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Getty Images Launches Artificial Intelligence Image Generator

On Tuesday (26), the Getty Images platform launched an Artificial Intelligence (AI) image generator for businesses, allowing users to create unique materials from the library of real photos.

According to the developer, Getty Images’ Generative AI is trained on the state-of-the-art Edify model architecture, which is part of the NVIDIA Picasso platform for creating generative AI models for visual design. This innovation was trained on real content from the company’s library, focusing on creative media.

“We are excited to launch a tool that explores the power of generative AI to meet the business needs of our customers, while also respecting the intellectual property of content creators,” said Craig Peters, CEO of Getty Images. “We have worked hard to develop a responsible tool that provides customers with confidence in the visual resources produced by generative AI for commercial purposes,” he added.

The AI seamlessly integrates with the platform’s library, allowing all customers to streamline the creative process and find the appropriate visual content for any need in one place. Images created using AI will have Getty Images’ standard royalty-free license, allowing the use of the content anywhere.

Getty Images’ generative AI is now available on GettyImages.com. Customers can also choose to integrate the service into their existing workflows and applications through an available API. Looking ahead, the developer has confirmed the possibility of customizing the AI with a unique style and language for businesses.

“We have talked to our customers about the rapid growth of generative AI – we have heard both excitement and hesitation – and we have tried to be intentional about how we develop our own tool,” said Grant Farhall, Chief Product Officer (CPO) at Getty Images.

“We have created a service that allows brands and marketers to adopt AI safely and expand their creative possibilities, while also compensating creators for including their visual resources in the underlying training sets,” he concluded.

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