This project addresses the inherent 12-second generation limit of OpenAI's Sora 2 model. It provides a method to create extended, high-quality AI-generated videos by intelligently segmenting user prompts and maintaining visual continuity across sequential generations. Sora Extend is designed for users requiring longer-form video content, offering an automated solution for seamless, extended AI video production.

How It Works

Sora Extend tackles the challenge of Sora 2's 12-second output limit by implementing a robust pipeline centered on prompt deconstruction and sequential video generation. The core innovation lies in its ability to intelligently break down a user's comprehensive prompt into smaller, manageable segments. Each segment is crafted not only to describe the next action or scene but also to incorporate contextual cues derived from the preceding generation, allowing Sora 2 to maintain a "sense of what happened before." These segments are then processed sequentially by Sora 2. A critical component of this process is the capture of the final frame from each generated clip, which is then fed as contextual input into the subsequent generation step. This frame-based context is crucial for ensuring visual consistency and thematic coherence across the entire extended video. The final output is a single, continuous video stream, automatically concatenated from these individual segments, aiming for seamless transitions and an uninterrupted viewing experience.

Highlighted Details

• Enables the generation of Sora 2 videos that significantly exceed the native 12-second limit.
• Achieves visual and thematic continuity across extended video sequences by using the final frame of each generation as context for the next.
• Automates the complex process of concatenating multiple generated clips into one cohesive, long-form video.
• Features intelligent prompt segmentation that breaks down user requests into coherent, context-aware sub-prompts.

Limitations & Caveats

The success of generating extended videos is inherently dependent on the capabilities of the underlying Sora 2 model and the effectiveness of the prompt segmentation algorithm. While the system is designed to produce seamless transitions, the ultimate quality, coherence, and absence of artifacts in longer videos may vary based on prompt complexity, the model's inherent consistency over extended temporal sequences, and potential compounding errors from sequential generation.