venice-video-model-routing

To maintain character consistency across multiple video shots in Venice AI, you can apply R2V-by-default routing with reference_image_urls and elements to anchor character identity across panels. This routing approach evaluates frame sourcing and prompt adaptation per shot.

Two-pass refinement is a strategy that routes Venice AI video models and reference images to maximize character consistency across panels and shots. Use it for multi-shot projects requiring per-shot model selection, reference attachments, and frame sourcing decisions.

Yes, routing Venice AI video models requires Python 3.10 or higher and a valid VENICE_API_KEY. These prerequisites allow the bundled scripts to execute image generation, video generation, image editing, and upscaling tasks.

The best way to preserve character identity in multi-shot talk-show formats is using frame-source and grouping rules. These rules route Venice AI models to manage per-shot reference attachments and adapt prompts to maintain consistent identity.

Character consistency fails without R2V routing because standard generation lacks default character anchoring via elements and reference_image_urls. Applying R2V-first routing and two-pass refinement ensures frame sourcing and prompt adaptation preserve identity across panels.