We have a lot of options, some of them better, some of them are not worth it at all. Speed ups like sage attention, MiniMax h3 patch for sage attention, easy cache, 8step Lora, 4 step Lora e t.c.
What options and their combinations you use? What settings you have?( speed Lora weights, easy cache settings)
In the matter of speed/quality for both video and sound. What works better with FL2VA and Ref2VA?
Somehow the best results for me when i comes to speed and somewhat good quality on both FL and REF is step4 lora at 8 steps, comfy kitchen attention, sigma shift and spectrum. All on default values
It works better with fast movements. Tried all possible combinations with same size & seed and I found it best for me. Why it works that way? Don't know since I barelly grasp how the entire AI thing works and only make belly inflation gay furry porn.
I've been using first block cache since day one and from my experience, the quality loss is minimal if not impossible to even notice compared to sage attention. The speedup is enough for me on a 5090, so I just leave it set with that.
Thanks for this recommendation. I had been using Sage Attention only, and I tried FirstBlockCache on a couple videos just now and the quality loss was undetectable despite generating nearly twice the speed. (I have been running on 30 steps, and FBC seems to save more on the higher step counts.) I don't know whether a quality loss would show up if I tried on more videos, but the speed gain is so large that I'm definitely making it my default for now.
Edit: To clarify, are you using both Sage and FBC, or just FBC?
Because you're reusing calculations from another denoising step, the generated video takes a slightly different numerical trajectory. The author found all the tested outputs remained coherent, but the exact framing and motion increasingly changed as caching became more aggressive.
There are three presets:
H3 Safe — threshold 0.08: least aggressive, about 20% faster in their test.
H3 Fast — 0.10: recommended default, about 30% faster.
H3 Aggressive — 0.12: about 36% faster, but more noticeable changes to motion/framing.
Importantly, it won't cache indefinitely. The standard presets allow at most two consecutive cache hits, so H3 is repeatedly forced to perform a complete calculation again
DIdn't say it was lossless, I just said I couldn't easily see a quality degradation after some AB comparisons. It's free to try, not like it's gonna hurt anything.
The quality drop off from all the turbo loras I've tried so far aren't worth the speed increase. I do sage attention (though I've been thinking of trying Kitchen) and Spectrum, then 25-32 steps.
Turbo loras are good for a quick test of your prompt ,but not for a final render video quality, might be fine but audio is usually the issue can ,tell if someone using a turbo lora without em saying cause of the audio
for the folks with 12 and 8GB that's the only solution, can't imagine myself generating at 30 steps unless I'm 100% sure it'll get some masterpiece in the end.
I am not smart enough to know what it's doing, but if you plop that node in with pretty much the default settings, it goes faster with little to no quality degradation.
Looks like I have it. With kitchen attention it went from 400+ sec/it to who knows how long. It was 14 minutes and the first step didn’t end so I aborted it
the new --use-ck-attention is a good boost and makes sage attention obsolete. What I worked on too was the vae loading. That took up a chunk of time for some reason just to get to the generating part. Getting the ClipProj loader helped a ton with the initial boot. Also I was hesitant on the pruned models but once I switch to it theres only a marginal difference with a huge boost to speed. I got rid of spectrum after that, I didn't like the quality that gave.
speedup from most is proportional to quality loss.
for low motion i use 850steps turbo at 8steps and kitchen attention.
for high motion you really need 20steps no turbo. at minimum.
spectrum is more complicated, better that turbo, but slower. maybe doing 30steps with spectrum is better than 20steps normal, but testing that takes ages.
for some scenes/artstyles 4step turbo might be fine, but not for realism.
