Multi-turn image editing fails because each edit shifts the image distribution away from training data. Training with self-forcing and asymmetric teacher-student conditioning keeps quality stable across many sequential edits.
This paper tackles quality degradation in multi-turn image editing by training models to handle their own outputs from previous edits. The key innovation is an asymmetric conditioning scheme where a student model edits sequentially on its own outputs while a teacher model stays anchored to the original image, using distillation to provide reliable training signals despite distribution shifts.