LumaFlux: Lifting 8-Bit Worlds to HDR Reality with Physically-Guided Diffusion Transformers

Shreshth Saini; Hakan Gedik; Neil Birkbeck; Yilin Wang; Balu Adsumilli; Alan C. Bovik

cs.CVApr 3, 2026

LumaFlux: Lifting 8-Bit Worlds to HDR Reality with Physically-Guided Diffusion Transformers

Authors: Shreshth Saini, Hakan Gedik, Neil Birkbeck, Yilin Wang, Balu Adsumilli, Alan C. Bovik

Organizations: The University of Texas at Austin · Google, Inc.

Abstract

The rapid adoption of HDR-capable devices has created a pressing need to convert the 8-bit Standard Dynamic Range (SDR) content into perceptually and physically accurate 10-bit High Dynamic Range (HDR). Existing inverse tone-mapping (ITM) methods often rely on fixed tone-mapping operators that struggle to generalize to real-world degradations, stylistic variations, and camera pipelines, frequently producing clipped highlights, desaturated colors, or unstable tone reproduction. We introduce LumaFlux, a first physically and perceptually guided diffusion transformer (DiT) for SDR-to-HDR reconstruction by adapting a large pretrained DiT. Our LumaFlux introduces (1) a Physically-Guided Adaptation (PGA) module that injects luminance, spatial descriptors, and frequency cues into attention through low-rank residuals; (2) a Perceptual Cross-Modulation (PCM) layer that stabilizes chroma and texture via FiLM conditioning from vision encoder features; and (3) an HDR Residual Coupler that fuses physical and perceptual signals under a timestep- and layer-adaptive modulation schedule. Finally, a lightweight Rational-Quadratic Spline decoder reconstructs smooth, interpretable tone fields for highlight and exposure expansion, enhancing the output of the VAE decoder to generate HDR. To enable robust HDR learning, we curate the first large-scale SDR-HDR training corpus. For fair and reproducible comparison, we further establish a new evaluation benchmark, comprising HDR references and corresponding expert-graded SDR versions. Across benchmarks, LumaFlux outperforms state-of-the-art baselines, achieving superior luminance reconstruction and perceptual color fidelity with minimal additional parameters.

Figures & tables

Figure 1: We present LumaFlux , a universal Inverse Tone Mapping (ITM) framework that expands dynamic range and color-gamut from 8 bit BT.709 SDR to 10 bit BT.2020 HDR with PQ encoding (i.e. SDRTV-to-HDRTV) of real-world LQ SDR videos. Figure shows examples from Luma-Eval benchmark. Each pair illustrates the SDR $\!\rightarrow$ HDR conversion result for one representative frame. LumaFlux restores a substantially broader dynamic range and corrects the color saturation lost during tone-mapping compression, recovering both diffuse texture and specular highlight detail.

Figure 2: Architectural Paradigms. (Left) Baseline Diffusion Transformer (DiT) architecture, where the backbone can be directly fine-tuned via LoRA or full-weight updates. Such adaptation often overfits small HDR datasets and leads to texture hallucination or unstable luminance restoration. (Right) The proposed LumaFlux introduces lightweight, physically interpretable modules- PGA , PCM , and the HDR Residual Coupler , inserted into the frozen MM-DiT backbone. These modules enable prompt-free, physically guided adaptation by modulating attention and MLP activations with luminance, frequency, and perceptual cues. This preserves the pretrained generative prior while allowing accurate ITM with only a few trainable parameters. (Best viewed zoomed in)

Figure 3: LumaFlux Overview. SDR input is split into physical ( $T_{\text{phys}}$ ) and perceptual ( $T_{\text{perc}}$ ) streams. With timestep/layer conditioning $\Psi(t,\ell)$ , In each Luma-MMDiT block (see Fig. 2 ), PGA injects luminance and spectrum aware LoRA updates into attention, PCM applies FiLM ( Perez et al., 2018 ) to modulate normalized features, and an HDR Residual Coupler fuses both cues. A frozen VAE decoder with an RQS tone-field head reconstruct HDR in PQ/BT.2020.

Method	HDRTV1K				HDRTV4K				Luma-Eval
Method	PSNR $\uparrow$	SSIM $\uparrow$	HDR-VDP3 $\uparrow$	$\Delta E_{\mathrm{ITP}}\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	HDR-VDP3 $\uparrow$	$\Delta E_{\mathrm{ITP}}\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	HDR-VDP3 $\uparrow$	$\Delta E_{\mathrm{ITP}}\downarrow$
HDRTVNet++ ( Chen et al., 2021 )	38.36	0.973	8.75	8.28	30.82	0.881	8.12	7.85	36.54	0.901	8.22	7.35
ICTCPNet ( Huang et al., 2023 )	36.59	0.922	8.57	7.79	33.12	0.977	8.90	6.75	34.45	0.919	8.74	6.93
HDRTVDM ( Guo et al., 2023 )	36.98	0.971	8.55	10.84	30.15	0.886	7.90	9.95	35.10	0.903	8.24	9.44
HDCFM ( He et al., 2022 )	38.42	0.973	8.52	7.83	33.25	0.908	8.20	7.42	36.78	0.915	8.29	7.20
Deep SR-ITM ( Kim et al., 2019b )	37.10	0.969	8.23	9.24	26.59	0.812	6.92	8.88	33.21	0.875	7.41	8.41

