随着基于扩散的方法的快速发展,使用扩散模型对图像进行编辑具有广泛的前景。一些在大规模文本图像数据上训练得到的扩散模型如 Stable Diffusion,具有丰富的先验知识,使得自由的真实图像编辑成为可能。 主流的基于扩散的图像编辑技术 根据编辑方式的不同,目前主流的基于扩散的图像编辑技术可以分为两大类: ...
DenseDiffusion BoxDiff 近年来,图像生成研究取得了巨大进展。过去几年,GANs 是最先进的技术,其 latent space 和conditional inputs 已经得到了深入研究,以实现可控的修改和生成。文本条件自回归和扩散模型已经展示出惊人的图像质量和概念覆盖,这是由于它们更稳定的学习目标和基于网络图像-文本对数据的大规模训练所致。
几篇论文实现代码:《Diffusion-based Blind Text Image Super-Resolution》(CVPR 2024) GitHub: github.com/YuzheZhang-1999/DiffTSR [fig3] 《Parameter Efficient Self-supervised Geospatial Domain Adaptat...
[CV] VideoBooth: Diffusion-based Video Generation with Image Prompts O网页链接 研究了一种新的视频生成任务,用图像提示生成视频。提出VideoBooth框架来解决该任务。VideoBooth使用两层的视觉嵌入来表达图像提示中的视觉特征。第一层是粗粒度的视觉嵌入,使用预训练的CLIP图像编码器提取图像特征,然后用MLP将特征映射...
Diffusion-Based Placement Algorithm for Reducing High Interconnect Demand in Congested Regions of FPGAscapacitycircuitsunder-resourcedAn FPGA has a finite routing capacity due to which a fair number of highly dense circuits fail to map on a slightly under-resourced architecture. The high-interconnect ...
论文翻译(扩散模型来了):Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data 利用发现的数据来创建合成声音是具有挑战性的,因为现实世界的录音通常包含各种类型的音频退化。解决这个问题的一种方法是使用增强模型对语音进行预增强,然后使用增强后的数据进行文本转语音(TTS)模型...
Faster Non-Log-Concave Sampling via Diffusion-based Monte Carlo Abstract: Efficient sampling from complex non-log-concave distributions is a cornerstone of statistical computing and machine learning, yet it is challenged by stringe...
Diffusion-based models have recently demonstrated notable success in various generative tasks involving continuous signals, such as image, video, and audio synthesis. However, their applicability to video captioning has not yet received widespread attention, primarily due to the discrete nature of captions...
ABSTRACT Diffusion-Based Placement Migration Placement migration is the movement of cells within an existing placement to address a variety of post-placement design closure issues, such as timing, routing congestion, signal integrity, and heat distribution. To fix a design problem,... H Ren,I ...
本文提出了一种Dataset Diffusion框架生成具有像素级语义分割的合成数据集。通过利用 Stable Diffusion,该框架能够从指定的对象类产生高质量的语义分割和视觉逼真的图像。实验结果表明,Dataset Diffusion在VOC和COCO中具有卓越的mIoU,优于当前的DiffuMask方法。为使用生成模型创建具有精确注释的大规模数据集提供了新的思路。