평범한 필기장

Paper : https://arxiv.org/abs/2504.13109 UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow ModelsFlow matching models have emerged as a strong alternative to diffusion models, but existing inversion and editing methods designed for diffusion are often ineffective or inapplicable to them. The straight-line, non-crossing trajectories of flow models posearxiv.orgAbstractDiffusion mo..

Paper : https://arxiv.org/abs/2411.15843 Unveil Inversion and Invariance in Flow Transformer for Versatile Image EditingLeveraging the large generative prior of the flow transformer for tuning-free image editing requires authentic inversion to project the image into the model's domain and a flexible invariance control mechanism to preserve non-target contents. However, thearxiv.org1. Introductio..

Paper : https://arxiv.org/abs/2412.07517 FireFlow: Fast Inversion of Rectified Flow for Image Semantic EditingThough Rectified Flows (ReFlows) with distillation offers a promising way for fast sampling, its fast inversion transforms images back to structured noise for recovery and following editing remains unsolved. This paper introduces FireFlow, a simple yet effarxiv.orgAbstract Rectified Flow..

Project Page : https://rf-inversion.github.io/ Litu Rout1,2 Yujia Chen2 Nataniel Ruiz2 Constantine Caramanis1 Sanjay Shakkottai1Wen-Sheng Chu2 1 The University of Texas at Austin,    2 Google ICLR 202" data-og-host="rf-inversion.github.io" data-og-source-url="https://rf-inversion.github.io/" data-og-url="https://rf-inversion.github.io/" data-og-image=""> RF-InversionSemantic Image Inversion and ..

Paper : https://arxiv.org/abs/2310.01506 Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of CodeText-guided diffusion models have revolutionized image generation and editing, offering exceptional realism and diversity. Specifically, in the context of diffusion-based editing, where a source image is edited according to a target prompt, the process comarxiv.orgProject Page : https:..