A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces (2211.07292v2)

Abstract: Recent advancements in the domain of text-to-image synthesis have culminated in a multitude of enhancements pertaining to quality, fidelity, and diversity. Contemporary techniques enable the generation of highly intricate visuals which rapidly approach near-photorealistic quality. Nevertheless, as progress is achieved, the complexity of these methodologies increases, consequently intensifying the comprehension barrier between individuals within the field and those external to it. In an endeavor to mitigate this disparity, we propose a streamlined approach for text-to-image generation, which encompasses both the training paradigm and the sampling process. Despite its remarkable simplicity, our method yields aesthetically pleasing images with few sampling iterations, allows for intriguing ways for conditioning the model, and imparts advantages absent in state-of-the-art techniques. To demonstrate the efficacy of this approach in achieving outcomes comparable to existing works, we have trained a one-billion parameter text-conditional model, which we refer to as "Paella". In the interest of fostering future exploration in this field, we have made our source code and models publicly accessible for the research community.

• The paper introduces a novel sampling scheme that uses token renoising to iteratively refine image synthesis conditioned on text and images.
• It leverages a convolution-based model in a quantized latent space to generate high-fidelity images in only 12 sampling steps with competitive FID scores.
• The study demonstrates that reducing sampling steps lowers computational demand, potentially democratizing generative AI for broader applications.

A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces: An Expert Overview

The paper "A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces" introduces a streamlined approach for text-to-image synthesis that diverges from the predominant transformer and diffusion-based methods. The authors propose a convolution-based model named Paella, which demonstrates impressive efficiency in generating high-fidelity images using a significantly reduced number of sampling steps compared to traditional methods.

The proposed method operates within a quantized latent space using a Vector Quantized Generative Adversarial Network (VQGAN), allowing for image encoding and decoding with modest spatial compression. By employing a convolutional paradigm rather than a transformer-based architecture, the model circumvents the quadratic memory expansion typical of transformers, facilitating the preservation of intricate image details while minimizing computational overhead.

Paella's distinctive contribution is its novel sampling approach, which utilizes token renoising rather than the conventional masking strategy. This deviation allows for iterative refinement of predictions during the sampling sequence, enhancing model accuracy. The model requires as few as 12 sampling steps to achieve visually appealing results, representing a substantial reduction in computational demand compared to existing state-of-the-art techniques.

Quantitatively, Paella demonstrates competitive performance with a zero-shot Fréchet Inception Distance (FID) score of 11.07 on the COCO dataset, achieved with only 12 sampling steps. This efficiency is noteworthy considering the model consists of a modest one billion parameters, significantly smaller than some of its state-of-the-art counterparts. The paper also explores the impact of classifier-free guidance (CFG) weight and the number of sampling steps on the FID and CLIP scores, revealing interesting insights into model performance and conditional alignment.

The implications of this research are twofold. Practically, the proposed sampling scheme democratizes the accessibility of generative AI technology by simplifying both the training paradigm and the sampling process, encouraging broader implementation across diverse sectors. Theoretically, the paper challenges conventional heuristics regarding the correlation between sampling steps and model performance, stimulating further exploration into efficient generative models. Future developments in AI could explore the integration of this method with other emerging techniques to refine and expand the capabilities of text-to-image synthesis systems.

This paper represents a significant stride in the field of text-to-image synthesis, offering a refreshing perspective with its innovative approach. By making the source code and model weights publicly accessible, the authors encourage future exploration and enhancement, contributing to the collective advancement of generative AI technology.