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For example, for generating images that look like ImageNet, the generator should be able to generate a picture of cat when given the class label "cat". In the original paper, [ 1 ] the authors noted that GAN can be trivially extended to conditional GAN by providing the labels to both the generator and the discriminator.
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [128] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [ 129 ] and reweighting training data.
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.
The Stable Diffusion model supports the ability to generate new images from scratch through the use of a text prompt describing elements to be included or omitted from the output. [8] Existing images can be re-drawn by the model to incorporate new elements described by a text prompt (a process known as "guided image synthesis" [ 49 ] ) through ...
Users can use Midjourney through Discord either through their official Discord server, by directly messaging the bot, or by inviting the bot to a third-party server. To generate images, users use the /imagine command and type in a prompt; [23] the bot then returns a set of four images, which users are given the option to upscale. To generate ...
Later, in 2017, a conditional GAN learned to generate 1000 image classes of ImageNet, a large visual database designed for use in visual object recognition software research. [ 51 ] [ 52 ] By conditioning the GAN on both random noise and a specific class label, this approach enhanced the quality of image synthesis for class-conditional models.
In the late 2010s, machine learning, and more precisely generative adversarial networks (GAN), were used by NVIDIA to produce random yet photorealistic human-like portraits. The system, named StyleGAN, was trained on a database of 70,000 images from the images depository website Flickr. The source code was made public on GitHub in 2019. [32]
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.