Table 1: Quantitative comparison across benchmarks. We compare LumaFlux with CNN-based and generative methods. Metrics are computed in PU21 space for luminance ( PSNR , SSIM ), HDR domain ( HDR-VDP3 ), and perceptual color (

\mathbf{\Delta E_{\mathrm{ITP}}}

, lower is better). LumaFlux achieves the best overall performance across datasets, consistently improving both dynamic range and colorimetric fidelity. Best results are in bold , second best are underlined .

TMO / Degradation	PSNR $\uparrow$	PSNR(Y) $\uparrow$	$\Delta E_{\mathrm{ITP}}\downarrow$	HDR-VDP3 $\uparrow$	HDR-LPIPS $\downarrow$	FR-HIDROVQA $\uparrow$
OCIOv2	37.85	38.94	5.41	9.12	0.078	82.6
2446c+GM	38.31	39.12	5.18	9.27	0.071	84.1
HC+GM	37.23	38.15	5.66	8.95	0.083	80.4
2446a	36.91	37.62	5.97	8.72	0.087	79.6
Reinhard	35.88	36.42	6.45	8.31	0.091	78.3
YouTube LogC	35.12	35.88	6.81	8.05	0.094	77.9

Table 2: Luma-Eval breakdown by TMO. Results show generalization of LumaFlux across SDR degradations.

Variant	PSNR $\uparrow$	PSNR(Y) $\uparrow$	$\Delta E_{\mathrm{ITP}}\downarrow$	HDR-VDP3 $\uparrow$	HDR-LPIPS $\downarrow$	FR-HIDROVQA $\uparrow$
Flux + LoRA only	33.42	34.28	8.58	7.82	0.136	72.9
+ PGA (no spectral)	34.94	35.81	7.62	8.18	0.122	75.2
+ PGA (with spectral gating)	35.18	36.02	7.31	8.29	0.116	76.3
+ PCM (SigLIP FiLM)	35.89	36.73	6.78	8.46	0.107	78.6
+ RQS (linear)	35.72	36.54	6.85	8.41	0.108	78.0
+ RQS (monotone spline)	35.98	36.84	6.09	8.61	0.087	80.8

Table 3: Ablations for LumaFlux. Highest performance boost arises from PGA, Spectral gating improves highlight roll-off and structure preservation. Adding PCM yields modest chroma stabilization, while RQS (linear) causes over-contrast and banding. The final RQS (monotone spline) restores perceptual smoothness and broadens effective dynamic range.

Method	Brightness $\uparrow$	Color $\uparrow$	Overall $\uparrow$
BT.2446c	2.8	2.9	2.7
HDRTVNet++	3.5	3.6	3.5
LEDiff	3.1	4.0	4.0
PromptIR	3.2	3.8	3.6
LumaFlux (ours)	3.8	4.5	4.2

Table 4: User study (10 raters, 10 clips). Mean Opinion Scores (MOS, 1–5) on perceived brightness, color naturalness, and overall HDR realism.

Panel of Figure 8 — Algorithm 1 SDR $\!\rightarrow$ HDR conversion using LumaFlux

	Expert SDR				BT.2446c SDR
Method	PSNR	$\Delta E_{\mathrm{ITP}}$	HDR - VDP3	HDR - LPIPS	PSNR	$\Delta E_{\mathrm{ITP}}$	HDR - VDP3	HDR - LPIPS
LEDiff	35.12	6.81	8.05	0.094	35.88	6.45	8.31	0.091
PromptIR	36.48	5.89	8.64	0.086	37.23	5.66	8.95	0.083
LumaFlux	37.11	5.53	9.03	0.079	38.31	5.18	9.27	0.071

Table 5: Per-family LIVE - TMHDR - Eval . Expert SDR values come from the “Expert Graded SDR” row in Table 2; BT.2446c SDR values come from the “2446c+GM” row.

Split	#Vid.	Peak	Notes
Train (HIDROVQA)	300	1000	controlled HDR
Train (HDR - UGC - 44K)	$\sim$ 3000	1000	diverse UGC
Train (LIVE - TMHDR)	30	1000	held-out HDR
Eval (HDRTV1K)	std.	1000	synthetic TM
Eval (HDRTV4K)	std.	1000	real+synthetic
Eval (LIVE - TMHDR - Eval)	10	1000	expert+synthetic SDR

Table 6: Unified PQ BT.2020 normalization and peak luminance across all splits.

HDR Imaging

High dynamic range imaging captures or reconstructs scene brightness beyond standard images, preserving detail in shadows and highlights.

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Abstract

HDR Imaging

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