Seedream 4.5 vs Nano Banana Pro: which AI model is better for character consistency in video production?

For stylized or animated video production, Seedream 4.5 — in a documented AI anime production it outperformed Nano Banana Pro at holding character and stylistic consistency across the series. Nano Banana Pro wins on prompt adherence and detailed character sheets, making it the pick for photoreal projects. Either way, video consistency comes from locked reference sheets, not the model alone.

Pick the model by your project's visual register, then lock references before any video generation.

Where Seedream 4.5 wins: stylized and animated consistency. In a documented AI anime production, Seedream 4.5 was used to lock the entire visual identity — character designs, backgrounds, architecture, and the visual language separating characters from environments — and it outperformed Nano Banana Pro for maintaining stylistic consistency across the production. If your film is animated, painterly, or otherwise non-photoreal, generate your character references in Seedream 4.5 and treat those images as the identity anchor for every downstream shot.

Where Nano Banana Pro wins: prompt adherence and character-sheet fidelity. Nano Banana Pro outperforms Nano Banana 2 for character sheet generation, and in one documented episode production it completed 11 of 11 marketing cover assets built from actual episode frames. The trade-off surfaced in testing: "Nano Banana Pro, it has insane prompt adherence. Something about these images felt extremely stock photo-y to me," as one filmmaker put it — precise execution with a photoreal bias that can flatten a stylized look. One practical caveat: it can garble rendered text on character images, so route logo and text fixes to Nano Banana 2.

Test both on your own character before committing. invideo is an agentic video creation tool with the current image and video models available in one place, so you can run the comparison inside a single project: set up a casting agent and instruct it to run the identical character prompt on two image models simultaneously, then pick the aesthetic. One documented production ran exactly this parallel casting test rather than testing models sequentially — it compresses the decision to a single generation round.

The model locks the reference; the workflow keeps it consistent on video. Whichever model produces your character, consistency across video shots comes from the pipeline around it: generate a multi-angle character sheet — one production used a 12-angle master sheet to lock a character's face, armor, and tattoos — lock it before any clip is rendered, keep it in the invideo agent's persistent context, and attach it to every video generation. Seedance 2.0 reference-to-video accepts character references directly, carrying the locked identity into motion. A 70-second short held two characters visually consistent across every scene this way with no LoRA fine-tuning, and another production locked each character's identity in about 5 generation attempts (~$9.78 per character). Include close-up panels on the sheet, not just wide views, so small details like scars and accessories survive the handoff between image and video models.

Watch some of these to see what works for you:

Nano Banana Pro, it has insane prompt adherence. Something about these images felt extremely stock photo-y to me.

— a filmmaker documenting a multi-agent AI film production