Diffusion Transformer Video Generation With Memory Vectors
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Solution Overview
Problem
Conventional systems struggle to generate long temporal sequences, such as videos, with high quality and coherence due to computational constraints and error accumulation, especially when conditioned on a small number of previous frames, leading to rapid quality degradation.
Innovation Solution
A latent denoising neural network is used to generate long temporal sequences by conditioning on a memory vector representing preceding segments, employing a diffusion Transformer architecture to update latent representations efficiently, reducing computational resources and maintaining quality over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If conventional diffusion models are used to generate videos autoregressively, then video generation is possible, but the window size is limited to a small number of previous frames due to computational constraints, resulting in limited long-term consistency
Solution Approach 1:
The patent extracts only the essential temporal context information from preceding video frames and stores it in a memory buffer, rather than processing all previous frames. This selective extraction allows the model to maintain long-term consistency without being burdened by the full computational complexity of processing entire video sequences autoregressively.
Solution Approach 2:
The patent changes the parameter representation from raw video frames to compressed temporal context vectors stored in memory. This parameter transformation reduces the dimensional complexity while preserving the essential temporal information needed for long-term consistency in video generation.
2Reliability
If autoregressive video generation is used with extended window sizes, then long-term consistency improves, but computing time and resources increase significantly
Solution Approach 1:
The patent performs preliminary processing of video frames by encoding them into compressed temporal context representations and storing them in memory before the autoregressive generation process. This preliminary action reduces the computational burden during actual video generation while maintaining access to long-term temporal information for consistency.
Solution Approach 2:
The patent introduces a memory buffer as an intermediary structure that stores compressed temporal context between the input video frames and the autoregressive generation process. This intermediary allows the model to access long-term temporal information without directly processing all intermediate frames, reducing computing time while maintaining reliability.
3Length of moving object
If autoregressive generation is used for longer videos, then video length increases, but quality degradation accelerates due to error accumulation
Solution Approach 1:
The patent implements a feedback mechanism where the generated video frames are encoded back into temporal context representations and stored in memory for subsequent generation steps. This feedback loop allows the model to maintain long-term temporal consistency and correct errors by referencing the compressed temporal context, thereby reducing quality degradation over extended video lengths.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output temporal sequence of data elements conditioned on an input. In one aspect, a method comprises: obtaining the input, wherein the input comprises a noise input comprising a respective latent representation for each of a plurality of segments of the temporal sequence; updating, for each segment, the latent representation for the segment using a latent denoising neural network, the updating comprising, for each segment other than the first segment: obtaining a memory vector representing one or more hidden states generated by the latent denoising neural network when updating the latent representations for one or more preceding segments; updating the latent representation for the segment at each of a plurality of iterations; and generating the output temporal sequence of data elements by processing the latent representations for the plurality of segments.