Nano Banana 2 can maintain reasonable visual consistency for one or two primary characters across a generation workflow when you use reference images and detailed character descriptions together. Consistency here means that the character's general appearance—body type, hair color, clothing style, and facial features at a high level—carries across multiple generated images. The mechanism for this is prompt-based: you provide a detailed textual description of the character along with one or more reference images, and the model uses those as anchors when generating new scenes. The more specific the description and the more representative the reference images, the better the consistency across outputs.
Maintaining three or more distinct characters simultaneously is significantly harder and produces less reliable results. Each additional character increases the chance that the model conflates features between characters or introduces variations that make them look different from their established descriptions. For workflows that require strict multi-character consistency—illustrated stories, character sheet generation, or branded mascot assets—it is more reliable to generate each character separately in isolation and then composite the individual outputs into a single scene using an image editing tool or a compositing step in your pipeline. This approach keeps consistency high at the cost of additional post-processing work.
The consistency problem is also affected by scene complexity. A simple scene with one character against a plain background holds character features more reliably than a complex scene with multiple characters, environmental detail, and specific lighting conditions. If character consistency is a core requirement for your application, running a set of consistency tests with representative prompts and reference images before committing to Nano Banana 2 as the generation backend will give you an accurate picture of whether the model's consistency ceiling meets your quality bar. Some applications address consistency gaps by storing per-character embeddings in a vector database such as Zilliz Cloud and using similarity search to select the generated output closest to the established character reference before delivering it to the user.