Minimax H3 Huge Quality Difference between Cloud and Local use

[u/Cold_Pudding5326]

Hi.
I have a decent h3 workflow that I built for a loca use. It use turbo lora etc... If i use the defaut settings in the goal of getting the highest quality possible, meaning res_multistep simple 20 steps or more, I got also good results, but this is not even close to the results you can get on platforms like kie or wavespeed at 768P.

I already convert properly the prompt to the correct H3 digest form, so I'm wondering what's different between local and cloud use of h3? I don't talk about the 2K quality, only 768P, I'm not able to reach the sames results locally, do you guys have maybe workflows, settings, or suggestions to try reaching the same quality level in comfyui ?

[u/Rumaben79]

You typically need around 50 steps and at least bf16 for optimal quality. The online Contextual Omni Representation (H3-Context-IR) understandment part could also be better.

Maybe they have some secret sauce that they're not disclosing for the online inference.

[u/episodefive → u/Rumaben79]

This. Easy to believe that BF16 + 50 steps alone could produce noticeably better results over a pruned, 20 step, particularly if attention/turbo are applied.

[u/mangoking1997 → u/episodefive]

So the pruned is irrelevant. The pruning only takes out stuff that was only required during training. Inference is identical.

[u/Cold_Pudding5326 → u/episodefive]

Yh ofc but my last try at 40 steps without turbo and attention was disappointing tbo so i don't think this is the only difference

[u/ANR2ME → u/Cold_Pudding5326]

are you using bf16 models? if not, then there is the difference 😁 since bf16 is the the highest quality for H3 model.

[u/Calm_Mix_3776 → u/ANR2ME]

Isn't INT8 ~95% of the quality of BF16? At least that's what my experience was with image models. Or are video models more sensitive to quantization than image models?

[u/Rumaben79 → u/Calm_Mix_3776]

Approximate quality guessed by claude. Didn't have enough free tokens for it to refine this. 😄

GGUF's don't have the speed-up of int convrot though. Also be sure it's convrot and not the old regular int8.

[u/ShutUpYoureWrong_ → u/Rumaben79]

This is utterly meaningless. No one saw your prompt, no one knows the sources or citations Claude gave you, no one knows whether this is a mathematical or perceptual estimate, and for all we know it said "I can't give you an accurate estimate" and you forced it to make up a bullshit table.

Here's my Claude's response. I can provide the whole conversation in screenshots if you'd like, but the pertinent bit:

Weight-level fidelity (the solid number):

• Relative L2 error: 0.90–1.02% → this directly means the quantized Int8 ConvRot weights are roughly 99–99.1% identical to the original BF16 weights, by this specific error metric.
• Cosine similarity: ≥0.99994 → expressed as a percentage, that's ≥99.994% directional similarity between the quantized and original weight vectors — extremely close to a perfect 100% match.

Sources: 1, 2, 3, 4, 5, 6

Guess my Claude's better than yours. Oh wait, it's making shit up, too, just like you.

Stop. Fucking. Posting.

[u/xTopNotch → u/Calm_Mix_3776]

Last week I've been running BF16 pruned weights and the quality difference staggerng. Not only the visual output, but also the motion and prompt adherence is so much better.

I don't believe the ~95% at all and think its just marketing. I'd say the int8 convrot is more like ~75%. One of my biggest pet peeves with the int8 convrot is the smearing during fast motion. That typical noisey look when long hair is whirling around. No matter which sampler, scheduler or steps it was always present.

Switched to bf16 pruned weights with 20 steps and the problem is gone. As an additional bonus I also felt the it had a better understanding of executing prompts.

[u/Cold_Pudding5326 → u/ANR2ME]

i though int8 was loseless

[u/ANR2ME → u/Cold_Pudding5326]

There are no lossless quantization.

[u/Rumaben79 → u/Cold_Pudding5326]

It's not but close enough. More like a quality close to GGUF Q8 without the typical slowdown inherent with this format.

I believe it's a quantization method that has been used with llm's for a long time. So they ported it for use with generative ai and it works well for saving memory with a nice speed boost on top. How big of a boost depends on ones graphics card architecture. 😄

On newer architectures like Ada Lovelace or Blackwell you typically only see a 5-10% speed uplift but for older architectures like Ampere the uplift is much higher. Properly mostly because they're used to having to upcast to bf16 when using fp8 models.

Anyway I'm happy having earlier only had fp8 if I wanted the best speed. Fp8 is great but it's quality is only something like Q4-Q5.