LumaFlux: Structure-Aware SDR-to-HDR Conversion with Monotone Luminance Flows

The rapid adoption of HDR-capable displays has created a growing need to con- vert legacy and user-generated 8-bit Standard Dynamic Range (SDR) content into perceptually faithful and physically plausible 10-bit High Dynamic Range (HDR). Existing inverse tone-mapping methods often rely on fixed tone map- pings or narrowly defined training degradations, which can lead to clipped high- lights, chromatic shifts, and temporally unstable tone reproduction. We intro- duce LUMAFLUX, an Diffusion Transformer (DiT) conditioned directly on phys- ical measurements of the SDR input, while training only 0.57% of total param- eters. LUMAFLUX combines Physically-Guided Adaptation (PGA), Perceptual Cross-Modulation (PCM), an HDR Residual Coupler, and a monotone Rational- Quadratic Spline (RQS) tone-field decoder to recover HDR appearance while preserving the luminance ordering of the input. HDR latents are reconstructed from an SDR-conditioned endpoint through direct rectified-flow transport in only 8 steps. The same image model extends to video inverse tone mapping without temporal modules or video-specific training, shared bridge noise and temporally smoothed RQS parameters stabilize deterministic framewise transport. We further curate a 314,396-pair UGC and PGC training corpus in PQ BT.2020 and introduce LumaEval, a unified evaluation protocol spanning eight SDR degradation types. On LumaEval, LUMAFLUX achieves 24.23 dB PSNR and 56.54 ∆EITP, improv- ing over the strongest baseline by 0.49 dB and 5.06, respectively, while reducing FR-HIDROVQA from 0.703 for HDRTVNet++ to 0.631. For video, temporal stabilization reduces signed excess flicker by 53.3%, while pooled raw flicker re- mains within 3.3% of the HDR reference. Code will be released.