Matryoshka Diffusion Models (2310.15111v2)

Abstract: Diffusion models are the de facto approach for generating high-quality images and videos, but learning high-dimensional models remains a formidable task due to computational and optimization challenges. Existing methods often resort to training cascaded models in pixel space or using a downsampled latent space of a separately trained auto-encoder. In this paper, we introduce Matryoshka Diffusion Models(MDM), an end-to-end framework for high-resolution image and video synthesis. We propose a diffusion process that denoises inputs at multiple resolutions jointly and uses a NestedUNet architecture where features and parameters for small-scale inputs are nested within those of large scales. In addition, MDM enables a progressive training schedule from lower to higher resolutions, which leads to significant improvements in optimization for high-resolution generation. We demonstrate the effectiveness of our approach on various benchmarks, including class-conditioned image generation, high-resolution text-to-image, and text-to-video applications. Remarkably, we can train a single pixel-space model at resolutions of up to 1024x1024 pixels, demonstrating strong zero-shot generalization using the CC12M dataset, which contains only 12 million images. Our code is released at https://github.com/apple/ml-mdm

Advancements in High-Resolution Image and Video Synthesis with Matryoshka Diffusion Models

Overview

The field of generative models has taken a significant stride with the development of Matryoshka Diffusion Models (MDM), focusing on generating high-resolution images and videos. Embracing a novel architecture and process, MDM pioneers a path in the synthesis domain, diverging from the traditional cascaded or latent diffusion models. This advancement is instrumental for applications requiring end-to-end high-resolution generation without the added complexity of multi-stage training or inference pipelines.

Key Contributions

Multi-Resolution Diffusion Process

At the core of MDM is a multi-resolution diffusion process that capitalizes on the hierarchical structure of data formation. This approach allows for the joint denoising of inputs across multiple resolutions, facilitated by a novel architecture termed Nested UNet. This architecture is crucial for enabling efficient generation across scales, significantly enhancing optimization for high-resolution content creation.

Progressive Training

A notable innovation within MDM is the integration of a progressive training schedule. This strategy initiates the training with low-resolution models progressively incorporating higher-resolution inputs. This phased training not only improves computational efficiency but also substantially boosts the model quality and convergence speed. Empirical results underscore the advantage of this approach, particularly in balancing training costs and model performance.

Empirical Validation

MDM has been rigorously evaluated across various benchmarks including class-conditioned image generation, and text-to-image as well as video generation. The model's performance is noteworthy, especially when trained on the CC12M dataset, achieving competitive results in high-resolution synthesis without necessitating the large data requirements typically associated with such tasks. Moreover, the model demonstrates robust zero-shot capabilities, further indicating its general applicability to video generation tasks.

Theoretical and Practical Implications

The introduction of MDM steers the conversation towards the efficiency and scalability of diffusion models for high-resolution synthesis. Its architectural design and training methodology offers a finer understanding of how multi-resolution processing can be harnessed, suggesting a potential paradigm shift in generative model training. Practically, MDM paves the way for more resource-efficient models capable of producing diverse, high-fidelity outputs, a critical development for fields such as digital content creation, medical imaging, and more.

Future Directions

While MDM represents a significant advancement, its exploration opens numerous avenues for future research. Further refinement of the Nested UNet architecture, exploration of alternative weight sharing mechanisms, and the integration of autoencoder-based approaches within the MDM framework are some of the potential areas for expansion. Additionally, investigating the model's applicability to other types of data beyond images and videos can broaden its utility across different AI domains.

Conclusion

Matryoshka Diffusion Models mark a pivotal development in the landscape of generative AI, providing an efficient, scalable method for high-resolution synthesis. This innovation underscores the evolving capabilities of diffusion models, moving closer to practical, real-world applications that necessitate high fidelity and diverse content generation. As the community delves deeper into the possibilities offered by MDM, the trajectory of generative models appears poised for remarkable transformations.