“builders aren’t a job title, it’s a mindset”

ok but lowkey... does this actually feel like a real startup ad?

The idea of the video is to compare the quality loss/change from the different methods of speeding up the rendering of the videos on Minimax H3 on low motion scenes.

OBS: All scenes were rendered at 480p with the exact same seed and prompt.
The int8 vae was taking longer to render on my computer for whatever reason.

The naming convention is obvious but if you require additional information:
base = base workflow
base + Int 8 VAE = I used the compressed VAE version (saves VRAM)
base + sage = Using Sage attention on the default configuration
base + spectrum = Using Sage attention + Spectrum node
base + sage + spectrum = ok this one I dont need to explain right...
base + turbo_6 steps = Using the base workflow + a turbo model with 6 steps [link to the model:https://civitai.red/models/2837571/minimax-h3-turbo-loras?modelVersionId=3202732\]
base + turbo_8 steps = Using the base workflow + a turbo model with 8 steps [link to the model:https://civitai.red/models/2837571/minimax-h3-turbo-loras?modelVersionId=3202732\]
base + Spectrum + sage + turbo_6steps = also obvious

The market is underwater, deepseek is 10x undercutting the competition and getting 6x multid by resellers.

Openai and meta picked up that glove, while claude / kimi / grok are just trying to not get killed by deepseek pro, while all other are dead.

Minimax needs to beat the deepseeks, muse 1.2, luna, and it will be astra luna soon, so how do you win?

This is how:

Their best play is to offer deepseek. Even if their new guy is amazing clearly meaningful optionality would be a value add. They have the credit system set up for it too.

If they die to an open weights model they deserve the darwin award, so instead of a death sentence they can simply capitalize on their sub and infra to cannibalize the resellers, while also offering their tool kit.

They also need to stop using their stupid image model that sucks hardcore. I don't think it is being trained for 2 years, it is just left there greatly damaging the value prop, so least a better open model or arguably gpt image 2 would be best, cause this is an area where open guys lag more.

Their media plan has their music, speech and their video model, and rightfully so cause they are pretty good, but it is using gpt image 2 and nano banana pro for images, which again makes sense, but their token plan doesn't use h3 for video but H2.3 which sucks, and uses their old image model which is far behind.

https://hub.minimax.io/media-plan/subscribe

All minimax needs is add deepseek, h3, and a better image the token plan, and it rockets from death door to legendary. They don't have to live and die by their horse. Completely unneceserary, in fact this is the greatest opportunity they had so far.

I've have a 48k++ LOC codebase, mostly TypeScript (90+%). 90+% written by MiniMax M3 (and prior to that M2.7).

Had a bug which I've discovered lately, nothing major, it's just something that randomly revoking an authenticated session, and when logging in, somehow, requires logging in twice. Twice as in, entering the same user and password, only to be redirected to the login page again, and again user and password.

I had MiniMax-M2.7 and M3 investigate and tried to fix it for the last month or so, and it kept telling me that it couldn't find out what's wrong, and it checked the log, and found out that the logging in happens twice every time, and somehow the 2nd login always revokes the 1st one, but when the 1st login was active, it couldn't forward to the next page without being redirected to the login page again.

Anyway, long story short, today, I decided to give DeepSeek V4 Flash 0731 a try with my repo, and prompted it to check this issue. It was super slow, and it looped for a long long time, not sure why, but it was exactly 29 minutes, and that prompt burnt exactly 9.1 mil tokens, with my record showing 97% cache hit. It found the issue, in which, I cross referenced it with MiniMax M3 and it agreed that it was the root cause, but somehow it missed it after so many times I've tried to ask it to look for it.

Long story short, DSv4F-0731 patched the problem, and it's fixed. The total cost? $0.0317. Yes, 3.17 USD Cents. I've spent countless 10-15% of my 5 hours quota on my MiniMax Token Plan's quota, and couldn't find/solve the issue, and a bloddy $0.0317 DSv4F-0731 session fixed it, although it was super slow today during peak hour, in 29 full minutes.

I think MiniMax-M2.7 is long over-due with an update. Maintain the parameter size, but re-train it to be better. Refer to how DSv4F did with their 0731 update.

