Image-to-Video
Diffusers
text-to-video
video-to-video
image-text-to-video
audio-to-video
text-to-audio
video-to-audio
audio-to-audio
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
ltx-2
ltx-2-3
ltx-video
ltxv
lightricks
Instructions to use Lightricks/LTX-2.3-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-2.3-nvfp4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-2.3-nvfp4", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Performance and qualityvs. GGUF Q8_0 (and FP8) ?
#6
by raffetazarius - opened
Has anyone tested this out, and/or have comparisons to share?
Curious which performs best on a 5090...
Q8_0 = ~23 GB
https://huggingface.co/unsloth/LTX-2.3-GGUF/blob/main/ltx-2.3-22b-dev-Q8_0.gguf
FP8 = ~29GB
https://huggingface.co/Lightricks/LTX-2.3-fp8/blob/main/ltx-2.3-22b-dev-fp8.safetensors
NVFP4 = ~22GB
https://huggingface.co/Lightricks/LTX-2.3-nvfp4/blob/main/ltx-2.3-22b-dev-nvfp4.safetensors
Preferably without Sage Attention since this affects the model's "pure" output.
IMO KJ FP8 transformer only is the best but its debatable
I think either can work and both win on different tests.
KJ FP8 vs gguf q8_0 on 16gb vram 5060
https://civitai.red/images/134162605