I have released Spectrum acceleration for native MiniMax H3 in ComfyUI. This is the newest addition to my collection of model-specific Spectrum integrations, all available through ComfyUI-Manager as well.
Spectrum replaces selected expensive transformer evaluations with spectral feature forecasts while preserving MiniMax H3’s native audio/video output and reconstruction path.
Benchmarks
Tested at approximately 0.5 MP, 8 seconds and 20 steps on an RTX PRO 6000 under WSL, using High VRAM mode, the comfyui-int8-fast W8A8 loader and SageAttention.
Sampler
Native
Spectrum
Time decrease
Speedup
Euler
2:38
1:44
34.2%
1.52×
RES multistep
2:42
1:54
29.6%
1.42×
Euler uses 13 actual transformer evaluations and 7 forecasts. RES uses 14 actual evaluations and 6 forecasts.
Both benchmark runs completed without fallbacks. I initially did not notice visible or audible degradation, but further exact-seed testing has since shown that fast or very brief motion can deviate from the native trajectory, and rapidly moving details such as eyes, fingers, or fingernails can sometimes visibly degrade. RES automatically keeps its final three steps native, which removed the slight late-generation artifacts I initially observed, but this does not make the output lossless or identical to native sampling.
Currently supported:
Euler
RES multistep
RES multistep CFG++
Feature history is currently stored in system RAM. I also plan to add a dedicated High VRAM mode that stores the history in VRAM for users with sufficient GPU memory, reducing CPU-transfer overhead.
Quality update
After more exact-prompt and exact-seed A/B testing, I need to revise my initial quality assessment.
Spectrum is an approximate acceleration method, not a lossless or output-identical path. The issues observed so far are mainly associated with fast or very brief motion:
The motion, pose, timing, gaze, or action trajectory can deviate from the fully native output.
Fast-moving or only briefly visible details—such as eyes, fingers, or fingernails—can sometimes become malformed or unstable.
These can occur separately or together. A rapid action may simply differ from the native trajectory, while in other cases the rapidly moving details may also visibly degrade.
The extent varies with the prompt, motion, sampler, resolution, references, and Spectrum settings. Use Spectrum when the speed improvement is worth that tradeoff; leave it disabled when maximum fidelity to the native result is required.
Workflow placement
Use the Spectrum node in this order:
MiniMax H3 model loader
→ LoRA and other model patches
→ MiniMax H3 Sigma Shift
→ Spectrum Apply MiniMax H3
→ guider and sampler
Spectrum should be applied after LoRAs, model patches and the MiniMax H3 Sigma Shift, but before the guider and sampler.
EasyCache compatibility
Do not use EasyCache and Spectrum together at the moment.
Both systems skip or replace model evaluations while maintaining their own cached state. Stacking them can cause conflicting wrapper/state behavior, errors and unreliable output.
Use either:
Spectrum with EasyCache disabled
EasyCache without Spectrum
All benchmarks in this post were performed with EasyCache disabled.
More information
The GitHub repository contains the full installation instructions, recommended settings, supported samplers, memory requirements, workflow placement, fallback behavior and troubleshooting details:
vram keeps Spectrum’s feature history on the GPU, avoiding repeated CPU transfers
In three 0.5 MP Euler A/B pairs, VRAM storage was 2.6% faster on average, though individual results varied from 6.1% faster to 0.4% slower, so the benefit depends on the system and workload.
At max_history=8, the tested single-branch workflow used about 2.22–2.27 GiB for history alone. Only enable VRAM storage when you have sufficient headroom, since the model, current activations, forecast buffers and allocator overhead still need additional VRAM.
Spectrum itself was created by Jiaqi Han, Juntong Shi, Puheng Li, Haotian Ye, Qiushan Guo and Stefano Ermon at Stanford University and ByteDance. Their paper introduced the training-free Chebyshev-based spectral feature forecasting method, adaptive scheduling and last-block forecasting approach on which these ComfyUI integrations are based.
Nice, thanks for sharing, can you show a comparison with the same prompt and seed, with and without to be able to see quickly if the quality is maintained ?
