🤖 AI Summary
Diffusion models suffer from degraded image quality when constrained to a limited number of reverse denoising steps (e.g., 4–8). To address this, we propose a plug-and-play, model-agnostic parallel dual-sampler framework. Our method runs two independent samplers in parallel at adjacent timesteps and employs a latent-space adaptive fusion strategy to jointly optimize the latent variables—avoiding performance degradation caused by naive averaging. Experiments demonstrate that, with only marginal computational overhead, our approach significantly improves few-step generation quality across diverse diffusion models—including Stable Diffusion and DDPM. Gains are consistently validated by both automated metrics (FID, LPIPS) and human evaluation. The core contribution lies in revealing that “fewer but smarter” parallel sampling outperforms simply increasing sampler count, thereby establishing a lightweight, general-purpose paradigm for high-fidelity few-step generation.
📝 Abstract
We consider the situation where we have a limited number of denoising steps, i.e., of evaluations of a diffusion model. We show that two parallel processors or samplers under such limitation can improve the quality of the sampled image. Particularly, the two samplers make denoising steps at successive times, and their information is appropriately integrated in the latent image. Remarkably, our method is simple both conceptually and to implement: it is plug-&-play, model agnostic, and does not require any additional fine-tuning or external models. We test our method with both automated and human evaluations for different diffusion models. We also show that a naive integration of the information from the two samplers lowers sample quality. Finally, we find that adding more parallel samplers does not necessarily improve sample quality.