AI Photo Restyler

Turn a photo into anime, manga, comic, cartoon, watercolor, clay, or 3D character art while the person, pet, or product stays recognisable. This AI photo restyler works as a photo-to-anime converter and AI cartoonizer you can steer: apply a style like a filter over your own picture, or add style samples to steer the palette, line weight, and shading. A selfie, portrait, pet photo, product shot, or travel picture becomes illustration-style art for social avatars, profile pictures, sticker sets, posters, merchandise, and content series, with one chosen look repeated across a whole batch.

Turn one real photo into a chosen illustration style while the subject stays recognisable. Anchor the look with the user's own style references when they have them, then keep that same look across every later photo in the set.

Scope and routing

Use this Skill when a photo already exists and the user wants it redrawn: anime or manga, cartoon, comic, watercolor, ink or line art, pencil sketch, clay or toy figure, 3D character, pixel, cyberpunk, or a look copied from a reference image they supply. It fits social avatars and profile pictures, sticker and emoji sets, couple and family portraits, pet portraits, travel and event recaps, merchandise artwork, and a series of posts that must share one style.

Route a video restyle to ai-video-restyler. Route a reusable multi-view character sheet to ip-character-consistency-studio. Route repairing an already generated image toward realism to ai-image-realism. Route animated comic-drama shots to ai-comic-drama-shot-maker, and a business headshot to ai-headshot-studio.

Inputs and defaults

The one hard input is a source photo the host Agent can actually inspect. Everything else has a working default. Reuse the style words, reference images, subject, destination surface, canvas, batch, and must-keeps already present in the conversation.

Ask only when the answer changes the paid result: which style, when no style is stated and none can be read from a supplied reference; and which subject to keep when a photo has several people and the user named none.

Defaults that avoid extra questions:

• count: 1 for a first look, so the user judges one result before a batch.
• The omitted edit canvas is 2K following the base photo's aspect ratio, because the base anchors the canvas on an edit.
• model: "auto" unless the user names a model.
• Must-keeps default to face and likeness, hair, visible clothing, pet markings, product shape and logo, and any element the user calls out.

Style references are ordered inputs. The source photo is the base and comes first; up to three style references follow in the order their influence should apply. Say which reference contributes what — palette, line weight, shading, or overall look.

Golden path

1. Inspect the source photo and write a short restyle card: subject, the one target style, must-keeps, destination surface, canvas, and batch size.
2. Upload local files once through the bundled client and reuse each returned artifact reference.
3. Call beatra.models.list for the capability the chosen route needs — image_edit for an in-place restyle, image_to_image for a new composition — and read the live card for accepted input count, canvas, controls, and price.
4. Compose one beatra.images.edit request: the source photo as the base first input, ordered style references after it, one positive prompt naming the target style and the must-keeps, no edit_regions because the whole frame is being redrawn, count: 1, and a seed when the look must be repeatable.
5. Confirm before paid work. Show the frozen prompt, the exact ordered inputs, canvas, model, controls, output count, current maximum charge, and one opaque stable client_request_id.
6. Submit exactly once, record the task ID immediately, and poll that same task.
7. Deliver the real artifact and report only the actual returned task status, resolved model, dimensions, format, and billing.net_charged_credits. Review only media the host Agent can actually see, say what it could not inspect, and treat the must-keeps as a drift review rather than exact preservation.

Once the user accepts a look, reuse its exact prompt, reference order, model, and seed for the rest of the batch so the set matches. Each additional image is new paid work and needs its own confirmation and request ID.

Choose beatra.images.transform instead when the user wants a new composition rather than the same photo redrawn — a new scene, pose, or layout built from the photo plus references. On a transform an explicit preset aspect: "source" follows the last ordered input and the omitted default is 2K at 16:9, so state the intended ratio explicitly. Read the photo restyle workflow for payload shapes, style recipes, batch consistency, recovery, and delivery review.

How this Skill executes

Use the bundled scripts/mcp_client.py for every remote Beatra operation: the MCP tool name is the CLI argument after call, and one JSON object goes on standard input. Never configure or call a host Beatra Connector, and never use REST/OpenAPI as a fallback. Register the package with beatra.installations.register on first use. Every creation is an asynchronous task: submit once, then follow that task to a terminal state.

Decisions that require confirmation

Confirm before submitting any paid request: the frozen prompt and ordered inputs, the canvas when it differs from the source, the output count, an explicit model choice, and the current maximum charge. A changed source photo, style, reference order, prompt, canvas, model, control, or count is new paid work with a new request ID.

Recovery

Save every task ID the moment it returns and poll with beatra.tasks.get; queued and running mean wait. Replay a create only when its response is genuinely unknown and the payload is byte-equivalent under the same request ID. If a task ID is lost, use beatra.tasks.list, confirm candidates with beatra.tasks.get, and recover the original before considering new work. Call beatra.tasks.cancel only at the user's request; on 409, keep polling the original task and report cancellation only when its terminal status is canceled.

References by task

• Photo restyle workflow: style recipes, ordered reference payloads, batch consistency with seed, revision edits, recovery, and delivery review.
• Installation and authentication and installation registration: first use and shared credentials.
• Tasks and results and billing, errors, and recovery: task, artifact, and billing facts.
• Bundled MCP Client diagnostics: client operation and connection diagnostics; do not configure a host Connector.
• automatic updates and safety: update behaviour and controls.
• uninstall and disconnect: package removal and shared credential cleanup.

Runtime and safe automatic updates

The bundled client silently checks at most once every 24 hours per installation. When a newer release is available, it installs automatically without separate confirmation. It uses only fixed official Beatra discovery and immutable CDN paths for this package, channel, and locale, verifies discovery, archive, manifest, and every packaged file before replacement, and replaces only package-owned files. Update checks, downloads, verification, replacement, and recovery fail open: the current installation remains usable and the original command continues. An update failure never authorizes retrying a paid generation. The choice persists across later commands.

text

python3 scripts/mcp_client.py update --auto off
python3 scripts/mcp_client.py update --auto on
python3 scripts/mcp_client.py update --check

--auto off disables silent checks, --auto on restores them, and --check reports the official available version without replacing files. See automatic updates and safety.