This is HQ Int8 Row ConvRot of LTX 2.3 Dev / Raw / Base model. Not the Distilled / Turbo fast model.
Made from official BF16 model with SECourses Musubi Trainer Quantization app
You can download and use Musubi Trainer app for both training and quantization from here : https://www.patreon.com/SECourses/posts/secourses-musubi-137551634
To be able to use this model with very best performance please use our Torch 2.13 CUDA 13 ComfyUI installer with ready presets : https://www.patreon.com/SECourses/posts/download-comfyui-installers-and-presets-105023709
I also recommend our SwarmUI installer with ready SwarmUI presets : https://www.patreon.com/posts/download-swarmui-installer-and-presets-114517862
With our HQ Int8 Row ConvRot quant conversation and app and preset, the model quality is able to surpass GGUF Q8
With our ComfyUI backend, Int8 Row ConvRot is able to generate faster than FP8 Scaled literally 100% faster on RTX 3000, 4000 and 5000 series GPUs
Model quantization is taking around 3-4 hours on RTX 5090 since we do training like quantization with prodigy optimizer
Check model screenshots to see and learn more
Description
This is HQ Int8 Row ConvRot of LTX 2.3 Dev / Raw / Base model. Not the Distilled / Turbo fast model.
Made from official BF16 model with SECourses Musubi Trainer Quantization app
You can download and use Musubi Trainer app for both training and quantization from here : https://www.patreon.com/SECourses/posts/secourses-musubi-137551634
To be able to use this model with very best performance please use our Torch 2.13 CUDA 13 ComfyUI installer with ready presets : https://www.patreon.com/SECourses/posts/download-comfyui-installers-and-presets-105023709
I also recommend our SwarmUI installer with ready SwarmUI presets : https://www.patreon.com/posts/download-swarmui-installer-and-presets-114517862
With our HQ Int8 Row ConvRot quant conversation and app and preset, the model quality is able to surpass GGUF Q8
With our ComfyUI backend, Int8 Row ConvRot is able to generate faster than FP8 Scaled literally 100% faster on RTX 3000, 4000 and 5000 series GPUs
Model quantization is taking around 3-4 hours on RTX 5090 since we do training like quantization with prodigy optimizer
Check model screenshots to see and learn more



