Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing demands with the practicality required by everyday users. However, editing models trained primarily for single-turn edits often break down in multi-turn editing—the natural interactive setting where a user iteratively refines an image based on the model’s own previous outputs. This failure stems from the all-or-nothing requirement, where a single failed turn compromises the entire sequence, and error propagation, where exposure bias leads to…
MT-EditFlow: Reinforcement Learning for Multi-Turn Image Editing with Flow Matching
Summary
Apple ML Research introduces MT-EditFlow, a multi-turn image editing framework based on flow matching, fine-tuned via reinforcement learning to overcome the failure of single-turn models in iterative editing due to all-or-nothing requirements and error propagation.
MT-EditFlow: Reinforcement Learning for Multi-Turn Image Editing with Flow Matching
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