Stable Attribution
Stable Attribution, the tool designed to credit artists whose work contributed to AI-generated images, is no longer active. Learn about its mission, shutdown, and ethical alternatives.
About Stable Attribution
What Stable Attribution Offered to the Creative Community
Stable Attribution was an experimental tool developed to identify the original sources behind AI-generated images. By analyzing visual outputs and comparing them to public training data, it aimed to give proper credit to the artists whose work was used in training AI models—promoting fairness and transparency in generative art.
Core Features That Set It Apart
The platform’s unique attribution algorithm could detect the most visually similar images from large-scale datasets. This process enabled the tool to identify contributing artists, enhance discoverability, and propose ways artists might be recognized—or even compensated—for their indirect contributions to AI-generated content.
Stable Attribution Has Been Discontinued
Platform Inactive and No Longer Accessible
Stable Attribution is no longer available. The official website has been taken offline, and users can no longer access its attribution tools or training data index. There are no public plans for reactivation or continued development.
Impact on Artists and AI Users
The shutdown of Stable Attribution affects artists and advocates who supported more ethical, transparent use of AI. It also limits researchers, creators, and developers who used the tool to explore responsible data sourcing and artist recognition in AI-generated visual content.
Alternatives and Ethical Considerations
Tools and Practices That Promote Artist Rights
While few tools offer the same attribution-focused capabilities, ongoing initiatives in the creative AI space continue to promote ethical model training and artist collaboration. Efforts like Spawning.ai, Have I Been Trained? , and open datasets with clear opt-out mechanisms support similar values.
Moving Toward Fairer AI Practices
The closure of Stable Attribution highlights the need for broader adoption of tools that prioritize transparency and consent. Users interested in supporting artists and responsible AI can choose platforms that disclose training sources and respect content licensing.