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版本更新 · v8arXiv:2603.26783

Can We Change "Stroke Size" to Improve Flow Matching?

Yunwei Bai, Ying Kiat Tan, Tsuhan Chen

原文摘要

Deep-learning-based image generation models are useful but difficult to train. Inspired by the varying stroke sizes used in human painting, we hypothesize that applying ``rough strokes'' can attenuate prediction biases and improve flow-matching image generation. We develop MultiStroke, which progressively varies the spatial granularity of the clean-signal component in the velocity regression target. A lightweight corrector aligns adjacent roughness levels during sampling. On ImageNet-1K, MultiStroke reduces FID by $22.5\%$ relative to a matched flow-matching baseline, despite the limited corrector capacity. Code is available at https://github.com/DaChengRuoQue/MultiStroke-Code. A quick video explanation is available at https://dachengruoque.github.io/MultiStroke-Web/.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05407

PEEK: One-Step-Look-Ahead Exposure Bias Correction for Diffusion Sampling

Yunwei Bai, Ying Kiat Tan, Tsuhan Chen

原文摘要

Diffusion image generators owe much of their practical success to accurate denoising networks and strong numerical samplers. However, during sampling, the denoiser predicts the next step from flawed inputs that differ substantially from real data. This train-test gap, or exposure bias, caps image generation quality (i.e., blurry images, missing details, or distorted shapes). To mitigate the problem, we propose PEEK, a training-free deterministic sampler plugin. At each eligible denoising step, PEEK first performs a provisional one-step update, evaluates the denoiser once at the look-ahead state, and interpolates the current clean prediction with this one-step-later prediction to form the next noisy state. By taking the additional step, we obtain a generally more image like prediction from the higher-signal noisy state, mitigating exposure bias. To optimize the interpolation positions, PEEK searches for an interpolation schedule with a greedy metric-driven search made efficient by the PEEK prefix and baseline noise-schedule-rebasing caches. Across pixel-space and latent-space diffusion models with unconditional, label-guided, and text-guided settings on CIFAR-10, LSUNBedroom,I mageNet, and MS-COCO,the look-ahead correction consistently improves over baselines matched by number of function evaluations (NFE), with Fréchet Inception Distance (FID) reductions up to 49.3%. The one-time per-model calibration, the only extra cost, takes at most 2.2 minutes in our experiments. Project website is available at https://dachengruoque.github.io/PEEK-Video/.

cs.CV · cs.AI

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