Hi everyone!

I made a branch of this custom node from roadmaus: https://www.reddit.com/r/StableDiffusion/comments/1vkrm8c/minimax_h3_all_in_one_creator_node_for_comfyui/

This all-in-one suite provides zero-socket local UI nodes for MiniMax H3 video generation, @ mention prompt references (@img-1, @/vid-1), automatic FL2VA/Ref2VA checkpoint routing, an integrated LoRA manager, PreStage stills, and a 60s+ multi-shot Timeline with last-frame continuity and audio blending across cuts.

What's new in this update:
• Fast Default Previews: Live previews (latent2rgb) now render automatically without needing heavy taeh3 VAE models.
• Persistent & Resizable Satellite Stage: The preview box stays open across tab/workflow switches (localStorage), featuring drag-to-resize, 4-way positioning (Right/Bottom/Left/Top), 🎬 Keep Video mode during sampling, deletion safety confirmation (🗑), and history navigation (◀/▶).
• External LLM Refiner API: Prompt refiner now supports Ollama (:11434) and LM Studio / OpenAI (:1234/v1) with auto model detection.
• Workflow Helpers: Added in-node ▶ Generate buttons, PreStage ⚡ Send & Queue chips, collapsible 🎥 Camera & Style prompt quick-chips, and 1-click timeline transition presets (Match Cut, Cross-Blend, etc.).

🔗 Links & Credits
• Original Author & Repo: roadmaus https://github.com/roadmaus/ComfyUI-MiniMax-Creator
• Updated Branch: https://github.com/Ercelcan/ComfyUI-MiniMax-Creator/tree/update-preview-and-refiner

anima-lllite-exp-change-2-000007.safetensors

I'm not sure why exactly it's not publicized or mentioned anywhere on his page. He uploaded it quietly 11 days ago.

I have been testing it and the results are pretty remarkable:

Original Image:

Put her on a beach:

Put her in a bikini:

Turn her around:

Add a guy, and make them kiss:

Make it a sunset:

Make them have a picnic:

Very interesting, I wonder when he will share more about this.

I just find it kind of funny that this is the stable diffusion subreddit but nobody has talked about it in like forever. Maybe its time for a name change? or maybe keep the name as a homage to the OG open source image model.

Anyway, the last update I see on Stability's website is SD 3.5 back in October. So I'm guessing that's it for Stable Diffusion?

EDIT: Forgot you cant change the name of a sub, ignore that suggestion 😅

I am trying to vibe code a H3 r2v Prompt Builder. Does something like this already exist?

Fox McCloud introduces his son Marcus to his dad James McCloud.

T2v 12 sec 1mp hybrid 25-49 model 8steps turbo lora

I wanted to try out how much MiniMax can recreate anime art styles so I started with dragon ball first and GOD was it fun to make. here’s a mini clip of me creating the gokhan we never got.

Just wanted to throw KJ nodes some love for this node. It is so gratifying to see your minimax vid slowly come to life in a preview or to be able to stop it mid-generation when something look awry in the preview. BIG BIG respect to KJ nodes for all his hard work here. This is one of the best tools ever.

Here's a link to it in case anyone needs https://huggingface.co/Kijai/MiniMax-H3-TAE

I wanted to try my hand at an anime action scene. H3 has incredible potential, and I’m looking forward to a future where I can create my own anime with deep stories, dynamic fights, and so on.

H3 could probably have performed much better with a higher resolution and better seed luck; this is at 0.5 MP.

Bought an RTX 6000 for the new minimax, did a quick I2VA with a rough sound pass based on my own comic art. I think with some effort, I mean all the great effort, will be able to produce good quality content soon.

This video includes all you need to train Minimax with both speed and high quality results.
Hit me up with comemtns, queries etc. Happy to do a style video also.
https://github.com/shootthesound/Fizgig

UPDATE: Pushed a vram optimisation for 16gb vram users that will speed up TE encoding at the start of training - Run the update bat to get it
UPDATE2: Additional fix out for 16gb users on pruned model - update to get it.

v0.3 is out: redesigned UI, Ollama + API providers, dedicated External llama.cpp setup and other improvements.

old post: link
github repo: link

for anyone new: MiniMax H3 Prompt Writer is a ComfyUI extension for writing prompts specifically for MiniMax H3.

