XMem VS latent-diffusion

Nvm just found the occlusion video on https://github.com/hkchengrex/XMem holy shit

XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model ( Added because of the Atkinson-Shiffrin Memory Model ) Paper: https://arxiv.org/abs/2207.07115 Github: https://github.com/hkchengrex/XMem

Yeah MiVOS would speed up your tagging a lot. I also was curious if you saw XMem which just came out. I found that worked really well too.

Have you check XMem?

CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models (github.com)

The "retrieval based model" refers to https://github.com/CompVis/latent-diffusion#retrieval-augmen..., which uses ScaNN to train a knn embedding searcher.

Stability is basically https://github.com/CompVis/latent-diffusion + training data.

Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here

DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.

Auto refuses to comply, and explains that the code he wrote is based upon research and development that was done quite some time ago already and is open-source. The function in question was published on December 21, 2021 here: https://github.com/CompVis/latent-diffusion/commit/e66308c7f2e64cb581c6d27ab6fbeb846828253b. But that is in fact still not the original source. The original source code was published on On August 3, 2021 here: https://github.com/lucidrains/perceiver-pytorch. The original code's license allows commercial use, so nobody is wrong for using it. The license can be read here: https://github.com/lucidrains/perceiver-pytorch/blob/main/LICENSE.

So am I. Latent Diffusion.