Image-to-Video
Diffusion Single File
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-video
lightricks
comfyui
ltx-2.5
Instructions to use tsolful/LTX_2.5_INT4_W4A8_ConvRot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use tsolful/LTX_2.5_INT4_W4A8_ConvRot with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Why does the distilled version weigh more than the dev version?
#1
by nosok12313 - opened
I mean, the quantization method is identical, so what's the deal? And would running the dev version + distilled LoRA actually reduce peak VRAM?
Also, been dying to know - what's the method/tool for quantizing down to w4a8?
I accidentally changed a value on the quantization, reuploading now. Currently, I only know https://github.com/NidAll/comfyui-mixed-quantizer and https://github.com/Starnodes2024/comfyui-starnodes-modelconverter these two. I've forked my own converter but haven't pushed the w4a8 update yet, it has old int4 mixed with int8 quantization not the new w4a8 https://github.com/tsolful/ComfyUI-INT8-Fast