Pre-Publication V2. A generative artificial intelligence model that can be directed by text or image prompts to intends to produces novel image or video content (images, video, audio, or other media) by starting from random noise and attempting to iteratively refine it into a coherent output, optionally guided by multi-modal inputs. through a two-step process beginning with forward diffusion, or adding “noise” until an image appears as static, and then reverse diffusion, or continually removing noise until a desired output is achieved. For example, an image generator would take a real image and slowly add random pixels until they become pure static and unrecognizable, then reverse this process to create a new clear, realistic image.
Deliberation Summary:
(1) Comment that the two-step description was technically inaccurate, because the noise-adding step occurs during training while generation begins from a blank latent and samples from what the model was trained on, rather than degrading an existing image. (2) Language edit on the use of “novel” or “new” terminology which may wrongly imply that the result was not derived from existing work. (3) Strike the additional explanatory clause to enhance clarity.
Pre-Publication V1. A generative artificial intelligence model that can be directed by text or image prompts to produce novel image or video content through a two-step process beginning with forward diffusion, or adding “noise” until an image appears as static, and then reverse diffusion, or continually removing noise until a desired output is achieved. For example, an image generator would take a real image and slowly add random pixels until they become pure static and unrecognizable, then reverse this process to create a new clear, realistic image.