Create a training job, or pick one on the left.
Settings
Adapter
Optimizer
Noise and captions
Preview images
Rendered by the burrow during training, with and without the LoRA. Leave empty for none. Write each prompt as you would write one to generate a picture: the trigger word, then what the picture should show (the trigger word alone says nothing about the scene). Every epoch is rendered with the same seed, so the epochs can be compared. Each preview has its own prompt, negative prompt, size, steps, CFG scale and seed. Previews are a plain single pass (no hires step), so a normal generation with the finished LoRA usually looks better than they do.
Advanced parameters (JSON, merged over the request)
Any parameter the burrow accepts, as a JSON object. Example:
{"noise": {"multires_iterations": 6}, "bucket": {"min": 768, "max": 1344}}
Dataset
.zip of them here, as many as you like at once. Caption files that come
with them are matched by name: fox.png takes fox.txt (tags) and
fox.natural.txt (a sentence). A zip is opened in your browser, and only the images
and caption files in it are sent.
The tagger is the one the bot already uses to describe images. Its tags go in
the tag caption of each image, where you can still edit them; the natural-language caption is
never touched. With "Fold what the images share" on (and a trigger word set), tags found in at
least four of every five captions (blue fur, goggles on every picture
of one character) are left out of training, so the trigger word alone brings them; what differs
between images stays something you ask for in the prompt. Your captions here are not changed,
and the job's log lists what was folded.