We all know the potential of a model reveals itself only when LoRAs are trained. However, when I first trained LTX-2, even after 11 hours, the results were subpar. Then, at day, I activated CREPA https://arxiv.org/pdf/2506.09229, which is extending the known REPA (representation alignment) technique for videos, and which has also helped me much with Kandinsky 5 trainability struggles. With CREPA and Prodigy as the optimizer (and TREAD, but I also used it for the failed training run), musubi block swap at 2, CREPA using backbone features, the iteration speed was 5.75s/it using SimpleTuner on a single 5090. The training VRAM usage is 30.4 GB.

Note, that this is not yet convergence (~90/100 of the training loss curve saturation), this is a "good enough" result to show the training speedup, because the results are diminishing later. Also, trained without sound because of SimpleTuner's implementation not finished completely at the time of the training.

There is a kink that the objects start subtly compressing before the press hits them, but it's a purely dataset related thing. (synthetic OmniVFX from Huggingface)

I think the CREPA-Prodigy-mix speedup is insane, because in r/StableDiffusion subreddit is was reported that with the official trainer LoRA training takes as much as 9 hours on a 5090 and 1 hour on rented Cloud hardware.

The training and dataset config are under config.json and ltx2-multiresolution-crush-t2v.json respectively.