RefGC-SR^2: Reference-guided Generated Content Super-Resolution and Refinement

Hugging Face Daily Papers Papers
reference-guided super-resolution diffusion-transformer image-refinement generative-artifacts

Summary

This paper introduces a new task, reference-guided generated content super-resolution-refinement (RefGC-SR²), which simultaneously recovers high-resolution details and refines generative artifacts using a frequency-aware diffusion transformer model. The method leverages a high-resolution reference image to improve the quality of AI-generated images during post-processing.

Reference-guided generation (e.g., object compositing, customization) has progressed rapidly, yet current pipelines share a fundamental limitation: the object-centric high-resolution reference image (HRRI) provided by users is downsampled to a fixed low-resolution (LR) before being fed into the model, so the fine-grained details are discarded before the output is even produced. In addition, the generation step then introduces its own artifacts (e.g., identity distortion) on top of this loss. Existing reference-guided generated content refinement (RefGCR) methods can correct some of these artifacts but still operate in the LR domain; reference-guided super-resolution (RefSR) methods recover resolution but assume natural-image degradations and ignore the artifact distribution of generative pipelines. To address both gaps in a single formulation, we introduce a new task: reference-guided generated content super-resolution-refinement (RefGC-SR^2), where the original HRRI is reused at the post-processing stage to recover lost details, refine generative artifacts, and upscale the output simultaneously. We construct the first real-world triplet data generation pipeline for this RefGC-SR^2 task, training a diptych-conditioned generator to synthesize paired low-quality anchors that public pretrained models cannot provide. We further present a frequency-aware diffusion transformer model for RefGC-SR^2 that selectively injects fine details from the HRRI while removing generative artifacts. Extensive experiments demonstrate that our RefGC-SR^2 model successfully (i) refines the object identity faithfully with respect to the reference, and (ii) recovers high-resolution details, so that the final result is significantly higher quality and practically more usable compared to existing RefGCR and RefSR baselines.
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Cached at: 06/17/26, 11:37 AM

Paper page - RefGC-SR^2: Reference-guided Generated Content Super-Resolution and Refinement

Source: https://huggingface.co/papers/2606.15158

Abstract

A new reference-guided generated content super-resolution-refinement task is introduced that simultaneously recovers high-resolution details and refines generative artifacts using a frequency-aware diffusion transformer model.

Reference-guided generation(e.g.,object compositing,customization) has progressed rapidly, yet current pipelines share a fundamental limitation: the object-centrichigh-resolution reference image(HRRI) provided by users is downsampled to a fixedlow-resolution(LR) before being fed into the model, so the fine-grained details are discarded before the output is even produced. In addition, the generation step then introduces its own artifacts (e.g., identity distortion) on top of this loss. Existing reference-guided generated content refinement (RefGCR) methods can correct some of these artifacts but still operate in the LR domain; reference-guided super-resolution (RefSR) methods recover resolution but assume natural-image degradations and ignore the artifact distribution of generative pipelines. To address both gaps in a single formulation, we introduce a new task: reference-guided generated contentsuper-resolution-refinement(RefGC-SR^2), where the original HRRI is reused at the post-processing stage to recover lost details, refinegenerative artifacts, and upscale the output simultaneously. We construct the firstreal-world triplet data generationpipeline for this RefGC-SR^2 task, training adiptych-conditioned generatorto synthesize paired low-quality anchors that public pretrained models cannot provide. We further present afrequency-awarediffusion transformermodel for RefGC-SR^2 that selectively injects fine details from the HRRI while removinggenerative artifacts. Extensive experiments demonstrate that our RefGC-SR^2 model successfully (i) refines the object identity faithfully with respect to the reference, and (ii) recovers high-resolution details, so that the final result is significantly higher quality and practically more usable compared to existing RefGCR and RefSR baselines.

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