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2. 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.

3. Microsoft Copilot - Wikipedia

   en.wikipedia.org/wiki/Microsoft_Copilot

   In March 2023, Bing incorporated Image Creator, an AI image generator powered by OpenAI's DALL-E 2, which can be accessed either through the chat function or a standalone image-generating website. [37] In October, the image-generating tool was updated to use the more recent DALL-E 3. [38]

4. Microsoft Bing - Wikipedia

   en.wikipedia.org/wiki/Microsoft_Bing

   Microsoft Bing, commonly referred to as Bing, is a search engine owned and operated by Microsoft. The service traces its roots back to Microsoft's earlier search engines, including MSN Search , Windows Live Search , and Live Search .

5. DALL-E - Wikipedia

   en.wikipedia.org/wiki/DALL-E

   AI-driven image generation tools have been heavily criticized by artists because they are trained on human-made art scraped from the web." [7] The second is the trouble with copyright law and data text-to-image models are trained on. OpenAI has not released information about what dataset(s) were used to train DALL-E 2, inciting concern from ...

6. Artificial intelligence art - Wikipedia

   en.wikipedia.org/wiki/Artificial_intelligence_art

   In the 2020s, text-to-image models, which generate images based on prompts, became widely used, marking yet another shift in the creation of AI generated artworks. [ 2 ] In 2021, using the influential large language generative pre-trained transformer models that are used in GPT-2 and GPT-3 , OpenAI released a series of images created with the ...

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.