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Bigger is not Always Better: Scaling Properties of Latent Diffusion Models (2404.01367)
Published 1 Apr 2024 in cs.CV and cs.LG
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models

Overview

  • The paper investigates how scaling the size of Latent Diffusion Models (LDMs) affects their efficiency in generating quality outputs, covering aspects like pretraining, downstream performance, and the impacts of diffusion samplers and distillation.

  • A study on text-to-image LDMs shows a correlation between model size and performance, but with diminishing returns beyond a certain scale, suggesting optimization opportunities for large models.

  • Smaller models, under certain conditions, can outperform larger ones in generating high-quality outputs efficiently, especially under constrained computational resources.

  • Future research directions are suggested, emphasizing the need for optimized pretraining strategies and the exploration of model and sampling efficiency to fully leverage LDMs' potential in resource varied computational settings.

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.

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Authors (6)
  1. Kangfu Mei (19 papers)
  2. Zhengzhong Tu (32 papers)
  3. Mauricio Delbracio (32 papers)
  4. Hossein Talebi (20 papers)
  5. Vishal M. Patel (203 papers)
  6. Peyman Milanfar (60 papers)