arXiv 2610.25844 · cs.LG · 2026-10-19
SWIFT: Self-guided Weak-to-strong dIFfusion models via Target extrapolation
Yanjin He, Cuifeng Shen, Wanzhou Lei, Maohua Li, Tao Lan, Kan Liu, Hanlin Tang, Qing Qu
alibaba
在最新榜 #295 本站收录于 2026-10-22
一句话要点
SWIFT 用冻结的早期弱模型给训练目标做外推,放大强模型欠拟合的部分,提升扩散模型质量且不增加推理开销。
SWIFT extrapolates training targets away from a frozen weak denoiser to boost a stronger diffusion model with no extra inference cost.
阅读 arXiv 原文 PDF Hugging Face 讨论 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。