PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution

Xin Di; Mingyu Shi; Yuanfei Bao; Long Peng; Yue Zhao; Jiaming Guo; Renjing Pei; Xueyang Fu

cs.CVSep 25, 2026

PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution

Authors: Xin Di, Mingyu Shi, Yuanfei Bao, Long Peng, Yue Zhao, Jiaming Guo, Renjing Pei, Xueyang Fu,

Organizations: University of Science and Technology of China · Huawei Technologies Ltd.

Abstract

Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while preserving faithful content. Diffusion-based SR benefits from strong generative priors but incurs substantial computational overhead, whereas feed-forward CNN and Transformer SR models are efficient yet often struggle to recover realistic high-frequency details. This motivates a natural question: can diffusion priors be transferred to existing diffusion-free SR networks without introducing diffusion components at inference time? To this end, we propose PhoenixSR, a generative heterogeneous distillation framework that transfers diffusion priors to independently designed feed-forward SR networks through score-based distribution matching. Rather than aligning heterogeneous features or imitating sampled diffusion outputs, PhoenixSR uses the pretrained diffusion model as distribution-level supervision, while paired SR supervision preserves reconstruction fidelity. To make distribution matching effective for fidelity-sensitive SR, we introduce Heterogeneous Distribution Adaptation, which adapts the target score to the SR domain, improves tracking of the evolving student distribution, and anchors training with paired supervision. We further employ Directional Reliability Weighting, a lightweight residual-consistency-based reweighting strategy that reduces unstable distributional guidance. All diffusion-related components are removed after training, leaving the original student architecture and inference cost unchanged. Experiments on three SR benchmarks and six feed-forward backbones, including SwinIR, HAT, Real-ESRGAN, and SeeMoRe, show consistent perceptual improvements with largely preserved reconstruction fidelity.

Figures & tables

Figure 1: Overview and performance of PhoenixSR. (a) PhoenixSR transfers diffusion generative priors to diffusion-free SR students through generative heterogeneous distillation. (b) Visual comparisons of SwinIR and HAT before and after PhoenixSR distillation. (c) Average and model-wise performance improvements brought by PhoenixSR on RealSR.

Figure 2: Motivation. CKA ( Kornblith et al., 2019 ) (left) reveals weak feature correspondence between DBSR and DFSR, while residual and zoom-in comparisons (right) show local discrepancies between DBSR outputs and paired HR targets.

Figure 3: Effect of fidelity anchoring on distribution matching. (a) Vanilla DMD causes unstable optimization and rapid PSNR degradation when directly applied to SR. (b) Sample-level fidelity supervision stabilizes distribution matching and preserves reconstruction quality. The optimization landscapes are conceptual illustrations.

Figure 4: Overview of PhoenixSR. HDA adapts real-score estimation, tracks the evolving student distribution, and anchors paired fidelity. DRW reweights sample-wise guidance according to residual-direction consistency.

Figure 5: t-SNE projection of feature representations. After distillation, DFSR representations move toward the region between real HR samples and DBSR outputs in t-SNE projection.

Model	PSNR $\uparrow$	LPIPS $\downarrow$	MANIQA $\uparrow$	NIQE $\downarrow$	MUSIQ $\uparrow$	CLIP-IQA $\uparrow$	LIQE-Mix $\uparrow$	ARNIQA $\uparrow$	TOPIQ $\uparrow$	Params	FLOPs	Runtime
Model	PSNR $\uparrow$	LPIPS $\downarrow$	MANIQA $\uparrow$	NIQE $\downarrow$	MUSIQ $\uparrow$	CLIP-IQA $\uparrow$	LIQE-Mix $\uparrow$	ARNIQA $\uparrow$	TOPIQ $\uparrow$	(M)	(G)	(ms)
DBSR Models
StableSR	24.4272	0.2636	0.5910	6.5779	61.8041	0.5644	3.5794	0.6772	0.5121	1410	162731	17870
SeeSR	25.1475	0.3007	0.6451	5.3958	69.8181	0.6703	3.6576	0.7152	0.6888	2511	131980	5451
SinSR	26.1309	0.3068	0.5432	6.1038	61.2905	0.6236	3.5595	0.6601	0.5309	119	5392	201
OSEDiff	25.1554	0.2928	0.6343	5.6243	69.1793	0.6669	4.1382	0.6935	0.6292	1761	4611	169

Table 1: Quantitative comparison on RealSR. PhoenixSR is applied to DFSR backbones, while DBSR methods are included as reference baselines. Gray cells denote results with PhoenixSR; red and blue denote performance improvement and degradation, respectively.

