# Towards demystifying the creativity of diffusion models

DevFeed: [Towards demystifying the creativity of diffusion models](<https://devfeed.tech/articles/towards-demystifying-the-creativity-of-diffusion-models-6909.md>)

Original publisher: [Read original article](<https://research.google/blog/towards-demystifying-the-creativity-of-diffusion-models/>)

Published: 2026-07-15T18:06:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [generation](<https://devfeed.tech/tags/generation.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [iclr](<https://devfeed.tech/tags/iclr.md>), [iclr-2026](<https://devfeed.tech/tags/iclr-2026.md>), [images](<https://devfeed.tech/tags/images.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [research](<https://devfeed.tech/tags/research.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

Google Research explains that diffusion models can generate novel data rather than merely memorize training examples. It attributes this creativity to neural networks learning a smoothed score function, which causes denoising to interpolate between training data points along a hidden data manifold.

## Source excerpt

Algorithms & Theory