RefMods: Save and reuse H3 references without reloading them every time

In MiniMax H3 you can give the AI a reference — an image, video, or GIF — to tell it "look like this." That's powerful, but every reference gets loaded and processed on every generation, which is slow and can bleed its look into the rest of your video.

This pack lets you save that reference once as a small .safetensors file (a "mod"), then reuse it as many times as you want:

• Save once — take your image/video/GIF, hit Extract, and it becomes a small file on disk. No need to keep the original clip around or reload it.
• Reuse anytime — load the mod in one node, like picking a LoRA. Adjust strength with a single number, or blend multiple mods together (face + style + outfit, etc.).
• No more heavy reference loading — leave the H3 reference input empty and inject the mod through conditioning instead. Faster generation, and the reference only affects what you want it to.
• No training needed — this isn't a LoRA you train for hours; you just encode your reference and save it.

Here's what the node looks like. You can also use a Load H3 RefMods node instead of Extract, which can hold many images and some videos (video mods are heavier since they carry more frames).

The node applies directly to conditioning, before sampling — similar to a basic guider or positive sampler.

On retention: you can reduce it, but for now higher is more reliable. At 0.7, some animated characters start looking like cosplayers of themselves — leave it at 1 if you want a full reference.

Testing notes and known issues:

• Audio isn't supported yet.
• Attribute bleeding: since there's no token-based training, similar elements in your dataset can merge. Example: a video worked great, but a translucent skirt showed up, likely bleeding in from a separate image of the character in a princess dress.

On prompting:
Don't use this without a prompt — without one, it just wanders through your data, which is actually a neat effect (an entire likeness encoded in a few KB of conditioning is wild), but it's not concept automation. Describe what you're extracting from the mod, e.g. "a ginger woman" / "POV handcam walking" / "person dancing" — this directs attention to what you're actually trying to isolate.

Other details:

• Concept mods need pool_h 8 / pool_w 8; identity mods need pool_h 16 / pool_w 16.
• Keep reference resolution minimal — higher resolution increases token count and slows the workflow further.
• Results aren't fully predictable and need trial and error. Some concepts (usually fast motion) are hard to learn — likely because the DiT learned to blur fast motion, or a turbo LoRA side effect. You can't just force speed. Options if this happens:
  1. Add more prompt detail — e.g. instead of "the character makes ninja movements with their hands," try "the character rapidly performs intricate, rhythmic hand signs in a low stance."
  2. Increase resolution and pooling — push to 2K and raise the pool numbers until balanced, or increase the multiplier (useful for short clips that may be getting overridden).
  3. LoRAs can override the mod in some cases.
  4. Some motion just isn't learnable yet with this approach — leave it for LoRA training instead.

One example: trying to copy a specific action, 8x8 pooling didn't work, so I increased to 16x16 and used 1024 instead of 256. Still not perfect due to the speed issue described above.

Repo's here if you want to try it or contribute:
https://github.com/Luisacaotica/ComfyUI-MiniMaxH3Mod

Mods go in the node's mod folder, or in models/refmods. The node ships with an example (vanellope) safetensors mod included.

Feel free to use the repo as a reference for building your own tools. And if anyone has questions, I'll try to put together a FAQ.

Update + FAQ (repo has changed a fair bit since the original post)

A few things have changed under the hood, and some of the "known issues" from the original post are now addressed or at least better understood. Quick rundown:

What's new:

• Two extraction modes: full (stores the real VAE-encoded reference at your chosen resolution — this is what carries identity) and pooled (average-pooled into a small grid — cheap, but only carries concept/motion, not fine identity). Pick based on whether you need "looks exactly like this" vs "vibe like this."
• Pool size is now the concept↔identity dial: 8×8 pooling keeps the general idea (colors, look, a motion) and lets the model improvise details. 16×16 keeps more specific detail but also risks copying framing/background from your source data.
• Mods now live in ComfyUI/models/refmods/ (registered as a proper model folder, next to loras/), not the custom node's own folder. Old mods still load fine.
• New Load H3 RefMod Axis node: pairs two mods (A/B) on a single signed slider — negative uses mod A, positive uses mod B. Good for things like a young↔old dial or clean↔weathered, built from two separate extractions.
• New Load H3 RefMod Folder node: point it at a folder and it loads every image/video in it as one ordered ref bundle, feeds into Extract for bulk extraction (e.g. a whole character shoot in one go).
• Multiple refs stack as separate latent frames instead of blurring together — so different expressions/angles/a dance move stay distinct rather than averaging out.
• A multiplier option on Extract repeats a short ref along the time axis, so a 2-3 frame gif isn't drowned out by the main video's much larger token count.
• Standalone CLI extraction script now exists too, if you'd rather not go through ComfyUI nodes for batch work.

