Z Image Base Trainer
general trainerfal-ai/z-image-base-trainer
The only trainer in the family that targets Z-Image-Base.
Trains on Z-Image-Base rather than the distilled Turbo variant, with a 2000 step default schedule. Despite what the names suggest, this is the family's only route to the base model — z-image-trainer actually trains Turbo. The catalog's only Z-Image inference endpoint runs Turbo, so test your base-trained LoRA there and compare against the dedicated Turbo trainers before committing to one.
What goes in the zip
Flat zip with one caption per image: 001.jpg next to 001.txt. Missing captions fall back to default_caption.
Good starting point
steps: 2000learning_rate: 0.0005Parameters
Schema facts come straight from the fal API; the notes are ours.
Required
image_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 the input data zip archive. The zip should contain pairs of images and corresponding captions. The images should be named: ROOT.EXT. For example: 001.jpg The corresponding captions should be named: ROOT.txt. For example: 001.txt If no text file is provided for an image, the default_caption will be used.
Optional
stepsintegerdefault: 200010 – 40000How 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 for
default_captionstringCaption used for any image that has no .txt caption file in the zip.
In the atelier: The note the painter assumes when a photo in the album has no note attached.
Tip: For edit trainers this often carries the whole instruction, like 'turn this sketch into a finished painting'.
Raw schema description
Default caption to use when caption files are missing. If None, missing captions will cause an error.
learning_ratenumberdefault: 5e-4How big each learning update is. Controls how aggressively the model changes per step.
In the atelier: The painter's eagerness. A high rate is frantic practice: fast but sloppy, and it can wreck habits he already had. A low rate is careful practice: slow, but precise.
Tip: Stay near the trainer's default unless you have a reason. If results look fried or oversaturated, lower it. If the subject barely shows after many steps, raise it slightly or add steps.
Watch out: Learning rate and steps trade off against each other. Doubling both at once is how datasets get burned.
Raw schema description
Learning rate.
Call it
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("fal-ai/z-image-base-trainer", {
input: {
"image_data_url": "https://your-cdn.com/dataset.zip",
"steps": 2000,
"learning_rate": 0.0005
},
logs: true,
});
console.log(result.data);