Parallel Diffusion Solver for Faster Image Generation
Analysis
This paper addresses the critical issue of slow sampling in diffusion models, a major bottleneck for their practical application. It proposes a novel ODE solver, EPD-Solver, that leverages parallel gradient evaluations to accelerate the sampling process while maintaining image quality. The use of a two-stage optimization framework, including a parameter-efficient RL fine-tuning scheme, is a key innovation. The paper's focus on mitigating truncation errors and its flexibility as a plugin for existing samplers are also significant contributions.
Key Takeaways
- •Proposes EPD-Solver, a novel ODE solver for faster diffusion model sampling.
- •Employs parallel gradient evaluations to mitigate truncation errors and improve image quality.
- •Introduces a two-stage optimization framework with a parameter-efficient RL fine-tuning scheme.
- •Offers flexibility as a plugin (EPD-Plugin) for existing ODE samplers.
Reference
“EPD-Solver leverages the Mean Value Theorem for vector-valued functions to approximate the integral solution more accurately.”