DBSR Models
Model	PSNR $\uparrow$	LPIPS $\downarrow$	MANIQA $\uparrow$	NIQE $\downarrow$	MUSIQ $\uparrow$	CLIP-IQA $\uparrow$	LIQE-Mix $\uparrow$	ARNIQA $\uparrow$	TOPIQ $\uparrow$
StableSR	28.4070	0.2726	0.4914	7.5141	51.4058	0.5046	2.6971	0.5762	0.4321
SeeSR	28.0712	0.3174	0.6052	6.4061	65.0873	0.6909	3.6846	0.6880	0.6572
SinSR	28.2012	0.3511	0.5034	6.7220	57.5658	0.6574	3.5766	0.6224	0.5288
OSEDiff	27.9321	0.2971	0.5874	6.5227	64.8184	0.6909	3.9768	0.6702	0.6005
AdcSR	26.2041	0.3156	0.5937	6.1738	66.8444	0.7206	4.2538	0.6985	0.6602

Table 2: Quantitative comparison on DRealSR.

DBSR Models
Model	PSNR $\uparrow$	LPIPS $\downarrow$	MANIQA $\uparrow$	NIQE $\downarrow$	MUSIQ $\uparrow$	CLIP-IQA $\uparrow$	LIQE-Mix $\uparrow$	ARNIQA $\uparrow$	TOPIQ $\uparrow$
StableSR	24.2395	0.3129	0.5506	5.6054	58.4136	0.5627	3.4477	0.6580	0.4851
SeeSR	23.6780	0.3194	0.6222	4.8095	68.6710	0.6935	4.0053	0.7301	0.6853
SinSR	24.2883	0.3222	0.5414	5.8005	63.2651	0.6530	3.7998	0.6745	0.5796
OSEDiff	23.7214	0.2942	0.6130	4.7203	67.9926	0.6611	4.2976	0.7030	0.6194
AdcSR	23.3567	0.2845	0.6071	4.3420	68.0922	0.6846	4.3009	0.7215	0.6574

Table 3: Quantitative comparison on DIV2K-Val.

Figure 6: Qualitative comparison on DIV2K-Val. PhoenixSR enhances high-frequency details of diffusion-free SR models, producing clearer and more natural wood textures while preserving the underlying image structure.

Figure 7: Qualitative comparison with diffusion-based SR methods. Diffusion-based baselines may over-sharpen the statue surface or introduce hallucinated textures that deviate from the underlying geometry. PhoenixSR improves details while retaining more faithful structural reconstruction.

	SwinIR-L			SwinIR-S			SeeMoRe
Method	PSNR $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	PSNR $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	PSNR $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$
Task-only strong FT	26.41	0.257	59.7	25.13	0.301	56.0	26.00	0.289	53.7
Output KD + GT	25.97	0.274	56.7	24.77	0.327	55.8	25.98	0.301	52.1
SDS + SFA	25.77	0.273	56.5	24.89	0.305	50.4	25.10	0.317	51.8
DMD2 + SFA	25.55	0.256	57.8	24.78	0.311	54.7	24.87	0.304	53.6
PhoenixSR w/o DRW	26.15	0.247	63.5	24.79	0.296	61.5	25.35	0.290	60.8

Table 4: Comparison of alternative diffusion-to-DFSR transfer strategies on RealSR.

Variant	SFA	Score Adapt.	DRW	PSNR $\uparrow$	MANIQA $\uparrow$	MUSIQ $\uparrow$	CLIP-IQA $\uparrow$
Base SwinIR-L	–	–	–	26.3071	0.5228	58.6388	0.4502
SFA only	✓	–	–	26.3124	0.5067	56.8245	0.4538
w/o SFA	–	✓	✓	20.7107	0.3512	40.7941	0.3071
SFA + Score Adapt.	✓	✓	–	26.1587	0.5678	63.5729	0.4963
Full PhoenixSR	✓	✓	✓	26.4409	0.5745	64.3102	0.5026

Table 5: Ablation of the main PhoenixSR components.

