Hello everyone

Want to get faster MiniMax H3 video generation without sacrificing quality? In this tutorial, I’m testing the new H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes to find the best combination for speed and quality. The goal is to push MiniMax H3 as far as possible while cutting generation times by up to 2×, then upscale the results with LTX Upscaler to reach a stunning 2432 × 1344 (2K-class) resolution. By combining both H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes I generated video at 0.8 megapixel using "**RTX3060 6GB 16GB RAM "**and I got

 13 minutes vs 41 minutes at 8 steps

 27 minutes vs 52 minutes at 20 steps

LTX 2.3 Upscaler 11 minutes to get 2432 × 1344 resolution

Workflow link

https://civitai.com/articles/34028/comfyui-tutorial-minimax-h3-4-steps-lora-upscaling-2x-faster-generation-best-settings-for-2k-ai

Video Tutorial link

https://youtu.be/ZUzeM9OEJ4Y

This is a generative re-render, not restoration or sharpening. It invents fine detail. In every test with one person it added freckles that weren't there.

The comparisons use MiniMax H3 clips at 640x384, 10 seconds long, upscaled 2x. They're Lanczos versus ReDetail at the same output size, so there isn't any bigger image sleight of hand.

On a motocross clip it redrew the jersey graphic and number plate. The new markings stayed fairly stable between frames, but they weren't the original markings. Logos, numbers and text are all fair game.

If reddit compresses this video to the afterlife again, see: https://civitai.com/models/2857731/redetail-ltx-25-generative-video-upscaler-workflow-cli

So it's useful for AI-generated or generally soft footage, where there isn't much real detail to recover. It's a bad fit if a face, label or logo has to be 100%.

• Silent clips fail because the model encodes audio and video jointly. Add a silence track first.
• Both output dimensions must divide by 64, not 32. Clip length must be `8n+1` frames or the model silently drops the tail.

I like 1.5x, not 2x. On one clip, 243 frames from 768x1408, 1.5x took 7 minutes and peaked at 65GB. 2x took 17 minutes and 80.5GB. The 2x result carries maybe more detail, but check between the two and it's hard to tell imo. On skin most of that extra is invented, not recovered. Faster render, less made up texture.

UPDATE! NOW WITH CACHED CONDITIONING

The text encoder is now optional. The graph runs with empty prompts, so its conditioning is a constant. It ships pre-computed at 26KB, which skips the 15GB download and takes peak VRAM from 30.4GB to 24.8GB on a 5090.

There's a Mac build in there now too, ReDetail_LTX25_upscale_MAC.json. It runs the GGUF transformer with no text encoder at all (the cached conditioning replaces it), so it's about 17GB of models total. On an M5 it did 33 frames from 640x384 to 1280x768 in 4.4 minutes. Per frame megapixel that's roughly 6x slower than a 5090, not the 30x I was expecting, so a 10s clip lands around 34 min at 2x or 19 min at 1.5x. Quality holds.

Repo: https://github.com/Bambushu/redetail

Workflow: https://drive.google.com/file/d/1B4kODxXQgJ1QOKRsEIkxHbgYmdruPpTK/view?usp=sharing

I made this experimental short scene using ComfyUI.

I generated the characters and backgrounds using the Krea 2 open-weight model with a Turbo LoRA, then animated them using MiniMax H3 with a reference-to-video workflow. With the Turbo LoRA, each 5-second clip took around 2–3 minutes to generate.

I edited everything in CapCut, added some color grading, bloom, and film grain, and it all came together nicely.

For the lo-fi track, I produced it on the Maschine MK3 using a free sample pack.

There always appears to be something missing from comfy templates. I don't see a spot to add a reference video or audio? And had to figure out that you have to duplicate image ones and connect them to get up to 9... Totally confused where to input the reference video. Load video node doesn't connect.

I have been working on this last week.

• What is this node ?

- One node with different MiniMaxH3 workflows.

• What its for ?

- If you hate spaghetti and hate doing workflows and dealing with errors.

https://github.com/LeonQ8/ComfyUI-ALLinONE-MinimaxH3

I’ve been working on a custom MiniMax H3 node for ComfyUI called **MiniMax H3 Motion Director**.

