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In digital photography and digital video, clipping is a result of capturing or processing an image where the intensity in a certain area falls outside the minimum and maximum intensity which can be represented. It is an instance of signal clipping in the image domain. The clipped area of the image will typically appear as a uniform area of the ...
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 linguistics, clipping, also called truncation or shortening, [1] is word formation by removing some segments of an existing word to create a diminutive word or a clipped compound. Clipping differs from abbreviation , which is based on a shortening of the written, rather than the spoken, form of an existing word or phrase.
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]
Clipping, in the context of computer graphics, is a method to selectively enable or disable rendering operations within a defined region of interest. Mathematically, clipping can be described using the terminology of constructive geometry .
The Nicholl–Lee–Nicholl algorithm is a fast line-clipping algorithm that reduces the chances of clipping a single line segment multiple times, as may happen in the Cohen–Sutherland algorithm. The clipping window is divided into a number of different areas, depending on the position of the initial point of the line to be clipped.
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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.
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