arXiv 2610.25404 · cs.CV · 2026-10-18
MT Forcing: Towards Stable and Consistent Multi-Turn Image Editing with Autoregressive Diffusion
Houyuan Chen, Fanzhou Wang, Xiangyu Fan, Zesong Qiu, Yukun Wei, Songlin Yang, Zhitao Yang, Lvmin Zhang and 5 more
SenseNova
In latest · #285 Added on 2026-10-22
One-line takeaway
MT Forcing trains autoregressive-diffusion editors with a single-turn distillation teacher and teacher-forcing guidance to curb multi-turn editing drift.
MT Forcing 用单轮蒸馏教师和教师强制引导训练自回归扩散模型，减少多轮图像编辑中的画质漂移和内容走样。
Read on arXiv PDF Hugging Face discussion Code Project page
Topics & disciplines
More on Visual generation
- 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
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-22.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.