Diffusion Image Editing via Asynchronous Token Decoding
Text-guided diffusion image editing aims to modify semantic attributes of an image while preserving its identity, layout, and background. However, naïvely switching the text condition during sampling often causes global drift, as denoising dynamics propagate changes across tokens and can disrupt unedited regions. To address this issue, we propose \textbf{A}synchronous \textbf{T}oken \textbf{D}ecoding \textbf{Edit} (ATDEdit), an inference-time framework that views each sampler step as a parallel update of a globally coupled token matrix and enables token-indexed condition switching with differentiated update policies. Instead of applying synchronous target-conditioned updates to all tokens...
| Authors | Yang Shi, Liangsi Lu, Minzhe Guo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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