Instructions to use MrReclusive/LTX-2.3-FP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MrReclusive/LTX-2.3-FP4 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("MrReclusive/LTX-2.3-FP4", 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
New version LTX-2.3-22B-distilled-1.1
Hi! First of all, I just wanna say THANK YOU for quantizing nvfp4 of LTX2.3, I have been using LTX-2.3-22B-Distilled-FP4ME on my RTX4060 laptop with 8GB Vram+32GB Ram, it works really really well and fast (Unet loader dequantized as FP8 e4m3 + sage attention2.2). And my previous quantized version was using ltx-2.3-22b-distilled-Q5_K_M by Unsloth, it often causes VRAM Overflows for my poor 8GB Vram, it was such a depressing experience to me. So really, thanks again for making such incredible nvfp4 quantized model.
Yesterday Lightricks has uploaded the newest 1.1 version of LTX-2.3, will you consider using the newing version to make the upcoming LTX-2.3-FP4MEL ? I'll be really looking forward to you next brilliant creations.
Best regards.
+1. This also works faster than both gguf's and fp8 with amd gpu's using rocm (with bitsandbytes) It is definately faster but the existing distill fpme is just a bit below what I get from the kijai's safetensors version.