KINESYS: Test-Time Adaptation for Reference-Based Effect Editing in Images

Visual effects are often easier to demonstrate via reference than to describe in words. We study effect-reference editing, where a reference image shows a desired visual effect and language identifies which properties should guide the edit. Existing single-pass editing techniques often fail to make fine-grained edits, resulting in subtle identity distortions or deviations from the reference effect. We introduce KINESYS, a test-time adaptation framework for effect-reference editing that combines reference-aware and source-aware feedback to transfer the desired effect while preserving the identity of the original shot. To ensure KINESYS can produce a diverse set of identity-preserving edits, we develop a robust optimization strategy in which the generative model is fine-tuned across multiple noise samples and optimization runs. Evaluation with VLM judges, source-preservation metrics (LPIPS, TPIPS), and a user study shows that KINESYS outperforms state-of-the-art image editors in source identity preservation; on effect transfer, it remains competitive with Nano Banana 2 and outperforms open-weight editors.