And to describe it accurately:

Yes, it is kind of a Frankenstein node.

Instead of pretending everything was invented from scratch, I basically took several H3-related ideas/projects that I liked, stitched the useful parts together, modified them, and then built a larger multi-segment Director around them.

The main “body parts” are:

* **AIMixer / ComfyUI_MiniMaxH3_Director**

The original Director project and one of the main foundations this project grew from.

* **NikoDemon80 / ComfyUI-H3-Motion-Context**

Motion Context / H3 runtime continuity work. This is integrated and modified directly inside the Director, so you should not load the standalone Motion Context node at the same time.

* **Carasibana / ComfyUI-H3-FaceRefine**

Face tracking, cropping, per-frame denoising, latent injection and stitching algorithms were adapted from this project.

* **Kijai / ComfyUI-KJNodes**

Some packed-latent preview normalization and temporal TAEHV decoder behavior were adapted/informed by KJNodes.

Then I started adding the parts I wanted for actually managing a longer H3 project instead of treating every generation as an isolated clip.

That eventually turned into this:

* T2V / I2V / FL2V / R2V / V2V / RV2V

* Multi-segment timeline

* Independent Prompt and assets per segment

* Selective rerun — regenerate only the failed segments

* Motion Context between segments

* Context Frames

* Latent Scale Lock

* Continue Generated Audio

* Color Re-anchor

* V2V / RV2V Source Bridge

* Shared/public assets

* Persistent material library

* External `SAMPLER` + `SIGMAS`

* Global Refine

* Integrated Face Refine

* Director-controlled live preview

* Segment / Multi / Final result management

So the basic idea is:

AIMixer Director

+

H3 Motion Context

+

H3 Face Refine

+

some KJNodes preview/TAEHV behavior

+

a lot of glue, UI and multi-segment management

↓

MiniMax H3 Motion Director

A proper ComfyUI **Frankenstein monster**.

The reason I went this route is that my H3 workflows kept turning into huge graphs once I wanted more than one shot.

Generating one 5–10 second clip is easy.

Managing:

Shot 1

↓

Shot 2

↓

Shot 3

↓

Shot 4

while keeping character appearance, motion, color, audio, references and generation settings reasonably consistent is a different problem.

And when Shot 3 sucks, I don’t want to regenerate Shots 1, 2 and 4 just because they happened to be part of the same workflow.

So Director treats the video more like a small project/timeline.

Each segment can have its own Prompt and assets, and failed segments can be selectively rerun.

For continuity, later segments can inherit information from earlier ones through Motion Context, Context Frames, audio continuation and other continuity controls.

For V2V / RV2V there is also a Source Bridge, because after splitting a Source Video into segments you also have to deal with motion discontinuities at the source boundaries.

I also didn’t want the Director to completely lock you into its sampler setup.

If you prefer your own ComfyUI sampling chain, you can connect external:

SAMPLER

SIGMAS

and let the Director handle the project/timeline side while your normal workflow handles sampling.

The node is now published on the **Comfy Registry**, so it should be installable through ComfyUI-Manager.

GitHub:

https://github.com/j955229/ComfyUI-MiniMax-H3-Motion-Director

The whole derivative project is GPL-3.0, and the repository includes the original licenses/notices for the upstream projects.

I’m still working on the long-form side in particular:

* better cross-segment continuity

* optional inheritance per segment

* reducing visual/motion drift

* better audio continuity

* stronger long-project context

* eventually making separate generation batches understand that they belong to the same larger project / episode

This is still very much evolving, so I’m interested in feedback from people actually using MiniMax H3 in ComfyUI.

Especially: **what currently breaks first for you when you try to make longer H3 videos?**

Continuity? Character drift? Audio? VRAM? Managing references? Rerunning bad shots? Something else?

Despite my limited experience and modest hardware — an NVIDIA RTX 5060 Ti with only 16 GB of VRAM — I was able to create a complete 3-minute video using MiniMax H3.

The process started with ChatGPT, which I used to develop the prompts and create the necessary reference images. I then rendered the project as 10 seconds individual video clips in MiniMax H3. The soundtrack was created separately using ACE-Step 1.5, and finally, I brought everything together in DaVinci Resolve, and completed the final video.

