arXiv AI
TraceSampler: Making Sampling Errors Predictable and Correctable for Accelerated Generative Sampling
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arXiv Computer Vision
1d ago
Accelerating Rectified Flow Models via Trajectory-Aware Caching
arXiv:2605.16789v2 Announce Type: replace Abstract: Diffusion and rectified flow (RF) models generate high-fidelity images and videos, but their iterative velocity-field evaluations are computational...
arXiv Computer Vision
Oct 12
A Lightweight Velocity Correction Framework for Improving Instruction Following in Few-Step Text-to-Image Generation
The paper introduces a lightweight correction framework that improves instruction following in few-step text-to-image generators without altering the generator’s parameters or increasing forward-pass count. The framework adds a small corrector module (<20 M parameters) that learns residual velocity adjustments at selected sampling steps, using On‑Trajectory Flow‑Bridge Supervision to target the actual states reached during corrected generation. Experiments show a +17.60‑point NetWin over a frozen baseline with only a 0.84% increase in end‑to‑end latency.
arXiv AI
Jun 19
Bi-Anchor Interpolation Solver for Accelerating Generative Modeling
arXiv:2601. 21542v3 Announce Type: replace-cross Abstract: Flow Matching (FM) models have emerged as a leading paradigm for high-fidelity synthesis.
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 Computer Vision
Oct 23
ParaLin++: Accelerating Parallel Diffusion Integrator via Intrinsic Partially Linear Structure
ParaLin++ is a training‑free, step‑parity parallel sampler that accelerates diffusion model sampling by preserving the full‑step discretization while removing the solver’s temporal dependency. The authors first review quality metrics for training‑free sampling and show that many step‑shortening methods lose quality on semantic‑composition and long‑text benchmarks, whereas parallel sampling remains near‑lossless. Experiments on Stable Diffusion‑v2, LSUN Church, FLUX.1‑dev, and Ming‑Flash‑Omni 2.0 demonstrate speedups of 2.6×–4.8×, 1.68×, and 1.21× respectively while maintaining the full‑step Euler quality profile.
arXiv AI
Aug 12
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.