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CLIP is a separate model based on contrastive learning that was trained on 400 million pairs of images with text captions scraped from the Internet. Its role is to "understand and rank" DALL-E's output by predicting which caption from a list of 32,768 captions randomly selected from the dataset (of which one was the correct answer) is most ...
CLIP has been used in various domains beyond its original purpose: Image Featurizer: CLIP's image encoder can be adapted as a pre-trained image featurizer. This can then be fed into other AI models. [1] Text-to-Image Generation: Models like Stable Diffusion use CLIP's text encoder to transform text prompts into embeddings for image generation. [3]
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
AI-based images have become more commonplace in art markets and search engines because AI-based text-to-image systems are trained from pre-existing artistic images, sometimes without the original artist's consent, allowing the software to mimic specific artists' styles.
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Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot), [85] Google Photos, [86] and the Adobe Suite (Adobe Firefly). [87] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [88] language model.
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