Bigger is not Always Better: Scaling Properties of Latent Diffusion Models (2404.01367v2)

Abstract: We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency. While improved network architecture and inference algorithms have shown to effectively boost sampling efficiency of diffusion models, the role of model size -- a critical determinant of sampling efficiency -- has not been thoroughly examined. Through empirical analysis of established text-to-image diffusion models, we conduct an in-depth investigation into how model size influences sampling efficiency across varying sampling steps. Our findings unveil a surprising trend: when operating under a given inference budget, smaller models frequently outperform their larger equivalents in generating high-quality results. Moreover, we extend our study to demonstrate the generalizability of the these findings by applying various diffusion samplers, exploring diverse downstream tasks, evaluating post-distilled models, as well as comparing performance relative to training compute. These findings open up new pathways for the development of LDM scaling strategies which can be employed to enhance generative capabilities within limited inference budgets.

• The paper reveals that scaling latent diffusion models boosts performance until diminishing returns appear beyond a compute threshold.
• It finds that extensive pretraining is key to downstream success, while smaller models can deliver superior sampling efficiency under fixed budgets.
• The study confirms that various diffusion samplers and distillation strategies exhibit consistent scaling trends, highlighting optimization opportunities.

Scaling Properties of Latent Diffusion Models: Insights and Implications

Introduction

Latent Diffusion Models (LDMs) have demonstrated significant potential in generating high-quality outputs across a range of generative tasks. A key area of interest is understanding how scaling model size impacts sampling efficiency. Our comprehensive analysis covers various aspects, including pretraining and downstream task performance, the influence of different diffusion samplers, and the effects of diffusion distillation.

Scaling Text-to-Image Performance

Our findings, derived from training 12 text-to-image LDMs ranging from 39M to 5B parameters, demonstrate a clear correlation between training compute and model performance. However, we observe diminishing returns beyond a certain compute threshold. This suggests potential scalability of LDMs with increased compute allocation. Crucially, models below 1G of training compute exhibited the most pronounced scalability in terms of performance improvement. Further scaling revealed that while larger models continue to outperform smaller counterparts, the rate of improvement is not linear, suggesting optimization opportunities in model architecture or training protocols for large-scale models.

Downstream Task Scaling

LDMs' performance in downstream tasks, such as real-world super-resolution and personalized text-to-image synthesis, also correlates with pretraining scale. Despite attempts to compensate with additional downstream training, smaller models fail to match the performance achieved by larger models pre-trained with more extensive datasets. This underscores the pivotal role of pretraining in establishing a foundational capability, which downstream tasks refine rather than fundamentally alter.

Sampling Efficiency Insights

Examining sampling efficiency across model sizes under equivalent inference budgets reveals that smaller models can outperform larger models in generating high-quality results. This counterintuitive finding suggests that smaller models might offer a more efficient pathway to high-quality generative outputs, especially under constrained computational budgets. Moreover, our analysis extends to different diffusion samplers and distilled LDMs, confirming that these trends hold across various configurations and optimization strategies.

Implications and Future Directions

Our systematic exploration into the scaling properties of LDMs uncovers several critical insights:

• Pretraining Scale as a Foundation: High-quality pretraining remains a cornerstone for advanced model performance in both direct generative tasks and downstream applications. This points to the importance of optimizing pretraining strategies to maximize the utility of available compute resources.
• Efficiency of Smaller Models: The observed efficiency of smaller models in certain contexts challenges the prevailing assumption that larger models invariably yield better results. This efficiency, especially under tight inference budgets, opens up new optimization avenues for deploying LDMs in resource-constrained environments.
• Sampler and Distillation Strategy Robustness: The consistency of scaling trends across different samplers and distillation approaches underscores the inherent properties of LDMs that transcend specific optimization techniques. Future research might explore how these properties can be leveraged to develop even more efficient training and inference methodologies for LDMs.

Conclusion

The implications of our findings are twofold: Practically, they offer a roadmap for more efficient deployment of LDMs in varied computational environments. Theoretically, they prompt a reassessment of scaling strategies for generative models, suggesting that optimization cannot be approached with a one-size-fits-all mentality. As we continue to push the boundaries of what LDMs can achieve, integrating these insights will be crucial in harnessing their full potential.