Accelerated Diffusion Models via Speculative Sampling (2501.05370v1)

Abstract: Speculative sampling is a popular technique for accelerating inference in LLMs by generating candidate tokens using a fast draft model and accepting or rejecting them based on the target model's distribution. While speculative sampling was previously limited to discrete sequences, we extend it to diffusion models, which generate samples via continuous, vector-valued Markov chains. In this context, the target model is a high-quality but computationally expensive diffusion model. We propose various drafting strategies, including a simple and effective approach that does not require training a draft model and is applicable out of the box to any diffusion model. Our experiments demonstrate significant generation speedup on various diffusion models, halving the number of function evaluations, while generating exact samples from the target model.

• The paper extends speculative sampling to continuous diffusion models, significantly reducing the computational cost of generative tasks.
• The paper evaluates multiple drafting strategies, including a frozen target draft model that accelerates sampling without retraining.
• The paper demonstrates, through rigorous theoretical and experimental analysis on datasets like CIFAR10, that efficiency gains are achieved while maintaining sample quality.

Accelerated Diffusion Models via Speculative Sampling

The paper "Accelerated Diffusion Models via Speculative Sampling" addresses the challenge of reducing the computational expense associated with denoising diffusion models (DDMs), which are utilized for generative tasks across various domains such as image, audio, music, and video generation. These models usually demand multiple function evaluations when simulating the reverse process of a Gaussian distribution to the data distribution. The work builds upon speculative sampling, a technique originally designed to enhance the efficiency of LLMs by utilizing a draft model to generate candidate samples quickly and a subsequent target model to validate and refine these samples.

Key Contributions

1. Extension to Continuous Diffusion Models: The speculative sampling technique, previously applied to discrete sequences in LLMs, is adapted here to the continuous context of diffusion models. This involves leveraging a fast draft model to propose a sequence of states in the diffusion chain, which are then validated against a computationally extensive target diffusion model.
2. Drafting Strategies: The authors explore several drafting strategies centered around diffusion models:
   • The first uses a simplified diffusion model requiring additional resources for training independently from the target model.
   • The second utilizes a speculative approach based solely on the target model. This technique, called "frozen target draft model," applies the target model's initial state across future states, allowing for rapid generation without retraining.
3. Efficient Implementation of Speculative Sampling: The target and draft states are coupled using a maximal coupling strategy known as reflection maximal coupling. Unlike typical naive implementations, which can significantly diminish efficiency, the paper proposes a deterministic approach to achieve coupling, enhancing performance by reducing target model evaluations whilst ensuring samples remain true to the intended distribution.
4. Complexity and Theoretical Analysis: The authors conduct a rigorous complexity analysis that demonstrates how speculative sampling, under certain conditions, can result in substantial efficiency gains. They also present a lower bound on the acceptance ratio, shedding light on the relationships between draft and target models and the gain in acceptance probability with increasing approximation quality.

Experimental Validation and Implications

The paper provides empirical results on various datasets, including CIFAR10, LSUN, and robotics data, demonstrating a reduction in target model evaluations by over half without a loss in sample quality. This is substantiated by metrics such as Fréchet Inception Distance (FID), Inception Score (IS), and other context-specific measures.

The speculative sampling approach substantially impacts the field of generative modeling by enhancing the scalability of diffusion models, making them applicable to larger datasets and more complex generative tasks. It offers a robust method for integrating cheaper, pre-existing models in a complementary manner to high-quality targets without the need for rigorous retraining. The implications of this method suggest potential for further innovations and applications in large-scale AI systems.

Future Directions

This research opens several new directions for exploration:

• Broader Application: Extending the speculative sampling methodology to other generative settings and models beyond diffusion models, such as GANs or autoencoders.
• Optimization and Adaptation: Fine-tuning the balance between draft efficiency and acceptance rates, and exploring alternate coupling strategies that might further streamline speculative sampling.
• Hybrid Models: Integrating speculative sampling with other acceleration techniques, such as neural network distillation or parallelization methodologies, to achieve even greater efficiencies.

In conclusion, the paper offers a comprehensive framework for speculative sampling in diffusion models, providing both theoretical insights and practical results that significantly reduce computation time while maintaining quality, encouraging the scalable deployment of diffusion models across various domains.