This is a simplified guide to an AI model called vidu/q2/reference-to-video/pro maintained by fal-ai. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.
Model overview
vidu/q2/reference-to-video/pro is a professional-grade video generation model from fal-ai that creates videos based on reference imagery. This model stands apart from other Vidu Q2 variants by prioritizing reference-based generation over text prompts alone, making it ideal for maintaining visual consistency across generated content. Compare this with vidu/q2/image-to-video/pro which focuses on single-image expansion, or the standard vidu/q2/reference-to-video for less intensive applications. For different needs, explore vidu/q2/text-to-video for text-driven generation or vidu/q2/video-extension/pro for extending existing footage.
Capabilities
The model delivers high-quality video generation with enhanced control over the creative output. It accepts visual reference material and translates that into coherent video sequences while maintaining the visual characteristics of the reference. This enables precise control over style, composition, and subject matter in the generated videos, reducing the variance that comes with purely text-based generation approaches.
What can I use it for?
Reference-based video generation serves numerous creative and commercial applications. Content creators can maintain consistent visual branding across multiple video assets by using reference imagery as a guide. Marketing teams can generate product demonstration videos that match existing brand aesthetics. Artists and designers can explore variations on a visual concept by treating reference images as creative starting points. Filmmakers can prototype visual effects or scene variations before committing resources to full production. Educational content creators can generate illustrative videos that match reference materials from textbooks or reference libraries.
Things to try
Experiment with high-quality photographs as references to see how the model interprets static imagery into dynamic motion. Try providing stylized artwork or illustrations as references to explore how the model handles non-photorealistic source material. Use detailed reference images with specific lighting conditions or color palettes to maintain those characteristics in generated videos. Test how the model handles multiple similar references to understand its approach to visual consistency across batches. Provide references with text or graphics to see how spatial elements translate into video generation.