Autoregressive Image Generation using Residual Quantization (2203.01941v2)

Abstract: For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. In this study, we propose the two-stage framework, which consists of Residual-Quantized VAE (RQ-VAE) and RQ-Transformer, to effectively generate high-resolution images. Given a fixed codebook size, RQ-VAE can precisely approximate a feature map of an image and represent the image as a stacked map of discrete codes. Then, RQ-Transformer learns to predict the quantized feature vector at the next position by predicting the next stack of codes. Thanks to the precise approximation of RQ-VAE, we can represent a 256$\times$256 image as 8$\times$8 resolution of the feature map, and RQ-Transformer can efficiently reduce the computational costs. Consequently, our framework outperforms the existing AR models on various benchmarks of unconditional and conditional image generation. Our approach also has a significantly faster sampling speed than previous AR models to generate high-quality images.

Autoregressive Image Generation using Residual Quantization

The paper at hand presents a novel approach to autoregressive image generation, introducing a two-stage framework comprised of a Residual-Quantized Variational Autoencoder (RQ-VAE) and an RQ-Transformer. The authors aim to address limitations in existing vector quantization (VQ) methods for high-resolution image generation, primarily their inability to effectively compress and accurately reconstruct images using a limited codebook size.

Key Findings and Contributions

The core innovation in this work is the Residual Quantization (RQ) technique applied within the RQ-VAE. This method stands out by employing a recursive quantization process that allows for the compressive representation of feature maps using a fixed-size codebook while ensuring a high degree of fidelity in image reconstructions. Unlike traditional VQ approaches that necessitate exponentially larger codebooks to maintain quality at lower resolutions, the RQ method recursively refines the feature map in a coarse-to-fine manner using a predefined depth, denoted as $D$. This capability enables the representation of high-resolution images, such as 256×256, with markedly reduced computational requirements.

The RQ-VAE is complemented by the RQ-Transformer, which incorporates an innovative strategy for predicting and generating image codes. This component benefits from the reduced code map size facilitated by the RQ-VAE, managing more efficient learning and inference processes. The design of the RQ-Transformer encompasses a spatial transformer and a depth transformer, which collectively handle spatial and depth-wise attention with improved efficacy. Consequently, the model achieves a significant improvement in reducing the costs associated with autoregressive modeling, allowing for faster sampling speeds and enhanced overall performance.

Numerical Results

The paper demonstrates strong empirical results, showcasing the superiority of the proposed method across various datasets and benchmarks. When evaluated on datasets such as LSUN Cats, Bedrooms, and Churches, as well as FFHQ, RQ-Transformer shows notable improvements in image quality compared to other autoregressive and generative adversarial network (GAN) models, achieving more competitive Frechet Inception Distance (FID) scores. The model's performance on ImageNet and CC-3M further substantiates its effectiveness, with impressive results in both class-conditioned and text-conditioned image generation tasks. This is particularly evident in the improved initialization and sampling techniques that mitigate exposure bias and facilitate efficient exploration of the latent space.

Implications and Future Directions

The introduction of RQ in the context of image generation has important implications for both theoretical advancements and practical applications. The ability to generate high-quality images with reduced computational resources aligns well with the ongoing need for scalable and efficient machine learning models, especially in scenarios requiring real-time generation capabilities.

Looking forward, there are several potential avenues for future research based on these findings. Exploring deeper integration of residual quantization into other forms of generative modeling could yield further performance improvements. Additionally, while the authors acknowledge the enhanced capabilities of their approach, future work could explore the regularization of autoregressive models to prevent overfitting, especially on small datasets. Scaling up the model and datasets for zero-shot learning applications, akin to other large transformer-based models, represents another promising direction. Furthermore, integrating bidirectional context modeling could expand the applicability of autoregressive models to tasks beyond generation, such as image inpainting and editing.

In conclusion, this work makes a substantive contribution to the field of autoregressive image generation by innovatively combining residual quantization with an efficient transformer model. The advancements shown here suggest a promising path toward more resource-efficient and high-fidelity image synthesis frameworks.