GLOW-FM: Efficient Generative Low-Light Image Enhancement with One-Step Latent Flow Matching

Ariel Lapid; Yuval Haitman; Reuven Peretz; Arnon Netzer; Idit Diamant

cs.CVOct 22, 2026

GLOW-FM: Efficient Generative Low-Light Image Enhancement with One-Step Latent Flow Matching

Authors: Ariel Lapid, Yuval Haitman, Reuven Peretz, Arnon Netzer, Idit Diamant

Organizations: Arm AI Research (AAIR), Arm Ltd

Abstract

Low-light image enhancement (LLIE) aims to recover well-exposed and perceptually faithful images from severely degraded observations. Recent diffusion-based generative methods achieve strong perceptual quality by leveraging powerful image priors, but their iterative sampling incurs substantial computational cost, limiting practical deployment. Despite recent efficiency gains in generative LLIE, high-quality enhancement with a single function evaluation and low overall computation remains challenging, particularly under diverse real-world low-light conditions. We introduce GLOW-FM, a one-step latent flow-matching framework for efficient generative LLIE. GLOW-FM performs conditional transport in the latent space of a frozen Tiny-VAE and leverages Terminal Velocity Matching (TVM) to enable enhancement with a single function evaluation. Matched comparisons with alternative one-step objectives support TVM as an effective design choice, and, to the best of our knowledge, this is its first adaptation to image restoration and specifically to LLIE. Extensive experiments on challenging real-world benchmarks demonstrate that GLOW-FM achieves competitive image quality and state-of-the-art cross-dataset generalization while requiring only a single function evaluation and 34 GFLOPs for the small variant. These findings highlight the potential of GLOW-FM to bring generative LLIE closer to practical deployment through single-step inference and compact latent modeling. Code will be made publicly available upon publication.

Figures & tables

Figure 1: GLOW-FM Performance. Comparison with efficient generative LLIE baselines on LSD-DLL. Metrics are min–max normalized across the compared methods, with lower-is-better metrics reversed so that farther from the center is always better; axis annotations show the corresponding absolute metric values. Full results are reported in Tab. 1 , while Perception Generalization uses LSD-U NIQE from Tab. 2 .

Figure 2: Overview of GLOW-FM Training. Paired low-light and normally exposed images are encoded into a frozen autoencoder latent space. During training, the model learns both the instantaneous flow-matching velocity and a terminal-velocity displacement map between arbitrary times. During inference, the enhanced latent is obtained with a single step and decoded back to image space.

Figure 3: Qualitative comparison on LSD-DLL. Representative examples from LSD-DLL comparing low-light inputs, outputs from ReDDiT, PyDiff, WCDM, and GLOW-FM, and the corresponding ground-truth normal-light images.

Category	Method	NFEs $\downarrow$	GFLOPs $\downarrow$	Params (M) $\downarrow$	LSD-DLL					UHD-LOL4K
Category	Method	NFEs $\downarrow$	GFLOPs $\downarrow$	Params (M) $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	NIQE $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	NIQE $\downarrow$
Non-Generative	TFFormer [ 45 ]	1	34	5.9	14.1	0.349	0.63	32.7	4.49	31.4	0.987	0.04	32.9	4.65
	Retinexformer [ 5 ]	1	17	1.6	16.9	0.567	0.54	28.1	4.64	30.3	0.983	0.05	34.0	4.47
	HVI-CIDNet [ 62 ]	1	8	2.0	15.2	0.495	0.56	23.5	4.42	33.7	0.989	0.02	33.1	5.03
	Multinex [ 4 ]	1	3	0.04	15.6	0.501	0.63	21.6	7.73	24.8	0.958	0.09	32.0	4.72
Generative	PyDiff [ 70 ]	4	473	97.9	17.3	0.544	0.56	33.2	7.38	22.5	0.956	0.17	32.4	4.83

Table 1: Results on paired low-light benchmarks. We evaluate on LSD-DLL and UHD-LOL4K benchmarks and report image quality and efficiency metrics. Best results are shown in bold and second-best results are underlined .

Category	Method	NFEs $\downarrow$	GFLOPs $\downarrow$	Params (M) $\downarrow$	NIQE $\downarrow$	MUSIQ $\uparrow$
Non-Generative	TFFormer [ 45 ]	1	34	5.9	4.42	24.1
	Retinexformer [ 5 ]	1	17	1.6	7.35	23.4
	HVI-CIDNet [ 62 ]	1	8	2.0	6.26	26.5
	Multinex [ 4 ]	1	3	0.04	6.96	27.2
Generative	PyDiff [ 70 ]	4	473	97.9	6.96	26.3
	WCDM [ 20 ]	10	90	22.1	6.16	23.7

Table 2: Results on LSD-U. Best results are shown in bold and second-best results are underlined .

