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      <title>Researchers Attempt to Uncover the Origins of Creativity in Diffusion Models</title>
      <link>https://www.infoq.com/news/2025/07/diffusion-model-creativity/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Stability+Diffusion-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2025/07/diffusion-model-creativity/en/headerimage/diffusion-models-creativity-1751814254644.jpeg"/&gt;&lt;p&gt;In a recent paper, Stanford researchers Mason Kamb and Surya Ganguli proposed a mechanism that could underlie the creativity of diffusion models. The mathematical model they developed suggests that this creativity is a deterministic consequence of how those models use the denoising process to generate images.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Generative AI</category>
      <category>Neural Networks</category>
      <category>Stability Diffusion</category>
      <category>AI, ML &amp; Data Engineering</category>
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      <pubDate>Sun, 06 Jul 2025 16:00:00 GMT</pubDate>
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      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2025-07-06T16:00:00Z</dc:date>
      <dc:identifier>/news/2025/07/diffusion-model-creativity/en</dc:identifier>
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