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CSS image replacement is a Web design technique that uses Cascading Style Sheets to replace text on a Web page with an image containing that text. It is intended to keep the page accessible to users of screen readers, text-only web browsers, or other browsers where support for images or style sheets is either disabled or nonexistent, while allowing the image to differ between styles.
Tailwind CSS is an open-source CSS framework. Unlike other frameworks, like Bootstrap , it does not provide a series of predefined classes for elements such as buttons or tables. Instead, it creates a list of "utility" CSS classes that can be used to style each element by mixing and matching.
Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs was founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.
To demonstrate specificity Inheritance Inheritance is a key feature in CSS; it relies on the ancestor-descendant relationship to operate. Inheritance is the mechanism by which properties are applied not only to a specified element but also to its descendants. Inheritance relies on the document tree, which is the hierarchy of XHTML elements in a page based on nesting. Descendant elements may ...
Bootstrap (formerly Twitter Bootstrap) is a free and open-source CSS framework directed at responsive, mobile-first front-end web development. It contains HTML , CSS and (optionally) JavaScript -based design templates for typography , forms , buttons , navigation , and other interface components.
Draw: add shapes or text to an image. Decorate: add a border or frame to an image. Special effects: blur, sharpen, threshold, or tint an image. Animation: assemble a GIF animation file from a sequence of images. Text and comments: insert descriptive or artistic text in an image. Image identification: describe the format and attributes of an image.
Ideogram was founded in 2022 by Mohammad Norouzi, William Chan, Chitwan Saharia, and Jonathan Ho to develop a better text-to-image model. [3]It was first released with its 0.1 model on August 22, 2023, [4] after receiving $16.5 million in seed funding, which itself was led by Andreessen Horowitz and Index Ventures.
Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model ), is adapted such that it can generate images of novel, user-provided concepts.