Method	NFEs $\downarrow$	GFLOPs $\downarrow$	Params (M) $\downarrow$	PSNR $\uparrow$	MUSIQ $\uparrow$	NIQE $\downarrow$
PyDiff [ 70 ]	4	473	97.9	21.9	74.2	5.40
WCDM [ 20 ]	10	90	22.1	26.3	66.9	4.52
ReDDiT [ 25 ]	2	128	17.4	20.8	71.7	5.38
GLOW-FM-XL	1	48	124	21.8	70.4	3.71
GLOW-FM-M	1	36	21.8	21.3	71.5	3.79
GLOW-FM-S	1	34	9.1	21.2	71.3	3.70

Table 3: Results on LOL-v1. Best results are shown in bold and second-best results are underlined .

Category	Method	GFLOPs $\downarrow$	Params (M) $\downarrow$	DICM		LIME		MEF		NPE		VV		Mean
Category	Method	GFLOPs $\downarrow$	Params (M) $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$
Non-Generative	SNR-Aware [ 61 ]	24	39.1	6.17	33.2	5.96	34.9	4.33	27.9	6.51	36.9	11.89	80.0	6.97	42.6
	DCC-Net [ 69 ]	64	13.2	3.44	27.0	4.26	25.4	4.13	30.2	3.51	25.1	3.55	26.2	3.78	26.8
	Zero-DCE [ 13 ]	5	0.079	2.84	19.3	3.80	18.2	4.02	26.3	3.59	26.4	3.21	29.1	3.49	23.8
	KinD++ [ 68 ]	371	8.3	3.10	20.5	4.72	23.3	4.20	34.1	3.79	27.6	2.68	26.5	3.70	26.4
	CPGA-Net [ 58 ]	2	0.025	3.62	28.1	4.59	21.5	4.18	28.1	3.73	26.4	4.22	35.3	4.07	27.9

Table 4: Cross-dataset generalization benchmark. Results on DICM, LIME, MEF, NPE, and VV. Lower is better. Best results are shown in bold and second-best results are underlined .

Figure 4: Qualitative generalization comparison. Results on unpaired real-world low-light benchmarks show that GLOW-FM recovers natural illumination while preserving scene structure across diverse inputs.

Figure 5: Effect of train-set size. Results on LSD-DLL for models trained with increasing train-set size.

Variant	Latent	GFLOPs $\downarrow$	Params (M) $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	NIQE $\downarrow$
Pixel Space (TVM)	None	1693	121	16.0	0.47	0.54	28.2	4.51
SD-VAE (TVM)	SD-VAE	471	205	19.6	0.50	0.47	40.6	3.37
No TVM (1 NFE)	TAESD	48	124	19.7	0.50	0.48	27.7	6.00
No TVM (25 NFE)	TAESD	415	124	20.1	0.54	0.37	35.6	3.00
ConsistencyFM	TAESD	48	124	19.4	0.52	0.50	32.8	3.53
MeanFlow	TAESD	48	124	19.5	0.52	0.49	31.7	3.59

Table 5: Ablation study. Results on LSD-DLL assessing the latent representation and one-step training objective.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Model	Input channels	Widths	Depths	Map. width	Map. depth	Patch	$d_{\mathrm{head}}$	NA kernel
GLOW-FM-XL	16	$(384,768)$	$(2,11)$	768	2	$2{\times}2$	64	$7{\times}7$
GLOW-FM-M	16	$(192,384)$	$(2,6)$	384	2	$2{\times}2$	64	$7{\times}7$
GLOW-FM-S	16	$(128,256)$	$(2,4)$	256	2	$2{\times}2$	64	$7{\times}7$

Appendix

Table 6: HDiT configurations used by GLOW-FM.

Figure 6: NFE ablation. PSNR and NIQE on LSD-DLL as the number of function evaluations varies for GLOW-FM with and without TVM.

Figure 7: Extended NFE ablation. SSIM, LPIPS-VGG, LPIPS-Alex, MUSIQ, and CLIP-IQA on the LSD-DLL validation split as the number of function evaluations varies.

Figure 8: One-Step Methods across NFEs. PSNR and NIQE on LSD-DLL for TVM, MeanFlow, ConsistencyFM, and No-TVM as the number of NFEs varies.

