Instructions to use Hippotes/LTX-2.3-quants with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Hippotes/LTX-2.3-quants with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Hippotes/LTX-2.3-quants", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Ltx 2.3 distill 1.1 request
so LTX just released a "Distill 1.1", i wonder if you can make a nvfp4 quant from that as you did with the first one? i loved it
fp8mixed and nvfp4mixed are up :)
idk if I will do non-mixed ones, the quality hit seems not worth saving a few gigs or 1-2s.
fp8mixed and nvfp4mixed are up :)
idk if I will do non-mixed ones, the quality hit seems not worth saving a few gigs or 1-2s.
genius, many many thanks
fp8mixed and nvfp4mixed are up :)
idk if I will do non-mixed ones, the quality hit seems not worth saving a few gigs or 1-2s.
i know you said it might not be worth, but your version of it let's me have more speed in wan2gp with some weird options, i ask here you to reconsider a bit and maybe do the full nvfp4 of the 1.1 as you did with the original
if you don't want to it's okay, but never hurts to ask, thanks for the others they work good
Hi! Where can I download the new workflow for
ltx-2.3-22b-distilled-1.1-nvfp4mixed.safetensors
fp8mixed and nvfp4mixed are up :)
idk if I will do non-mixed ones, the quality hit seems not worth saving a few gigs or 1-2s.i know you said it might not be worth, but your version of it let's me have more speed in wan2gp with some weird options, i ask here you to reconsider a bit and maybe do the full nvfp4 of the 1.1 as you did with the original
if you don't want to it's okay, but never hurts to ask, thanks for the others they work good
welp, I can't vouch for the quality but weights are up: 1.1-nvfp4 and 1.1-fp8
Hi! Where can I download the new workflow for
ltx-2.3-22b-distilled-1.1-nvfp4mixed.safetensors
1.1 is only a better distillation of dev model, drop-in replacement in your existing workflows
fp8mixed and nvfp4mixed are up :)
idk if I will do non-mixed ones, the quality hit seems not worth saving a few gigs or 1-2s.i know you said it might not be worth, but your version of it let's me have more speed in wan2gp with some weird options, i ask here you to reconsider a bit and maybe do the full nvfp4 of the 1.1 as you did with the original
if you don't want to it's okay, but never hurts to ask, thanks for the others they work goodwelp, I can't vouch for the quality but weights are up: 1.1-nvfp4 and 1.1-fp8
Hi! Where can I download the new workflow for
ltx-2.3-22b-distilled-1.1-nvfp4mixed.safetensors1.1 is only a better distillation of dev model, drop-in replacement in your existing workflows
thanks brov, seriously, thanks
1.1 is only a better distillation of dev model, drop-in replacement in your existing workflows
Did you also make new Distilled Lora 1.1 with lower rank and size?
1.1 is only a better distillation of dev model, drop-in replacement in your existing workflows
Did you also make new Distilled Lora 1.1 with lower rank and size?
Kijai did: https://huggingface.co/Kijai/LTX2.3_comfy/tree/main/loras, using adaptive rank, best it can be
1.1 is only a better distillation of dev model, drop-in replacement in your existing workflows
Did you also make new Distilled Lora 1.1 with lower rank and size?
Kijai did: https://huggingface.co/Kijai/LTX2.3_comfy/tree/main/loras, using adaptive rank, best it can be
Thanks.
By any chance you know with which strength I need to use this lora? Is it 1.0, 0.5 or other?
Thanks.
By any chance you know with which strength I need to use this lora? Is it 1.0, 0.5 or other?
I'm not sure... It is meant to be used at 1.0, that should bring dev + lora closest to the distilled model. But even official ComfyUI template uses 0.5, I have seen gens with 0.6~0.7 that are less saturated and with more natural motion, and some 2 stages workflows use 0.4 -> 0.7
I often stick with the distilled model for that exact reason of not having to guess or trial & error the correct strength for the specific thing I'm generating at the time