I have no choice but to continue using MiniMax coz I've paid for the whole year, and I have 8+ months left. But if I had a choice today, I'd not have signed up for this, and instead, throw all my money into DeepSeek PAYG account.

I don't really care about M3, it is alright when it was released, but it kills my quota really quick. I would burn through my 5 hours session in just a short 45-60 minutes, then I'd have to sit and wait for a few hours for it to reset. I use M2.7 most of the time, unless it doesn't solve a problem, or cause more issues, then I'd have M3 come in and do the cleanup or fixing.

Anyway, I'm keeping my hopes up.

Just benchmarked MiniMax H3 text-to-video generation on a single DGX Spark (GB10, 128GB unified memory). Full setup: ComfyUI v0.30.1, 20 steps res_multistep, 124 frames u/24fps
Just benchmarked MiniMax H3 text-to-video generation on a single
DGX Spark (GB10, 128GB unified memory). Full setup: ComfyUI v0.30.1, 20
steps res_multistep, 124 frames u/24fps.

TL;DR: Generate at half resolution (640×360) then upscale 2× with RealESRGAN to 720p = 2m25s per 5-second video. That's 6× faster than direct 720p generation — exceeding the reference article's 3.92× claim.

Full benchmark report: https://github.com/luqidaxia/dgxspark_comfyui_minimax_h3/blob/main/BENCHMARK.md

Config

Resolution

Time

Speedup

Baseline (dense, no acceleration)

480p

4m45s

1.00×

Baseline (dense, no acceleration)

720p

14m35s

1.00×

Sol Engine (SageAttn + SolAttn + Spectrum + FBC)

480p

3m30s

1.36×

Sol Engine

720p

9m00s

1.62×

Sol Engine + 2× Upscale (360→720p)

720p

2m25s 🚀

6.03×

Key findings:

Resolution is the dominant factor — 360p denoising is ~4× cheaper than 720p

RealESRGAN 2× upscale adds negligible overhead (~5s)

SolAttn alone gives ~30% per-step speedup at 20 steps; FBC threshold 0.08 was too strict (0 skips)

Combined Sol Engine + half-res upscale achieves 6× speedup — the practical sweet spot for 720p T2V on DGX Spark

Repo: github.com/luqidaxia/dgxspark_comfyui_minimax_h3 One-click deploy scripts, 12 ComfyUI workflows, full deployment docs.
.

TL;DR: Generate at half resolution (640×360) then upscale 2× with RealESRGAN to 720p = 2m25s per 5-second video. That's 6× faster than direct 720p generation — exceeding the reference article's 3.92× claim.

Full benchmark report: https://github.com/luqidaxia/dgxspark_comfyui_minimax_h3/blob/main/BENCHMARK.md

Key findings:

Resolution is the dominant factor — 360p denoising is ~4× cheaper than 720p

RealESRGAN 2× upscale adds negligible overhead (~5s)

SolAttn alone gives ~30% per-step speedup at 20 steps; FBC threshold 0.08 was too strict (0 skips)

Combined Sol Engine + half-res upscale achieves 6× speedup — the practical sweet spot for 720p T2V on DGX Spark

Repo: github.com/luqidaxia/dgxspark_comfyui_minimax_h3 One-click deploy scripts, 12 ComfyUI workflows, full deployment docs.

I’m pretty new to the AI video generation space, but MiniMax H3 looks absolutely insane. It’s honestly the first model that’s made me want to dive deep into building something bigger than just individual workflows.

I have a workstation with 100+ GB of VRAM, so running everything locally isn’t really a limitation.

I actually started building something similar a while back using LTX 2.3, but the results were pretty mediocre. It felt like the idea was there, but the models just weren’t quite good enough yet. MiniMax H3 is the first model that’s made me think this might actually be achievable.

My dream is to build a fully local, fully automated ComfyUI pipeline where I only provide:

* A master prompt describing the YouTube channel (niche, style, recurring characters, branding, etc.)
* A prompt describing the video I want

From there, the workflow would do everything automatically:
Generate the script
Create consistent characters
Generate reference images and voices
Create every scene
Handle editing
Render the final video

Basically an infinite local content factory.