Variant	Latent	GFLOPs $\downarrow$	Params (M) $\downarrow$	LSD-DLL						LSD-U
Variant	Latent	GFLOPs $\downarrow$	Params (M) $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	NIQE $\downarrow$	CLIP-IQA $\uparrow$	NIQE $\downarrow$	MUSIQ $\uparrow$	CLIP-IQA $\uparrow$
Pixel Space (TVM)	None	1693	121	16.0	0.47	0.54	28.2	4.51	0.386	6.15	25.2	0.210
SD-VAE (TVM)	SD-VAE	471	205	19.6 $\mathord{\pm}$ 0.0287$$	0.504 $\mathord{\pm}$ 0.003,42$$	0.472 $\mathord{\pm}$ 0.0326$$	40.58 $\mathord{\pm}$ 0.0984$$	3.372 $\mathord{\pm}$ 0.001,04$$	0.609 $\mathord{\pm}$ 0.000,77$$	4.35 $\mathord{\pm}$ 0.001,20$$	30.4 $\mathord{\pm}$ 0.005,74$$	0.362 $\mathord{\pm}$ 0.000,16$$
No TVM (25 NFE)	TAESD	415	124	20.1	0.54	0.37	35.6	3.00	0.626	4.75	28.8	0.341
ConsistencyFM	TAESD	48	124	19.46	0.52	0.50	32.8	3.53	0.573	5.17	27.3	0.327
MeanFlow	TAESD	48	124	19.49	0.52	0.49	31.7	3.59	0.567	5.32	27.8	0.344

Appendix

Table 7: Extended ablation study. LSD-DLL and LSD-U results assessing the latent representation and flow-matching objective. SD-VAE results are reported as the mean

\pm

sample standard deviation across five seeds. All other variants are deterministic and are therefore reported as single values. Best mean results are shown in bold .

Dataset	Setting	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	NIQE $\downarrow$	MUSIQ $\uparrow$
LOL-v1	SD-VAE Rec.	28.14	0.854	0.048	4.339	72.45
	TAESD Rec.	30.33	0.909	0.031	3.735	72.70
	GLOW-FM	21.8	0.828	0.29	3.71	70.4
LSD-DLL	SD-VAE Rec.	26.40	0.687	0.086	3.719	37.94
	TAESD Rec.	29.49	0.816	0.069	3.350	35.77
	GLOW-FM	20.3	0.548	0.48	2.92	36.0

Appendix

Table 8: Autoencoder reconstruction analysis. Direct target reconstructions

\mathcal{D}(\mathcal{E}(x_{0}))

are compared with the corresponding GLOW-FM-XL enhancement outputs.

Autoencoder	LOL-v1	LSD-DLL	UHD-LOL4K
TAESD	1.181	1.792	0.266
SD-VAE	1.963	4.579	0.693

Appendix

Table 9: Autoencoder reconstruction error. MSE

\downarrow

is reported in units of

\times 10^{-3}

Figure 9: TVM warmup ablation. PSNR and NIQE on LSD-DLL with and without TVM warmup. Curves are smoothed for visualization.

Training $\sigma$	LSD-DLL			LSD-U	Cross-dataset
Training $\sigma$	PSNR $\uparrow$	LPIPS $\downarrow$	NIQE $\downarrow$	NIQE $\downarrow$	Mean NIQE $\downarrow$
0.00	20.05	0.49	2.93	4.72	2.87
0.01	20.30	0.48	2.92	4.33	2.86
0.05	19.91	0.50	2.90	4.88	2.90
0.10	19.62	0.51	2.84	4.67	2.98

Appendix

Table 10: Effect of source-latent perturbation. LSD-U measures unseen-device generalization, while cross-dataset mean NIQE is averaged over DICM, LIME, MEF, NPE, and VV. Best results are shown in bold .

