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...
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:2601. 21542v3 Announce Type: replace-cross Abstract: Flow Matching (FM) models have emerged as a leading paradigm for high-fidelity synthesis.
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.
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:2506. 13058v2 Announce Type: replace-cross Abstract: Diffusion probabilistic models (DPMs) have demonstrated remarkable success in visual generation.
arXiv:2606. 27978v1 Announce Type: cross Abstract: Pixel-space continuous-token autoregressive (AR) generation directly models images as sequences of raw pixel patches, avoiding discrete tokenization or a separately pretrained tokenizer.
arXiv:2606. 24140v1 Announce Type: new Abstract: Discrete flow matching (DFM) provides a principled framework for generative modeling on discrete state spaces via continuous-time Markov chain dynamics.
arXiv:2607. 06114v1 Announce Type: cross Abstract: Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs).
arXiv:2512. 19311v2 Announce Type: replace-cross Abstract: This paper studies the training-testing discrepancy (a.
The paper introduces PEEK, a training‑free deterministic sampler plugin that corrects exposure bias in diffusion image generators by performing a provisional one‑step update and interpolating between predictions at a look‑ahead state. PEEK evaluates the denoiser at this higher‑signal noisy state, thereby producing more image‑like predictions and reducing the train‑test gap that often leads to blurry or poorly detailed outputs. Experiments on eight pixel‑space and latent‑space diffusion models—including unconditional, label‑guided, and text‑guided settings—show consistent improvements over NFE‑matched baselines, with FID gains up to 49.3 % and minimal additional computation.
arXiv:2610.26025v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) deliver high-fidelity image and video generation, but inference requires many sequential model evaluations. Two...