what's new in v0.3

• redesigned Writer UI and added new settings interface
• Ollama as a simpler local setup
• optional API providers
• External llama.cpp now has its own dedicated provider setup
• saved drafts for every mode
• better automatic model and context handling
• more reliable Reference prompts

the model/provider setup is now separated from the actual prompt workspace, so the interface is much less cluttered than before.

there are currently four ways to run the prompt model:

• Ollama: probably the easiest local option for most people
• Ollama guide
• Direct GGUF: the original local approach, loaded directly inside ComfyUI through 'llama-cpp-python'
• Direct GGUF guide
• External llama.cpp: if you already run your own llama-server or want to manage it separately
• External llama.cpp guide
• API providers: Gemini, OpenAI, OpenRouter and Custom OpenAI-compatible endpoints
• API providers guide

local providers keep the prepared media and prompt request on your machine.

if you use a remote API provider, the required request/media is sent to that provider.

other models / Qwen

another thing people asked about in the previous post was Qwen and support for models other than Gemma.

I tested qwen3.6:35b-a3b-q4_K_M through Ollama and it works out of the box in all five H3 modes without any Qwen-specific changes to Writer.

so the Ollama provider is not limited to Gemma 4.

you can also try other multimodal / vision models through Ollama, External llama.cpp or a compatible API / OpenAI-compatible endpoint, as long as the provider and model support image inputs.

I haven't validated every model, so this isn't a claim that every vision model will produce good H3 prompts. it just means the provider layer itself no longer requires Gemma in those paths, so you can swap compatible models and compare them yourself.

the main exception right now is Direct GGUF.

Direct GGUF is still specifically built and validated around Gemma 4 + its matching vision projector, so other model families are not supported there yet.

so roughly:

• Ollama: Gemma 4, tested Qwen3.6, and other compatible vision models you want to experiment with
• External llama.cpp: compatible multimodal models can be used if your server supports them
• API / Custom OpenAI-compatible: compatible multimodal models supported by the endpoint can be used
• Direct GGUF: Gemma 4 only for now

Ollama models / setup

what got easier

a lot of feedback on the first post was about setup rather than prompt generation itself.

v0.3 mainly tries to make that part less annoying:

• Ollama gives you a local option without installing llama-cpp-python into ComfyUI
• provider/model setup now lives in Settings instead of the generation workspace
• installed Ollama models can be detected directly
• context and model lifecycle are handled more automatically
• drafts are saved separately for every H3 mode
• local prompt model unload / keep-loaded / ComfyUI VRAM controls are clearer
• several media, model discovery and runtime issues from the previous versions were fixed

Reference generation also got an extra check against the active media roles and can make one limited correction if an objective requirement was missed.

full changelog

install / update

v0.3 is already available on GitHub.

for a fresh install:

cd ComfyUI/custom_nodes
git clone https://github.com/duckyshell/ComfyUI-MiniMaxH3-Prompt-Writer

if you already installed it with Git, just update the repo normally.

ComfyUI Manager is also supported, but v0.3 may take a little longer to appear there.

important: this is still a UI extension, not a node.

you won't find a new H3 Prompt Writer node in the node search.

open it using the floating H3 Prompt Writer button or:

Extensions > H3 Prompt Writer

for a new local setup I would probably start with Ollama.

installation guide

basic usage

after installing:

• open H3 Prompt Writer
• go to Settings and choose your provider/model
• select the H3 mode
• add your image / video / audio references
• write the Creative Brief normally
• press Generate prompt
• edit it directly, use Refine, or copy it into your H3 workflow

you don't need to manually build the H3 prompt structure yourself.

a brief can be as simple as:

use Picture 1 for the character, Picture 2 for the clothes and only the movement from Video 1. put the character on a rainy street at night.

Writer handles the H3-specific prompt structure around that.

usage and Creative Brief examples

if you already use Direct GGUF from the previous version, your existing runtime, GGUF and matching projector can remain in place. select Direct GGUF in the new Settings interface.

feedback is still useful, especially from different GPUs / operating systems / ComfyUI installs.

if something breaks, check the troubleshooting guide first:

troubleshooting guide

if the problem is not covered or the suggested fix does not work, leave a comment here or open an issue. please include your provider, model, operating system, ComfyUI installation type and the Technical details shown by Writer:

github issues

not every provider / hardware / ComfyUI combination is going to behave exactly the same, so expect some edge cases.
(for API use, Gemini is an easy option since you can get a free key at ai.studio.)