Appendix figures & tables10 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Task	Transfer mechanism	Deployed model	Native DFSR	Diffusion-derived inference
DTKD ( Park & Song, 2026 )	SR	Frequency-aware output KD	Transformer SR student	✓	No
DM-SR ( Park & Lee, 2026 )	SR	Distribution alignment	Pretrained diffusion model with learned input mapping	–	Yes
Cross-Space Distillation ( Nguyen et al., 2026 )	Image generation	Distribution-level diffusion distillation	One-step diffusion student	–	Yes
PhoenixSR	SR	Score-based distribution matching	CNN / Transformer / lightweight SR students	✓	No

Appendix

Table 6: Positioning of PhoenixSR with closely related diffusion-transfer methods. “Native DFSR” denotes a conventional feed-forward SR architecture designed independently from the diffusion teacher. “Diffusion-derived inference” indicates whether the deployed model still relies on a diffusion model or a diffusion-derived generator.

Figure 8: Analysis of diffusion guidance for SR distillation. (a) Effect of timestep selection. Low-noise states preserve more input-consistent structures, while stronger noise makes diffusion guidance increasingly dominated by the generative prior and may introduce unfaithful details. (b) Visualization of real-score adaptation. The frozen real-score model may convert restoration artifacts into plausible but unfaithful textures, while LoRA adaptation alleviates such artifact-to-texture degeneration and provides more SR-compatible guidance.

Student	Method	PSNR $\uparrow$	LPIPS $\downarrow$	MANIQA $\uparrow$	MUSIQ $\uparrow$	CLIP-IQA $\uparrow$
SwinIR-L	Base checkpoint	26.30	0.253	0.522	58.6	0.45
	PhoenixSR w/o DRW	26.20 $\pm$ 0.03	0.251 $\pm$ 0.004	0.557 $\pm$ 0.007	62.9 $\pm$ 0.3	0.481 $\pm$ 0.005
	PhoenixSR	26.40 $\pm$ 0.04	0.247 $\pm$ 0.008	0.568 $\pm$ 0.006	64.1 $\pm$ 0.5	0.493 $\pm$ 0.007
SeeMoRe	Base checkpoint	26.0	0.295	0.347	48.7	0.381
	PhoenixSR w/o DRW	25.41 $\pm$ 0.07	0.277 $\pm$ 0.009	0.511 $\pm$ 0.008	60.9 $\pm$ 0.4	0.519 $\pm$ 0.009
	PhoenixSR	25.94 $\pm$ 0.03	0.269 $\pm$ 0.005	0.531 $\pm$ 0.005	61.7 $\pm$ 0.3	0.530 $\pm$ 0.006

Appendix

Table 7: Robustness across random seeds on RealSR. PhoenixSR variants are independently trained with three random seeds and reported as mean

\pm

standard deviation. The base checkpoints are fixed references evaluated once.

Table 16

Table 17

Table 18

Figure 9: Additional qualitative comparisons on RealSR. PhoenixSR improves fine structures and local texture reconstruction across different feed-forward SR backbones while preserving the underlying scene content and geometry. Best viewed by zooming in.

Figure 10: Additional qualitative comparisons on DRealSR. PhoenixSR recovers clearer local structures and more natural texture details across different feed-forward SR backbones while largely preserving reconstruction fidelity. Best viewed by zooming in.

Figure 11: User study across six DFSR backbones.

Backbone	Original	PhoenixSR	$\Delta$ (95% CI)
Real-ESRGAN	6.38 [6.14, 6.64]	7.89 [7.71, 8.06]	+1.51 [1.36, 1.66]
HAT	6.78 [6.56, 7.00]	8.29 [8.15, 8.40]	+1.51 [1.39, 1.63]
SwinIR-L	6.67 [6.43, 6.92]	8.24 [8.10, 8.37]	+1.57 [1.44, 1.70]
SwinIR-M	6.22 [5.92, 6.51]	7.50 [7.31, 7.67]	+1.28 [1.14, 1.42]
SwinIR-S	6.08 [5.95, 6.21]	6.87 [6.60, 7.11]	+0.79 [0.63, 0.92]
SeeMoRe	5.74 [5.58, 5.89]	6.81 [6.60, 7.04]	+1.07 [0.87, 1.31]

Appendix

Table 14: Detailed user-study statistics. Scores are reported as image-level mean scores with 95% bootstrap confidence intervals.

\Delta

denotes PhoenixSR minus the original backbone.

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