All are Worth Words: A ViT Backbone for Diffusion Models (2209.12152v4)

Abstract: Vision transformers (ViT) have shown promise in various vision tasks while the U-Net based on a convolutional neural network (CNN) remains dominant in diffusion models. We design a simple and general ViT-based architecture (named U-ViT) for image generation with diffusion models. U-ViT is characterized by treating all inputs including the time, condition and noisy image patches as tokens and employing long skip connections between shallow and deep layers. We evaluate U-ViT in unconditional and class-conditional image generation, as well as text-to-image generation tasks, where U-ViT is comparable if not superior to a CNN-based U-Net of a similar size. In particular, latent diffusion models with U-ViT achieve record-breaking FID scores of 2.29 in class-conditional image generation on ImageNet 256x256, and 5.48 in text-to-image generation on MS-COCO, among methods without accessing large external datasets during the training of generative models. Our results suggest that, for diffusion-based image modeling, the long skip connection is crucial while the down-sampling and up-sampling operators in CNN-based U-Net are not always necessary. We believe that U-ViT can provide insights for future research on backbones in diffusion models and benefit generative modeling on large scale cross-modality datasets.

• The paper introduces U-ViT, a ViT-based backbone that treats inputs as tokens and utilizes long skip connections for enhanced image generation.
• The paper demonstrates U-ViT’s competitive performance with FID scores of 2.29 for class-conditional ImageNet and 5.48 for text-to-image MS-COCO tasks.
• The paper challenges the dominance of CNN-based U-Nets by showcasing the scalability and flexibility of transformer architectures in diffusion models.

Overview of "All are Worth Words: A ViT Backbone for Diffusion Models"

This paper introduces a novel approach for enhancing image generation in diffusion models using Vision Transformer (ViT) architectures. Traditionally, convolutional neural networks (CNNs), especially U-Nets, have dominated diffusion models for image generation tasks. The authors propose U-ViT, a ViT-based backbone, which treats inputs such as time, condition, and noisy image patches as tokens, incorporating long skip connections between shallow and deep layers.

Key Contributions

1. ViT Architecture for Diffusion Models: U-ViT employs a ViT architecture, differing from the conventional CNN-based U-Nets. It adapts the transformer methodology by treating multiple facets of the input as tokens.
2. Performance Evaluation: The new architecture is evaluated on unconditional, class-conditional, and text-to-image generation tasks. U-ViT showcases competitive, if not superior, performance compared to similarly sized CNN-based U-Nets.
3. Notable Results: The model achieves impressive Fréchet Inception Distance (FID) scores of 2.29 for class-conditional image generation on ImageNet 256×256 and 5.48 for text-to-image generation on MS-COCO without relying on large external datasets. This highlights the effectiveness of the ViT architecture in diffusion models.
4. Significance of Long Skip Connections: Long skip connections are deemed crucial for performance improvement, while the typical down-sampling and up-sampling operations found in CNN-based U-Nets are not always necessary.

Implications and Future Directions

The research challenges the prevailing assumption that CNN-based architectures are essential for diffusion models in image generation. By demonstrating the efficacy of a transformer-based architecture, this work opens pathways to further explorations in applying transformers to various generative modeling tasks across large-scale and multi-modal datasets.

This shift could lead to substantial changes in how future diffusion models are conceptualized, focusing on scalability and flexibility afforded by transformer architectures. Future research might explore more sophisticated tokenization techniques or investigate alternative ways to leverage the transformer’s attention mechanism for improved generative performance.

In terms of theoretical implications, U-ViT provides evidence supporting the adaptability of transformer-based structures beyond traditional language processing tasks, underscoring their potential versatility across domains.

Conclusion

The work presented in "All are Worth Words: A ViT Backbone for Diffusion Models" offers significant insights into how ViT can be leveraged to possibly surpass conventional CNN-based architectures in diffusion models for image generation. As AI continues to evolve, embracing such innovations could drive advancements in generative modeling, contributing to more effective and efficient algorithms. This contribution underscores a pivotal shift in diffusion models, suggesting transformers could play a central role in future developments.