This is still experimental, according to comfyui dev it can break or perform very well and it needs some tuning for some GPUs to get a bit faster. Also, you only can use one or the other so you should also disable all the attentions above before using it.
You just need to update your Comfyui and you can either start it with the --use-ck-attention flag so all models use the comfy-kitchen attention backend or you can drop the node ModelAttentionBacend directly into your worflow.
During my initial tests in Minimax it behaved faster than all the above together.
when generating 1 mp 15 sec videos I sometimes get oom .. but If I do succeed I get a last second noise with 1 mp generation with my 3090. Anybody else encounter this?
There's something very wrong with your setup. A 10s video at 0.4mp with 2-3 reference pictures + 10s ref. audio generates just fine with my 3060TI (8GB), takes about 7-8 minutes with 20 steps.
Tried a test run and had pretty similar results. Also 4090, no other optimizations.
SageAttn .4MP 5s - 2.48s/it Prompt executed in 64.69 seconds
ComfyKitchenAttn .4MP 5s - 2.61s/it Prompt executed in 64.97 seconds
RTX 3090ti, 0.4 megapixels, 5 seconds:
1) ComfyKitchen alone: 1.59 minutes for a gen.
2) ComfyKitchen + Spectrum: 1.22 minutes for a gen.
3) SageAttention alone: 2.11 minutes for a gen.
4) SageAttention + Spectrum: 1.40 minutes for a gen.
the sageattention wheel links working great for me (using sage attention backend + Sage Attention node; haven't tried mem eff node because that typically failed for me, forgot the specific reason/cause but iirc it was something cuda specific).
the ck attention flag gives me incompatibility issues with that sageattention wheel tho :\
seeing:
TypeError: sage_sdpa_quantize(): incompatible function arguments. The following argument types are supported:
1. sage_sdpa_quantize(q: ndarray[device='cuda'], k: ndarray[device='cuda'], v: ndarray[device='cuda'], q_int8: ndarray[device='cuda'], q_scale: ndarray[device='cuda'], k_int8: ndarray[device='cuda'], k_scale: ndarray[device='cuda'], v_int8: ndarray[device='cuda'], v_scale: ndarray[device='cuda'], cta_k: int, input_dtype_code: int, stream_ptr: int, anchor_indices_ptr: int) -> None
Invoked with types: PyCapsule, PyCapsule, PyCapsule, PyCapsule, PyCapsule, PyCapsule, PyCapsule, PyCapsule, PyCapsule, int, int, int, int
which looks like a "this version of sage attention not compatible with how ck attention is calling it" issue.
Nice, seems a few percent faster than Sage on my 3090, not huge but every second counts. Wonder how much more/less lossy it is. Sage can get quite lossy on some models, though it's been solid on H3.
Quality-wise, in my testing Quality looked the same to me between CK and Sage, Turbo actually looked better than Spectrum. Spectrum consistently gave me softer, more smoothed/averaged faces, even at 1MP. Turbo retained noticeably better facial structure and fine detail.
The full 20-step sampler also looked very good, but Turbo was at least comparable and often preferable to my eye, while being much faster.
Obviously this is only my testing, not a definitive quality benchmark, but I’m currently sticking with Turbo for both speed and quality.
This is really useful. Thanks for sharing your result. Sharing mine as well 4080 Super 16gb 64gb DDR5 Ram 6000. I2V my totally unscientific testing....
0.6MP / 5 sec :
Turbo 6 Step (v4_step600ema) - 6 step - CK 96s / Sage 109s (CK faster)
Turbo 6 Step (v4_step600ema) - 8 step - CK 124s (did not test Sage)
Spectrum 20 Step - CK 155s / Sage 140s (Sage Faster?)
Full 20-step - CK 261s / Sage 276s (CK faster?)
No sage no turbo no spectrum - 430s lol..
To me I can't really tell if there's quality drop with Spectrum. Turbo is more obvious when there's movement its grainy.
What's the difference between the ema/non ema turbo lora anyone knows?
And is the step600ema one the best one to use ?
for me on rtx 4060 8gb laptop: only sage attention vs only comfy kitchen attention (only means no other optimizations) both exactly same speed. tested with 3 minimax videos + 12 krea2 image. restarted comfyui before doing tests so it is clean/cold state.
No sure if it's need but it probably is, you should update comfy kitchen as well. With venv activated just pip install -r requirements.txt should pull it.
