Diffusion Transformer Video Generation in Compressed Latent Space
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Solution Overview
Problem
Conventional systems struggle to generate high-resolution and high-frame-rate videos efficiently, often requiring cascaded diffusion models that suffer from limited detail and quality due to low-resolution generation, and mismatched training and test distributions.
Innovation Solution
A single latent diffusion model processes video frames through latent representations, downsampling to a compressed latent space, and leveraging neural network blocks on a shorter sequence of tokens to generate high-quality videos with reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a cascaded diffusion model is used to generate high resolution videos, then spatial resolution can be improved, but manufacturing precision deteriorates due to limited detail generation capability
Solution Approach 1:
The video generation process is segmented into two distinct stages: a first diffusion model generates lower resolution video frames to establish temporal coherence, while a second diffusion model generates higher resolution video frames to achieve detailed quality. This segmentation allows each model to specialize in different resolution requirements, resolving the contradiction between spatial resolution and detail generation quality.
2Measurement precision
If a cascaded diffusion model is used to generate high frame rate videos, then temporal resolution can be improved, but device complexity increases
Solution Approach 1:
The temporal resolution improvement is achieved by segmenting the generation process into two models with different temporal characteristics. The first model operates at lower temporal resolution to establish base temporal coherence, while the second model enhances temporal resolution for final output. This segmentation avoids the complexity of a single cascaded model while maintaining high frame rate capability.
3Productivity
If extensive compression is applied to reduce computational resources, then productivity can be improved, but loss of information increases
Solution Approach 1:
The computational process is segmented into two passes: the first diffusion model processes compressed representations to establish temporal coherence efficiently, while the second diffusion model processes the full-resolution data to preserve video quality. This segmentation allows aggressive compression during the computationally intensive first pass while maintaining quality in the final output pass.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a temporal sequence of data elements conditioned on an input. One of the methods includes obtaining the input, wherein the input comprises a noise input comprising a plurality of latent representations for the output temporal sequence; updating each latent representation using a latent denoising neural network; and generating the output temporal sequence of data elements by processing the updated latent representations using a decoder neural network.