This project shows that with the right tools, some creativity, and a lot of patience, it is possible to achieve decent results even without high-end hardware or extensive experience.

Finally got the flow and prompt structure figured out for H3- now I'm having so much fun.

Will post specs in comments.

I wanted to test it a bit with creature animation, snow, wind, and atmosphere. I started by generating still keyframes with Seedream pro, then used image-to-video to generate videos in .

Prompts and keyframe here :https://www.patreon.com/u8638148/posts/minimax-h3-and-166452641?utm\_medium=clipboard\_copy&utm\_source=copyLink&utm\_campaign=postshare\_creator&utm\_content=join\_link

Is there a way to convert the last frame to a true image like a png?

I noticed I could create longer vids by taking the last frame and use that as a refrence image, but when I do that like in my screenshot, generation time gets much larger, I think it's b/c internally Minimax reads that last image actually as a video, thus behaving much differently.

Crappy demo clip as I was short on time, but handy to see in context of the screenshot of this post

https://github.com/shootthesound/ComfyUI-H3Studio

Lots of hopefully clear instructions in the Github link and a basic example workflow.

Its my first time making a video editor after 15 years of using one every day, so there is a lot of carried over UX, and more I'll refine.

If you fancy it, this plays nicely with what is now a fast and high quality results Minimax Lora Trainer (getting good quality training to work in minimax has been a nightmare, but its there now): https://github.com/shootthesound/Fizgig

Just sharing my results using the Turbo LoRA that was created for H3.

They said it’s still a work in progress, and the audio is still a little bit stretchy in some parts, but the results are already fantastic. I mean, it’s only the third day since H3 was released and we already have a functional Turbo LoRA.

I generated all the clips in this video with the Turbo LoRA enabled, using 10 steps at 0.4MP.

The first three clips were T2V, and the last two were FLF2V.

The only thing I manually added was the soundtrack at the end.

*EDIT: It was T2V not I2V.

I managed to run MiniMax H3 Ref2VA locally on hardware below the model’s recommended VRAM requirement.

My setup:

• GPU: NVIDIA GeForce RTX 2080 Ti, 11GB VRAM
• CPU: Intel Core i9-9900KF, 8 cores / 16 threads
• System RAM: 16GB
• OS: Windows
• PyTorch: 2.11 with CUDA 13.0
• ComfyUI running in LOW_VRAM mode
• DynamicVRAM and asynchronous weight offloading enabled
• Mixed INT4/INT8 ConvRot MiniMax H3 checkpoint

Here are the results from my local tests:

Output	Video length	Configuration	Generation time
256×160	0.21s	2-step smoke test	38 seconds
512×288	5.17s	Base model, 10 steps	4m 00s
640×352	5.17s	Base model, 10 steps	7m 35s
864×480	5.17s	Base model, 10 steps	30m 31s
352×640	5.17s	Qwen3-VL-32B, Turbo, 4 steps	4m 23s
576×1024	5.17s	Qwen3-VL-32B, Turbo, 4 steps	15m 40s
288×512	8.00s	Qwen3-VL-32B, Turbo, 4 steps	5m 06s
384×672	8.00s	Qwen3-VL-32B, Turbo, 4 steps	11m 26s
288×512	15.00s	Qwen3-VL-32B, Turbo, 4 steps	13m 51s
384×672	15.00s	Qwen3-VL-32B, Turbo, 4 steps	40m 55s

The results show how quickly both spatial resolution and video duration increase the inference cost. For example, moving from 288×512 to 384×672 increased the 15-second generation time from 13m 51s to 40m 55s.

MiniMax H3 also aligns the requested duration to its required frame grid. For example, a requested five-second video becomes 124 frames at 24 FPS, producing an actual duration of approximately 5.17 seconds.

I also tested the Qwen3-VL-4B INT4 encoder with the Turbo sampler at four steps. It reduced the text encoder memory requirement, but the generated quality was noticeably worse in my tests. Subject consistency, motion quality, prompt understanding and fine detail were all weaker than with the native Qwen3-VL-32B encoder.

For that reason, Qwen3-VL-32B remains the default. The 4B path is included only as an experimental lower-memory option.

