ICLight模型下载:https://huggingface.co/lllyasviel/ic-light/tree/main网盘链接:https://pan.quark.cn/s/95cd3173ce2f模型放置位置:ComfyUI/models/unet几个关键插件:ComfyUI-IC-Light:https://github.com/kijai/ComfyUI-IC-Light.gitC, 视频播放量 13227、弹幕量 1、
seperate SegmentAnythingUltra V2 into nodes (#291)* seperate SegmentAnythingUltra V2 into nodes * refine the code * refine the code * refine the code * refine the code * refine the code * add code file * refine the code * refine the code * refine the code * test * refine the code...
I have used the SegmentAnythingUltra V2 node in one of my workflows and I am hosting that workflow in 'Baseten' platform to get it as an API endpoint, the custom nodes have loaded properly but when I ran the workflow then it is giving the below error, there is some issue with sam_...
分割任何内容以获得稳定的扩散 WebUI。通过单击或文本提示自动生成图像的高质量分割/遮罩。旨在将WebUI和ControlNet与Segment Anything和GroundingDINO连接起来,以增强稳定扩散/ ControlNet修复(单个图像和批处理),增强ControlNet语义分割,自动化图像垫并创建LoRA / Ly
2023 年 4 月,Meta 公司发布了 Segment Anything Model (SAM),号称能够「分割一切」,犹如一颗重磅炸弹震荡了整个计算机视觉领域,甚至被很多人看作是颠覆传统 CV 任务的研究。 时隔1 年多,Meta 再度发布里程碑式更新—— SAM 2 能够为静态图像和动态视频内容提供实时、可提示的对象分割,将图像与视频分割功能整合...
Ultralytics 源代码地址:https://github.com/ultralytics/ultralytics.git. SAM 论文地址:https://arxiv.org/abs/2304.02643 SAM2 官方源代码:https://github.com/facebookresearch/segment-anything-2.git SAM2 论文地址:https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-vide...
比如,图像分类(2015)、基础阅读理解(2017)、视觉推理(2020)、自然语言推理(2021)、多任务语言理解任务(2024.1, Gemini Ultra)。但是,在复杂认知的任务上,AI仍然不及人类,比如视觉常识推理、竞赛级的数学问题。如下图所示,虚线是人类的水准,其它实线是AI在不同任务下的得分。 随着算法模型的迭代,某些经典测试基准的...
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(1)模型:基于yolov8-seg的模型,具体可以查看ultralytics. (2) 实例分割:yolov8-seg实现了实例分割,结果包含了检测和分割分支。检测分支输出box和类别cls,分割分支输出k(默认为32)个mask分数,检测和分割分支是并行的。看推理代码的模型,这块其实就是yolov8的segment网络,具体可以看yolov8的segment训练代码。
The Segment Anything model (SAM) has brought significant changes to the segmentation field with its superior performance, but its extensive computational resource requirements remain a limiting factor. Many works, such as MobileSAM, Edge-SAM, and MobileSAM-v2, have explored lightweight solutions. How...