MTEdit: Packed Trajectories Beat Rollouts for Multi-Turn Image Editing

Key points

• Multi-turn image editing aims to modify an image according to a sequence of instructions, making it better aligned with human-AI collaborative creation than single-turn editing.1
• However, constructing high-quality multi-turn training data remains challenging, as conventional sequential rollout accumulates errors and introduces visual-quality drift across editing turns.1
• We further introduce MTEdit-Bench, a benchmark designed to evaluate both scenarios by assessing models' ability to follow editing instructions, maintain content consistency, resist visual-quality drift, and effectively use historical context across turns.1
• Experimental results show that Qwen-Image-MTEdit achieves the best performance among the evaluated open-source models, demonstrating the effectiveness of MTEdit in improving models' multi-turn image editing capabilities.1

Sources (2)

• [1]MTEdit: Packed Trajectories Beat Rollouts for Multi-Turn Image Editing
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 04:56 PM

  To address this challenge, we introduce MTEdit, a multi-turn image editing dataset built through a pipeline centered on packed trajectory canvas (PTC) generation.

  Multi-turn image editing aims to modify an image according to a sequence of instructions, making it better aligned with human-AI collaborative creation than single-turn editing.

• [2]MTEdit: Packed Trajectories Beat Rollouts for Multi-Turn Image Editing
  Hugging Face Daily Papers · Oct 16, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.