Release v1.2.64-minimax-h3-video-lora-training · inlineresearch/Inline-Studio

H3 LoRAs can now train on short video clips, so the adapter learns motion instead of just appearance.

Set Clip length (seconds) in the Trainer's Adjust panel to switch a run from stills to clips
Leave it empty and nothing changes: stills work exactly as before
Both write the same adapter file, and one dataset can hold images and clips together
Clips shorter than 22 frames (0.92s at 24fps) are skipped and named in the log, not fatal
Works on all four H3 nodes, including Reference to Video

Measured, not projected.

Clips cost about 4x per step and almost nothing extra in VRAM
The caption pass sets the peak, which is why resolution and clip length barely move it
A 500-step clip run is roughly 27 minutes of training on a 32GB card, plus about 7 minutes to
load the checkpoint
Caching scales with dataset size, not step count: 173 clips takes about 20 minutes

On 16GB cards. Stills train at a 12.7GB peak, unchanged from v1.2.63.

The first clip LoRA was trained on
trojblue/test-HunyuanVideo-pixelart-videos,
173 short anime pixel art clips with captions.

hf download trojblue/test-HunyuanVideo-pixelart-videos \
  --repo-type dataset --local-dir ~/dataset

Captions ship in metadata.jsonl, so convert them to NNNN.txt sidecars first
Point Load from path at the train folder and the sidecars are picked up automatically
6 of the 173 clips fall under the frame floor and are skipped, leaving 167 to train on
500 steps at 512px with a 1 second clip length produced a usable adapter

Load from path: import a whole folder without a file picker, caption sidecars included
Clips render in the dataset grid, the Load Dataset node and on canvas, instead of drawing black
Load Dataset fills with as many thumbnails as fit, rather than a fixed 6, and grows as you
resize the node
Hover to play, double click to open on any dataset tile
Copy logs, all of them or a single line, straight from the Train LoRA node, with text
selection working inside the node
Precache reports progress item by item, so a long cache no longer looks like a hang
The log keeps 3000 lines instead of 400, so a failed run's setup lines survive to be copied

Your open project survives a page refresh and a Core restart. It used to live only in memory, so a
reload dropped you on the launcher and a Core restart left an open tab failing every call with
"No project is open"
One clip below the frame floor no longer throws away a 20 minute precache. Too-short clips are
skipped individually and the run continues
A checkpoint the kernel cannot memory map is now refused in seconds with the exact fix in the
message, rather than after the precache with a raw Cannot allocate memory (12) that reads like a
corrupt download

The app reads its version from Core's installed metadata, so reinstall or it keeps reporting the old
one:

cd core && uv pip install --python .venv/bin/python -e . --no-deps

If you hit unable to mmap ... Cannot allocate memory on a machine with less RAM than the
checkpoint, the new preflight will tell you this:

sudo sysctl -w vm.overcommit_memory=1
# to keep it across reboots
echo 'vm.overcommit_memory = 1' | sudo tee /etc/sysctl.d/99-inline-studio.conf

What's Changed

Full Changelog: v1.2.63...v1.2.64