models, tools, tests, and documentation it points to. Thank you to the MiniMax, ComfyUI, SGLang, vLLM, NVIDIA, and Unsloth teams, and to the independent maintainers who keep testing H3 on real hardware.

The structure and much of the resource discovery come from the community-maintained wildminder/awesome-minimax-H3. This page follows that work and adds MiniMax's own GitHub and Hugging Face scan.

More specific thanks go to:

• Comfy-Org for the official ComfyUI conversions, workflow templates, and day-one support.
• ModelTC / LightX2V for the Turbo distillation work and public training configuration.
• Larryvrh for the checkpoint comparisons behind the Turbo guidance.
• Kijai for the NVIDIA Sol-Attn implementation and benchmark notes.
• IAmIronMan42 for the training work and the documented fixes.
• Salvatore Sanfilippo (antirez) for the standalone H3 C/Metal inference engine.
• Every quantizer and workflow maintainer represented above. Their files, testing time, and write-ups make local H3 use much easier.

If you spot a wrong number, broken link, or missing compatibility note, please open an issue or send a correction.

Contact

For MiniMax H3 questions, contact model@minimax.io.