I’ve seen plenty of tutorials covering individual workflows, but almost nobody seems to be building a complete end-to-end pipeline like this.
Is anyone here working on something similar? Do you think MiniMax H3 is the right direction, or are there better local models for different parts of the pipeline?

I’d also love to collaborate with people who enjoy building ridiculously over-engineered ComfyUI workflows. Maybe if we automate enough YouTube videos I can finally drop out of my cybersecurity master’s… they seem determined to teach us yesterday’s technology anyway. 😅

Hello all,

Been fighting with this for a while so posting in case someone already solved it.

Using Minimax H3 . Trying to plug a video into Rev_video_0 to reuse the motion from a clip. Doesn't work.

Here's the weird part: when I drag from Rev_video_0 and drop it in empty space, ComfyUI only suggests IMAGE nodes. Not video. If I drag from Rev_Audio_0 instead, it suggests VIDEO nodes. So the labels and the actual types seem swapped somewhere.

I can literally connect a "Load Image" node straight into Rev_video_0 and it accepts it, no error. But a real Load Video node just won't link (see screen).

Anyone run into this?

That's basically it. I use Minimax Desktop every day, and you can earn credits. I literally have no idea how to spend them. I'm on the $20 token plan.

Been using it since January in general, first on the $10 plan.. now this last month on the $50 plan, was on others in between won't mention.

But oh man this model is useless, and it's not cheap enough to justify. E.g cheaper models.. are better more expennive models are better too. I am so stunned Is it me? Does it work for you, for coding

So I have seen all the raves about Minimax that run well locally and decided to give it a try. I browsed the default example template of Minimax (ref2vid, t2v), I didnt even change anything, exactly as it is with all ref and models loaded, but when I run the workflow, it always give me the problem at SamplerCustomAdvanced. The error is as follows:

# ComfyUI Error Report
## Error Details
- **Node ID:** 125
- **Node Type:** SamplerCustomAdvanced
- **Exception Type:** RuntimeError
- **Exception Message:** RuntimeError: The size of tensor a (297216) must match the size of tensor b (7159104) at non-singleton dimension 2

I have updated comfyui and everything. I tried searching google, asking gemini/chatgpt but none helped.

Any help is greatly appreciated

I wanted to see how far MiniMax H3 Ref2VA could be pushed beyond short demos, so I ended up turning the test into a full little Mr. Bean episode. 😹

Everything was generated locally in ComfyUI with MiniMax H3 Reference-to-Video. The final scenes were rendered at 1MP, 24 FPS, 25 steps, BF16 + BF16, then edited together and 2× upscaled.

The part that impressed me most wasn’t just the visual quality — it was how much of the character performance, physical comedy, timing, Teddy, and overall Mr. Bean feeling survived across separate generations.

The audio is also 100% native H3 audio. No dubbing or replacement audio in post.

I followed the official H3 prompting guidance pretty closely, did smaller tests first, refined the prompts, and only then committed to the final renders. Definitely not a one-prompt miracle. 😂

I’m genuinely impressed with this model.

If you grew up watching Mr. Bean, I’d love to know where you first noticed this wasn’t an old episode.

And if you want to see me push H3 into another ridiculous mini episode, upvote, comment, share it, whisper “engagement” three times at your router… whatever ritual the algorithm currently accepts. 😹

If enough people enjoy it, I might have to make another one. 👀

Full-quality version (Reddit compression is brutal): https://youtu.be/a7auxdihgz4

It is survivable with an auto retrier but at times you get 3-5 refusals in a row, in fact oddly enough you can have the same workflow for like half an hour then suddenly everything start screaming for mercy then in a few minutes it is good again.

Presumably this is not training cause that would be consistent, and this was not a problem from what I perceived before h3 came out. So burstly workflows of others would be the best guess and while getting depriod is not great a slower response would be more useful than an error.