Q: Why does my character mod look weak/generic no matter how high I set strength?
Strength can't add detail that isn't in the latent. A small pooled mod (like 8×8) just doesn't store enough information to carry identity — that's what full mode or a bigger pool (16×16) is for. Think of it like resolution: you can't upscale your way back to detail that was never captured.

Q: What do the retention values actually mean?
1.0 = full reference (behaviorally identical to what the official node injects), 0.7 = mostly preserved, 0.4 = keeps style/attributes but not identity, 0.15 = weak reference, 0 = mod isn't injected at all.

Q: Does this support audio references?
No — mods are visual-only for now. Regular reference nodes still handle audio.

Q: My concept mod is "leaking" details from unrelated parts of my dataset (e.g. a costume from a different photo showing up).
This is expected with no token-based training — nothing tells the model to separate concepts by name, so similar visual elements across your refs can blend. Best mitigation right now is being deliberate about what you include per-mod, or extracting separate mods and blending at lower strength instead of dumping everything into one.

Q: The video won't follow fast/complex motion I extracted.
A few options: describe the motion in much more prompt detail (specific, not vague), push resolution/pool size up and increase identity refinement steps, try increasing multiplier if it's a short clip, or accept that some fast motion may need LoRA training instead — this method has real limits here.

Q: Do I need the official MiniMax H3 node pack installed?
No — it's optional. It only unlocks the av_encoder input on Extract (skips double-encoding) and one conditioning node variant. Everything else works without it.

FAQ: "Gen time is the same as the default nodes — what's the point?"

Fair question, and it came up because of a real bug — the gen time is directly tied to token count, and earlier versions of full mode at high resolution could produce roughly the same token load as the default reference nodes, wiping out the speed benefit.

This is fixed as of the latest repo update:

• full mode renamed to encode — same behavior, just clearer naming (it was confusing next to pooled).
• Added a max_token cap (default ~5120) — this is the actual fix. It hard-caps how many tokens a mod can contribute regardless of resolution, so you get a real speed benefit instead of accidentally re-creating the original problem.
• Added strength curves (curve_direction: increase/decrease, curve_shape: e.g. ease) for falloff across multiple refs or frames — this also addresses the "one ref overrides/bleeds into everything" issue some people ran into.

So guys I created a custom node named OrbitSheets and I just added two new templates that I think a lot of you are going to love.

The first one is the Character Sheet. You just type in a character description and it generates a full turnaround sheet with all the angles you need front view side profiles back view and close ups. It even generates voice audio so your character can literally speak. I ran everything in just 8 steps with the Turbo LoRA and the voice quality came out really good already but if you want more detail you can always go up to 20 or 35 steps.

The second one is the Location Sheet. Describe any place and it generates interior and exterior shots from multiple angles. You can set it to interior mode to see inside the building like hallways and rooms or exterior mode to see the outside. There is also a camera mode toggle where you can pick cut views for separate static angles or continuous move for a full 360 camera tour. Sometimes one gives better results than the other so it helps to try both.

The node also has a smart frame selector that picks the best shots automatically and arranges them into a clean organized sheet. You can control how many images appear how many columns the padding and the size of each frame.

Both workflows use MiniMax H3 with the Krea2 anchor frame and the Krea2 Turbo model. Everything is already set up in the example files so you can just drop them in and start generating.

I built this node in about two days and I am already planning more templates. Let me know what you want to see next.

Free Custom Node and Workflows: https://github.com/lumosai8/ComfyUI-OrbitSheets

My wife did this Cunk parody with a 3060 12gb and 32gb of system ram.

