Stable Release - LTX 2.5 (22B) FP8 (e4m3fn) for ComfyUISelective FP8 (float8_e4m3fn) quantization converted directly from the official LTX 2.5 22B BF16 Transformer weights.Key Features:- Selective Quantization: Converts 2D weight matrices to FP8 (e4m3fn) while preserving LayerNorms, AdaLN modulations, and dual-stream audio/video conditioning blocks in original BF16 to eliminate visual noise and audio-video desync.- Fits 100% inside 24GB VRAM (tested on RTX 4090 + 64GB RAM).- Fast generation time (~99s) with ZERO PCIe RAM offloading.- 100% native PyTorch implementation (no experimental torchao or Blackwell dependencies required).ComfyUI Pro-Tip: Place a VRAM Cleanup / Free GPU VRAM node right before your sampler to purge Gemma 4 12B Text Encoder from VRAM and achieve full generation speed! Hugging Face: <a target="_blank" rel="ugc" href="https://huggingface.co/guillaume127/LTX-2.5-FP8">https://huggingface.co/guillaume127/LTX-2.5-FP8</a>GitHub Code: <a target="_blank" rel="ugc" href="https://github.com/Guillaume-127/LTX-2.5-FP8">https://github.com/Guillaume-127/LTX-2.5-FP8</a>

LTX 2.5 FP8 (ComfyUI)

Initial release of LTX 2.5 (22B Distilled) quantized to FP8 (e4m3fn) with selective BF16 preservation for ComfyUI.

Files with SHA256: 140f02b01dbf7000f1aae7a5e2903a2ae184be2970c1d9dff675d39aebbd47f3