arXiv 2610.14136 · cs.AI · 2026-10-09
Correction Training for Diffusion Models
Donghoon Ahn, Runqian Wang, Alexei A. Efros, Taesung Park, Yash Belhe
University of California, Berkeley
在最新榜 #203 本站收录于 2026-10-22
一句话要点
为缓解扩散模型的曝光偏差，额外训练一个“校正器”在每个去噪步修正预测，在 DiT、SiT、JiT 上明显提升质量。
Trains a corrector that refines the denoiser's prediction at each step to reduce exposure bias, improving DiT, SiT and JiT samples.
阅读 arXiv 原文 PDF Hugging Face 讨论 代码仓库 项目页
主题与学科
同主题：视觉生成
- MaLiang-Harness: A Programmable Path to Image and Video Generation ▲367
- Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation ▲364
- DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models ▲325
- UltraText Bench: A Comprehensive Bilingual Benchmark for Evaluating Visual Text Rendering in Image Generation ▲197
- Matrix-Game 4.0: Harnessing Video Models for Real-Time Playable Game Worlds ▲182
数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-22。
要点由本站根据摘要自写，不转载摘要原文，请阅读原文。