Cutting LTX-2 22B Peak VRAM by 40% with fp8_cast — and Why optimum-quanto Was a Trap
The article discusses the LTX-2.3 video generation model from Lightricks, which features audio support and can animate entire scenes. It highlights the challenges of managing its large VRAM requirements and the successful implementation of a quantization method to reduce memory usage. Additionally, it critiques the compatibility issues with another quantization approach, optimum-quanto.
- ▪LTX-2.3 is a video generation model that can generate lip sync, facial expressions, and head/hair motion from a single image and audio prompt.
- ▪The model originally requires 43 GB of VRAM, which poses challenges for running alongside other services.
- ▪The author successfully reduced the peak VRAM usage from 40 GiB to 24 GiB using the native fp8_cast quantization method.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3945785) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } shinji shimizu Posted on May 22 • Originally published at kotonia.ai Cutting LTX-2 22B Peak VRAM by 40% with fp8_cast — and Why optimum-quanto Was a Trap #ai #machinelearning #python #gpu Introduction LTX-2.3 is a video generation model from Lightricks that includes audio support. In A2V (Audio-to-Video) mode, it takes a single image + audio + prompt and generates lip sync, facial expressions, and head/hair motion all at once.
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