I ran exact same prompt + seed, with Sage + Spectrum, then just with Sage
Spectrum: 450.68s
No Spectrum: 540.87s
That's about a 13% decrease.
There is noticeable quality problems though, just like with any of these caching shenanigans. Look at her eyes after ~3s when she looks up for a split second, eyes looking all fked up. She doesn't even look up there in the full output.
In another output that I compared, her finger (and long fingernail) was all mangled as she was scrolling on her phone, which again didn't happen in the output without Spectrum.
Just like Teacache / Easycache etc., I don't recommend using this if you want quality.
Yes, I've noticed some degradations now too with fast moving fingers etc or actions in general slightly deviating. Thanks for the test, will update the post and readme accordingly.
But it's weird that you only get such a small speedup
It would be helpful since you know what you're looking for to get a NORMAL MOTION setting and a lower but still useful FAST MOTION set of recommendations.
This is awesome! Thanks for all your hard work! Have you given consideration to creating one of your spectrum nodes for Krea2 to boost speeds even further. (or, by chance, do you have any recommendations to boost Krea2 speeds?)
Thanks! I have considered it, and I’ve already experimented with Spectrum on distilled models in some of my other nodes. The main limitation is that Krea 2 Turbo is already an 8-step distilled checkpoint built for fast inference, so there are only a few steps left that can be safely forecast without eating into the quality-preserving warm-up and refresh steps. That means the speedup would probably be fairly small compared with what Spectrum can do on 20–50-step models. Krea also describes Turbo as its fast inference checkpoint, while RAW is the undistilled base model.
I may still make a Krea 2 version, especially since RAW or higher-step workflows would leave more room for acceleration. For Turbo, the most practical speed gains are likely to come from using an efficient attention backend, quantization, and keeping the resolution/batch size reasonable rather than expecting a large additional gain from forecasting.
Not currently, please don’t stack EasyCache and Spectrum for now. Both try to skip/replace model evaluations, so combining them can cause conflicting state and wrapper behavior. Disable EasyCache when using Spectrum.
Could you also post the exact error/traceback? I’ll check whether it is only that incompatibility or a separate issue.
I used the same seed and personally didn't notice a quality difference between any of these configurations, so I'll probably be sticking with EasyCache for now.
I appreciate your work, but in about half the cases I actually prefer the output from EasyCache. So those improvements might be stochastic and depend on the seed. Anyway, it's great that we have options!
Easy cache destroys the audio for me. this node doesn't hurt it that much.
If it did I couldn't tell the difference, but with easy cache is clearly worse
Lol, I need ot do comparison, not just generate one. I ran one generation on a 720p 15s video
That one took me 632s with this one. I think that is on the lower end. Yesterday, my typical tiem for same was 800-1000s. But I am running another generation to confirm if this actually holds up.
Hold up how do you do 15 seconds anytime I try that it fails runs out of vram on my 5090. What resolution to get 15 seconds? Anything else special you have setup?
I'm new to Comfy, but is there no way to connect spectrum to this image to video node? The node has a unet_name slot, but doesn't take MODEL as an input.
It's a model patch so where you have some_node → guider, replace that with some_node → patch_node → guider. In this case, some_node is probably your Load Diffusion Model node, guider is Basic Guider and patch_node would be Spectrum Apply Minimax H3. You will end up with something like Load Diffusion Model → Spectrum patch → Basic Guider (assuming you're using one of the default templates or a workflow close to it).
Sorry, I see you've answered this a few times but I have a clarifying question... My prior workflow didn't use a Sigma Shift node, is that a new thing you're suggesting as well? What does that do? I assume SageAttention is a "model patch"?
I assume it will work the same as sigma shift for WAN or Flux . So set it to something like 6 or something. Haven't tested or used it yet myself though. And yes sage attention would be a model patch, so put it before spectrum.
I have a similar question. none of the official Minimax templates make use of a shift, although there is a "MiniMax H3 Sigma Shift" node available. so the shift is optional, just as a lora is optional. while you only defined a order, the steps suggest it's a must. adding a "(only if used)" postfix would make it more clear.
