Stable Attribution is a tool designed to credit artists whose works have been used in training AI image generation models. By analyzing AI-generated images, it identifies and attributes the original human-created images that influenced the output. This process ensures that artists receive proper recognition and potential compensation for their contributions, fostering ethical collaboration between AI technologies and the creative community. Key Features and Functionality: - Attribution Identification: Decodes AI-generated images to determine the most similar examples from the training data, effectively tracing back to the original artists' works. - Ethical Collaboration: Promotes transparency by highlighting the human-created images that influenced AI outputs, enabling users to discover and support original artists. - Compensation Mechanism: Opens avenues for artists to earn passive income by sharing revenue generated from AI applications that utilize their works. Primary Value and User Solutions: Stable Attribution addresses the ethical concerns surrounding AI-generated content by ensuring artists are credited and compensated for their contributions. It solves the problem of lost attribution in AI training processes, providing a transparent link between AI outputs and original human creations. This not only upholds artists' rights but also enhances the integrity and trustworthiness of AI-generated content.