Scalable State Space Diffusion Models for Efficient Image Generation

Core Concepts

This paper presents Diffusion State Space models (DiS), a simple and general state space-based framework for efficient image generation using diffusion models. DiS treats all inputs, including time, conditions, and noisy image patches, as concatenated tokens, and adopts a state space backbone to effectively model long-range dependencies.

Abstract

The paper introduces Diffusion State Space models (DiS), a novel architecture for diffusion-based image generation that leverages a state space backbone. The key highlights are:

DiS treats all inputs, including time, conditions, and noisy image patches, as concatenated tokens, and processes them using a state space backbone. This unified approach enables effective modeling of long-range dependencies.

Extensive experiments on unconditional and class-conditional image generation tasks demonstrate that DiS achieves comparable or superior performance to CNN-based and Transformer-based U-Net models, while exhibiting better scalability characteristics.

The authors analyze the scalability of DiS by studying the impact of model depth, width, and input token count. Increasing the model complexity consistently improves the generation quality, as measured by the FID metric.

On the class-conditional ImageNet dataset at 256x256 and 512x512 resolutions, DiS-H/2 achieves state-of-the-art FID scores, outperforming both CNN-based and Transformer-based U-Net diffusion models.

The authors posit that the insights gained from DiS can inform future research on backbone architectures for diffusion models, contributing to advancements in generative modeling across large-scale multimodal datasets.

Stats

The paper provides detailed model configurations and computational complexity analysis:
"Given a sequence X ∈R1×J×D and the default setting E = 2, the computation complexity of a self-attention and SSM operation are delineated as:
mathcal {O}(\text {SA}) = 4JD^2 + 2J^2D,\ mathcal {O}(\text {SSM}) = 3J(2D)N + J(2D)N^2,
where we can see that self-attention is quadratic to sequence length J, and SSM is linear with respect to sequence length J."

Quotes

"Motivated by the successes observed in language modeling with Mamba, a pertinent inquiry arises: whether we can build SSM-based U-Net in diffusion models?"
"Experimentally, we assess the performance DiS across both unconditional and class-conditional image generation tasks. In all settings, DiS demonstrate comparative, if not superior, efficay when juxtaposed with CNN-based or Transformer-based U-Nets of a similar size."
"Moreover, experiments yield impressive results, with DiS achieving a comparable FID scores in class-conditional image generation conducted on ImageNet at a resolution of 256×256 and 512×512."

Key Insights Distilled From

Scalable Diffusion Models with State Space Backbone

by Zhengcong Fe... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2402.05608.pdf

Deeper Inquiries

How can the state space backbone in DiS be further optimized to improve its performance and efficiency for high-resolution image generation tasks?

To enhance the performance and efficiency of the state space backbone in DiS for high-resolution image generation tasks, several optimization strategies can be implemented:

Hierarchical State Space Modeling: Introducing a hierarchical state space structure can help capture multi-scale features in high-resolution images more effectively. By hierarchically organizing the latent states, the model can learn representations at different levels of abstraction, leading to improved image generation quality.

Attention Mechanisms: Integrating attention mechanisms within the state space backbone can enhance the model's ability to focus on relevant image regions during the generation process. Attention can help the model capture long-range dependencies more efficiently, especially in high-resolution images where contextual information is crucial.

Adaptive State Space Dimensionality: Adapting the dimensionality of the state space dynamically based on the complexity of the input image can optimize the model's capacity to represent intricate features. By adjusting the state space dimensionality according to the input image characteristics, the model can achieve better performance and efficiency.

Regularization Techniques: Incorporating regularization techniques such as dropout or weight decay can prevent overfitting and improve the generalization ability of the state space backbone. Regularization helps the model learn robust representations from the data, leading to enhanced performance on high-resolution image generation tasks.

Parallel Processing: Implementing parallel processing techniques can accelerate the computation within the state space backbone, especially for high-resolution images. Utilizing parallelization strategies can distribute the computational load efficiently, leading to faster inference and training times for large-scale image generation tasks.

How could the DiS framework be adapted to enable efficient and high-quality generation of diverse real-world images across a broader range of datasets and applications?

The DiS framework can be adapted and extended in the following ways to enable efficient and high-quality generation of diverse real-world images across a broader range of datasets and applications:

Multi-Modal Condition Handling: Extend the DiS architecture to handle multi-modal conditions beyond class labels, such as textual descriptions or additional image modalities. By incorporating diverse input modalities, the model can generate images that align with complex and varied conditioning information, enhancing its versatility across different datasets and applications.

Transfer Learning and Fine-Tuning: Implement transfer learning techniques to pre-train the DiS model on large-scale datasets and fine-tune it on specific real-world image datasets. By leveraging pre-trained models and fine-tuning them on target datasets, the model can adapt to different data distributions and generate high-quality images across diverse domains.

Data Augmentation and Augmented Training: Introduce data augmentation strategies and augmented training methodologies to enhance the model's robustness and generalization capabilities. By augmenting the training data with diverse transformations and perturbations, the model can learn more robust representations and generate high-quality images across a broader range of scenarios.

Domain-Specific Architectural Modifications: Tailor the DiS architecture to specific domains or applications by incorporating domain-specific architectural modifications. Customizing the model architecture based on the characteristics of the target dataset can improve its performance and efficiency in generating real-world images that meet domain-specific requirements.

Ensemble and Diversity: Explore ensemble learning techniques and diversity-promoting strategies to enhance the diversity and quality of generated images. By combining multiple DiS models or introducing diversity-promoting objectives during training, the model can generate a wider range of realistic and diverse images across different datasets and applications.