Character Consistency

Keep the same character, person, or product looking identical across new scenes, poses, outfits, and styles. Use when the user says "the same character again", "keep her face consistent", "my mascot in a different scene", "same product, new background", or wants a reference, expression, or outfit sheet. The number-one thing people struggle with in image generation, so reach for it whenever identity must persist across images. To hold a composition or pose fixed rather than identity, use controlled-generation. To fuse separate photos into one scene, use composite-scene.

Produce new images of an established subject (a character, a real person, a mascot, a product) that stay recognizably the same across scenes, angles, and styles. The lever is reference images plus prompt phrasing that ties the new image back to them, not re-describing the subject from scratch.

Inputs to collect

• The subject's reference image(s). One clear shot is enough; several angles/expressions improve fidelity. (Ask only if none provided.)
• What changes in the new image: scene, pose, outfit, style, or all of these.
• How many outputs and the target use (single hero, a reference sheet, a set of expressions/outfits).
• Optional: a locked style or palette to carry across the set.

Models

• Default: Google Nano Banana 2 (google:4@3) - accepts up to 14 reference images and holds identity strongly across scenes and styles. Best general pick.
• For a trained, reusable identity (a recurring brand character used at scale): train a LoRA via train-style-model, then generate with it - more setup, maximum consistency.
• Reference-guided alternatives: IP-Adapter on a FLUX/SDXL base, or any image model that accepts referenceImages. Confirm support and the exact field via runware-models + runware-run before calling.

Workflow

1. Resolve the model schema (runware-run) and confirm the reference-image field and its max count.
2. Upload the subject reference(s) into inputs.referenceImages.
3. Run imageInference synchronously with a prompt that names the subject as "the same … from the reference" and then describes only what's new.
4. For a set (reference sheet, expressions, outfits), reuse the same references across calls and vary only the scene/pose clause. Keep a fixed seed if you want tighter repeatability.
5. Review for identity drift; retry the outliers with an added or clearer reference.

Technique

• Anchor, then vary. State the immutable identity once ("the same woman from the reference image, same face and hair") and let the rest of the prompt change freely (new pose, lighting, outfit, setting). Do not re-describe the face from imagination - that invites drift.

  Fill this character-anchor template, then send it as positivePrompt:

  text

  The same <subject> from the reference image <new scene, pose, lighting, or medium>. Keep <the immutable identity features: face, hair, key details> identical.
  

  The first clause is the immutable anchor (do not vary it). The middle clause is the only part that changes per image. The closing clause names the features to hold hardest.

  Load references/examples.md for worked end-to-end recipes (single subject, hidden-detail reference set, two locked subjects).

• More references = more stability. A single front shot works; adding profile/expression shots locks identity harder across angles.

• Composition is a sibling move: to place the subject with other real elements (product, backdrop), give each as a separate reference and describe how they fit - see composite-scene.

• For a whole set, hold references and style constant and change only one variable per image. That's what makes a reference/expression/outfit sheet read as one character.

Parameters that matter

• inputs.referenceImages - up to 14 on Nano Banana 2; order is not significant.
• Prompt phrasing carries the consistency, not a strength dial - lead with "the same … from the reference".
• seed - fix it for tighter repeatability across a set; vary it for alternates.
• Confirm exact field names against the live schema (runware-run); never guess.

Quality bar

• Face/identity is recognizably the same across every output (no morphing between siblings).
• Only the intended variables changed (pose/scene/outfit), not the subject.
• For a set, the images read as one character, not cousins. Retry any that drift with a clearer or extra reference.

Related skills

runware-run, runware-models, runware-prompting; composite-scene (subject + other elements), train-style-model (reusable identity), product-photography (same-product across shots).