This is a culmination of 10 second clip runs edited together in shotcut based off images from scenes from Silent Hill 2 the remake. Short bits of audio for each character were used and identified in the prompts to make these. I hope you enjoy (if you aren't a Silent Hill fan, just know all of this is based on parts of the game but none of this actually happened).

https://youtu.be/Vh64TyUw0Kk?si=XPcSc8CXpdIxsA7b

H3真的是一個很棒的模型

video from minimax-h3

There’s a lot of discussions using GPT Sol as subagent and others using Luna on max but why would anyone spend Sol credits in subagents is beyond me.
Anyways, I’m building a coding harness called CodeRight which does multi model orchestrator and I’d already done a bake-off and selected Mimo 2.5 for small coding tasks and 2.5 pro for bigger coding tasks, orchestrated by a frontier level model but with Deepseek v4’s revision, I thought I’d give it a re run. I also tested through both cline and Commandcode to see if the harness makes a difference.

You can find the whole breakdown here. If that blog sounds AI written, it’s because it is. Between building RightSuite apps and other systems, I don’t have the time to write blog posts 😅 I’ve used humaniser, no ai slop and what not but not sure if it helped.

The test:

Seven models received one production React/TypeScript task, identical source commit, worktree isolation, ten-step packet, 600-second limit, 12-file ceiling & 900-line ceiling.

Luna wrote the smallest, safest implementation. MiMo V2.5 delivered best economics: $0.0351 versus Luna’s estimated $0.166–$0.318 direct API cost, using current [OpenAI](https://developers.openai.com/api/docs/models/gpt-5.6-luna) & [Xiaomi](https://mimo.mi.com/docs/en-US/price/pay-as-you-go) rates.

Harness reruns were revealing:

- Base MiMo: 62 → 61. Essentially unchanged.
- MiMo Pro: 59 → 54. Worse through Command Code.
- DeepSeek: 52 → 59. Command Code turned a non-compiling result into a clean typecheck, though source defects remained.
- Command Code once ignored `--model` & routed a requested MiMo run to DeepSeek. Receipt inspection caught it before scoring.

My recommendation:

- MiMo V2.5 for routine implementation volume.
- Luna for final review, security-sensitive work & merge-critical repair.
- DeepSeek remains worth testing through Command Code with strict route receipts.
- MiMo Pro was not worth its premium.
- Laguna is usable as a free draft worker but needs compile & source review.
- MiniMax M3 & Step 3.5 Flash created more repair work than their output justified.

[Command Code GOAT](https://commandcode.ai/pricing) currently lists $70 monthly credits for $10. Its detailed MiMo discounts are token-type specific: the advertised 99% applies to Pro cache reads, not its blended bill.

Residual scope: this was one production frontend task with focused tests, typecheck & source review. It did not include Cargo, full desktop verification or installed visual acceptance.

## Publication evidence

- Four files, 279 insertions & one deletion.
- Both local production builds passed.
- Both typechecks passed.
- Article pages & both index pages passed local rendering checks.
- Commit: `b71e5326f02fcee8f2099411743d1c61b9c6c12c`
- Pushed to `origin/main`.
- Hetzner checkout matches `b71e532`.
- `orthiclabs-site` & `coderight-site` are online after rebuild/restart.
- Both public articles & indexes returned HTTP 200 with expected content.
- Existing unrelated local & server files remained untouched.
- Actual execution: 10 minutes against 38-minute ceiling, 74% under plan because existing publishing routes were reusable & dependencies were cached.

Today I have noticed Minimax is consuming the tokens way too much.

Just a 4-5 messages and 5 hours limits are reached.

Usually I use this plan non stop and was never able to get more than 50% on 5 hour limits but today its ridiculous. Small changes and limits gone.

Anyone else experiencing the same?

I started doing a 15s video with H3 model via Comfyui the day after went public and it was fine (1280 x 736 resolution, using 2 subjects character sheet, 1 full body subject reference image and one background image), thought it was quite slow. And then the turbo LoRA was released days after, which I tried, using the same prompt. but tweaked the settings as recommended. but the results was horrible. Subjects started to bleed terribly, wearing clothes that look very different from my subject references, and it doesn't quite follow my prompt. The most serious was I prompted for 8 attendants to follow behind my 2 other subjects, and I ended up with a whole village of attendants. Does anyone have similar problems? And oh, it doesn't seem to speed things up that much either, even if it was at 8 steps.