Method	NFEs $\downarrow$	GFLOPs $\downarrow$	Params (M) $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	NIQE $\downarrow$	CLIP-IQA $\uparrow$
PyDiff [ 70 ]	4	473	97.9	21.9 $\mathord{\pm}$ 0.2044$$	0.895 $\mathord{\pm}$ 0.0206$$	0.11 $\mathord{\pm}$ 0.0032$$	74.2 $\mathord{\pm}$ 0.1738$$	5.40 $\mathord{\pm}$ 0.1014$$	0.44 $\mathord{\pm}$ 0.0230$$
WCDM [ 20 ]	10	90	22.1	26.3 $\mathord{\pm}$ 0.0118$$	0.887 $\mathord{\pm}$ 0.0225$$	0.12 $\mathord{\pm}$ 0.0006$$	66.9 $\mathord{\pm}$ 0.0104$$	4.52 $\mathord{\pm}$ 0.0112$$	0.40 $\mathord{\pm}$ 0.0003$$
ReDDiT [ 25 ]	2	128	17.4	20.8 $\mathord{\pm}$ 0.1545$$	0.883 $\mathord{\pm}$ 0.0022$$	0.20 $\mathord{\pm}$ 0.0022$$	71.7 $\mathord{\pm}$ 0.0714$$	5.38 $\mathord{\pm}$ 0.0189$$	0.70 $\mathord{\pm}$ 0.0043$$
GLOW-FM-XL	1	48	124	21.8	0.828	0.29	70.4	3.71	0.554
GLOW-FM-M	1	36	21.8	21.3	0.826	0.27	71.5	3.79	0.547
GLOW-FM-S	1	34	9.1	21.2	0.822	0.27	71.3	3.70	0.549

Appendix

Table 11: Five-seed evaluation results on LOL-v1.

Method	NFEs $\downarrow$	GFLOPs $\downarrow$	Params (M) $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	NIQE $\downarrow$	CLIP-IQA $\uparrow$
Retinexformer [ 5 ]	1	17	1.6	16.9	0.567	0.54	28.1	4.64	0.32
HVI-CIDNet [ 62 ]	1	8	2.0	15.2	0.495	0.56	23.5	4.42	0.37
Multinex [ 4 ]	1	3	0.04	15.6	0.501	0.63	21.6	7.73	0.39
TFFormer [ 45 ]	1	34	5.9	14.1	0.349	0.63	32.7	4.49	0.45
PyDiff [ 70 ]	4	473	97.9	17.3 $\mathord{\pm}$ 0.0851$$	0.544 $\mathord{\pm}$ 0.0013$$	0.56 $\mathord{\pm}$ 0.0053$$	33.2 $\mathord{\pm}$ 0.1199$$	7.38 $\mathord{\pm}$ 0.0658$$	0.425 $\mathord{\pm}$ 0.0017$$
WCDM [ 20 ]	10	90	22.1	17.1 $\mathord{\pm}$ 0.1346$$	0.553 $\mathord{\pm}$ 0.0019$$	0.59 $\mathord{\pm}$ 0.0007$$	25.5 $\mathord{\pm}$ 0.0288$$	5.94 $\mathord{\pm}$ 0.0442$$	0.269 $\mathord{\pm}$ 0.0007$$

Appendix

Table 12: Five-seed evaluation results on LSD-DLL.

Method	NFEs $\downarrow$	GFLOPs $\downarrow$	Params (M) $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	NIQE $\downarrow$	CLIP-IQA $\uparrow$
Retinexformer [ 5 ]	1	17	1.6	30.3	0.983	0.05	34.0	4.47	0.286
HVI-CIDNet [ 62 ]	1	8	2.0	33.7	0.989	0.02	33.1	5.03	0.281
Multinex [ 4 ]	1	3	0.04	24.8	0.958	0.09	32.0	4.72	0.293
TFFormer [ 45 ]	1	34	5.9	31.4	0.987	0.04	32.9	4.65	0.267
PyDiff [ 70 ]	4	473	97.9	22.5 $\mathord{\pm}$ 0.3310$$	0.956 $\mathord{\pm}$ 0.0020$$	0.17 $\mathord{\pm}$ 0.0576$$	32.447 $\mathord{\pm}$ 0.7027$$	4.83 $\mathord{\pm}$ 0.0435$$	0.331 $\mathord{\pm}$ 0.0059$$
WCDM [ 20 ]	10	90	22.1	26.8 $\mathord{\pm}$ 0.0469$$	0.957 $\mathord{\pm}$ 0.000,02$$	0.16 $\mathord{\pm}$ 0.0312$$	31.728 $\mathord{\pm}$ 0.0964$$	4.08 $\mathord{\pm}$ 0.0388$$	0.231 $\mathord{\pm}$ 0.0097$$

Appendix

Table 13: Five-seed evaluation results on UHD-LOL4K.

