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Stable attribution was built by jeff huber and anton troynikov, with the help and feedback of many others Packy and anton discuss the development of stable attribution, how to understand the data underlying these sophisticated ai models, and the implications of attribution for creators and artists. We built stable attribution because we saw a way to solve a real problem, for real people, using technology we understand.

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Upload an image from stable diffusion to trace back to the original creators and easily share their attribution link, ensuring proper recognition for their work. Stable attribution is at the forefront of ai image generation technology, offering sophisticated tools that bridge the gap between artificial creation and attribution integrity. A tool which lets anyone find the human creators behind ai generated images

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Features of consistent attribution are present when an a.i

Model is taught to produce images based on text, using an extensive dataset of images and their corresponding captions. Stable attribution is the human tendency to infer that events and behaviors are due to unchanging factors Heavily based on past outcomes, both positive and negative, these types of inferences rely heavily on both luck and effort. Stable attribution lets you upload an image to find similar pictures from the stable diffusion training dataset

It searches for images that closely match your upload and shows you a list with similarity scores, helping you see which content may have influenced the image.