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Awesome Generative AI AwesomeTrack Awesome List <!-- omit in toc -->

A curated list of Generative AI projects, tools, artworks, and models

Repository Introduction

Welcome to our Awesome List of Generative AI resources! This repository is a curated collection of references in the dynamic field of Generative AI, equipped with various sources such as academic papers, technical articles, online courses, tutorials, and software.

Structure

  1. Sections: Each section represents a different Generative AI-related category (e.g., LLMs, prompt engineering, image synthesis, educational resources, etc.). The Inboxes are the more general references of a category. When a new category emerges, it becomes a specific subsection.

  2. References within sections: Inside each section, references are listed in reverse chronological order, with the most recent one at the top. This order signifies the ever-evolving landscape of Generative AI, keeping you up-to-date with the latest developments.

This repository is designed to offer you the most recent advancements at your fingertips, allowing you to explore the depth of older resources at your own pace. It's regularly updated, ensuring you're always on track with the rapidly progressing world of Generative AI.

Contribute to our Repository

Your contributions are welcome and greatly appreciated! If you have a valuable resource that you believe should be on this list, or if you see any outdated information, please make a Pull Request. This will help us maintain the quality and relevance of our Awesome List.

Follow this roadmap, keep learning, and enjoy your journey through Generative AI!

Generative AI Area

Generative AI history, timelines, maps, and definitions

Ethics, Philosophical questions and Discussions about Generative AI

Critical Views about Generative AI

Generative AI Processes and Artifacts

<img src="https://user-images.githubusercontent.com/299057/226114498-c9b8a717-31e2-4630-b0ab-752b69005146.png" width=75% height=75%> <details> <summary>More info</summary>

Generative AI is a branch of artificial intelligence that focuses on creating new data based on patterns learned from existing data. Here's a step-by-step explanation of the process:

  1. Starting with Data: Every Generative AI process begins with data. This can be in various forms such as text, images, sounds, or other datasets. This data serves as the foundational material that the AI uses to recognize and understand patterns.

  2. Training the AI: With the data in hand, the next step is 'training'. During this phase, the AI processes the data multiple times to learn and internalize the patterns present. The outcome of this stage is a 'model', which acts like a digital representation of the knowledge derived from the data.

  3. Fine-Tuning: At times, there's a need for the AI to focus on specific nuances or characteristics. In such cases, an additional set of data is used to 'fine-tune' the already trained model, enhancing its capabilities in the desired direction.

  4. Using the Model: After training, the model is prepared to make inferences, which means using its acquired knowledge to process new data and come up with relevant outputs. This inference process can be executed locally on a machine or can be accessed remotely through an 'API'. The choice between local execution and API access often depends on factors like computational resources, application needs, and user preferences. Whether locally or via an API, the goal is to leverage the model's capabilities to derive meaningful results from new data inputs.

  5. Generating New Data: With the model set up, the AI can now produce or 'generate' new data. By giving the AI certain 'input parameters' or guidelines, it returns with 'generated output', which is the newly created content.

  6. Applications: The output generated by the AI can be incorporated into a range of applications, be it websites, mobile apps, or other digital platforms. The 'interface' refers to the user-facing portion of these applications, enabling users to interact with and benefit from the AI's capabilities.

In essence, Generative AI is about feeding an AI system vast amounts of data, training it to grasp underlying patterns, and then utilizing that trained knowledge to produce novel data. The potential applications and benefits of this technology are vast and continue to grow as the field evolves.

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Generative AI Tools Directories

Courses and Educational Materials

Human-AI Interaction

Papers Collection

Online Tools and Applications

Text

Small Language Models

Large Language Models (LLMs)

Programming Frameworks for LLMs

Prompt Engineering

Prompt Optimizers

Prompt Engineering for Text-to-text

Prompt Engineering for Text-to-image

Mamba

Running LLMs Locally

Function Calling

GPTs and Assistant API

Retrieval-Augmented Generation (RAG)

Embeddings and Semantic Search

Autonomous LLM Agents

Multi-agents

LLM Evaluation

LLMOps

AI Engineering

Attacks on LLMs

LangChain

ChatGPT

Text-related Generative Tools

Research AI Tools

AI Tools for Research

AI Tools for Searching

Image

Image Synthesis

Inbox: Stable Diffusion

Stable Diffusion Deployed Web Tools

Web UI for Stable Diffusion via Google Colab

References Collection about Stable Diffusion

Hypertechniques

ControlNet

Textual Inversion

DreamBooth

Deforum

Creative Uses of Generative AI Image Synthesis Tools

Image Upscaling

Image Restoration

Image Segmentation

Video and Animation

Audio and Music

Speech

Text-to-speech (TTS) and avatars

Podcast generators

Speech-to-text (STT) and spoken content analysis

Games

Code and Programming

Multimodal

Multimodal Embedding Space

Datasets

Misc

AI and Education

People and works

Interesting Twitter Accounts

Interesting Instagram Accounts, Posts and Reels

Interesting Youtube Channels

Interesting GitHub Repositories

Artists and Artworks

Galleries

Related Awesome Lists

Bio experiments

Jobs in Generative AI

Improving Google Colab experience

Auxiliary tools and concepts

Dimension reduction techniques

Roadmaps, Tracks, Rails

Stargazers over time

Stargazers over time

Contribute

Contributions welcome! Read the contribution guidelines first.

License

CC0

To the extent possible under law, Filipe Calegario has waived all copyright and related or neighboring rights to this work.

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