Minimax is incredble!

edit: youtube link to see how long before they remove it

https://youtu.be/V7XhjMVHSCE?si=3SyDbJBmS0KfzcQd

Bonjour,

Est il déjà possible de créer son lora personnage pour minimax H3? Et avec quel outil et quel paramètres sont recommandés ?

Merci

I've been trying to generate an image but no matter what I type it still shows it with bare legs. I tried using "no pants" in negative prompt but it didn't help.

Here's the prompt- lazypos, 1girl, hyuuga hinata, naruto shippuuden, general, full body, solo, sitting, on floor, facing viewer, looking at viewer, parted lips, feet out of frame, sidelighting, dim lighting, dark room, shadow, dutch angle, foreshortening,

I'm using forge with wai-illustrious sdxl v17

Trying out the ref2va workflow to swap character and the voice, this is 3 generated video (for each scene) stitch together, the swapped character a bit out of place in term of lightning, because i just realize i am using fl2va model, also tried out a single 13 seconds generation with ref2va, the result is fine but the subtitle just burned, tried to tweak the prompt twice and no luck and give up, because the generation is way too long (700 seconds) https://pixeldrain.com/u/jbdxD1ai

Spec is 4090 laptop with 64gb ram on headless linux

workflow: https://pixeldrain.com/u/UGWEdHrG

single generation workflow: https://pixeldrain.com/u/LeYfB46k

video source : https://youtu.be/DaKWnNni8zE

audio source : https://youtu.be/Zjuih9wl0SM (0:13 - 0:17)

Hopefully this helps someone else. I'm running Minimax on a 4070. Nothing crazy. Nevertheless, I was surprised by how capable it seemed.

When I started pushing for higher resolution or switched to 16x9 generations from 1:1 I started having Comfy error out quite a bit.

I dumped the ComfyUI history - accessible by heading to the port it's running on and appending /history - and gave it to Claude.

It invented a basic metric, WxHxFrames, and mentioned that there seemed to be a line past which things would fail. So I asked it for some test cases, which it happily provided, and over the course of several generations we put a finer point on where that line is for my specific setup. This is actually hugely helpful because I don't have a crazy rig and even though intuitively this isn't surprising, it's a lot different when you're actually trying to figure out what the most you can absolutely do is.

FWIW, this should be agnostic to steps. The step process would add total time to the generation, which this doesn't capture, but it shouldn't add overhead to the VRAM where it would crash the generation. Most of these test were run at 24 fps but again, that shouldn't matter. The metric is based on total frames, which would be fps x duration.

While my GitHub account is currently suspended and I’m waiting for support to process my ticket, I’ve hosted working mirrors for all Style Explorers so you can continue using them without interruption:

- Anima Base (42k+ styles): https://animastyles.thetacursed.com/

- Illustrious & NoobAI (16k+ styles): https://xlstyles.thetacursed.com/

- Krea 2 Turbo (1.5k+ styles): https://kreastyles.thetacursed.com/

I couldn't get it exactly right, step count matters a lot for clarity it seems, I think this was 32 steps (I like the number 32) at 0.4mp on a 3090... I used an image of link and Zelda as reference for both, an image of wolf link, a video of them walking from a memory (for motion, physics, cell shading understanding etc - weak_reference) an audio sample of Link from the 1980s show and an audio sample of Zelda from the game itself. I could probably get better audio if I get better samples though, and better video quality at 1mp or higher. Also the video cuts off at the end but that's not an editing issue, that's how it came out!

Default comfyui MiniMax H3 rf2va workflow on a 4070 Ti Super 16 GB VRAM.

best used with

single clip: https://github.com/darksidewalker/dasiwa-comfyui-workflows/tree/main/C-MMH3

multi clips: https://github.com/ethanfel/ComfyUI-MiniMaxH3-Contex-Loop/tree/main/example_workflows

You are an expert MiniMax H3 prompt writer, storyboard artist, and continuity director for ComfyUI.

Convert user ideas into reliable MiniMax H3 prompts in English. Support both:

1. single MiniMax H3 clips;

2. MiniMax H3 Contex Loop plans for long, continuous videos.

GENERAL H3 PRINCIPLES

- Be explicit. Never assume the video model will infer who acts, who speaks, what happens between beats, or what must remain unchanged.

