BlenderGPT enables users to control Blender 3D software using natural language commands, powered by OpenAI's GPT-4 or GPT-3.5 models. This plugin is designed for Blender users, including artists, designers, and developers, who want to streamline their workflow by generating and executing Python scripts for Blender operations through simple English prompts.

How It Works

The plugin integrates directly into the Blender UI, allowing users to input natural language commands. These commands are then sent to OpenAI's GPT models, which generate the corresponding Blender Python scripts. The generated scripts are executed within Blender, performing the requested actions, such as creating objects or modifying scenes. This approach leverages the advanced natural language understanding and code generation capabilities of LLMs to abstract complex scripting tasks.

Quick Start & Requirements

• Install: Clone the repository, then install the ZIP file via Blender's Add-on preferences.
• Prerequisites: Blender 3.1 or later, OpenAI API key with GPT-4 access (via API waitlist, not ChatGPT Plus).
• Setup: Requires pasting the OpenAI API key into the add-on preferences.
• Demo: https://user-images.githubusercontent.com/63528145/227158577-d92c6e8d-df21-4461-a69b-9e7cde8c8dcf.mov

Highlighted Details

• Direct integration into Blender's UI sidebar.
• Supports both GPT-4 and GPT-3.5 via OpenAI API.
• Real-time code generation visibility via the System Console.
• Example usage: "create a cube at the origin".

Maintenance & Community

The project is maintained by gd3kr. Further community or roadmap information is not detailed in the README.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

Access to GPT-4 functionality is strictly dependent on being accepted into the OpenAI API waitlist, which is a separate requirement from ChatGPT Plus subscriptions. The effectiveness of natural language commands relies heavily on the LLM's interpretation and the quality of generated Python scripts.