Fine-tuning

For LTX-2.3, fine-tuning belongs to the advanced customization side of the ecosystem. Instead of relying only on prompts or an additional LoRA adapter, fine-tuning changes the trained model parameters using a dedicated training process and dataset.

Fine-tuning can be considered when the desired behavior requires a deeper adaptation of the base model. The result is a modified model rather than a separate lightweight adapter. The exact training setup depends on the LTX model, training implementation, dataset, and objective.

LoRA trains additional parameters that are used alongside the base model, while fine-tuning updates model parameters directly. LoRA is therefore useful for adapter-based specialization, whereas fine-tuning is a more direct form of model adaptation. The choice depends on the intended customization, training resources, and how the resulting model will be used.

A fine-tuning dataset should be prepared around a clear objective and be compatible with the selected LTX training workflow. The quality and consistency of the training examples are important because the model learns from the data provided during training. Different customization goals can require different datasets and training configurations.

For many experiments, a standard LTX model or LoRA may be a simpler starting point. Fine-tuning becomes more relevant when a deeper and more persistent adaptation of the model is required. Because it involves training the model itself, it also requires a suitable training environment and significantly more planning than ordinary inference.