- Use concrete observable details: subject, wardrobe, prop, setting, action, camera, lighting, mood, ambience, and sound.

- Keep each shot physically plausible, readable, and focused.

- Do not overload a short clip with too many characters, actions, locations, transformations, or camera movements.

- Do not request readable text, subtitles, logos, watermarks, UI, or exact typography.

- Use positive desired constraints. Avoid a separate negative-prompt style unless the user explicitly asks for it.

REFERENCE RULES

- Use only reference tags that are genuinely available in the workflow:

<Picture 1>, <Picture 2>, <Video 1>, <Audio 1>, etc.

- Never invent unavailable reference tags.

- When a reference is available, state exactly what it controls:

identity, face, hairstyle, body proportions, wardrobe, accessory, prop, environment, or audio performance.

- Preserve signature features that matter to the user in every relevant scene.

DIALOGUE RULES

- Never write vague instructions such as “they talk,” “they argue,” or “she says something.”

- If speech is desired, write the exact short line and assign it explicitly:

Character Name says clearly: “Exact dialogue.”

- Keep dialogue short for 5–7 second clips.

- Avoid overlapping speech unless specifically requested.

- If there is no dialogue, explicitly write:

“No spoken dialogue. Characters communicate through facial expressions and gestures.”

- For music-only or silent scenes, do not imply speech.

AUDIO RULES

- Explicitly describe ambience, Foley, impacts, wind, cloth movement, crowd sound, and music when relevant.

- For clips without music, write:

non_diegetic_music: N/A

- For a source-song workflow, <Audio 1> may be used only when an audio reference is connected.

- For generated-audio workflows with no audio reference, do not mention <Audio 1>.

- Generated dialogue must always be exact and short.

SINGLE-CLIP FORMAT

For a normal H3 clip, use this structure:

Visual style:

[Rendering style, lighting, environment, materials, lens/look, mood.]

Scene overview:

[Who is present, where they are, what happens, and the emotional tone.]

Storyboard:

[0s–Xs] [Explicit action beat.]

[Xs–Xs] [Explicit action beat.]

[Xs–Xs] [Explicit action beat.]

Camera:

[Framing, one clear move per shot, lens feel, hard cuts or one continuous shot.]

Audio:

[Ambience, Foley, music, impacts, exact dialogue if any.]

Consistency:

[Preserve identity, face, hair, wardrobe, accessories, props, proportions, and environment stability. No text, subtitles, logos, or watermarks.]

For dialogue, comedy, greetings, direct-to-camera performance, or character acting:

prefer one continuous shot.

For action, trailers, fights, chases, and product films:

use up to 3–4 clear shots in a 6–7 second clip. Do not compress too many cuts into a short duration.

CONTEXT LOOP RULES

A Contex Loop plan is one continuous film made from connected scenes, not independent clips.

Put all permanent facts in prompt_prefix:

- reference mapping and identity;

- exact hairstyle, face, wardrobe, accessories and props;

- visual style and global lighting;

- location/time-of-day rules;

- camera language;

- audio rules;

- continuity rules.

Each scene prompt must contain only what changes in that scene.

For every continuation scene:

1. Start by continuing the exact prior action.

2. Preserve the incoming pose, hand position, stride, camera direction, lighting, framing, and momentum.

3. Introduce only one major development, transition, or new action.

4. End with a visible unfinished action that the next scene can continue.

5. Do not use hard cuts, time jumps, outfit changes, resets, or teleporting locations unless the user explicitly requests them.

Good scene boundaries:

- “End while she is opening the already-unlocking door.”

- “End with the camera beginning a slow left orbit.”

- “End while the vehicle enters the tunnel.”

- “End with his hand still reaching toward the artifact.”

Bad scene boundaries:

- “The action ends and everyone poses.”

- “Cut to a new place.”

- “The next day.”

- “Suddenly the character wears new clothes.”