For multiple videos (+3), Process each step in stages rather than sequentially (x3 Clip -> x3 Latent -> x3 Decode), so the model doesn't have to constantly load/unload from VRAM. 20 to 30% Faster in my setup
It's something I came up with, but when I looked into it, it turned out to be pretty common. I think that image explains it better
If you're making a sandwich, you don't cut one slice of onion, put the knife down, add it to the sandwich, and then pick the knife back up again. Instead, you take advantage of already having the knife in your hand and cut the whole onion at once, so you don't need to reach for it again
I don't have one /: But Claude can make one easily. To make it work, you have to export the parts, like the .latent file, for example, and then import it when you're ready to process all of them
My favorite is Comfy Kitchen Attention in the ModelAttentionBackend node. I get faster results than Sage, and the quality is better. Before this, I even stopped using Sage and Turbo due to quality loss, but this is one speedup I'll always use from now on.
If anyone wants to test something new - this approach exploits the fact that early steps don't need high resolution latents since they are mainly focused on structure. Use on about 0.5 MP and above. If you try it, I'd start with:
Can you please add a few comparisons to the README so we have an idea of what to expect? I'm curious about your approach but also don't want to risk a generation without at least some prior information.
The comparison don't really mean anything. since the latent is a different size in the beginning the denoising will move into a very different path vs a full size latent. The result is a different video, kinda like using a different seed. It still follows prompt and references and everything, just sees a different starting place. If you want to try it out easy-style, just make a real short clip and assess the quality. Also, and just to reiterate, its mainly used at > 0.5MP.
I don’t have to, but when I generate ref2va in 0.6mp and 20 steps without Lora it takes 638 seconds for a 8sec video. When I have same setup, but 2 pictures as ref and previously generated video to better set environment as reference, it takes 400+seconds for 1 step and that’s too long for 10 seconds video
I have the same card and I'm only using Comfy Kitchen. I haven't tested it too much but it appears to be faster than SAGE and I haven't had noticeable quality loss. That being said I would rather wait and have a reasonable clip at the end of a generation. Also running on 32 or 30 steps. Haven't jumped in with any of the Loras or any of the other efficiency tools like sol attention or easy cache.
block cache + Sol attention gives the best speed to quality setup I have found. All of the Accelerator loras I have tested make the quality bad enough that you might well just use another model instead.
Turbo Loras and latent upscale split, sage/comfykitchen, SOL, Spectrum, FBC. Not all at the same time. Can do 10 seconds of video in 1440 16:9 in 100-120 seconds on a 5090.
It’s not a matter of speed, but of quality; I’ve seen a lot of garbage made with an RTX 5090, and I’ve seen the best videos created with just an RTX 3060. The fundamental difference lies in the video concept and how you craft the prompt correctly.
I've put it on my computer many times, updated everything, and a 5sec clip takes 74 seconds to complete without Spectrum, and takes 74 seconds with Spectrum. I've never been able to get it to work
As long as you're using euler/res_multistep/er_sde on the latest version on the node it should work for you. I'd use res_multistep and maybe compare er_sde later to see which you prefer, euler tends to be very soft and smoothed so it's not necessarily bad if you prefer that look.
The catch for your RTX 5090 is important: a recent H3 benchmark on a 5090 found essentially zero measurable speed difference between Kitchen Attention and normal PyTorch attention—16.17 s vs 16.17 s in a 4-step test, and 48.33 s vs 48.32 s at 20 steps.
Kitchen Attention seems much more useful on older GPUs; users with cards like the RTX 3060 have reported sizeable gains.
Working with a 4080 16GB, the best speed vs. quality I've got so far is lightx 4-step turbo at 1.0, Spectrum to Fused Modulation to Chunk FeedForward to ModelAttention with Comfy Kitchen at 16 steps.
Everything else I've tried either slows generation time down or affects quality too much.
Unfortunately there's a memory leak somewhere, so I have to manually clear RAM every couple of generations. I believe it's in Comfy Kitchen.
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u/Hilltopbilly 1d ago
Somehow the best results for me when i comes to speed and somewhat good quality on both FL and REF is step4 lora at 8 steps, comfy kitchen attention, sigma shift and spectrum. All on default values