license: cc0-1.0
tags:
- art
- stable-diffusion
- text-to-image
CC0_rebuild_attempt
Version Number: 0.1
Summary
CC0_rebuild_attempt is a text-to-image model based on the Stable Diffusion 1.5 architecture. It is trained exclusively on CC0 images and other permissive content, aiming to produce high-quality artistic images from given text prompts. The goal is to create a robust and versatile model while ensuring the dataset used is entirely within the public domain, allowing for unrestricted use.
Training Overview
Input: Manual captioned images
Output: Images
Architecture: Stable Diffusion 1.5
Performance Limitations
CC0_rebuild_attempt may face challenges in generating highly detailed or realistic images due to the constraints of the CC0 and permissive content datasets. Additionally, the model may underperform in specific domains where high-quality, diverse CC0 images are less prevalent.
Training Dataset Limitations
The model is trained on images and content from the following sources:
- Pexels: Pexels License
- LIBRESHOT: CC0
- Unsplash: Lite Dataset License
- opengameart.org: CC0
- Authors: CC0
- Contributors: CC0
These datasets may not cover all possible themes or subjects comprehensively. The dataset may lack representation of certain modern or niche topics due to the limited availability of such content under these licenses. Additionally, the model was trained and developed with a focus on compliance with the Brazilian Copyright Act, which imposes stricter regulations compared to other jurisdictions due to the absence of fair use provisions.
Associated Risks
- The model might struggle with generating highly detailed text within images.
- There may be limitations in creating complex scenes that require deep compositional understanding.
- The quality and diversity of generated images are dependent on the availability and variety of CC0 and permissive content.
- Potential bias towards subjects and styles that are more commonly found in CC0 and permissive content datasets.
Intended Uses
- Generative art and design projects
- Educational tools and research in generative AI
- Creative experimentation and artistic expression
- Safe deployment in environments where restrictive content licenses are a concern