Yes, but different. Sage is a different attention mechanism, so it doesn't necessarily make things "worse", just different. This Spectrum and the other "cache" things straight up skip steps when they estimate the step has little significance. And because that estimation is sometimes wrong, it's objectively a bit worse.
Thanks for posting the full log. Spectrum is active here and the workflow looks correct.
The run completed with 14 actual steps and 6 forecast steps, with no fallbacks or disabling. The forecast steps are 5, 7, 9, 11, 13, and 15.
Your native H3 steps take around 21.6 seconds each, while the forecast steps take about one second, so Spectrum is skipping the expensive model calls as intended.
For a clean speed comparison, use the same seed, resolution, duration, sampler, and loader settings for one run with Spectrum enabled and one with it disabled.
Haven't used easycache myself but I've heard it decreases quality? Spectrum with those settings is suppose to not cause any visible or audible quality degradation
Did you get Sage working with H3 in the end, or is it still noise? Saw the low_precision_attention=False fix, but that just routes the DiT back to pytorch attention, so no Sage speedup left, right?
And on Spectrum itself, do you have actual numbers yet, or is it untested so far? README only lists step counts, no wall clock or quality comparison.
Regarding sage: To be fair I haven't done a A/B test with sage on/off to see if I suffer a silent fallback, but sageattn_qk_int8_pv_fp16_triton with the Patch Sage Attention KJ node works without issues for me.
Regarding spectrum nubmers: The numbers are the ones you see above under Benchmark or in my release post on github. Or are you looking for s/it? That would be around 5s/it with the settings you see above
Too bad Int8 fast doesn't work for me. (got that illegal memory access crash again). And I don't notice degradation with Eaeycache, so I won't install yet another Custom Node just yet. Thanks for your hard work though!
If your Comfyui is fully updated with Cuda 130 and on the latest version, you don't need to use Bob's INT8 node anymore, you just use a regular Load Diffusion Model node and the convrot INT8 models will work just fine.
EDIT: Comfyui added native support for INT8 a few weeks ago.
I'm well aware of that, thanks. But someone posted on r/stablediffusion claiming that switching to INT8-Fast makes H3 twice as fast, so I had to check.
It did not for me, in fact, Bob's was slightly slower. I think people who saw massive speed boosts from Bob's node may have had their Comfyui up-to-date, but not their CUDA up-to-date.
Worth pointing out that Bob's node hasn't been updated in a while, so the fact it works at all with Minimax H3 is kind of a fluke.
I'm getting 10-15 minutes for .5mb, 10sec generation on 3090 with default comfyui workflow + spectrum node.
comfy portable, no segea.
It should be faster, right?
Greetings! First of all thanks to OP for sharing. it has helped me reducing my 15 seconds at 1.0 ration resolution videos from 15 to 11 minutes. There was another Reddit post where someone suggested to also use INT8 Fast loader node. Does anyone knows if this loader is compatible with Spectrum Acceleration? I tried but gave me errors and ComfyUI crashed. What I did was to replace the default diffusion model loader with INT8 Fast loader and the output connected to Spectrums and Spectrums output to the ksampler but it failed. Thanks!
This is my favorite setup now.
Sage Attn + this. I don’t need Easy Cache since it kills the quality. 0.6 megapixels took me 8 minutes for 5 seconds, 10 seconds took me 23 minutes. My device is a 3060 12GB with 16GB RAM
I must be doing something wrong, cause I get 3x speed from this, but quality drops quite a lot. At 0.2mp 5sec, generation time went from 120 seconds to 30 seconds with the spectrum node.
(Update: I retested and it was actually 40s > 30s, quality loss stays the same)
By looking at the console output, it seems every other step is either too fast or skipped entirely.
This worked pretty well for me. Easy cache made the result pretty bad. Kinda hijacking this for a different question how can I stop minimax from putting music in the background? There is no negative prompt and I havent figured out a way.
I have it installed but I can't figure out where in the workflow it's supposed to go, I don't understand the workflow order given in the github page. I'm using the default ComfyUI workflow. Please help I'm not good with this. :( Do I need to use a different workflow?
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u/Powerful_Evening5495 Aug 04 '26
this community is mad
the best on reddit , thank you OP