MT Forcing: Towards Stable and Consistent Multi-Turn Image Editing with Autoregressive Diffusion

Key points

• Multi-turn image editing aims to modify an image according to a sequence of instructions, enabling more effective interactive visual content creation for users.1
• Specifically, we propose Single-Turn Distribution Matching Distillation (Single-Turn DMD), which supervises student self-rollouts with a teacher that applies cumulative edits to the source image in a single turn, providing visual-quality guidance without recursive error accumulation.1
• Additionally, we introduce Teacher-Forcing Guidance, which supervises history-dependent transitions on reference trajectories during warmup and as a regularizer during distillation, encouraging cross-turn content consistency beyond what per-turn distribution matching alone can ensure.1
• Extensive experiments demonstrate that our approach significantly improves the multi-turn image editing capabilities of UMMs, yielding more stable visual quality and stronger cross-turn consistency.1

Sources (2)

• [1]MT Forcing: Towards Stable and Consistent Multi-Turn Image Editing with Autoregressive Diffusion
  Hugging Face Daily Papers · Oct 18, 12:00 AM

  To address these challenges, we propose MT Forcing, a novel training paradigm for multi-turn image editing with unified multimodal models (UMMs) based on autoregressive diffusion.

  Multi-turn image editing aims to modify an image according to a sequence of instructions, enabling more effective interactive visual content creation for users.

• [2]MT Forcing: Towards Stable and Consistent Multi-Turn Image Editing with Autoregressive Diffusion
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 05:01 PM · same content

Extractive summary: sentences quoted from the sources.