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목록rectified flow (5)
평범한 필기장
[평범한 학부생이 하는 논문 리뷰] Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing (CVPR 2025)
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..
[평범한 학부생이 하는 논문 리뷰] FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing (arXiv 2412)
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..
[평범한 학부생이 하는 논문 리뷰] Flow Straight and Fast : Learning to Generate and Transfer Data with Rectified Flow (ICLR 2023)
Paper : https://arxiv.org/abs/2209.03003 Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified FlowWe present rectified flow, a surprisingly simple approach to learning (neural) ordinary differential equation (ODE) models to transport between two empirically observed distributions π_0 and π_1, hence providing a unified solution to generative modelingarxiv.orgAbstract 본 논문..
[평범한 학부생이 하는 논문 리뷰] Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations (ICLR 2025)
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 ..
[평범한 학부생이 하는 논문 리뷰] InstaFlow : One Step is Enough for High-Quality Diffusion-based Text-to-Image Generation (ICLR 2024)
Paper : https://arxiv.org/abs/2309.06380 InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image GenerationDiffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attemparxiv.orgAbstr..