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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. 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. Ideogram (text-to-image model) - Wikipedia

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

   Ideogram is a freemium text-to-image model developed by Ideogram, Inc. using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The model is capable of generating legible text in the images compared to other text-to-image models. [1] [2]

5. Google’s new AI tool uses image prompts instead of text

   www.aol.com/google-ai-tool-uses-image-214855525.html

   Since OpenAI initially launched its text-to-image creation tool, Dall-E, in 2021, the concept of AI-generated artwork has swamped social media and become a focus of consumer products. Google’s ...

6. Here's how to spot AI-generated images, videos, audio and text

   www.aol.com/heres-spot-ai-generated-images...

   AI-generated images: Look at the context for red flags. It’s hard to spot fakes, so the most reliable cause for suspicion is often the context in which you see an image.

7. Stable Diffusion - Wikipedia

   en.wikipedia.org/wiki/Stable_Diffusion

   Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...