arXiv AI
Training-Free Hidden-State Refinement for Flow-Matching Image Generators
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arXiv AI
1d ago
PEEK: One-Step-Look-Ahead Exposure Bias Correction for Diffusion Sampling
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 AI
Aug 3
WaiT for the Signal: Simple Frequency-Aware Flow-Matching
arXiv:2607. 28760v1 Announce Type: cross Abstract: As image generation models scale to ever higher resolutions, global coherence, local detail, and texture fidelity become critical axes for generation quality.
Hugging Face Trending Papers
Aug 6
Energy-Guided Flow Matching
Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flow matching interpolates noise toward a fixed clean-image endpoint, leaving the spectral evolution to be learned implicitly.
arXiv AI
Aug 6
AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers
arXiv:2602. 13357v3 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive inference due to their iterative denoising structure.
arXiv Machine Learning
Sep 3
CAT-Flow: Curvature-Adaptive sTeps for Flow Matching
The paper introduces CAT-Flow, a pair of lightweight, training‑free algorithms—CAT‑OV and CAT‑OT—that adapt step‑sizes during Flow Matching inference by estimating curvature in time or state space. These methods avoid extra neural evaluations and achieve constant‑order truncation error bounds. Experiments show that CAT‑OV and CAT‑OT improve image quality metrics across four text‑to‑image Flow Matching models, cutting the required generation steps by up to 40%.
arXiv AI
6d ago
How to Loop Your Transformer: Loop Scaling, Specialization, and Parallel Inference
The paper investigates how looping—reusing transformer weights to deepen models without adding parameters—affects performance under different constraints. Experiments on dense decoder‑only transformers up to 1 B parameters show that looping the full transformer yields best quality for a fixed parameter budget, while looping a middle block is preferable when training FLOPs are limited. Adding loop‑specific low‑rank adapters improves quality, but decoding slows linearly with loops; a parallel loop design that passes hidden states across token positions can mitigate this slowdown, though careful handling of token embeddings is needed to retain gains.