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An approach to generate high-quality images faster

Diffusion models, like Stable Diffusion and DALL-E, create highly detailed images by gradually removing random noise from each pixel. This process is repeated multiple times (sometimes 30+ steps), allowing the model to refine its output and fix mistakes, resulting in high-quality images. However, this method is slow and requires significant computing power. Autoregressive models, often used for text prediction, can also generate images. They do this by sequentially predicting small patches of an image, a few pixels at a time. These models are faster because they skip the iterative process but can’t correct errors once made. They use an autoencoder Read Full Article At: An approach to generate high-quality images faster