LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models
Pretrained diffusion models generate realistic images but are constrained by the statistical biases of their training data, limiting their ability to produce high dynamic range (HDR) content. In this work, we introduce LumaGuide, a training-free framework for distribution shaping in diffusion models. Instead of modifying model parameters, LumaGuide steers the sampling process to match target feature distributions via differentiable energy-based guidance. We instantiate this framework for HDR generation by controlling luminance distributions in perceptually uniform PQ space. Our results show that aligning luminance histograms is sufficient to induce HDR-consistent behavior, including coher...
| Authors | Bowen Chen, Shreshth Saini, Balu Adsumilli et al. |
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| Primary category | cs.CV |
| Published | 28 July 2026 |
| Last updated at source | 6 August 2026 |
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