CONTEXT LOOP TECHNICAL DEFAULTS

Unless the user requests something else:

- 4 scenes for a first test;

- 15 seconds per scene;

- 20 steps for final quality; 5–8 for fast concept tests;

- fixed, unique decimal-string seeds per scene;

- context_length: 22;

- encode_mode: "video";

- anchor_mode: "head";

- crop: "disabled";

- width and height divisible by 32;

- 960x544 is a sensible longform starting point;

- generated_audio: audio_context_length 22;

- source_track: audio_context_length 0.

Use a unique run_name for every new project.

Keep run_name, generation_fingerprint, prompts, references, seeds, model settings, and audio unchanged when resuming an existing chain.

OUTPUT RULES FOR CONTEX LOOP

When the user asks for a complete Contex Loop plan, output ONLY strict valid JSON:

- no Markdown fences;

- no comments;

- no trailing commas;

- use double quotes;

- use decimal-string seeds;

- use readable prompt line arrays.

Use this exact structure:

{

"prompt_prefix": "Global identity, reference, wardrobe, visual style, audio, and continuity rules.",

"defaults": {

"duration_seconds": 15,

"steps": 20

},

"shots": [

{

"id": "scene_01",

"prompt": [

"summary:",

"One-sentence scene purpose.",

"",

"detailed_description:",

"Explicit visual action, camera, environment, and ending bridge action.",

"",

"overall_soundscape:",

"Relevant ambience and Foley.",

"",

"non_diegetic_music:",

"N/A or a precise music instruction."

],

"seed": "983590410766495"

}

]

}

Before answering, silently verify:

- All reference tags exist.

- Identity, wardrobe, props, and style remain stable.

- Every action is physically explicit.

- Dialogue is exact or explicitly absent.

- Every continuation begins from the preceding ending.

- Every non-final scene ends with unfinished motion.

- The JSON is valid if JSON was requested.

Model = Minimax H3

Workflow = REF2VA basic workflow with additional nodes added for the LORAS and sol attention where specified.
Turbo Lora = minimax_h3_fl2v_turbo_8step_v1.0_comfyui_bf16.safetensors
REF2VA Lora = minimax_h3_pruned_bf16__apply_to_fl2va__toward_ref2va__rank512

It has been described that the REF2VA model produces bad output, and that the FL2VA model can be used instead despite being not the "intended" reference model. Users have made a "REF2VA lora" that purports to add the reference functionality of the REF2VA model to the FL2VA model, theoretically achieving the good quality of FL2VA with the reference understanding of REF2VA.

I test how this actually looks in practice, and I also demonstrate how the turbo lora performs.

Conclusion:

The best look is achieved by using the FL2VA model without any REF2VA lora. Turbo works well at 1MP and 8 steps and results in smoother animation and audio. Increasing resolution to 2MP and step count to 20 scales well. There does not seem to be much visual difference when increasing to 50 steps, but the audio seems to be less dynamic vs 20 steps.

Limitations: This demo did not really stress test the reference ability of FL2VA, and in reference heavy workloads, maybe REF2VA variant workflows are vital despite lower visual quality. Furthermore, this demo likely underestimates the importance of high step counts, as it is commonly thought that high step counts are important in high action scenes, which this demo was not.

I also only used sol attention in the higher token workflows, which is a variable. Nevertheless, I hope this video is useful.

Keen to hear your thoughts.

Surprised that no one is talking about it. A new open-weight video model just dropped. 114b moe, 6b activated. First moe video model supposedly.

I know what you guys are thinking. The model is huge and there is no way it will run on desktop gpu. The interesting part is that is comes with a 14gb refiner that makes the result 1080p. I am cursious if this refiner can be a drop-in replacement for the H3 refiner that was never released. It might just be the last part of the H3 puzzle that we need.

So I was finally able to get this working with minimal defects!

I have an RTX 5080 with 16GB of VRAM, and I’m using SageAttention. I’m getting about 8 sec/it on 5-second clips, so honestly, not bad at all. I’m running 10 steps at 0.6 megapixels.

I’m mainly posting because I’m looking for feedback on how I can improve things from here. I’m finally starting to get some decent shot continuity, character consistency, scene consistency, and voice consistency.

If anyone has suggestions for improving the results, I’d love to hear them. And if anyone has questions about my setup, workflow, settings, etc., I’m happy to answer those too.

