평범한 필기장

Paper : https://arxiv.org/abs/2212.09748 Scalable Diffusion Models with TransformersWe explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Darxiv.orgPaper : https://arxiv.org/abs/2403.03206 Scaling ..

Paper : https://arxiv.org/abs/2406.03293 Text-to-Image Rectified Flow as Plug-and-Play PriorsLarge-scale diffusion models have achieved remarkable performance in generative tasks. Beyond their initial training applications, these models have proven their ability to function as versatile plug-and-play priors. For instance, 2D diffusion models can sarxiv.orgAbstract 본 논문은 기존 diffusion model의 prior..

Paper : https://arxiv.org/abs/2412.20413 EraseAnything: Enabling Concept Erasure in Rectified Flow TransformersRemoving unwanted concepts from large-scale text-to-image (T2I) diffusion models while maintaining their overall generative quality remains an open challenge. This difficulty is especially pronounced in emerging paradigms, such as Stable Diffusion (SD) v3arxiv.org0. Obstacles in migrati..

Paper : https://arxiv.org/abs/2507.01496 ReFlex: Text-Guided Editing of Real Images in Rectified Flow via Mid-Step Feature Extraction and Attention AdaptationRectified Flow text-to-image models surpass diffusion models in image quality and text alignment, but adapting ReFlow for real-image editing remains challenging. We propose a new real-image editing method for ReFlow by analyzing the interme..

Paper : https://arxiv.org/abs/2411.14430 Stable Flow: Vital Layers for Training-Free Image EditingDiffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT), and employed flow-matching for improved training and sampling. However, thearxiv.orgAbstract 최근에는 UNet보다는 DiT를 많이 이용하고 ..