Method	NFEs $\downarrow$	GFLOPs $\downarrow$	Params (M) $\downarrow$	NIQE $\downarrow$	MUSIQ $\uparrow$	CLIP-IQA $\uparrow$
Retinexformer [ 5 ]	1	17	1.6	7.35	23.4	0.205
HVI-CIDNet [ 62 ]	1	8	2.0	6.26	26.5	0.281
Multinex [ 4 ]	1	3	0.04	6.96	27.2	0.301
TFFormer [ 45 ]	1	34	5.9	4.42	24.1	0.271
PyDiff [ 70 ]	4	473	97.9	6.96 $\mathord{\pm}$ 0.0542$$	26.3 $\mathord{\pm}$ 0.1942$$	0.276 $\mathord{\pm}$ 0.0012$$
WCDM [ 20 ]	10	90	22.1	6.16 $\mathord{\pm}$ 0.0241$$	23.692 $\mathord{\pm}$ 0.2839$$	0.183 $\mathord{\pm}$ 0.0034$$

Appendix

Table 14: Five-seed evaluation results on LSD-U.

Method	NFE	NVIDIA H100 GPU										Apple M4 Max (SME/SME2)
		$256{\times}256$		$512{\times}512$		$1024{\times}1024$		$2048{\times}2048$		$4096{\times}4096$		$1024{\times}1024$
		Lat. (ms)	Mem. (MB)	Lat. (ms)	Mem. (MB)	Lat. (ms)	Mem. (MB)	Lat. (ms)	Mem. (MB)	Lat. (ms)	Mem. (MB)	Lat. (ms)	Mem. (MB)
ReDDiT	2	31.4	291.3	65.7	859.1	286.8	3130.4	1791.4	12215.2	39704.3	48554.7	7484.1	25000.2
PyDiff	4	80.7	1099.9	191.7	1949.3	738.6	5346.7	4485.5	18936.2	80846.0	73294.4	23682.8	27056.0
WCDM	10	166.8	195.4	172.6	430.1	229.8	1369.1	974.7	5125.1	7606.8	35277.1	4987.1	4047.0
GLOW-FM-XL	1	40.9	559.3	45.8	708.3	65.0	1304.6	237.4	3689.6	1149.1	13229.6	3710.3	4557.8

Appendix

Table 15: Runtime efficiency on an NVIDIA H100 GPU and Arm SME/SME2. We report mean end-to-end latency and peak memory usage with batch size one after 20 warm-up iterations and over 100 timed inferences. GPU results cover five input resolutions and use PyTorch peak allocated memory. SME/SME2 results are measured at

1024{\times}1024

on an Apple M4 Max using ONNX, with memory measured as the peak resident-set size (RSS) of the inference process. Best results are shown in bold and second-best results are underlined.

Figure 10: Qualitative results on LSD-U.

Figure 11: Qualitative results on UHD-LOL4K.

Figure 12: Qualitative results on LOL-v1.

Panel of Figure 13 — Figure 13: Qualitative results on LSD-DLL. Each full-image comparison row is followed by a magnified crop from the full-image.

Panel of Figure 14 — Figure 14: Qualitative results on LSD-DLL. Each full-image comparison row is followed by a magnified crop from the full-image.

Figure 15: Qualitative comparisons on the unpaired LIME, DICM, MEF, NPE, and VV datasets. Each full-image comparison row is followed by a magnified crop from the full-image.

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Low-Light Image Enhancement Image Restoration

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Low-light image enhancement (LLIE) is an im- portant component of visual sensing systems operating under degraded illumination, including nighttime surveillance, au- tonomous navigation, remote sensing, and inspection in poorly lit industrial environments. Most LLIE methods rely on output- level reconstruction losses that supervise only the final restored image, leaving the intermediate feature recovery process weakly constrained. This paper proposes MirrorDistill, an illumination- aware latent distillation framework that links the low-light and clean domains through feature mirroring. During training, a shared encoder and an exponential-moving-average teacher decoder process the clean reference image to generate clean- domain latent targets. These targets supervise the low-light student at two levels: raw encoder features and standardized multi-scale decoder projections. The alignment is applied layer by layer, while a proposed illumination-aware weighting scheme gives greater emphasis to underexposed regions. The teacher and reference branches are used only during training, so inference requires only the lightweight student encoder-decoder and in- troduces no teacher-side computational cost. Under evaluation on the standard LOL benchmarks, MirrorDistill outperforms the state-of-the-art methods on the real-captured LOL-v2-Real set, while having the lowest compute complexity (GMACs) and while remaining competitive on the LOL-v1 and LOL-v2-Synthetic datasets. Ablation studies further show the contributions of the encoder mirror, decoder mirror, and illumination-aware weighting. Finally, we release our code as open-source for the benefit of future research.

Abstract

Low-Light Image Enhancement

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