To extend the scalability demonstrated by the model to generate personalized video content with dynamic multi-ID preservation, a similar approach could be applied to video data. By incorporating video frames as input data and leveraging temporal information, the model could generate personalized video content with multiple identities seamlessly integrated. The model could utilize a combination of frame-level features, text prompts, and pose control information to generate dynamic video sequences that preserve individual identities across frames. Additionally, exploring techniques from video generation models, such as spatiotemporal attention mechanisms and motion modeling, could enhance the model's ability to generate personalized video content with multi-ID preservation. By adapting the existing architecture to handle video data and incorporating temporal dependencies, the model could generate personalized video content with the same level of scalability and versatility demonstrated in the image generation process.