In the context of machine learning, diffusion refers to a type of generative AI model that can create random noise (think of the fuzzy snow/white noise that you could see on a TV) from an input, and then “de-noise” that fuzzy noise that was generated from the input into a comprehensible image. Examples of such models can be found in Stable Diffusion and DALL-E. Applications of such that could be used within animation include: Image generation from text. Making images that come out “noisy” (think taking a picture in the dark) from a CG/3D render look correct without having to re-do the expensive and time-consuming computer process that made the image.

Diffusion | Animation Guild

Diffusion

Kim Fay / September 10, 2024

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In the context of machine learning, diffusion refers to a type of generative AI model that can create random noise (think of the fuzzy snow/white noise that you could see on a TV) from an input, and then “de-noise” that fuzzy noise that was generated from the input into a comprehensible image.

1. Examples of such models can be found in Stable Diffusion and DALL-E.
2. Applications of such that could be used within animation include:
  1. Image generation from text.
  2. Making images that come out “noisy” (think taking a picture in the dark) from a CG/3D render look correct without having to re-do the expensive and time-consuming computer process that made the image.

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