Storyboard GUI for Multimodal Video Generation Under Memory Limits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI tools face limitations in memory capacity, primarily affecting video and long-form text generation, and interfaces are nascent, with most prompts being text-based, lacking versatility for multi-modal inputs.
Innovation Solution
A visual media generative response engine utilizing a diffusion-transformer architecture that processes both image and text prompts, capable of generating high-quality videos with varied aspect ratios, resolutions, and lengths by breaking frames into spacetime patches and using a timeline interface for user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI tools use traditional text-based chat interfaces or APIs for prompting, then the interface implementation is simple, but the user input versatility is limited
Solution Approach 1:
The graphical user interface enables users to input prompts through multiple modalities including text boxes, image uploads, video uploads, and audio recordings, allowing a single interface to serve multiple input functions simultaneously. This multi-functional approach resolves the contradiction by expanding input versatility without requiring separate specialized interfaces for each input type.
Solution Approach 2:
The interface is divided into distinct functional components: a text box for text input, an image upload button for visual input, a video upload button for temporal visual input, and an audio recording button for auditory input. Each segment handles a specific input modality independently, allowing the system to process diverse input types through organized, manageable interface elements rather than a monolithic complex interface.
2Adaptability or versatility
If AI tools process video generation tasks, then creative capabilities are enhanced, but memory capacity limitations are exceeded
Solution Approach 1:
The video generation process is divided into discrete frame processing steps, where the model generates one frame at a time rather than processing the entire video sequence simultaneously. This segmentation reduces the memory burden by breaking down the large-scale video generation task into smaller, manageable units that fit within available memory capacity.
Solution Approach 2:
The system employs iterative generation where the model produces video frames in periodic steps, generating a frame, evaluating it, then proceeding to the next frame. This periodic action allows memory to be released and reallocated between generation steps, enabling video generation capability while managing memory constraints through rhythmic processing cycles.
Data Source
AI summary
The present technology pertains to presenting a storyboard user interface that includes a visual media timeline and a representation of a first frame on the timeline along with a prompt to generate visual media. The input prompt is then sent to a visual media generative response engine, which generates output visual media in response to the input prompt.


