v1.2.69 - LTX2.5: Generation & Training #48
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LTX-2.5, running on your own GPU, Train Clip & IC LoRa
Lightricks' 22B open-weights video model is now a local model in Inline Studio.
It generates video and its soundtrack in the same pass, on your own hardware,
with no account and no per-second billing.
Three nodes. Text to video, image to video, and first-and-last frame. Each
has a fast mode (distilled, 12 steps) and a quality mode (the dev build, guided),
and each takes a LoRA. Clips run from 1 to 20 seconds at 24fps.
Bring the weights. Drop the checkpoint pack into
models/the way you wouldany other model. The download is about 71GB for fast mode, and the app tells you
what is missing and fetches it for you if you would rather not do it by hand.
What it needs. An Ampere card or newer. A 2 second clip at 960x576 renders in
about 229 seconds on a 48GB card once the model is warm. On a card too small to
hold the transformer it streams the weights from system RAM instead, which works
but takes roughly four times as long.
Train a LoRA on it(Clip & IC Lora)
Two kinds, both trained on the dev transformer, both teaching the picture rather
than the sound.
Clip LoRA learns a look and how it moves, from single clips. Feed it a folder
of footage and the adapter carries that style into anything you generate
afterwards.
Control LoRA learns a transform rather than a look. Every item is two clips:
a reference and the result you want from it. Lightricks call this an IC-LoRA.
Training is available now and produces a standard adapter file. Driving a render
with a reference clip is not wired up yet, so this release covers training a
Control LoRA rather than generating with one.
Training peaks at 42GB, so a 48GB card is the floor rather than a comfort. There
is no 4-bit path for this architecture, and the Trainer says so plainly instead
of accepting the setting and ignoring it. A step takes about two thirds of a
second at 512px, so the startup costs more than the training on a short run.
A new way to add training data
The dataset controls are now one dialog, Add/Manage Training Data, which both
builds a dataset and edits one you already have.
paired, and the download size before anything is fetched.
gigabytes through the browser.
dataset.jsonormetadata.jsonlis read wherethe dataset ships one, and filenames are matched otherwise, so
bear.mp4andbear_reference.mp4become a single training item rather than two.fills only the rows that have none rather than overwriting good text.
loading a folder cannot change a dataset by accident, and Import stays greyed
out until something has actually changed.
Smaller things
rather than all at once, play on hover, and open full screen on a double click.
A paired item opens both halves side by side, which is the only way to see
whether a reference and its target line up.
inlinestudio.art/lora-training/ltx-2-5-lora-training-locally,
with measured VRAM, an example dataset, and the licence terms.
Before you start
The weights are gated. Open the model page on Hugging Face, accept the LTX-2
Community License, and make sure the account you accepted with is the one your
token belongs to. Without that, every download comes back as a permission error
rather than a file.
Upgrading
From the repo (the usual way):
What's Changed
Full Changelog: v1.2.68...v1.2.69
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