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Google’s Whisk is an image-to-image generator, building upon the popular concept of text-to-image generators. ... hairstyle or skin tone as the prompt images, Google said in a blog post.
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.
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 ...
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [116] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [117] and reweighting training data. [118]
It can generate images based on a prompt that mixes images and text. No further information available. [70] Imagen 3 (2024-05) is too. No further information available. [71] Veo (2024) generates videos by latent diffusion. The diffusion is conditioned on a vector that encodes both a text prompt and an image prompt. [72]
A depth-guided model, named "depth2img", was introduced with the release of Stable Diffusion 2.0 on November 24, 2022; this model infers the depth of the provided input image, and generates a new output image based on both the text prompt and the depth information, which allows the coherence and depth of the original input image to be ...