DLSF: Dual-Layer Synergistic Fusion for High-Fidelity Image Syn-thesis
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913948301787136 |
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| author | Chen, Zhen-Qi Yang, Yuan-Fu |
| author_facet | Chen, Zhen-Qi Yang, Yuan-Fu |
| contents | With the rapid advancement of diffusion-based generative models, Stable Diffusion (SD) has emerged as a state-of-the-art framework for high-fidelity im-age synthesis. However, existing SD models suffer from suboptimal feature aggregation, leading to in-complete semantic alignment and loss of fine-grained details, especially in highly textured and complex scenes. To address these limitations, we propose a novel dual-latent integration framework that en-hances feature interactions between the base latent and refined latent representations. Our approach em-ploys a feature concatenation strategy followed by an adaptive fusion module, which can be instantiated as either (i) an Adaptive Global Fusion (AGF) for hier-archical feature harmonization, or (ii) a Dynamic Spatial Fusion (DSF) for spatially-aware refinement. This design enables more effective cross-latent com-munication, preserving both global coherence and local texture fidelity. Our GitHub page: https://anonymous.4open.science/r/MVA2025-22 . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DLSF: Dual-Layer Synergistic Fusion for High-Fidelity Image Syn-thesis Chen, Zhen-Qi Yang, Yuan-Fu Graphics With the rapid advancement of diffusion-based generative models, Stable Diffusion (SD) has emerged as a state-of-the-art framework for high-fidelity im-age synthesis. However, existing SD models suffer from suboptimal feature aggregation, leading to in-complete semantic alignment and loss of fine-grained details, especially in highly textured and complex scenes. To address these limitations, we propose a novel dual-latent integration framework that en-hances feature interactions between the base latent and refined latent representations. Our approach em-ploys a feature concatenation strategy followed by an adaptive fusion module, which can be instantiated as either (i) an Adaptive Global Fusion (AGF) for hier-archical feature harmonization, or (ii) a Dynamic Spatial Fusion (DSF) for spatially-aware refinement. This design enables more effective cross-latent com-munication, preserving both global coherence and local texture fidelity. Our GitHub page: https://anonymous.4open.science/r/MVA2025-22 . |
| title | DLSF: Dual-Layer Synergistic Fusion for High-Fidelity Image Syn-thesis |
| topic | Graphics |
| url | https://arxiv.org/abs/2507.13388 |