I also created a simple web interface where you can try it yourself:

[Try MiniMax H3 online] https://hackcha.cn/minimax-h3/index-en.html

Please keep in mind that the service is running on my personal RTX 2080 Ti, so generation can be quite slow, especially for longer videos or higher resolutions. Tasks are processed through a queue, and waiting times will increase when multiple people are using it.

The code is available here:

github.com/hackcha/minimax_h3_infer

This project is a command-line and worker wrapper around the official ComfyUI MiniMax H3 implementation. It:

• Runs the mixed INT4/INT8 ConvRot MiniMax H3 Ref2VA checkpoint
• Accepts one or more reference images with a text prompt
• Supports the native Qwen3-VL-32B encoder
• Includes an optional Qwen3-VL-4B INT4 path through ClipProj
• Supports the MiniMax H3 Turbo LoRA and its dedicated four-step sampler
• Automatically starts and communicates with a local ComfyUI backend
• Uses grouped loading, DynamicVRAM and asynchronous offloading
• Generates an MP4 containing both video and audio
• Includes a configuration and environment validation command
• Includes a database worker for automated task processing

The worker can atomically claim queued database tasks, download reference images, run local inference, upload completed videos to Alibaba Cloud OSS, and update each task with either a result URL or an error message. Multiple workers can claim tasks safely.

Resolution tiers in the worker are interpreted using the short edge, similar to how video platforms describe resolution. For example:

• Vertical 9:16 at 288p: 288×512
• Vertical 9:16 at 384p: 384×672
• Vertical 9:16 at 480p: 480×864
• Vertical 9:16 at 768p: 768×1376
• Horizontal 16:9 at 768p: 1376×768

The long edge is aligned to a multiple of 32 because that is required by H3. These are direct inference dimensions, not post-generation upscaling.

The model files are not committed to GitHub, but the README lists all required checkpoints, their download sources and the expected directory structure.

Running H3 with only 11GB VRAM is possible, but it is operating close to the hardware limit. Higher resolutions can still result in CUDA OOM errors, and 16GB of system RAM causes substantial paging during model staging and offloading. I would recommend at least 32GB of system RAM, preferably 64GB, for a more practical setup.

This post is half a joke and half a plea and public service announcement.

Some people have been complaining they don't get results as good as other people with Minimax H3 videos, or have the following issues:

• Dialogue being spoken by the wrong characters
• Dialogue that is just gibberish or random
• Random video cuts they didn't ask for
• Characters talking over each other or too fast
• Prompts not being followed

These things can all be prevented and avoided and not encountered at all if you follow the official prompting guides. Yes, there are two. Both are on the official Huggingspace page for Minimax H3.

One is the Official Prompting Guide for the Text to Video and Image to Video Model.

The other is the Official Prompting Guide for the Reference Video Model.

There is some overlap, but for the most part, each model has it's own prompting syntax, and in particular, the Reference Video Model for H3 is very picky about you using the right keywords and instructions to get what you want.

"But I get decent results with just a couple of sentences typed in natural language of what I want."

That's great, but you're really just relying on the Qwen 32b vision model guessing what you want. It's like pulling a slot machine lever and hoping you get cherries. Only this slot machine can take a few minutes to nearly an hour to stop spinning, based on your hardware.

The great thing about Minimax H3 is for the first time we can truly direct our own AI videos like a director would on set, with the AI providing the actors, scenery, and props. If you write a properly formatted and detailed prompt for Minimax H3, it looks almost like a shooting script.

Why spend time waiting to hit a jackpot when you can take a few minutes to write a detailed, properly formatted prompt that follows the official guides, and get those bright lights and tokens falling into your lap on the first lever pull?

Okay, quick fire problem solving for people who still won't RTFM:

>Dialogue from the wrong characters?

>Dialogue that is just gibberish or random?

Walter White says, <d>[English in Walter White's voice from Breaking Bad] My product is pure, Jesse! There will be no chili powder in my meth.</d>

Always specify the character speaking, either by name, or using the <Subject 1> system in the official guide. In the Text to Video and Image to Video model, always use the <d>[Language Spoken]</d> tags. This will fix BOTH of those issues.

