arXiv Computer Vision
Analyzing and Improving Fast Sampling of Text-to-Image Diffusion Models
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arXiv AI
Sep 16
Efficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models
The paper introduces an adaptive step schedule controller for text‑to‑image diffusion models, allowing the number of denoising steps to vary based on the complexity of the input prompt. By mixing step schedules of different sizes and monitoring error discrepancies at each timestep, the method switches schedules to maintain image quality while reducing inference time. Experiments on COCO and DiffusionDB demonstrate that this approach achieves faster generation without sacrificing visual fidelity.
arXiv Machine Learning
Aug 19
Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization
The paper introduces Optimizing Your Sampling (OYS), a method that treats diffusion model timestep selection as a black-box optimization problem and uses Bayesian optimization to directly improve the target metric. OYS outperforms default schedules and the Align Your Steps approach on text-to-image generation, inpainting, and other image tasks, as shown by both quantitative and human evaluations. It works without additional training, applies to distilled models, and enhances both simple and sophisticated samplers, achieving 89–94% of a 50‑step schedule’s quality with only 5 steps and a tenfold reduction in inference cost.
arXiv Machine Learning
Oct 5
Adaptive Second-Order Solvers for Fast Stochastic Diffusion Sampling
The paper introduces adaptive second‑order solvers for diffusion models, employing a proportional‑integral (PI) step‑size controller based on a diffusion noise‑normalised error estimator. Unlike existing adaptive methods that react only to the current error, the PI solver incorporates past error information, producing smoother step adaptations. The authors also demonstrate that per‑sample trajectories share structure, allowing aggregation into a fixed schedule that largely preserves adaptive benefits. Experiments on natural‑image and language datasets show that the fixed schedule outperforms the commonly used EDM schedule for image sampling with the stochastic Heun sampler, and that the PI adaptive solver achieves lower FID than most baselines, though it does not surpass the EDM‑churn sampler at low neural‑network‑evaluation counts. In language diffusion, the PI solver improves perplexity over EDM and entropy schedules at low‑to‑medium NFE, albeit with reduced token entropy. The study concludes that per‑sample adaptivity is highly beneficial for simple 1D toy problems but only marginally useful for image and language data, where an average schedule can sometimes outperform the PI‑adaptive approach.
arXiv AI
Jul 1
OTCache: Optimal Transport for Geometry-Aware Caching in Diffusion Models
arXiv:2606. 31026v1 Announce Type: cross Abstract: We propose OTCache, a training-free framework for accelerating diffusion sampling via caching schedule prediction.
arXiv Machine Learning
Jul 2
Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling
arXiv:2607. 00773v1 Announce Type: new Abstract: Discrete diffusion models are widely used for learning and generating discrete distributions.
arXiv Machine Learning
Sep 30
Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps
arXiv:2602.12624v2 Announce Type: replace Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited by h...