BlueMoon AI

---

BlueMoon AI

Original author(s)	BlueMoon AI
Developer(s)	BlueMoon AI
Initial release	December 12, 2023
Stable release	0.0.1 / January 6, 2024
Repository	https://github.com/BlueMoonAI/BlueMoonAI
Written in	Python
Engine
Operating system	Any that support CUDA kernels
Type	Text-to-image model
License	Apache License 2.0 and Creative ML OpenRAIL-M
Website	https://bluemoonai.github.io/

Search BlueMoon AI on Amazon.

BlueMoon AI is an open-source text-to-image model based on stable diffusion, released in the later part of 2023. It is recognized as a component of the ongoing open-source AI model landscape.

The primary function of BlueMoon AI is to generate detailed images based on textual descriptions. Additionally, it can be employed for tasks such as inpainting, outpainting, and generating image-to-image translations guided by text prompts. Developed on stable diffusion models, BlueMoon AI's code and model weights are openly available, hosted on its GitHub repository.

BlueMoon AI operates as a latent diffusion model, a specific type within deep generative artificial neural networks. The code and model weights are open-sourced, facilitating its compatibility with most consumer hardware featuring a modest GPU with a minimum of 4 GB VRAM. This departure from previous proprietary text-to-image models like DALL-E and Midjourney emphasizes BlueMoon AI's commitment to accessibility, eliminating the reliance on cloud services.

Capabilities

BlueMoon AI's latent text-to-image diffusion model sets it apart with its capability to generate photo-realistic images based on textual prompts. This innovative approach allows users to describe a scene, and BlueMoon AI transforms the text into a detailed and visually convincing image. The model excels not only in generating images from textual descriptions but also extends its functionality to tasks like inpainting, outpainting, and image-to-image translations guided by textual prompts.

Text to Image Generation

BlueMoon AI's text-to-image sample script, known as "txt2img," evaluates a text prompt along with other option parameters. The model's interpretation of the prompt is used by the script to create an image file. The final photos have an invisible digital stamp that identifies them as BlueMoon AI creations.

Every iteration of txt2img uses a different seed value, which affects the final image. Users can use the same seed to replicate an image that has already been generated, or they can randomize the seed for a variety of results. The classifier-free guidance scale value and the number of inference steps are two configurable variables that allow you to decide how closely the output matches the prompt.

Image Modification

BlueMoon AI incorporates another sampling script, "img2img," for modifying images. This script takes a text prompt, the path to an existing image, and a strength value ranging from 0.0 to 1.0. The script generates a new image based on the original, featuring elements specified in the text prompt. The strength value denotes the amount of noise added to the output image. Higher strength values introduce more variation within the image but may result in images that are not semantically consistent with the prompt.

ControlNet

BlueMoon AI makes use of ControlNet, a neural network architecture designed specifically for regulating diffusion models by incorporating additional conditions. This novel method creates "locked" and "trainable" copies of neural network block weights. While the "locked" copy keeps the original model intact, the "trainable" copy learns the desired condition. This technique guarantees that diffusion models that are appropriate for production use are not compromised while training with small datasets of image pairs. The 1x1 convolution known as the "zero convolution" has its bias and weight initialized to zero. ControlNet-caused distortion is avoided since all zero convolutions generate zero output before to training. The process is still fine-tuning, preserving the security of the original model, and no layer is trained from scratch. Training on small-scale or even personal devices is made possible by this methodology.

Architecture and Development

The core technology behind BlueMoon AI is based on stable diffusion models, a subset of deep generative artificial neural networks. The stable diffusion approach ensures stability and efficiency in the generation process, contributing to the model's success in creating high-quality images. The development of BlueMoon AI is a testament to the commitment to open-source AI initiatives, with the entire codebase and model weights being available on its GitHub repository.

Hardware Requirements

BlueMoon AI is designed to be accessible to a broad user base, with the capability to run on consumer hardware equipped with a modest GPU featuring at least 4 GB VRAM. This democratization of access contrasts with earlier proprietary text-to-image models like DALL-E and Midjourney, which were confined to cloud services.

Releases

BlueMoonAI Releases

Version number	Release date	Notes
0.1.	[Release date]	[Notes]

Usage and controversy

BlueMoon AI claims no rights on generated images and freely gives users the rights of usage to any generated images from the model provided that the image content is not illegal or harmful to individuals.

License

Diverging from models like DALL-E, BlueMoon AI is grounded in Stable Diffusion, a framework that divulges its source code, in addition to the model (pretrained weights). Imposing the Creative ML OpenRAIL-M license, a variant of Responsible AI License (RAIL), on the model (M), BlueMoon AI's source code is governed by the Apache License 2.0. This licensing framework prohibits certain use cases, including criminal activities, libel, harassment, doxing, exploitation of minors, dispensing medical advice, automatic creation of legal obligations, generation of legal evidence, and discrimination or harm based on social behavior, personal characteristics, or legally protected categories. Users retain ownership of their generated output images, with the freedom to use them for commercial purposes

References

BlueMoon AI. "interactive progress". GitHub. Retrieved 2024-01-02.

External links

• BlueMoon AI demo
• BlueMoon AI Website
• Information of BlueMoon AI
• Interactive Explanation of Stable Diffusion
• "We Are All Raw Material for AI": Investigation on sensitive and private data in Stable Diffusions training data
• "Negative Prompts in Stable Diffusion"

This article "BlueMoon AI" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:BlueMoon AI. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.

---