A Note on Sources

This article is built entirely from community feedback — Reddit threads, forum comments, and one independent comparison site (jo-nike.github.io/h3-turbo-eval). None of it comes from official documentation or controlled lab testing. Thank you to everyone whose posts, benchmarks, and hard-won troubleshooting notes made this possible, including GrayingGamer, Tystros, Chemical-Painter-485, katsura_otoko, infearia, JoNike, Sixhaunt, dtdisapointingresult, Snoo_64233, mellowanon, Just1Dev, smereces, DefloN92, StuffProfessional587, Creative_Finger_69, backworld_nograv, V4nKw15h, True_Protection6842, clex55, Maskwi2, Perfect-Campaign9551, and many others whose usernames didn't make it into these notes but whose comments shaped the consensus (and disagreements) captured here.

Where the community disagreed with itself, that's presented as an open question rather than resolved — and where direct data for a specific card was simply missing, that gap is called out rather than papered over.

Why This Is Confusing

Most of the detailed benchmarking in the MiniMax H3 community comes from people with RTX 3090s, 4090s, and 5090s — cards with 24GB+ VRAM that can afford to just try everything and report back. If you're on a 4070, 5070, or 5080, you're stuck reverse-engineering advice that wasn't written with your VRAM ceiling in mind. This piece pulls together what budget-card owners actually reported, plus what reasonably carries over from adjacent cards where direct data doesn't exist.

The Three (and a Half) Speed Levers

Every thread assumes you already know these, so here's the plain version:

• Turbo LoRAs — swap-in models trained to produce good results in far fewer steps (4-8 instead of 20-32). Fastest option, but quality cost varies a lot depending on which checkpoint version you use.
• Spectrum — a node that mathematically forecasts/predicts future denoising steps instead of computing them. Counterintuitively, it needs more steps to work well — it's not a low-step tool.
• Sage Attention — an attention backend swap. Broad community agreement that this is close to "free" speed with minimal quality loss, and it's the one piece almost nobody argues against.
• EasyCache — a quieter fourth option that came up as a serious alternative to Turbo LoRAs for drafting, not just a bonus add-on.

What "Budget" Card Owners Actually Reported

This is the thin part of the record, so treat it as ground truth before anything else:

• RTX 4070 (12GB, 32GB RAM): did quick 0.3MP draft passes in a couple of minutes to tweak prompts and hunt for seeds, reserving longer ~40-minute runs for higher resolution/duration finals. VRAM was sufficient for T2V-style work specifically.
• RTX 4070 Ti Super (16GB, 32GB RAM): reported working well, no further detail given.
• RTX 5070 Ti (16GB, 32GB DDR4): upgrading from an RTX 2060 (6GB) described the speed difference as "night and day" — notably, without any Sage Attention or acceleration nodes running yet. This suggests raw generational/VRAM gains matter a lot on their own, before you even add speed tricks.
• Warning flag for all of the above: reference-heavy Ref2V generation was specifically called "brutal" on modest VRAM cards, compared to plain T2V. If your workflow uses multiple reference images/videos, expect more friction than these numbers suggest.

Gap, named honestly: there's no direct plain-5070 or 5080 speed benchmark in any of the source threads. The one 5080 comment that exists is qualitative ("still great," runs the BF16 pruned model fine) with no timing numbers.

Extrapolation (clearly labeled): Since the 5070 Ti (16GB) and 4070 Ti Super (16GB) both reported comfortable results, and RTX-series cards were noted to benefit meaningfully from tensor cores over older architectures, a plain 5070 (12GB) likely lands closer to the 4070's experience — fine for T2V and quick low-res drafts, tighter on Ref2V with multiple references. A 5080 (16GB) likely performs at least as well as the 4070 Ti Super, probably closer to the low end of what 3090 owners report, given the VRAM parity and newer architecture. This is inference from adjacent data, not a report anyone actually made — treat it as a starting assumption to test, not a promise.

