1D Image Latent Tokenization for Scalable Transformer Reconstruction
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Solution Overview
Problem
Existing image generation models face limitations in scaling up image resolution or contextual length due to the assumption of a 2D latent space structure, which restricts the ability to leverage image redundancy and results in constrained latent capacity.
Innovation Solution
A transformer-based framework that tokenizes images into a compact one-dimensional (1D) sequence, utilizing a vision transformer encoder, vector quantizer, and decoder, with a two-stage training paradigm to stabilize training and improve image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a 2D latent space structure is used to represent images, then the mapping relationship with image patches is maintained, but the ability to leverage image redundancy is restricted and latent capacity is constrained
Solution Approach 1:
The patent transforms the traditional 2D latent space structure into a 1D sequence representation. By flattening the 2D latent features into a one-dimensional sequence, the model can leverage transformer architectures more effectively while maintaining the ability to represent image patches. This dimensional change enables better exploitation of image redundancy through self-attention mechanisms that operate across the entire sequence, resolving the contradiction between information loss and structural complexity.
2Quantity of substance
If image resolution or contextual length is increased, then more detailed image representation is achieved, but the constrained latent capacity prevents effective scaling
Solution Approach 1:
The patent introduces a dynamic approach where the latent representation is not fixed in dimensionality but can be flexibly transformed. The 2D latent features are dynamically flattened into 1D sequences, allowing the model to adapt to different image resolutions and contextual lengths. This dynamic transformation enables the latent capacity to scale effectively with increased image detail while maintaining representational flexibility through the transformer architecture.
3Manufacturing precision
If more tokens are used in latent representation, then higher image quality is achieved, but training time and computational resources increase
Solution Approach 1:
The patent replaces the traditional mechanical approach of using more tokens to improve image quality with a transformer-based attention mechanism. Instead of increasing token count linearly with image detail, the self-attention mechanism allows the model to focus computational resources on the most relevant image patches and features. This substitution enables high-quality image reconstruction with fewer tokens by dynamically weighting the importance of different latent features, thereby reducing training time while maintaining or improving reconstruction quality.
Data Source
AI summary
The present disclosure describes techniques for generating latent representations of images using a machine learning model. An image is split and flattened into a series of patches. The series of patches is concatenated with a sequence of latent tokens. The concatenated patches and latent tokens are input into an encoder of the machine learning model. A one-dimensional (1D) latent representation of the image is generated by the encoder. Vector quantization is performed on the 1D latent representation of the image by a vector quantizer of the machine learning model to generate quantized latent tokens. The image is reconstructed based on the quantized latent tokens by a decoder of the machine learning model.