DPM-Solver++: Accelerating Guided Sampling for Diffusion Probabilistic Models
The paper "DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models" addresses a significant challenge in the efficiency of guided sampling within the framework of diffusion probabilistic models (DPMs). DPMs have demonstrated substantial success in generating high-resolution images, particularly in the domain of text-to-image synthesis. However, the inherent inefficiency of existing guided sampling methods, which often require extensive computational resources, presents a notable bottleneck. This research proposes DPM-Solver++, a novel high-order solver aiming to improve the speed and quality of guided sampling processes in diffusion models.
Problem Statement
Guided sampling is a pivotal technique enhancing DPMs' sample quality by employing external guidance, often through classifier-free methods or explicit classifier involvement. However, prevalent methods such as DDIM, a first-order solver, necessitate between 100 to 250 function evaluations to yield high-quality samples. This limitation hinders the practical application of DPMs, particularly in scenarios demanding fast and efficient generation.
Methodological Contributions
- Instability in High-Order Solvers: Initial analysis in the paper reveals instability issues in existing high-order solvers when applied to guided sampling with large guidance scales. These solvers often perform worse than first-order methods, such as DDIM, due to their reduced convergence radius in high-guidance settings and mismatch between training and test distributions.
- DPM-Solver++ Development: The core innovation is the introduction of DPM-Solver++, an advanced high-order solver constructed to address the documented shortcomings. By leveraging a data prediction model parameterization, DPM-Solver++ effectively solves diffusion ODEs and improves stability. This approach allows for the straightforward integration of dynamic thresholding methods to counter the "train-test mismatch" issue.
- Multistep Variant: To address numerical stability concerns associated with large guidance scales, a multistep variant of DPM-Solver++ is devised. This version reduces the effective step size, further enhancing the robustness and efficiency of the sampling process.
Experimental Evaluation
Empirical results are robust, demonstrating that DPM-Solver++ can approximate high-quality samples in a mere 15 to 20 steps. This marks a significant reduction in computational demand compared to traditional methods. It consistently outperforms existing approaches in both pixel-space and latent-space domains, across varying degrees of guidance scale. Additionally, ablation studies validate the necessity and effectiveness of each component within DPM-Solver++.
Implications and Future Direction
This research provides a practical solution to the computation-intensive nature of guided sampling in DPMs, greatly enhancing their utility in real-time applications. The introduction of a multistep solver variant suggests new avenues for further reduction in computational overheads. Future work could explore integrating these solvers with an expanded range of guidance models or extending their application into domains beyond image synthesis, such as natural language processing or voice synthesis.
In conclusion, DPM-Solver++ represents an important advancement in optimizing the guided sampling process for diffusion models. It achieves a balance between computational efficiency and sample fidelity, poised to significantly impact the scalability and adoption of diffusion models in commercial and research applications.