Train Flux LoRA
subject trainerfal-ai/flux-lora-fast-training
The classic FLUX.1 LoRA trainer: trigger word in, character out.
The simplest mental model of LoRA training on fal. Upload a zip, optionally set a trigger word, pick steps, done. It auto-captions, can auto-mask subjects, and has a style switch that turns the same pipeline into a style trainer.
What goes in the zip
Flat zip of images. Captions optional; the trainer generates them and injects your trigger word.
Good starting point
steps: 1000Parameters
Schema facts come straight from the fal API; the notes are ours.
Required
images_data_urlstringrequiredURL to a zip archive of your training images, optionally with matching .txt caption files.
In the atelier: The album you hand the painter. It is the single biggest factor in what the LoRA becomes.
Tip: 15 to 30 sharp, varied images beat 200 sloppy ones. Vary angle, lighting and background; keep the subject consistent.
Watch out: Duplicate or near-duplicate images push the LoRA toward memorizing instead of learning.
Raw schema description
URL to zip archive with images. Try to use at least 4 images in general the more the better. In addition to images the archive can contain text files with captions. Each text file should have the same name as the image file it corresponds to.
Optional
trigger_wordstringA unique word or phrase baked into your captions that activates the LoRA at inference time.
In the atelier: The skill's calling word. Say it in the prompt and the painter knows to use the bracelet.
Tip: Pick something that is not a real word, like TOK or OHWX, so it does not collide with anything the base model already knows.
Watch out: If you train with a trigger and forget it in your prompts later, the LoRA will seem weak or broken.
Raw schema description
Trigger word to be used in the captions. If None, a trigger word will not be used. If no captions are provide the trigger_word will be used instead of captions. If captions are the trigger word will not be used.
create_masksbooleandefault: trueAutomatically segments the subject so training focuses on it instead of the background.
In the atelier: Cropping each album photo so the painter studies the subject, not the wallpaper behind it.
Tip: Keep it on for people and objects. Turn it off for style training, where the whole image matters.
Raw schema description
If True segmentation masks will be used in the weight the training loss. For people a face mask is used if possible.
stepsinteger1 – 10000How many training iterations the model runs on your dataset. More steps means the LoRA sees your images more times.
In the atelier: Practice repetitions. Too few and the painter never picks up the skill. Too many and he stops learning and starts memorizing your exact photos.
Tip: Around 1000 is a solid default for a 15 to 30 image subject dataset. Small datasets need fewer steps, not more.
Watch out: If outputs start reproducing your training photos almost exactly (same pose, same background), you overtrained. Go back down.
Raw schema description
Number of steps to train the LoRA on.
is_stylebooleandefault: falseSwitches training from subject mode to style mode, disabling subject-focused tricks like masking.
In the atelier: Telling the painter: do not learn this person, learn this way of painting.
Tip: Style datasets should show the style across many different subjects, or the style and the subject will fuse.
Raw schema description
If True, the training will be for a style. This will deactivate segmentation, captioning and will use trigger word instead. Use the trigger word to specify the style.
is_input_format_already_preprocessedbooleandefault: falseTells the trainer your zip is already in its internal processed layout, skipping preprocessing.
Tip: Leave off unless you exported a preprocessed dataset from a previous run.
Raw schema description
Specifies whether the input data is already in a processed format. When set to False (default), the system expects raw input where image files and their corresponding caption files share the same name (e.g., 'photo.jpg' and 'photo.txt'). Set to True if your data is already in a preprocessed format.
data_archive_formatstringArchive format of the upload, like zip. Usually auto-detected from the file.
Tip: Just use a plain zip and let the trainer detect it.
Raw schema description
The format of the archive. If not specified, the format will be inferred from the URL.
Call it
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("fal-ai/flux-lora-fast-training", {
input: {
"images_data_url": "https://your-cdn.com/dataset.zip",
"steps": 1000,
"trigger_word": "TOK"
},
logs: true,
});
console.log(result.data);