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Awesome-AIGC-Tutorials

luban-agi/Awesome-AIGC-Tutorials

Curated tutorials and resources for Large Language Models, AI Painting, and more

GitHub

GraphCanon updated 3w · GitHub synced 3w

4.5k stars303 forksLast push 2y MIT

Decision brief

Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry.

Good fit when

  • If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources.
  • Ideal when seeking curated educational content that zeroes in on both the technical and creative aspects of large language model development and AI art

Avoid when

  • Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples.
  • Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis.
Requirements:
No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.

Observed Jul 16, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Dormant (848d since push)
As of 3w
Provenance
Not a fork · Organization account
As of 3w
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

git clone https://github.com/luban-agi/Awesome-AIGC-Tutorials

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Provides educational content on LLMs, NLP, prompt engineering, and AI-generated art.

Capability facts

No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).

Categories

Developer ToolsLLM FrameworksModel Training

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

LangChain integrationLangChain

Source: README excerpt (regex_v1, Jul 28, 2026)

- [LangChain for LLM Ap
Source link
Python runtimePython

Source: README excerpt (regex_v1, Jul 28, 2026)

automating workflows using language models, creating prompt chains, integrating Python, and designing chatbots, all through hands-on Jupyter notebook exercises with j
Source link
Works with ChatGPTChatGPT

Source: README excerpt (regex_v1, Jul 28, 2026)

- [ChatGPT Prompt Engineering for Developers - DeepLearning.AI](https://www.deeplearning.a
Source link

Tags

aiaigcchatgptdeep-learningllmmidjourneymultimodalnlp

README

Awesome AIGC Tutorials

English | 中文版

Awesome AIGC Tutorials houses a curated collection of tutorials and resources spanning across Large Language Models, AI Painting, and related fields. Discover in-depth insights and knowledge catered for both beginners and advanced AI enthusiasts.

🔔 Recent Updates

🌱 How to Contribute

We warmly welcome contributions from everyone, whether you've found a typo, a bug, have a suggestion, or want to share a resource related to AIGC. For detailed guidelines on how to contribute, please see our CONTRIBUTING.md file.

📜 Content

  • 👋 Introduction
  • 💬 Large Language Models
    • 💡 Prompt Engineering
    • 🔧 LLMs in Practice
    • 🔬 Theory of LLMs
  • 🎨 AI Painting
    • 🧑‍🎨 Art Fundamentals and AI Painting Techniques
    • 🌊 Stable Diffusion Principles and Applications
  • 🔊 AI Audio
  • 🌈 Multimodal
  • 🧠 Deep Learning
  • 💻 AI System
  • 🗂 Miscellaneous
    • ✨ Star History
    • 🤝 Friendship Links

👋 Introduction

  • AI for Everyone - Andrew Ng

    • "AI for Everyone" is a beginner's guide to understanding AI's practical applications, its limitations, and its societal impact, ideal for business professionals and leaders alike.

  • Practical AI for Teachers and Students - Wharton School

    • Wharton Interactive's crash course delves into the mechanics and impacts of LLMs, spotlighting models like OpenAI's ChatGPT4, Microsoft's Bing in Creative Mode, and Google's Bard.

  • Artificial Intelligence for Beginners - Microsoft

    • This 12-week Microsoft curriculum dives deep into AI methodologies, spanning symbolic AI to neural networks, while highlighting TensorFlow and PyTorch frameworks, yet omits business applications, classic machine learning, and certain cloud-specific topics.

  • Generative AI learning path - Google Cloud

    • This learning path offers a comprehensive journey from the basics of Large Language Models to deploying generative AI solutions on Google Cloud.

💬 Large Language Models

💡 Prompt Engineering

  • ChatGPT Prompt Engineering for Developers - DeepLearning.AI

    • Co-taught by OpenAI and DeepLearning.AI, this course guides learners in leveraging Large Language Models for tasks like summarizing and text transformation, with hands-on experiences in a Jupyter notebook environment.

  • Building Systems with the ChatGPT API - DeepLearning.AI

    • Led by experts from OpenAI and DeepLearning.AI, this course teaches automating workflows using language models, creating prompt chains, integrating Python, and designing chatbots, all through hands-on Jupyter notebook exercises with just basic Python knowledge required.

  • [LangChain for LLM Ap

For agents

This page has a .md twin and JSON over the API.

/tools/luban-agi-awesome-aigc-tutorials.mdGET /api/graphcanon/tools/luban-agi-awesome-aigc-tutorialsGET /api/graphcanon/graph?tool=luban-agi-awesome-aigc-tutorials

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