arXiv AI
Jun 16
Region-Adaptive Sampling for Diffusion Transformers
arXiv:2502. 10389v2 Announce Type: replace-cross Abstract: Diffusion models (DMs) have become the leading choice for generative tasks across diverse domains.
SynBoost: A Synergistic Framework for Fast Sampling of Diffusion Models
arXiv:2506. 13058v2 Announce Type: replace-cross Abstract: Diffusion probabilistic models (DPMs) have demonstrated remarkable success in visual generation.
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arXiv Computer Vision
Sep 1
Memory-Efficient Training-Free Acceleration of Diffusion Transformers with BaryCache
arXiv:2608.28670v1 Announce Type: new Abstract: Diffusion Transformers achieve high-fidelity image and video generation, but their iterative sampling remains expensive, for each denoising step requir...
arXiv AI
Jun 2
Efficient Weighted Sampling via Score-based Generative Models
arXiv:2502. 04646v2 Announce Type: replace-cross Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is a fundamental technique with wide-ranging applications in variance reduction, biased sampling, data augmentation, and more.
arXiv AI
Jul 17
Integration Matters: Rollout-Based Training for Constrained Diffusion Models
arXiv:2607. 14398v1 Announce Type: cross Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution.
arXiv Machine Learning
3d ago
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...
arXiv Computer Vision
Aug 25
Bridging Restoration and Generation in One-step Diffusion for Real-World Image Super-Resolution
arXiv:2604.24136v3 Announce Type: replace Abstract: Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohi...
arXiv AI
Jun 2
Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
arXiv:2602. 03211v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent.
arXiv Machine Learning
23h ago
Learned End-to-End Guidance Schedules for Diffusion Models
The paper introduces Learned End-to-End Guidance Schedules (LEEGS) for diffusion models, which train a time‑dependent guidance schedule to balance data quality and requirement satisfaction while reducing sampling steps. LEEGS minimizes the guidance function over a small set of examples using stochastic gradient descent and employs a gradient approximation to cut training time by a factor of four. Experiments on tasks such as image inpainting, noisy image inverse problems, face‑ID‑guided generation, and PDE problems show that LEEGS outperforms baselines at the same computational budget or matches constant guidance with only 10% of the steps.
arXiv AI
Aug 25
ChebBooster: A Training-Free Approach for Efficient Diffusion Transformer Inference via Chebyshev-Inspired Extrapolation
ChebBooster is a training‑free extrapolation framework that accelerates Diffusion Transformers (DiTs) by using Chebyshev polynomial theory. It employs a Barycentric formulation for numerically stable evaluation and separates the process into an offline weight precomputation phase and a lightweight online application stage. Experiments on DiT‑XL/2, PixArt‑Σ, and FLUX.1‑dev show consistent visual quality gains and up to 3.68× latency speedup and 5.12× FLOPs reduction compared to existing training‑free baselines.
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 Computer Vision
Sep 2
ASSERT: Adaptive Stochastic Sampling for Robust Diffusion Models on Analog Compute-in-Memory Hardware
arXiv:2609.00955v1 Announce Type: new Abstract: Diffusion models achieve strong image generation quality but incur high iterative denoising costs. Analog compute-in-memory (CIM) can accelerate matrix...
arXiv AI
Sep 2
Training-Free Refinement of Flow Matching with Divergence-based Sampling
The paper introduces Flow Divergence Sampler (FDS), a training‑free method that refines intermediate states in flow‑matching models by using the divergence of the marginal velocity field to detect and correct misguidance toward low‑density regions. FDS operates during inference, requires no additional training, and can be applied as a plug‑and‑play module with standard solvers and existing flow backbones. Experiments show that FDS consistently improves fidelity in tasks such as text‑to‑image synthesis and inverse problems.