layer-image

Layer Image Generation

Overview

The loop is the one the layer skill teaches: list_base_models with filter.use_case: "text_to_image", get_base_model, estimate, execute, poll. What separates a usable image from a near miss is the prompt and the per-model contract, not the model choice, which the curated ranking already handles.

Editing an image that already exists is a different use case with different rules: see layer-image-editing. Sprites, icons, and game UI have their own constraints: see layer-game-assets. Holding one look across a set: see layer-reference-sets.

If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add layerai/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

What a prompt must name

A model fills every gap you leave, and it fills it with the most average answer in its training data. Each of these is a gap worth closing, in roughly this order of impact:

1. Subject, concretely. "A dwarf blacksmith" beats "a fantasy character".
2. Style register, as a production term rather than an artist's name: hand-painted, cel-shaded,

flat vector, painterly semi-realism, stylised PBR render, gouache, 90s airbrush.

1. Shot and framing: full body, three-quarter portrait, top-down, isometric, extreme close-up.

This is the single most common omission and the most common cause of a reroll.

1. Lighting: key direction, hardness, time of day, practical sources. "Rim-lit from behind,

warm forge glow below" is a decision; "good lighting" is not.

1. Palette, named or constrained: "muted teal and rust, two accent hues only".
2. Background treatment: flat colour, isolated on transparent, environment implied, full scene.

Assets that will be composited need this stated, not assumed.

Leave out what does not matter. A prompt that specifies everything specifies nothing, because the model weights the whole string and dilutes the parts you cared about.

Sizing, and the fields that differ

Aspect ratio and resolution are per-model contracts. Two models that both do text-to-image disagree about whether they take an aspect ratio string, explicit width and height, or a fixed set of named sizes, so read get_base_model rather than reusing the shape that worked last time.

When the deliverable has a fixed ratio, filter for it. filter.capabilities is an object of booleans, not a list of names, so it is {portrait_9_16: true}: square, portrait_9_16, landscape_16_9, or multiple_aspect_ratios when the user will want several crops from one setup. Set only what the task needs, since false filters rather than defaults.

Negatives, references, and seeds

negative_prompt is a capability, not a universal parameter. Filter for it when the run needs one, and do not send it blindly: on a model without it, it is either ignored or an error, and on a model with it, a long negative list costs more than it saves. Reach for it to suppress one specific, recurring defect.

Three reference types steer a text-to-image run, all passed through guidance_files after an upload:

• reference_image with the image_editing capability, for "reproduce this subject" and for new

art that has to match a reference. style_reference is the older style-only transfer: it moves a look across, it does not carry a subject, so reaching for it to hold a character is the usual reason a cast drifts.

• pose with character_pose, for "put the character in this position".
• depth, canny, lineart, or scribble with structure or outline, for "keep this layout".

A reference is worth more than three adjectives. When a user has an image and is describing it in words, upload the image.

Seeds make a run repeatable, which matters when iterating: hold the seed and change one clause to see what that clause actually does. They do not make two different prompts consistent with each other. For a look held across a set, train it: see layer-reference-sets.

Text in the image

Most image models garble text. When the deliverable needs readable words, filter for a model whose description claims typography, keep the string short, and put it in quotes in the prompt. If it still fails after two attempts, stop rerolling: generate the art clean and composite the text, which is also what makes it editable and localisable later.

Worked example

"Key art for our roguelike, 16:9, for the Steam page."

1. list_base_models with filter.use_case: "text_to_image" and

filter.capabilities: {landscape_16_9: true}. Take the first result.

1. get_base_model, which reports the sizing fields it accepts and whether it needs a trigger word.
2. Compose against the six points above: "Hooded rogue mid-leap over a collapsing stone bridge, seen

three-quarter from below, hand-painted semi-realism, hard moonlight from the upper left with cold blue rim light, warm torchlight pooling below, muted slate and amber, storm-lit ruins receding into fog behind."

1. estimate_forge_price with batch_size: 4, to see four compositions of one idea.
2. Under 20 CUs, so execute, then poll at poll_interval_seconds.
3. Present all four. Pick one, hold its seed, and iterate one clause at a time.

Common mistakes

• Omitting shot and framing, then rerolling the same prompt hoping for a different crop.
• Describing a reference image in words instead of uploading it.
• Sending negative_prompt to a model that does not declare the capability.
• Reusing the sizing fields from a different model rather than reading get_base_model.
• Using batch_size for N different assets. It makes variations of one prompt.
• Chasing readable text through a sixth reroll instead of compositing it.
• Stacking five style adjectives, which averages them into none of them.