From 2694a6c63ad37fc02531dd2f7ca6ea5ede0c44a2 Mon Sep 17 00:00:00 2001 From: Zachary Mueller Date: Mon, 13 Feb 2023 13:28:55 -0500 Subject: [PATCH] Update integrations (#1063) --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 52cd4135823..edc2754814c 100644 --- a/README.md +++ b/README.md @@ -208,13 +208,16 @@ You shouldn't use 🤗 Accelerate if you don't want to write a training loop you ## Frameworks using 🤗 Accelerate -If you like the simplicity of 🤗 Accelerate but would prefer a higher-level abstraction around your training loop, some frameworks that are built on top of 🤗 Accelerate are listed below: +If you like the simplicity of 🤗 Accelerate but would prefer a higher-level abstraction around its capabilities, some frameworks and libraries that are built on top of 🤗 Accelerate are listed below: * [Animus](https://github.com/Scitator/animus) is a minimalistic framework to run machine learning experiments. Animus highlights common "breakpoints" in ML experiments and provides a unified interface for them within [IExperiment](https://github.com/Scitator/animus/blob/main/animus/core.py#L76). * [Catalyst](https://github.com/catalyst-team/catalyst#getting-started) is a PyTorch framework for Deep Learning Research and Development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop. Catalyst provides a [Runner](https://catalyst-team.github.io/catalyst/api/core.html#runner) to connect all parts of the experiment: hardware backend, data transformations, model train, and inference logic. * [fastai](https://github.com/fastai/fastai#installing) is a PyTorch framework for Deep Learning that simplifies training fast and accurate neural nets using modern best practices. fastai provides a [Learner](https://docs.fast.ai/learner.html#Learner) to handle the training, fine-tuning, and inference of deep learning algorithms. +* [InvokeAI](https://github.com/invoke-ai/InvokeAI) is a creative engine for Stable Diffusion models, offering industry-leading WebUI, terminal usage support, and serves as the foundation for many commercial products. * [Kornia](https://kornia.readthedocs.io/en/latest/get-started/introduction.html) is a differentiable library that allows classical computer vision to be integrated into deep learning models. Kornia provides a [Trainer](https://kornia.readthedocs.io/en/latest/x.html#kornia.x.Trainer) with the specific purpose to train and fine-tune the supported deep learning algorithms within the library. +* [Open Assistant](https://projects.laion.ai/Open-Assistant/) is a chat-based assistant that understands tasks, can interact with their party systems, and retrieve information dynamically to do so. * [pytorch-accelerated](https://github.com/Chris-hughes10/pytorch-accelerated) is a lightweight training library, with a streamlined feature set centred around a general-purpose [Trainer](https://pytorch-accelerated.readthedocs.io/en/latest/trainer.html), that places a huge emphasis on simplicity and transparency; enabling users to understand exactly what is going on under the hood, but without having to write and maintain the boilerplate themselves! +* [Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) is an open-source browser-based easy-to-use interface based on the Gradio library for Stable Diffusion. ## Installation