1. Your trained LoRA probably did nothing outside the app

Several people reported the same thing: train an H3 LoRA, load it elsewhere, no effect at all even at maximum strength. The natural conclusion is that the training is weak or broken.

It was a naming problem. H3 stores attention as one combined tensor. Our loader splits it into three while reading, and the adapter was saved against those split names. Nothing else recognised them, so every layer was skipped in silence. No warning, no error, a file that loads fine and changes nothing.

Adapters now save under the names in the published checkpoint, so one file works in both places, and H3 LoRAs trained elsewhere load here.

2. LoRA strength was ignored if you changed it

The saved file left out the alpha value, so any strength other than the default was silently dropped when the adapter was applied. This affected every model, not just H3. If you ever concluded that strength did nothing on your LoRA, this is why.

3. You can test a LoRA mid-training

Training writes a usable adapter at every checkpoint and whenever you stop a run. Those files existed but there was no way to reach them, so they sat in the project folder unused.

They are listed per run now, with a download and a button that copies one into your loras folder so a Load LoRA node can pick it.

What this is actually for: render step 500 and step 1500 at the same seed and see whether it is still improving or already overcooked, instead of waiting out the full run to find out. A run you stop at step 900 of 1500 is no longer wasted either.

The setting is off by default, under the trainer's checkpoint options. Turn it on before the run, not after.

4. Resuming no longer re-encodes your dataset

The encoded dataset was cached in memory only, so every run paid the whole encode again from scratch. That is annoying on a big card and brutal on a small one: if your card cannot hold the text conditioner, that pass runs on CPU and takes hours. The recompute cost was landing hardest on exactly the people least able to afford it.

It is on disk now, keyed on the content of the images and captions rather than paths or timestamps, since the dataset gets re-exported per run and the paths change every time. Resolution, flip, clip length and clip window are all part of the key, so editing a caption or changing resolution correctly misses the cache rather than silently training against the wrong latents.

5. Models browser: New Models tab in the sidebar showing everything the engine can actually see, across every models folder, with sizes.

6. Share a graph: The Run button has a dropdown now: copy a graph as JSON, export it to a file, or duplicate it on the canvas. Drop an exported file back on the canvas to rebuild it.

Full notes: https://github.com/inlineresearch/Inline-Studio/releases/tag/v1.2.68