Text-to-Video Latent Compression for Long 720p Video Generation
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Solution Overview
Problem
Existing generative AI systems for video generation are computationally expensive and slow due to the large size of video and image data, particularly in training and inference processes, which is exacerbated by the high dimensionality of latent spaces used in variational auto-encoders.
Innovation Solution
A video-specific variational auto-encoder (VAE) with spatial and temporal compression is combined with a video diffusion transformer (vDiT) to reduce the token length of latent representations, allowing for efficient training and inference of long videos, using a video encoder, text encoder, and video decoder framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video data is processed at high spatial resolution (720p) with large frame count (100+ frames), then video quality is improved, but computational cost and processing time increase exponentially
Solution Approach 1:
The video is segmented into multiple clips of manageable length (e.g., 16 frames each), which are processed independently through the VAE and diffusion model. This segmentation allows the system to handle high-resolution video data in smaller batches, reducing memory requirements and computational overhead while maintaining overall video quality through proper clipping and concatenation of generated segments.
Solution Approach 2:
The system transforms the video data from pixel space to latent space through the VAE encoder, effectively reducing the dimensionality of the data. By operating in this compressed latent representation rather than full-resolution pixel space, the diffusion model can process long videos with high spatial resolution without the exponential computational cost that would otherwise be required.
2Measurement precision
If the latent space dimensionality is increased to preserve video details, then reconstruction accuracy is improved, but the token length and processing complexity increase
Solution Approach 1:
The system applies partial compression by retaining only the most essential latent representations needed for video generation. Rather than preserving all possible video details through high-dimensional latent spaces, the VAE is configured with compression ratios that balance reconstruction quality with processing efficiency, discarding redundant information while maintaining critical visual features.
Solution Approach 2:
The system dynamically adjusts latent space dimensionality and compression parameters based on the specific video processing requirements. By modifying the VAE architecture parameters (such as encoder/decoder filter sizes and latent dimensions), the system optimizes the balance between reconstruction accuracy and processing complexity for different video resolutions and durations.
Data Source
AI summary
Embodiments described herein provide a generation model comprising a video-specific variational auto-encoder (VAE) for effective compression of video pixel information with reduced spatial and temporal dimensions and a video diffusion transformer (vDiT) to generate latent representations of frames. Specifically, the VAE may, instead of encoding each frame independently, incorporate both temporal and spatial compression. This significantly decreases the token length, improves the computational cost of training and inference, and facilitates the generation of long videos. The encoded training video, in the form of latent representations from a VAE encoder may then be passed to the vDiT to reconstruct the latent representations during training. The trained vDiT may then generate latent representations of a video in response to a text input, and the latent representations may be converted to a video output by a VAE decoder.