title: LightLab: Controlling Light Sources in Images with Diffusion Models
summary: We present a simple, yet effective diffusion-based method for fine-grained,
  parametric control over light sources in an image. Existing relighting methods
  either rely on multiple input views to perform inverse rendering at inference
  time, or fail to provide explicit control over light changes. Our method
  fine-tunes a diffusion model on a small set of real raw photograph pairs,
  supplemented by synthetically rendered images at scale, to elicit its
  photorealistic prior for relighting. We leverage the linearity of light to
  synthesize image pairs depicting controlled light changes of either a target
  light source or ambient illumination. Using this data and an appropriate
  fine-tuning scheme, we train a model for precise illumination changes with
  explicit control over light intensity and color. Lastly, we show how our method
  can achieve compelling light editing results, and outperforms existing methods
  based on user preference.
paper_authors._id: 68262ba929fe8333cdfec69c
paper_authors.name: Nadav Magar
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-15T18:00:14.378Z
paper_authors._id: 6698f6ee004e418e71fbbdef
paper_authors.avatarUrl: /avatars/3c2d521c5ece4fc5a0a483a3238bf659.svg
paper_authors.fullname: Nadav Magar
paper_authors.type: user
paper_authors.user: NadMag
paper_authors._id: 68262ba929fe8333cdfec69d
paper_authors.name: Amir Hertz
paper_authors._id: 68262ba929fe8333cdfec69e
paper_authors.name: Eric Tabellion
paper_authors._id: 68262ba929fe8333cdfec69f
paper_authors.name: Yael Pritch
paper_authors._id: 68262ba929fe8333cdfec6a0
paper_authors.name: Alex Rav-Acha
paper_authors._id: 68262ba929fe8333cdfec6a1
paper_authors.name: Ariel Shamir
paper_authors._id: 68262ba929fe8333cdfec6a2
paper_authors.name: Yedid Hoshen
paper_title: LightLab: Controlling Light Sources in Images with Diffusion Models
paper_summary: We present a simple, yet effective diffusion-based method for fine-grained,
  parametric control over light sources in an image. Existing relighting methods
  either rely on multiple input views to perform inverse rendering at inference
  time, or fail to provide explicit control over light changes. Our method
  fine-tunes a diffusion model on a small set of real raw photograph pairs,
  supplemented by synthetically rendered images at scale, to elicit its
  photorealistic prior for relighting. We leverage the linearity of light to
  synthesize image pairs depicting controlled light changes of either a target
  light source or ambient illumination. Using this data and an appropriate
  fine-tuning scheme, we train a model for precise illumination changes with
  explicit control over light intensity and color. Lastly, we show how our method
  can achieve compelling light editing results, and outperforms existing methods
  based on user preference.
paper_ai_summary: A diffusion-based method fine-tuned on real and synthetic image pairs provides precise control over light sources and ambient illumination in images, offering more effective relighting than existing methods.
paper_ai_keywords: diffusion-based method fine-tuning diffusion model relighting inverse rendering photorealistic prior linearity of light light intensity light color light editing