Summary
Veteran AI presents a detailed three-part guide to training custom character LoRAs for LTX Video 2.3, covering dataset preparation, training configuration, and final deployment. The tutorial opens by demonstrating a real Anne Hathaway character LoRA and honestly diagnosing its visual inconsistencies — a result of training data that lacked environmental variety — before explaining how to do better.
A key focus is what distinguishes LTX 2.3 training from the more familiar Wan 2.2 workflow: LTX 2.3 requires actual video clips (not static images), generates audio alongside video, and consequently needs roughly double the training steps — 5,000 to 6,000 versus the 2,500–3,000 typical for Wan 2.2. The guide uses Compshare for GPU access (RTX 5090 recommended) and AI Toolkit for training. Configuration details covered include rank 32 (yielding a ~600 MB LoRA), FP8 quantization, mandatory Layers Offload to avoid out-of-memory errors, and Cache Text Embeddings for speed. Training is structured in two phases: an initial High Noise bias phase to quickly establish character features, followed by a Balance or Low Noise phase for detail refinement, with checkpoints saved every 250 steps.
Dataset preparation guidance addresses the real-world challenge of gathering diverse training footage, explains how to write dual prompts for video content and audio (audio descriptions must be enclosed in quotation marks), and covers file naming conventions. Trained LoRAs are validated in RunningHub’s ComfyUI workspace, with candid discussion of quality trade-offs from the chosen training data.
📺 Source: Veteran AI · Published April 13, 2026
🏷️ Format: Tutorial Demo