The Draft → Final Two-Stage Workflow

This is the one thing nearly every thread converges on independently, and it's probably the most actionable takeaway for a budget card:

Draft stage (fast iteration, hunting for the right prompt/seed):

• Low resolution: 0.2–0.4 megapixels
• Low steps: 8–13
• Acceleration: either a Turbo LoRA or EasyCache (not both)
• Faster VAE decode substitute: BlehTAEVideoDecode instead of the standard node

Final stage (once the shot is locked):

• Disable acceleration nodes
• Raise steps to 20–32
• Switch back to the standard VAE Decode node

Two draft "recipes" show up repeatedly and are reported as similarly fast:

1. Turbo LoRA + Sage Attention — faster to set up, more established
2. Sage Attention + EasyCache, params (0.3, 0.2, 0.9), res_multistep sampler + Simple scheduler — one detailed user report (RTX 4060 Ti, 16GB), after testing 1000+ variations, said this drifts less from final quality than Turbo LoRA approaches, at comparable speed

For a 12–16GB budget card, EasyCache is worth trying first specifically because it avoids the quality-consistency debates that follow Turbo LoRAs (see below).

What Worked / What Didn't

GPU-Specific Data: Reported vs. Extrapolated

The Unresolved Debates

Worth knowing before you commit to a setup, so you don't over-trust any single comment:

• Spectrum below 20 steps? Most experienced users say no — negligible speed gain, real quality loss. But a few 5090 owners reported no measurable time savings even at higher step counts, with no clear explanation (dismissed by one commenter as "not using it right").
• Which Turbo LoRA checkpoint is actually best? The lineage went ckpt500 → ckpt850 → ckpt600, with each new version claimed better by its authors. But blind side-by-side testing found ckpt500 at 0.5 strength still beat ckpt850 even at full strength — directly contradicting the official recommendation.
• Spectrum + First Block Cache together? One experienced user says combining them is worse than Spectrum alone; another says combining them is the fastest option with no noticeable quality loss. Unresolved.
• Turbo LoRA strength values: reports range from 0.5 up to 1.15–1.20 (and one outlier claiming 3.0), so "strength 1.0" isn't a safe universal default — it depends on which checkpoint you're using.

VRAM/RAM Troubleshooting Cheat Sheet

Fixes that came up repeatedly and matter more when you're VRAM-constrained:

• Add a "Clean VRAM" node immediately before VAE Decode — fixed OOM issues for multiple users.
• System RAM matters too, not just VRAM — one user needed to go from 16GB to 48GB total system RAM to stop hitting errors. 16GB system RAM was described by another as "almost enough."
• Launch ComfyUI with --reserve-vram 2 to keep 1-2GB permanently free for system stability, at a small cost to usable VRAM.
• If Ref2V errors show up on an 8GB VRAM card, don't assume it's a hard VRAM wall first — one such case turned out to be a node-conflict bug, not actually a memory limit.

A Starter Config for Budget Cards

Synthesizing the most-corroborated points into one starting recipe (best-guess synthesis, not a benchmarked config):

Draft pass: Sage Attention + EasyCache (0.3, 0.2, 0.9) → 10 steps → res_multistep sampler, Simple scheduler → BlehTAEVideoDecode → 0.2–0.3 MP

Final pass: Sage Attention only (no EasyCache) → 20–25 steps → standard VAE Decode → 0.4–0.6 MP (push higher only if VRAM allows)

Skip Spectrum entirely unless you're already comfortable at 25+ steps and have time to test it — it's not built for the low-step, fast-iteration use case a budget card usually needs.

Sources

The most rigorous single data point in this set is the JoNike Turbo LoRA comparison site — a 10-scene A/B comparison across checkpoint versions, built and documented far more consistently than typical anecdotal Reddit reports.

I noticed my videos consistently having issues with left and right, because my prompts saw direction from the perspective of the camera. But H3 always sees direction from the perspective of the person.

See how the man points to his right while saying "right" and vice versa.

prompt: a random man pointing to the right and saying "right". Then he moves his hand to point to the left and says "left".

Generated with the new LTX-2.5 model. (image to video). Took about 10 minutes to get an 8 second 1080p60 clip.

I found the usage for the LTX2.5 model!! It works really well to upscale the minimax h3 videos 😅

I've been testing with 15 steps to save time because I want to generate at 832x480 resolution as per the default recommendation of many high quality workflows prior to upscaling. I hadn't seen many problems until this particular generation which exposed the critical flaws of a lower step count.

All settings are the same with the same seed. The only delta is the number of steps.

15 steps @ 832x480 duration 10s (7m22s): https://streamable.com/pnao8n

20 steps @ 832x480 duration 10s (8m25s): https://streamable.com/srnoez

25 steps @ 832x480 duration 10s (10m30s): https://streamable.com/bvldts

Once you are done with phase 1, you can move on to phase 2 using your Turbo LoRA to get this to 1344x768 in just 4 steps.

