Rethinking Scribble-Guided Image Editing: A Progressive Supervision Curriculum for Coherent Multi-Task Editing

Scribble-guided image editing combines text with coarse strokes to specify both what to edit and where. However, current image editors often fail to follow scribble instructions, especially for multiple requests. We diagnose two gaps in the required training supervision. First, precise supervision and visually coherent supervision play complementary roles: the former supports reliable local editing, while the latter improves visual coherence with the source image. Second, exposure to multiple scribbles alone is insufficient for multi-task editing; complete multi-task supervision is needed for reliable binding between scribbles, instructions, and edits. Based on these findings, we propose a Progressive Supervision Curriculum. Layered Synthetic Data provide precise supervision for single-task edits. Multi-Task Mosaicking extends these samples into complete multi-task tuples. Visually Coherent Synthetic Data provide supervision for visual coherence. Stage I learns single-task editing and multi-task binding from Layered Synthetic Data and mosaicked tuples. Stage II refines visual coherence with Visually Coherent Synthetic Data. We render scribbles directly into the input image and train only a LoRA adapter. On VIBE, our method achieves the best overall single-task result and reaches performance comparable to closed-source models on multi-task editing. We will release our dataset and model.