SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based
  Image Editing
Authors: name: Ming Li, name: Xin Gu, name: Fan Chen, name: Xiaoying Xing, name: Longyin Wen, name: Chen Chen, name: Sijie Zhu

Due to the challenges of manually collecting accurate editing data, existing
datasets are typically constructed using various automated methods, leading to
noisy supervision signals caused by the mismatch between editing instructions
and original-edited image pairs. Recent efforts attempt to improve editing
models through generating higher-quality edited images, pre-training on
recognition tasks, or introducing vision-language models (VLMs) but fail to
resolve this fundamental issue. In this paper, we offer a novel solution by
constructing more effective editing instructions for given image pairs. This
includes rectifying the editing instructions to better align with the
original-edited image pairs and using contrastive editing instructions to
further enhance their effectiveness. Specifically, we find that editing models
exhibit specific generation attributes at different inference steps,
independent of the text. Based on these prior attributes, we define a unified
guide for VLMs to rectify editing instructions. However, there are some
challenging editing scenarios that cannot be resolved solely with rectified
instructions. To this end, we further construct contrastive supervision signals
with positive and negative instructions and introduce them into the model
training using triplet loss, thereby further facilitating supervision
effectiveness. Our method does not require the VLM modules or pre-training
tasks used in previous work, offering a more direct and efficient way to
provide better supervision signals, and providing a novel, simple, and
effective solution for instruction-based image editing. Results on multiple
benchmarks demonstrate that our method significantly outperforms existing
approaches. Compared with previous SOTA SmartEdit, we achieve 9.19%
improvements on the Real-Edit benchmark with 30x less training data and 13x
smaller model size.