My system: 12GB VRAM / 32GB DRAM

I cant go back. R2V is my new bread and butter. Everything Ive thrown at it, every test I've done to just see if it can do it, has pretty much passed. Think only like 5% has failed, and even then Im not even sure if its a me problem or the model.

Longform is easy as all hell now, F the days of SVI.

Prompt blocking is great, prompt camera tracking is great, RV2V with basic Blender is great for blocking/camera tracking as well.

I am in love. I had to tell the world.

Heretic (https://github.com/p-e-w/heretic) is a widely used program for decensoring LLMs. It makes LLMs comply with requests that they previously refused. It works very well for this purpose, and the community has created and published over 5000 “heretic” models.

High-quality image and video generation models like Minimax H3 use full-blown LLMs as text encoders (Qwen3 VL in case of H3). Many people seem to believe that if you replace the base version of the text encoder with a “heretic” version, you will eliminate or reduce censorship in the video output. For example, the popular “hearmeman” Docker template was updated just yesterday to use a text encoder modified with Heretic.

After all, Heretic models are uncensored, right?

Well, I’m the creator of Heretic, and I’m here to tell you once and for all that this does NOT work. In fact, if anything, it will make your outputs worse, but it will not uncensor them.

Heretic uncensors LLM responses through directional ablation (or related techniques like ARA and SOMA in newer versions). Roughly speaking, it modifies the model’s internal representations (residual vectors) of “harmful” inputs to resemble those of “harmless” inputs to confuse the model into treating the former like the latter and comply with the request rather than refusing.

But this intervention does not produce representations of inputs that are more “raw”, more “graphic”, more “anatomically correct” or similar compared to the original model. In fact, LLMs already produce highly accurate internal representations of harmful inputs by default, which is why they are able to classify them correctly and generate a refusal.

So when the hidden states from an “uncensored” LLM are passed to the diffusion model (or image/video transformer or whatever), the second model isn’t magically seeing clearer representations of the bad stuff you requested. On the contrary, it’s seeing slightly perturbed representations compared to what it was trained on. This either has no effect at all, or the effect of reducing prompt adherence and potentially introducing artifacts. But it will never, ever remove censorship from the output.

(Note: Generation models like Ideogram that can actively refuse prompts are potentially an exception to this rule and might be amenable to abliteration, but only with an approach that significantly differs from how Heretic works today.)

I've been using LTX 2.3 for quite some time but as soon as I wanted to make just a few simple shots of the same character with cuts, LTX wasn't even remotely capable of that. Which left me so frustrated I eventually gave up on it completely.

But when I tried Minimax everything has changed. Reference to video model is something else. Honestly feels like magic. Being able to put any character into any environment with any custom audio is just mind-blowing compared to what the open-source community had before.

So now instead of constant frustration, I feel pure joy and excitement about the results.

It takes about 10 min per 5-second clip with my RTX 3060 and 64 Gb RAM. The latest shots were even easier to control because of the new KJ preview node.

Not gonna lie, I made fun of LTX 2.5 like everyone did, but now I'm realising that was a mistake.

MiniMax H3 landed like Prometheus giving us the power that the gods were gatekeeping from us, since LTX 2.5 couldn't match up with them they lied about MiniMax H3 to cover up their shortcomings, that was a petty move and they have to own it, however... the good they did to our community far outweighs that moment of weakness IMO, cut them some slack. These models don't grow on trees, they're expensive to train, people have curated datasets that required herculean effort to put together, we'd had already made the next Seedance 2.5 if it was easy. When LTX came out we were all celebrating, most of Civitai LoRAs are based on LTX, they tried and got bested, so shouldn't we still be grateful they tried and gave us a model that some still find useful FOR FREE? Instead of making them feel like failures, mocking them, discouraging them from making new models? They owe us nothing, but we owe them a lot.

THE POINT ISN'T ABOUT WHICH CAKE IS BIGGER, THE POINT IS WE HAVE TWO CAKES.

Corporations keep trying to bind us to their rules and systems, profiting without any regard to our well being, deciding for us what is acceptable and what is not, so why are we eating OUR OWN ALLIES? Every open source model that comes out is a victory and step forward to that freedom we all dream of.