Vector Quantized Diffusion Model for Text-to-Image Synthesis (2111.14822v3)

Abstract: We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.

Vector Quantized Diffusion Model for Text-to-Image Synthesis

The paper presents the Vector Quantized Diffusion (VQ-Diffusion) model, a novel approach to text-to-image generation. This is achieved by incorporating a vector quantized variational autoencoder (VQ-VAE) with a conditional variant of the Denoising Diffusion Probabilistic Model (DDPM). The paper aims to address notable limitations in existing autoregressive (AR) methods, such as unidirectional bias and error accumulation, by introducing a latent-space approach that includes a mask-and-replace diffusion strategy.

Key Contributions

1. Model Architecture: The VQ-Diffusion model leverages a VQ-VAE to encode images into discrete tokens, which are then used in a diffusion model to gradually denoise data back to original images. By formulating the reverse diffusion process conditioned on text, the model effectively generates images that are semantically aligned with input textual descriptions.
2. Elimination of Unidirectional Bias: In contrast to AR models that predict images using a fixed order, the proposed method uses bidirectional attention. This allows the model to integrate information from the entire image context during prediction, thus removing unidirectional constraints and improving image coherence.
3. Error Mitigation through Mask-and-Replace Strategy: The paper proposes a hybrid diffusion strategy which combines masking with random token replacement. This allows the network to focus on masked areas explicitly while enabling corrections to erroneous tokens, effectively preventing error propagation.
4. Improved Computational Efficiency: Through reparameterization and fast inference strategies, VQ-Diffusion achieves significant improvements in computational efficiency. The model is noted to be fifteen times faster than traditional AR methods, offering a compelling solution for real-time applications.

Experiments and Results

The paper reports extensive experiments over diverse datasets such as CUB-200, Oxford-102, and MSCOCO. VQ-Diffusion shows superior performance in terms of image quality compared to GAN-based and AR text-to-image models. Notably, it can handle complex scenes and generate high-fidelity images with a higher degree of detail and visual realism. Furthermore, the model demonstrates scalability when trained on larger datasets like Conceptual Captions and LAION-400M, maintaining strong performance on specific subset categories.

Implications and Future Work

The VQ-Diffusion model has profound implications for both theoretical and practical domains:

• Theoretical: This work challenges the adequacy of current AR models, paving the way for potentially redefining paradigms in text-to-image generation. The mask-and-replace diffusion strategy introduces a novel method to counteract common issues such as error accumulation and unidirectional biases.
• Practical: By substantially enhancing inference speed with minimal compromise on image quality, VQ-Diffusion offers practical adaptability for applications requiring rapid synthesis of high-quality images.

Future research could explore further optimization of the diffusion process, improvements in model scaling for larger datasets, and potential extensions to other domains such as video generation or more sophisticated scene constructions. Additionally, integrating more complex text comprehension mechanisms could also enhance the model's ability to capture nuanced textual cues.

Overall, the VQ-Diffusion model represents a significant advancement in the field of text-to-image synthesis, providing a versatile, efficient, and high-quality generative framework.