Three Creates All: You Only Sample 3 Steps
arXiv:2603.22375v1 Announce Type: new Abstract: Diffusion models deliver high-fidelity generation but remain slow at inference time due to many sequential network evaluations. We find that standard timestep conditioning becomes a key bottleneck for few-step sampling. Motivated by layer-dependent denoising dynamics, we propose Multi-layer Time Embedding Optimization (MTEO), which freeze the pretrained diffusion backbone and distill a small set of step-wise, layer-wise time embeddings from reference trajectories. MTEO is plug-and-play with existing ODE solvers, adds no inference-time overhead, and trains only a tiny fraction of parameters. Extensive experiments across diverse datasets and backbones show state-of-the-art performance in the few-step sampling and substantially narrow the gap between distillation-based and lightweight methods. Code will be available.
arXiv:2603.22375v1 Announce Type: new Abstract: Diffusion models deliver high-fidelity generation but remain slow at inference time due to many sequential network evaluations. We find that standard timestep conditioning becomes a key bottleneck for few-step sampling. Motivated by layer-dependent denoising dynamics, we propose Multi-layer Time Embedding Optimization (MTEO), which freeze the pretrained diffusion backbone and distill a small set of step-wise, layer-wise time embeddings from reference trajectories. MTEO is plug-and-play with existing ODE solvers, adds no inference-time overhead, and trains only a tiny fraction of parameters. Extensive experiments across diverse datasets and backbones show state-of-the-art performance in the few-step sampling and substantially narrow the gap between distillation-based and lightweight methods. Code will be available.
Executive Summary
This article proposes Multi-layer Time Embedding Optimization (MTEO), a novel approach to improving the inference speed of diffusion models in few-step sampling tasks. By distilling a small set of step-wise, layer-wise time embeddings from reference trajectories, MTEO achieves state-of-the-art performance while adding no inference-time overhead and only training a tiny fraction of parameters. The method is plug-and-play with existing ODE solvers and shows substantial improvement over lightweight methods. Extensive experiments on diverse datasets and backbones demonstrate the efficacy of MTEO, highlighting its potential as a game-changer in the field of diffusion models.
Key Points
- ▸ MTEO distills a small set of step-wise, layer-wise time embeddings from reference trajectories
- ▸ MTEO adds no inference-time overhead and trains only a tiny fraction of parameters
- ▸ MTEO achieves state-of-the-art performance in few-step sampling tasks
Merits
Improved Inference Speed
MTEO significantly accelerates inference without compromising model performance, making it an attractive solution for real-world applications.
Scalability and Flexibility
MTEO's plug-and-play design enables seamless integration with existing ODE solvers, allowing for easy adaptation to diverse datasets and backbones.
Efficient Parameter Training
MTEO trains only a tiny fraction of parameters, reducing the computational burden and making it a more resource-efficient solution.
Demerits
Limited Evaluation of Generalizability
While the article presents extensive experiments on diverse datasets and backbones, the evaluation of MTEO's generalizability to other scenarios remains limited.
Lack of Theoretical Analysis
The article does not provide a comprehensive theoretical analysis of MTEO's dynamics and convergence properties, which may limit its adoption in more complex scenarios.
Expert Commentary
The authors' innovative approach to diffusion models demonstrates a deep understanding of the underlying dynamics and the limitations of current methods. By distilling time embeddings from reference trajectories, MTEO tackles the key bottleneck of sequential network evaluations, achieving state-of-the-art performance while maintaining efficiency. This breakthrough has significant implications for the field, as it enables faster and more scalable inference. However, the article's limitations, such as the lack of theoretical analysis and limited evaluation of generalizability, should be addressed in future work to further solidify MTEO's position as a leading approach.
Recommendations
- ✓ Future research should focus on developing a comprehensive theoretical analysis of MTEO's dynamics and convergence properties.
- ✓ The authors should investigate the generalizability of MTEO to other diffusion-based generative models and more complex scenarios.
Sources
Original: arXiv - cs.LG