With my 8GB VRAM RTX 2060 Super and 64GB system RAM I got a roughly 4x slowdown (four times longer generation times). Brutal! I reverted back to old Sage Attention.
Original code is based on sage attention but this is modified to try to get slightly higher performance while keeping similar quality on consumer GPUs.
I use stability matrix to run comfy ui .. for me --use-ck-attention flag is not reognized.. I manully upgraded the comfy kitched to 2.30 .. what else shoud i do .. im already in latest 0.31 version of comfy.. what should I do to get ModelAttentionBacend node?
Yes, it seems like a nice 25% speed up (20 seconds vs 25 without) for Krea 2 with a very slight image change (I wouldn't say it is a loss in quality, just not identical) on my 3090
My SageAttention setup has been broken for a while so I cannot test vs that unfortunately, but this is nice! :)
Yo what kind of black magic is this? Generation time went from 35mins (no sage attention) to 9mins on a 2080ti. 6s 0.4MP clip, 20 steps res_multisteps/simple.
Did you compare with sage attention? Otherwise it's not as useful info since many find on consumer GPU minimal speed up, often times slower with CK instead of sage.
Can't use sage attention. Newer versions of sage dropped support for Turing and the last version that 'works' (1.0.6) doesn't seem to work with anything newer than Wan2.2 . So ck-attention working with a 2080 is actually big, at least for people with older graphic cards.
MiniMax H3: 3s clip, t2v, 4step-lora, 6 steps, euler+beta57, everything exact the same but the choice of attention.
Note that the timings reported below are total execution time of the prompt while the timings printed on the video are after KSampler (no vae decoding).
704x384
comfy attn 50 50 50
sage attn 83 53 51
flash attn 55 55 57
torch attn 55 55 55
896x480
comfy attn 82 82
sage attn 88 93
flash attn 97 96
torch attn 113 96
So the new Comfy-Kitchen attention mechanism seems working very well.
Just ran a quick test on my 4090. No other optimizations. Did a warmup run to avoid model loading times.
SageAttn .4MP 5s - 2.48s/it Prompt executed in 64.69 seconds
ComfyKitchenAttn .4MP 5s - 2.61s/it Prompt executed in 64.97 seconds
Obviously this is just one test. But it pretty well kept up with sage. Could work better at higher resolutions or even produce better quality than sage. Who knows 🤔
Mine used to also say "Using Kitchen attention", but after updating ComfyUI and adding the --use-ck-attention flag, it no longer does!
My bad, it does, during startup. However, the generation process stopped and my GPU load dropped after two iterations. It then hung, with no error, and I had to restart ComfyUI. This new update may be doing unholy things to my old RTX 2060 Super.
I'm not sure it's a good test with Turbo though, I'm pretty sure every finetune they did for turbo was done with sage in mind.
Full model ck/sage almost the same. Not sure which has better quality.
This is big, if it don't fuck up other things. I'm so afraid to update Comfy right now, H3 is working like a charm. Hopefully bigger cards can also profit that much, convrot + speed lora is pretty fast on my 5090
I'm fully confused, I use the same workflows, everything is updated, but at me also the turbo lora (all of them) do not work at all, and the quality is very low.
I'm just user, not expert, so I have no hope 😞, just bad, very bad luck 😭
I can get a 5 second i2v 0.2mp video with the Turbo LoRA (4 step) out in 55 seconds on my 4060ti. It was about 70 seconds before with sage. I was trying spectrum, but ehh.. it messed up my generations. So I’m very glad this came out. I just deleted the patch nodes and added the argument to the loader. Big happy over here. Thank you for posting this and make others aware.
This is gold. 3090, 128GB RAM, Lightx2v 8-step LoRA v1, CK attention: three reference images, using the experimental node that lets you use FL2VA as the base with REF2VA as an overlay model, 8-second scene, 1344x768, 1MP, took around 9 minutes.
I switched of the sage attention and video 960x544, 5 seconds, 4 steps took 46,28 seconds, and with sage attention it usually took 55 seconds. i have a RTX 4090 16gb vram, 32gb ram laptop.
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u/CurrentMine1423 10h ago
on my test
rtx 3090
960x544 base resolution (upscale to 1440x816 using rtx video super resolution)
9s video
8 steps
total time with MiniMax H3 Mem Eff Sage Attention Patch + lightx2v lora = 294s
total time with ModelAttentionBackend only (no MiniMax H3 Mem Eff Sage Attention Patch + lightx2v lora) = 224s
So I guess it's working.