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Before the rise of deep learning, [when?] attempts to build text-to-image models were limited to collages by arranging existing component images, such as from a database of clip art. [2] [3] The inverse task, image captioning, was more tractable, and a number of image captioning deep learning models came prior to the first text-to-image models. [4]
DALL-E was developed and announced to the public in conjunction with CLIP (Contrastive Language-Image Pre-training). [23] 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 ...
In text-to-image retrieval, users input descriptive text, and CLIP retrieves images with matching embeddings. In image-to-text retrieval, images are used to find related text content. CLIP’s ability to connect visual and textual data has found applications in multimedia search, content discovery, and recommendation systems. [31] [32]
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.
An example of prompt usage for text-to-image generation, using Fooocus. Prompts for some text-to-image models can also include images and keywords and configurable parameters, such as artistic style, which is often used via keyphrases like "in the style of [name of an artist]" in the prompt [88] and/or selection of a broad aesthetic/art style.
This is one of the largest collections of public domain images online (clip art and photos), and the fastest-loading. Maintainer vets all images and promptly answers email inquiries. Open Clip Art – This project is an archive of public domain clip art. The clip art is stored in the W3C scalable vector graphics (SVG) format.
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Re-captioning is used to augment training data, by using a video-to-text model to create detailed captions on videos. [ 7 ] OpenAI trained the model using publicly available videos as well as copyrighted videos licensed for the purpose, but did not reveal the number or the exact source of the videos. [ 5 ]