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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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hivemind
Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
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artbot-for-stable-diffusion
A front-end GUI for interacting with the AI Horde / Stable Diffusion distributed cluster
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Merge-Stable-Diffusion-models-without-distortion
Adaptation of the merging method described in the paper - Git Re-Basin: Merging Models modulo Permutation Symmetries (https://arxiv.org/abs/2209.04836) for Stable Diffusion
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InvokeAI
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Or you just copy in this link and never complain about this stuff again stable diffusion for all!!!!
Anyone can download and set up SD, from anywhere. We have multiple one-click SD installers, and one-click online versions. The barrier of entry is low, and it's only going to get easier over time.
https://github.com/learning-at-home/hivemind https://twitter.com/SamuelAinsworth/status/1569719494645526529 Technically, you can. It'll just require someone to write the code. Someone who understands neural networks well enough to do so.
Wouldn't "applying the permutation" simply swap all the parameters in a model so they match on both models? For example, in https://github.com/samuela/git-re-basin/blob/main/src/cifar10_vgg_weight_matching.py, on line 184 they apply the permutation, and on line 192 they lerp from model A's params to the permuted model B's params. This lerp is basically a weighted sum merge, isn't it? At a lerp of 0.5, it would be somewhere in between model A and the permuted model B.
Look into InvokeAI, pretty nice looking IMO... https://invoke-ai.github.io/InvokeAI/