Kyujinpy (166) 썸네일형 리스트형 [DoRA 논문 리뷰] - Weight-Decomposed Low-Rank Adaptation *DoRA를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! DoRA paper: https://arxiv.org/abs/2402.09353 DoRA: Weight-Decomposed Low-Rank AdaptationAmong the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA and its variants have gained considerable popularity because of avoiding additional inference costs. However, there still often exists an accuracy gap between these methods and fullarxiv.org D.. [UniCon 논문 리뷰] - A SIMPLE APPROACH TO UNIFYING DIFFUSION BASED CONDITIONAL GENERATION *UniCon를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! UniCon paper: https://arxiv.org/abs/2410.11439 A Simple Approach to Unifying Diffusion-based Conditional GenerationRecent progress in image generation has sparked research into controlling these models through condition signals, with various methods addressing specific challenges in conditional generation. Instead of proposing another specialized techn.. [WAN-Alpha 논문 리뷰] Video Generation with Stable Transparency via Shiftable RGB-A Distribution Learner *WAN-Alpha를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! WAN-Alpha paper: https://arxiv.org/pdf/2509.24979 WAN-Alpha github: Wan-Alpha Wan-AlphaWan-Alpha Video Generation with Stable Transparency via Shiftable RGB-A Distribution Learner 1Haotian Dong, 2Wenjing Wang, 2Chen Li, 2Jing Lyu, 1Di Lin 1Tianjin University, 2Individual Researcherdonghaotian123.github.io Contents1. Simple Introduction2. Backgroun.. [CIKM' 25 LOD] - Learnable Orthogonal Decomposition for Non-Regressive Prediction for PDE LOD Github: https://github.com/voltwin-dev/LOD-ML GitHub - voltwin-dev/LOD-ML: (CIKM '25 Oral) Learnable Orthogonal Decomposition for Non-Regressive Prediction for PDE(CIKM '25 Oral) Learnable Orthogonal Decomposition for Non-Regressive Prediction for PDE - voltwin-dev/LOD-MLgithub.com Paper: https://dl.acm.org/doi/10.1145/3746252.3761364 ACM Kudos: Learnable Orthogonal Decomposition for Non-Reg.. [OmniInsert 논문 리뷰] - Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models *OmniInsert를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! OmniInsert paper: https://phantom-video.github.io/OmniInsert/ OmniInsertMask-Free Video Insertion of Any Reference via Diffusion Transformer Models * Equal contribution, † Corresponding author, ‡ Project lead Intelligent Creation Lab, Bytedance Research Paper GitHubphantom-video.github.ioContents1. Simple Introduction2. Background Knowledge: Diffusi.. [WANAlign2.1⚡- Awesome-Training-Free-WAN2.1-Editing] WANAlign2.1⚡ is released!!Awesome-Training-Free Video Editing Open Source Project with WAN2.1@!!Awensome-OpenSource!!! WANAlign2.1⚡ github: https://github.com/KyujinHan/Awesome-Training-Free-WAN2.1-Editing GitHub - KyujinHan/Awesome-Training-Free-WAN2.1-Editing: Training-Free (Inversion-Free) methods meet WAN2.1-T2VTraining-Free (Inversion-Free) methods meet WAN2.1-T2V - KyujinHan/Awesome-Traini.. [FlowAlign 논문 리뷰] - Trajectory-Regularized, Inversion-Free Flow-based Image Editing *FlowAlign를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! FlowAlign paper: https://arxiv.org/abs/2505.23145 FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image EditingRecent inversion-free, flow-based image editing methods such as FlowEdit leverages a pre-trained noise-to-image flow model such as Stable Diffusion 3, enabling text-driven manipulation by solving an ordinary differential equatio.. [FlowDirector 논문 리뷰] - Training-Free Flow Steering for Precise Text-to-Video Editing *FlowDirector를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! FlowDirector paper: https://arxiv.org/abs/2506.05046 FlowDirector: Training-Free Flow Steering for Precise Text-to-Video EditingText-driven video editing aims to modify video content according to natural language instructions. While recent training-free approaches have made progress by leveraging pre-trained diffusion models, they typically rely o.. [FlowEdit 논문 리뷰] - Inversion-Free Text-Based Editing Using Pre-Trained Flow Models *FlowEdit를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! FlowEdit paper: https://arxiv.org/abs/2412.08629 FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow ModelsEditing real images using a pre-trained text-to-image (T2I) diffusion/flow model often involves inverting the image into its corresponding noise map. However, inversion by itself is typically insufficient for obtaining satisfactory.. [Rectified Flow 간단한 설명] - Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow *Rectified flow를 위한 간단한 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! Rectified flow: 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 u.. [KO-VQA 벤치마크 제작기🤗] 시각화자료질의응답 데이터셋을 활용한 한국어 VLM 능력 평가 벤치마크 KO-VQA Benchmark GithubGithub: https://github.com/Marker-Inc-Korea/KO-VQA-Benchmark GitHub - Marker-Inc-Korea/KO-VQA-Benchmark: AIHUB 시각화자료질의응답 데이터셋을 기반으로 만든 VLM 벤치마AIHUB 시각화자료질의응답 데이터셋을 기반으로 만든 VLM 벤치마크 데이터셋. Contribute to Marker-Inc-Korea/KO-VQA-Benchmark development by creating an account on GitHub.github.comIntroduction😋한국어 문서 기반 VLM 능력을 평가하기 위한 KO-VQA 벤치마크 제작기🔥안녕하세요! 어느덧 2025년의 절반도 지나가 무더.. [InfEdit 논문 리뷰 + DDIM Inversion] - Inversion-Free Image Editing with Natural Language *InfEdit를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! InfEdit paper: https://arxiv.org/abs/2312.04965 Inversion-Free Image Editing with Natural LanguageDespite recent advances in inversion-based editing, text-guided image manipulation remains challenging for diffusion models. The primary bottlenecks include 1) the time-consuming nature of the inversion process; 2) the struggle to balance consistency witha.. 이전 1 2 3 4 ··· 14 다음
