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2. Optical character recognition - Wikipedia

   en.wikipedia.org/wiki/Optical_character_recognition

   Video of the process of scanning and real-time optical character recognition (OCR) with a portable scanner. Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and ...

3. Text-to-image model - Wikipedia

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

   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.

4. 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.

5. 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 was founded by Robin Rombach, Andreas Blattmann, and Patrick Esser. [2] As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.

6. DALL-E - Wikipedia

   en.wikipedia.org/wiki/DALL-E

   DALL-E has three components: a discrete VAE, an autoregressive decoder-only Transformer (12 billion parameters) similar to GPT-3, and a CLIP pair of image encoder and text encoder. [22] The discrete VAE can convert an image to a sequence of tokens, and conversely, convert a sequence of tokens back to an image.

7. Contrastive Language-Image Pre-training - Wikipedia

   en.wikipedia.org/wiki/Contrastive_Language-Image...

   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.