>Random cuts in the video you didn't ask for?

[Shot 1] A medium close-up of Jesse Pinkman from Breaking Bad, pacing back and forth, agitated. He looks up towards the camera, opens his mouth as if he's about to speak, then seems to change his mind, closing his mouth and shaking his head. [Shot 2] At 00:06:000 the camera cuts to a static camera shot framing Walter White from Breaking Bad, sitting on a cheap white plastic lawn chair, his arms crossed and glaring at Jesse. [Shot 3] At 00:10:500 the camera pans quickly back to Jesse, doing a Push In at slow speed to his face as he stops pacing and narrows his eyes at Walter.

This is how you control not only the camera work, but the PACING of your video. You NEVER include a time code on your first shot. You can omit the time code from ALL shots if you want the model to decide on it's own, based on your prompt, when to cut.

BUT, for ultimate control, you want to use time codes. Look at my example above. I just told the model to have Walter glare at Jesse for 4.5 seconds, because I told the model that camera shot starts at 6 seconds into the video, and the next cut doesn't happen until 10.5 seconds into the video. That lets you control the pacing and timing for jokes, punchlines, acting, everything.

>Characters talking over each other or too fast?

This is an old one that anyone familiar with prompting for video models should know by now - what you are asking for in your prompt and the length of your video in time need to match.

The model will try its best to cram every action and piece of dialogue into your video that you asked for, and if that would naturally take 10 seconds and you've only given it 5 seconds? Well, now everything is crammed together, overlapping, or being cut-off.

My recommendation is to generate just a quick 0.2 MP version of your video first after you type your prompt, generate, and see how the timing is working. Is it too fast? Too slow? Do the actions have enough time to happen? Do you want more breathing room?

This is the time to decide all that and lock in a video length. The low resolution of 0.2 MP is quick to generate on most set-ups (mine for this post's video took 3.5 minutes for a 14 second video) and let you work out any issues in your prompt before going in for the long generation at higher resolution.

>Prompts not being followed?

It's because you didn't read the manual!

--------------------------------------------------------------------------------------------------
Now, with all that said, here is the prompt for the video I made:

integrated_multimodal_description: [Shot 1] Live-action film footage of the American drama series Breaking Bad, professionally color graded with a warm color grade, with slightly desaturated colors for a premium film feel, a continuous camera shot with no cuts, medium close-up POV shot of Walter White, bald with a goatee and glasses, as portrayed by Bryan Cranston. He is standing in the Arizona desert next to a parked RV. He is wearing a white PPE protective suit and yellow rubber dish gloves. He is looking directly at the viewer with barely constrained anger. At 00:01:300 he reaches out towards the camera and points his finger at the POV camera with one hand, the camera shaking slightly from the movement. Walter then says angrily, <d>[English with Walter White's voice] Listen, you want to cook Mini Max H3 videos, you follow the recipe!</d>. At 00:04:500 Walter raises his other hand revealing he is holding a thin stack of white paper pages in portrait orientation. The front of the paper visible on top of the thin paper stack is blank except for the large black printed text "Minimax H3 Official Prompting Guide". The papers are held in front of the camera on the right side of the screen for a moment in portrait orientation, so the text can be clearly read, while Walter glares at the viewer on the left side of the screen. At 00:07:000 Walter then shakes the papers at the camera, then says angrily, <d>[English with Walter White's voice] Read the fucking manual!</d>. At 00:10:000 the camera does Pan Right and a Pull Out to show a close-up of Jesse Pinkman from Breaking Bad, with his hands held up by his face with fingers spread, an annoyed look on his face. Then he says in frustration, <d>[English in Jesse Pinkman's voice from Breaking Bad] Alright! Damn, Mr. White! I just want to generate memes.</d>, overall_soundscape: Ambient sounds of an Arizona outdoor desert during the day, non_diegetic_music: none

For those interested, this video was generated at 1 MP on a 3090, using Sage Attention and the Spectrum Node for H3. The final video of 14 seconds at 1 MP took 40 minutes to generate and then was upscaled using RTX Super Resolution.

The workflow was the default Text to Video Minimax H3 template that comes in the latest update of Comfyui.

Now get out there and go cook some memes, everyone!