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Optical generative models.

Shiqi Chen1,2,3, Yuhang Li1,2,3, Yuntian Wang1,2,3

  • 1Electrical and Computer Engineering Department, University of California Los Angeles, Los Angeles, CA, USA.

Nature
|August 27, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed optical generative models that use light to create novel images, inspired by digital diffusion models. These models offer a fast, energy-efficient alternative for AI-generated content, comparable to digital methods.

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Area of Science:

  • Artificial Intelligence
  • Optics and Photonics
  • Computer Vision

Background:

  • Digital generative models are expanding into diverse applications like image synthesis and NLP.
  • Scalable and energy-efficient inference presents a significant challenge for large digital generative models.

Purpose of the Study:

  • To introduce and demonstrate optical generative models for AI-generated content.
  • To explore energy-efficient and scalable inference using optical methods.

Main Methods:

  • Developed optical generative models utilizing a digital encoder and a free-space reconfigurable optical decoder.
  • Employed phase patterns as optical generative seeds processed all-optically to generate images.
  • Leveraged visible light for experimental demonstration, generating various image types.

Main Results:

  • Successfully generated monochrome and multicolour images of handwritten digits, fashion items, butterflies, human faces, and artworks.
  • Achieved performance comparable to digital neural network-based generative models.
  • Demonstrated energy-efficient image synthesis with minimal computing power consumption during inference.

Conclusions:

  • Optical generative models offer a promising pathway for energy-efficient and scalable AI inference.
  • This approach leverages optics and photonics for novel artificial intelligence-generated content.
  • The developed models show potential for future applications in AI and content generation.