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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.
DALL-E was revealed by OpenAI in a blog post on 5 January 2021, and uses a version of GPT-3 [5] modified to generate images.. On 6 April 2022, OpenAI announced DALL-E 2, a successor designed to generate more realistic images at higher resolutions that "can combine concepts, attributes, and styles". [6]
Example of prompt engineering for text-to-image generation, with Fooocus. In 2022, text-to-image models like DALL-E 2, Stable Diffusion, and Midjourney were released to the public. [67] These models take text prompts as input and use them to generate AI-generated images.
In December 2022, Midjourney was used to generate the images for an AI-generated children's book that was created over a weekend. Titled Alice and Sparkle, the book features a young girl who builds a robot that becomes self-aware. The creator, Ammaar Reeshi, used Midjourney to generate a large number of images, from which he chose 13 for the ...
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 ...
In July 2023, the fact-checking company Logically found that the popular generative AI models Midjourney, DALL-E 2 and Stable Diffusion would produce plausible disinformation images when prompted to do so, such as images of electoral fraud in the United States and Muslim women supporting India's Hindu nationalist Bharatiya Janata Party.
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During training, at first only , are used in a GAN game to generate 4x4 images. Then G N − 1 , D N − 1 {\displaystyle G_{N-1},D_{N-1}} are added to reach the second stage of GAN game, to generate 8x8 images, and so on, until we reach a GAN game to generate 1024x1024 images.