ResetEdit: 通过可重置的起始潜在实现生成图像的精确文本引导编辑
ResetEdit: Precise Text-guided Editing of Generated Image via Resettable Starting Latent
AI总结：
本文提出ResetEdit框架，通过在生成过程中嵌入可恢复的潜在信息，实现高精度文本引导编辑，优于现有方法。
摘要：
最近扩散模型的进步使得高质量图像生成成为可能，导致对生成后编辑的需求增加，这种编辑需要修改局部区域同时保持全局结构。实现这种灵活和精确的编辑需要高质量的起始点，即提供多样修改自由度和精细区域特定控制精度的潜在表示。然而，现有的基于倒置的方法如DDIM倒置往往产生不满意的起始潜在，导致编辑保真度下降和结构不一致。理想情况下，最合适的编辑锚点应是生成过程中使用的原始潜在，因为它本质上捕捉了场景的结构和语义。然而，由于存储每个生成图像的潜在不现实，因此需要解决这一挑战。为此，我们提出了ResetEdit，一种主动扩散编辑框架，将可恢复的潜在信息直接嵌入生成过程。通过将干净和扩散潜在之间的差异注入扩散轨迹并在倒置过程中提取，ResetEdit重建了一个可重置的潜在，其近似于真实的起始状态。此外，一个轻量级的潜在优化模块补偿了由VAE不对称性引起的重建偏差。基于Stable Diffusion，ResetEdit无缝集成现有无调优编辑方法，并在可控性和视觉保真度方面一致优于最先进的基线方法。
英文摘要：
Recent advances in diffusion models have enabled high-quality image generation, leading to increasing demand for post-generation editing that modifies local regions while preserving global structure. Achieving such flexible and precise editing requires a high-quality starting point, a latent representation that provides both the freedom needed for diverse modifications and the precision required for fine-grained, region-specific control. However, existing inversion-based approaches such as DDIM inversion often yield unsatisfactory starting latents, resulting in degraded edit fidelity and structural inconsistency. Ideally, the most suitable editing anchor should be the original latent used during the generation process, as it inherently captures the scene's structure and semantics. Yet, storing this latent for every generated image is impractical due to massive storage and retrieval costs. To address this challenge, we propose ResetEdit, a proactive diffusion editing framework that embeds recoverable latent information directly into the generation process. By injecting the discrepancy between the clean and diffused latents into the diffusion trajectory and extracting it during inversion, ResetEdit reconstructs a resettable latent that closely approximates the true starting state. Additionally, a lightweight latent optimization module compensates for reconstruction bias caused by VAE asymmetry. Built upon Stable Diffusion, ResetEdit integrates seamlessly with existing tuning-free editing methods and consistently outperforms state-of-the-art baselines in both controllability and visual fidelity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
