Latent Diffusion Transformer for High-Resolution Video Generation
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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 result in limited quality 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
1Manufacturing precision
If a cascaded diffusion model is used to generate high-resolution videos, then the spatial resolution can be improved, but the temporal coherence and quality at final resolution deteriorate due to limited ability to generate highly detailed videos
Solution Approach 1:
The patent merges spatial and temporal processing into a single unified diffusion model that operates on latent representations of video frames. This integrated approach allows the model to simultaneously optimize for both spatial resolution and temporal coherence, eliminating the trade-off present in cascaded models where spatial and temporal processing are separated into different stages.
Solution Approach 2:
The patent transforms the video generation problem into a latent space representation problem by applying compression to convert high-resolution video frames into compressed latent representations. This dimensional transformation enables the diffusion model to operate in a reduced feature space where temporal relationships are preserved, allowing high-resolution generation with improved temporal coherence.
2Manufacturing precision
If a cascaded diffusion model is used to generate high-resolution videos, then the spatial resolution can be improved, but the computational resources and time required increase significantly
Solution Approach 1:
The patent applies compression to transform high-resolution video frames into compressed latent representations, reducing the dimensionality of the data while preserving essential information. This allows the diffusion model to perform computations on a smaller representation, significantly reducing computational time and resource requirements while maintaining high spatial resolution output.
Solution Approach 2:
The patent uses latent representations as compressed copies of the original video frames. By operating on these compressed representations rather than the full-resolution frames throughout the diffusion process, the model achieves the same generative capability with reduced computational complexity, effectively copying the essential features at lower computational cost.
3Adaptability or versatility
If conventional diffusion models are used, then the training and test distributions can be matched, but the quality of generated videos at inference deteriorates due to distribution mismatch
Solution Approach 1:
The patent changes the representation parameters by working in compressed latent space rather than original pixel space. This parameter transformation allows the model to learn patterns in a compressed domain that better generalize to inference time, resolving the distribution mismatch issue while maintaining training-test alignment.
Data Source
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.