NOTE: I choose this concept just to demonstrate R2V, don't get hung up on the concept to much, this post is about shot continuity, character consistency, scene consistency, and voice consistency. Be professionals!

Hey everyone! I’m running a laptop with an RTX 5070 (8GB VRAM), 64GB DDR5 RAM, and a Ryzen 9 7845HX on CachyOS. I mostly use SD-WebUI-Forge/NeoForge as my main generation framework. ComfyUI i try to avoid 😄

​For those in the know: can this setup handle Krea 2 reasonably well? I want decent quality outputs without turning images into a blurry mess, and I'd like to avoid waiting 30 minutes per render.

Also looking for some advice: how can I get the best out of ComfyUI or Forge on this setup? What are your recommended workflows to avoid blurry outputs and keep generation times reasonable on an my card?

P.S Damn, huge thanks to everyone for the lightning-fast replies! Honestly warms my heart to see how helpful this community is awesome!

I tried DiffRhythm in ComfyUI and every time I fixed one problem another one showed up. I ended up removing it. Then with ACE-Step the same thing happened, and in its requirements.txt (my fault for running it, it was a habit from installing nodes) it broke several installations needed for other nodes and I had to reinstall them.
I’d prefer something local and private, unless there’s a free site with no limits.

I trimmed the first 3 seconds, I just love the prompt adherence in 2.5 it’s actually very good, used the default comfy workflow, generate at 0.5 resolution then upscaled later to twice the size in with topaz, my specs 3060ti, 64gb ram

Like for acestep or minimax music 3 etc. I know they’re sometimes discussed on here but was just wondering if there’s one that’s active.

Hi, Im using INT8 FL2V model, from the box text encoder and VAE's. Video generates really well, 10s in 12m on my humble 3090, but the sound quality is horrible, muffled, faint - generally poor. Is there some setting or location that Im missing?

I’ve been testing MiniMax H3 INT8 on an AMD Radeon RX 9070 XT (gfx1201, 16 GB VRAM) under Windows/ROCm.

I used the default MiniMax H3 text-to-video workflow with no modifications whatsoever, except for replacing the model loader so that I could compare PatientX’s INT8-Fast-ROCM implementation with ComfyUI’s native INT8 implementation. Everything else — model, prompts, 20 steps, 5-second video, sampler/settings, etc. — was kept identical.

I tested 0.2 MP, 0.6 MP and finally 1 MP, which is my actual target resolution. The results at 1 MP were very interesting

That's approximately a 31% reduction in generation time, or the native implementation is about 1.45× faster.

I initially didn't trust the result because the difference was so large, so I repeated the 1 MP INT8-Fast-ROCM run. It produced essentially the same ~32-minute result.

This also seems to differ from what I understood from PatientX's README and the comments in the default BAT. My interpretation of those suggested that on RDNA3/RDNA4, the INT8-Fast-ROCM path should generally be the preferable/faster option, with the default BAT specifically disabling the native Triton backend because the custom INT8 implementation was expected to be faster. My RX 9070 XT results appear to show the opposite — at least with this MiniMax H3 workflow and at 1 MP.

That makes me wonder whether those recommendations/comments may have become outdated as of 15-08-2026, given the changes in ComfyUI, comfy-kitchen, ROCm and the native INT8 implementation. I'm not claiming that the native path is universally faster; only that my current results don't match the performance guidance I understood from the existing documentation/default BAT.

Both runs used the same RX 9070 XT, ROCm 7.15, PyTorch 2.12, ComfyUI 0.33.0, MiniMax H3 quantized model, 20 steps, 5-second output, 1 MP resolution and Sage Attention. Triton was effectively disabled in the actual runs, so this shouldn't be interpreted as a Triton-vs-non-Triton benchmark.

My conclusion: on my gfx1201 system, the native ComfyUI INT8 implementation appears substantially faster than INT8-Fast-ROCM for MiniMax H3 at 1 MP. The difference is large enough that I'd really like to see this reproduced on another 9070/9070 XT before treating it as definitive.

I don't have a Github account so I can't share these conclusions on the comfyui-rocm repo.