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license: cc0-1.0 |
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tags: |
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- art |
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- stable-diffusion |
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- text-to-image |
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--- |
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# CC0_rebuild_attempt |
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**Version Number:** 0.1 |
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## Summary |
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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. |
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A mixed technic was used to create te capitons of images, the datatset was segmented my subject and for each it was used a different method such as: GIT for realistic and photo images and CLIP for illustrations at the final everything was human correct. |
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### Training Overview |
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**Input:** Manual captioned images |
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**Output:** Images |
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**Architecture:** Stable Diffusion 1.5 |
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## Performance Limitations |
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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. |
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## Training Dataset Limitations |
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The model is trained on images and content from the following sources: |
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- **Pexels:** Pexels License |
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- **LIBRESHOT:** CC0 |
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- **Unsplash:** Lite Dataset License |
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- **opengameart.org:** CC0 |
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- **Authors:** CC0 |
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- **Contributors:** CC0 |
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- **Met Museum Open Access** CC0 |
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Dataset sample: |
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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. |
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It is important to note that while every effort has been made to ensure the model generates ethical and high-quality content, it is not possible to guarantee that the model will always avoid producing unwanted content or achieve the highest quality in every instance. This project represents an attempt to create an ethical model within the constraints of the available datasets and legal considerations. |
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Copyright laws differ from country to country, and this project acknowledges the necessity of establishing guidelines for considering public domain content. It is hoped that this research will inspire others to build more responsible models, taking into account the complexities of copyright laws and the ethical use of training data. |
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## Associated Risks |
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* The model might struggle with generating highly detailed text within images. |
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* There may be limitations in creating complex scenes that require deep compositional understanding. |
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* The quality and diversity of generated images are dependent on the availability and variety of CC0 and permissive content. |
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* Potential bias towards subjects and styles that are more commonly found in CC0 and permissive content datasets and the manual capition. |
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* Chek the original open rail |
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## Intended Uses |
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* Generative art and design projects |
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* Educational tools and research in generative AI |
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* Creative experimentation and artistic expression |
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* Reference for ethical development |
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* Please check OpenRAIL and make responsable use of open framework |
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## Authors' Note |
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I firmly believe that this work will quickly be forgotten. Many massive models have used, and continue to use, unauthorized content, and this is unlikely to change. It's a sad reality that has happened before and will keep happening. However, this is the first time these technologies are open source and widely available for everyone. Neither governments nor companies will decide the fate of society, but the work and actions of individual people will. |
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We might have to change how we think about what is considered original or creative. This shift in perspective is crucial as we navigate the evolving landscape of AI and copyright. I hope this work inspires more responsible and ethical development practices in the field of generative AI. |
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