Character consistency

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