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2. Flux (text-to-image model) - Wikipedia

   en.wikipedia.org/wiki/Flux_(text-to-image_model)

   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 were founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.

3. DALL-E - Wikipedia

   en.wikipedia.org/wiki/DALL-E

   The discrete VAE can convert an image to a sequence of tokens, and conversely, convert a sequence of tokens back to an image. This is necessary as the Transformer does not directly process image data. [22] The input to the Transformer model is a sequence of tokenized image caption followed by tokenized image patches.

4. Inkscape - Wikipedia

   en.wikipedia.org/wiki/Inkscape

   Inkscape is a vector graphics editor.It is used for both artistic and technical illustrations such as cartoons, clip art, logos, typography, diagrams, and flowcharts.It uses vector graphics to allow for sharp printouts and renderings at unlimited resolution and is not bound to a fixed number of pixels like raster graphics.

5. Dia (software) - Wikipedia

   en.wikipedia.org/wiki/Dia_(software)

   Dia has special objects to help draw entity-relationship models, Unified Modeling Language (UML) diagrams, flowcharts, network diagrams, and simple electrical circuits. It is also possible to add support for new shapes by writing simple XML files, using a subset of Scalable Vector Graphics (SVG) to draw the shape.

6. diagrams.net - Wikipedia

   en.wikipedia.org/wiki/Diagrams.net

   Its interface can be used to create diagrams such as flowcharts, wireframes, UML diagrams, organizational charts, and network diagrams. [5] diagrams.net is available as an online web app, and as an offline desktop application for Linux, macOS, and Windows. [5] Its offline application is built using the Electron framework.

7. Text-to-image personalization - Wikipedia

   en.wikipedia.org/wiki/Text-to-image_personalization

   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.