This video snippet explains the basic concept of how diffusion models generate images. It likens the process to a Rorschach inkblot test, starting with a blurry, noisy canvas and gradually refining details until a recognizable image emerges. This concise analogy helps viewers grasp the iterative nature of diffusion models, which differ from other AI image generation methods.
Several popular image generators utilize diffusion models (these are some, as of the time of this posting):
Stable Diffusion: An open-source model known for its flexibility and customization options. It's available through various platforms and software.
DALL·E 2: Developed by OpenAI, this model creates images from text prompts and offers features like inpainting and variations.
Imagen: Google's text-to-image model that produces high-quality photorealistic images. It's currently not publicly available.
Midjourney: A model accessible through a Discord server, known for its artistic and dreamlike image generation style.
These are just a few examples, and the field of diffusion models is rapidly evolving, with new models and applications emerging constantly.
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Dave Grenier
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