Openclaw Skills for character consistency generates new AI images that keep the same subject recognizable across scenes, poses, outfits, and styles.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install character-consistency
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install character-consistency using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Character Consistency?
Character consistency in Openclaw Skills is built to generate new images of an established subject without losing identity. It uses reference images plus an anchor-first prompt strategy so the model preserves immutable details like facial structure, hair, product geometry, mascot markings, and other key traits while you change the scene, pose, outfit, lighting, or art direction.
This is the right skill whenever the same character, person, or product needs to appear again in a way that still reads as the same subject. Openclaw Skills supports strong reference-guided generation for single hero images, expression sheets, outfit variations, and brand asset sets, with Google Nano Banana 2 as the default path and LoRA-based workflows available when you need reusable identity at scale.
Character Consistency Use Cases
Use Openclaw Skills when identity has to persist across outputs:
- Recreate the same character in a new scene, pose, outfit, or medium without losing recognizability.
- Generate a mascot across a campaign with consistent proportions, markings, and style.
- Produce product shots with the same item in different backgrounds, angles, or lighting setups.
- Build reference sheets with expressions, outfits, or pose variations for an established subject.
- Keep a real person recognizably consistent across editorial, social, or concept art outputs.
- Use Openclaw Skills when a prompt calls for the same character again, keep her face consistent, or same product, new background.
How Character Consistency Works
- Resolve the live model schema with
runware-runand confirm the exact reference-image field and max count. - Collect one clear reference image or several angles and expressions for stronger fidelity.
- Upload the references to
inputs.referenceImagesand describe only what changes in the new image. - Generate synchronously with
imageInference, anchoring the prompt around the same subject from the reference and varying the scene, pose, outfit, or style. - Reuse the same references across a set, keep
seedfixed for repeatability, and retry any drifted outputs with an added or clearer reference. - If you need a reusable identity for scale, train a LoRA via
train-style-modeland generate with it.
Character Consistency Setup
No local package install is required. Openclaw Skills works by providing reference images and a schema-validated generation request.
- Verify the live model and schema before generating.
runware-models
runware-run
- Prepare 1 to 14 reference images for the subject, depending on the model you choose.
- Pass the images through
inputs.referenceImagesand anchor the prompt around the same subject from the reference. - Use the
imageInferencecall in your agent workflow to render the new image set. - If you need scalable reuse for a recurring brand character, train a reusable identity model.
train-style-model
- Review the outputs for identity drift, then add a clearer or extra reference if a result needs correction.
Character Consistency Data Schema & Taxonomy
Openclaw Skills does not impose a fixed folder layout. It organizes work around a subject, a reference set, a prompt anchor, generation parameters, and the resulting image outputs.
| Artifact | Purpose | Key metadata |
|---|---|---|
| Subject profile | Defines the immutable identity to preserve | subject type, face and hair markers, product geometry, costume details |
| Reference set | Anchors the subject across generations | inputs.referenceImages, image count, angle, expression, clarity |
| Prompt anchor | Locks identity in the prompt | same subject from the reference, immutable features, changeable scene clause |
| Generation config | Controls repeatability and model choice | model id, seed, batch size, schema version |
| Output set | The rendered image assets | hero image, variation grid, reference sheet, expression sheet, outfit sheet |
| QA record | Tracks drift and retries | pass or fail, drift notes, extra-reference usage, revision count |
Suggested metadata taxonomy
subject: character, person, mascot, productidentity: face, hair, markings, shape, labels, key detailsvariation: scene, pose, outfit, lighting, stylereference: count, angle, expression, clarity, sourcegeneration: model, seed, batch size, schema mappingquality: consistency, drift, retry status, acceptance
Character Consistency Advanced Features
- Up to 14 reference images on Google Nano Banana 2 for strong identity locking.
- Anchor-first prompt strategy that keeps the subject stable while letting scene, pose, and style change.
- Reference-sheet workflows for expressions, outfits, and pose studies that still read as one character.
- Fixed-seed reruns for tighter repeatability when you need near-identical alternates.
- LoRA-based reusable identity workflows for recurring brand characters at scale.
- Cross-skill composition with
composite-scene,train-style-model, andproduct-photographywhen the subject must interact with other elements or products. - Live schema verification with
runware-runandrunware-modelsso you confirm field names instead of guessing.
SKILL.md
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