They ship a complete checkpoint for easily management (inference & training) in their own infrastructure. Moving to a LUT would make training on these layers impossible. BTW, these are not useful for lightweight fine-tuning, but might still be useful if you do serious post-training work.

Of course, these are also not an issue for things like FLUX.2 which adopts DiT-Air arch, that doesn't have this wasted space issue.

It may or may not be true. The people who made this modification and the other commenters didn't do anything rigorous to verify what they did. They just eyeball it. They could very well make some other error - this has happened frequently - that developing on prod, not knowing what they are doing, has and hopefully will again solve.

People on reddit have definitely pointed out that sageattention will speed up the renders.

And it's literally the first day. Someone will make a distilled 4-8 step LoRA and we're off to the races.

Edit: did a couple of 10 second long 864/480 i2v videos on my RTX Pro 6000: sageattention bumps them up 33%, that is to say, 140.89 seconds without sageattention becomes 105.69 with sageattention on (if using the KJ Sageattention node, "allow_compile" doesn't seem to affect it, just "sage_attention" set to "auto" works fine).

EasyCache also appears to work, but does affect quality, at least with the default threshold or even down to 0.10. Still, at 0.10 threshold the same render above, with sageattention, is down to 71.33 seconds, so depending on your use case the quality hit might be worth it. Also it seems that with EasyCache the video still matches the un-EasyCached video (with the same seed), so you could use it to do seed hunting.

What’s the vram usage? I’ve got a 5090 so as long as I can fit it in memory the gen times should be roughly equivalent ~10%

    diffusion model: minimax_h3_fl2va_bf16.safetensors
    text encoder: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
    video VAE: minimax_h3_video_vae_fp16.safetensors
    audio VAE: minimax_h3_audio_vae_fp32.safetensors

Waiting for the “FWIW on a B200” reply

spice is a very bad simulation. it's not like a unit test or a VM. it works very differently to the real thing, only approximating it in some highly controlled situations.

> I have seen MCPs for measurement equipment also, maybe they can even close the physical loop?

I'm working on this but for various reasons can't have my physical lab up at the moment. But yes there are many options for connected test equipment that could rather trivially be interacted with via LLM or pretty easy to write libraries.

> AFAIK, everything we can "know" can be written down, one way or another, even analog circuits.

writing down circuit diagrams is like cooking about music.

> what SOTA LLMs are you referring to?

I have done a survey among analog electronics designers just a couple weeks ago and they all said that their forays into LLMs were great for digital electronics, code, and firmware, but for analog they were pretty terrible, with a variety of LLMs, according to everyone.

> everything we can "know" can be written down

Do you know how to ride a bike?

what sorts of things have you designed with it?

i bet they're good for it. however robotics aren't electronics. that's a completely different space.

reroll

AI psychosis gets everyone, like our human context window drifts the further the session goes on and discernment quality decreases

> A year and a half behind Seedance 2.0? That is a bold claim that needs evidence.

SOTA a year ago was Kling 2.5, and H3 does not look or perform at that level.

The Artificial Analysis rankings are whack. They rank Omni first, which is incredulously wrong. Google's models broadly suck, and there they all are - right at the top.

Artificial Analysis has notoriously ranked models such as Grok Imagine highly and continues to rank Happy Horse as a good model despite the model being absolute garbage.

Could it be because they are subject to broad based statistical attacks? It's easy to encode information about the origin of media in either its metadata or output frames. Or maybe there's simply no overlap between creatives and people who click on ELO scores.

I've spent thousands upon thousands of dollars generating video. I will stand by the claim that nothing touches Seedance 2.0 / 2.5.

It's good that we're getting better open weights. It puts price pressure on the foundation model companies. But these open models are not a substitute for Kling or Seedance yet. Not even close.

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The fundamental difference between AI and all the previous advances like that is that people actually liked and wanted films, synchronized sound, color, CGI, etc. and queued down the block for them. Same thing with electronic music, people enjoy listening to it. Not really the same thing with AI, most of the praise is about the impact it will have on the production process, cost-effectiveness, etc., what actually comes out tends to take the back seat. From what we've seen it's pretty difficult getting people to like the output as well.

Worth if you plan to use it for production use. If you use it for personal or as mockup, then what they don't know won't hurt them? ;)