INT8 ConvRot comfy compatible quantizations of LTX2.3 video model
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Thank you. I'm looking forward to trying this out. Had good luck with int8 with Krea2.
Whats the difference between INT8 convot and the 1.1 distilled?
Both are INT8 convrot, 1.1 distilled is the latest distilled version of dev
two version of 2.3 1.0 and 1.1.
I downloaded this model to check if the quality would be noticeably better than the model I've been using so far (GGUF - Q4) and how much longer the generation would take. And boom! Not only did the quality skyrocket with your model, but the generation time dropped by about 30-40% at 1080p, and it's more than twice as fast at 768p! I have a potato PC with an RTX 3060 (12GB VRAM + 32GB RAM), and dropping from 10 minutes to just over 4 minutes is an absolute game-changer! Thank you!
no way, 4 minutes on a 3060 with ltx? really? would you share a video with your workflow?
@hughchungus420
[INFO] Model LTXAV prepared for dynamic VRAM loading. 22404MB Staged. 0 patches attached. Force pre-loaded 608 weights: 3303 KB.
100%|████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:53<00:00, 6.64s/it]
[INFO] Model LTXAV prepared for dynamic VRAM loading. 22404MB Staged. 0 patches attached. Force pre-loaded 608 weights: 3303 KB.
100%|████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:49<00:00, 36.54s/it]
[INFO] 0 models unloaded.
[INFO] Model VideoVAE prepared for dynamic VRAM loading. 1384MB Staged. 0 patches attached.
[INFO] Prompt executed in 258.65 seconds
1344x768, 8 seconds, 24 fps (LTX 2.3 distill INT8 from here + Gemma GGUF Q2)
My workflow, here: https://drive.google.com/file/d/196C4pEUHj6MEh92b1iVYLHYXSWYKB2Op/view?usp=sharing
Btw, I generate clips for music video - so audio VAE is off.
@sheben11 damn boss real minmaxxing there with the q2'd text encoder. cool. i may give ltx one last try with how runnable it is under those circumstances.
@hughchungus420 I think it is worth trying. I will test INT8 version of Gemma tonight, I let you know how this setup works
@sheben11 Same specs here, real gamechanger using int8. How's prompt adherence using the Q2 text encoder? I use Q4 personally
It is possible to make INT8 version of spacial upscaler model? 🙏
I'll run a few tests with the int8 version of it, but I think the performance increase will be minimal, as the upscaler upscales the latent, which then gets passed to the sampler