UTD Press Journals
Convergence in Science and Society

CusEnhancer - A Zero-Shot Scene and Controllability Enhancement Method for Photo Customization via ResInversion: Robustness Under Distribution Shift

Read & download PDF
Abstract

Robustness depends on whether conclusions remain stable when the data distribution, case mix, prevalence, or operating environment differs from the reported setting. This structured evidence review evaluates "CusEnhancer: A Zero-Shot Scene and Controllability Enhancement Method for Photo Customization via ResInversion" alongside nine author-disjoint, topically matched publications in controllable image personalization. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through robustness under distribution shift, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda uses prespecified shift scenarios, subgroup analysis, calibration checks, and post-deployment monitoring to locate failure boundaries.

Keywords
controllable image personalizationrobustness under distribution shiftevidence synthesisreproducibilityresearch evaluation
References
  1. Ren, M., Vaddamanu, P., Xu, J., & Frade, F.-D.-L.-T. (2025). CusEnhancer: A Zero-Shot Scene and Controllability Enhancement Method for Photo Customization via ResInversion. arXiv. https://doi.org/10.48550/arXiv.2509.20775 DOI
  2. Soboleva, V., Alanov, A., Kuznetsov, A., & Sobolev, K. (2026). T-LoRA: Single Image Diffusion Model Customization Without Overfitting. Proceedings of the AAAI Conference on Artificial Intelligence, 40(11), 9051-9059. https://doi.org/10.1609/aaai.v40i11.37861 DOI
  3. Yu, H., Lin, X., & Wang, J. (2025). GeoDiff-SR: Structure-Preserving Image Super-Resolution via Riemannian Diffusion Inversion. . https://doi.org/10.2139/ssrn.5421704 DOI
  4. Ma, L., Fang, X., & Qi, G.-J. (2024). Equilibrated Diffusion: Frequency-aware Textual Embedding for Equilibrated Image Customization. Proceedings of the 32nd ACM International Conference on Multimedia, 196-204. https://doi.org/10.1145/3664647.3680729 DOI
  5. Lee, K., Kwak, S., Sohn, K., & Shin, J. (2024). Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion models. Advances in Neural Information Processing Systems 37, 103269-103304. https://doi.org/10.52202/079017-3281 DOI
  6. Jiang, Z., Po, L.-M., Xu, X., Wang, Y., Wu, H., Liu, Y., & Li, K. (2025). RMP-adapter: A region-based Multiple Prompt Adapter for multi-concept customization in text-to-image diffusion model. Expert Systems with Applications, 274, 126936. https://doi.org/10.1016/j.eswa.2025.126936 DOI
  7. Hase, A., & Kancharla, P. (2026). Spatial Distortion-Guided Diffusion Inversion for Blind Image Restoration. 2026 National Conference on Communications (NCC), 412-417. https://doi.org/10.1109/ncc68160.2026.11479176 DOI
  8. Chihaoui, H., Lemkhenter, A., & Favaro, P. (2024). Blind Image Restoration via Fast Diffusion Inversion. Advances in Neural Information Processing Systems 37, 34513-34532. https://doi.org/10.52202/079017-1088 DOI
  9. Kumari, N., Zhang, B., Zhang, R., Shechtman, E., & Zhu, J.-Y. (2023). Multi-Concept Customization of Text-to-Image Diffusion. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1931-1941. https://doi.org/10.1109/cvpr52729.2023.00192 DOI
  10. Cheng, J., Qiu, X., Zhou, Q., Li, M., Li, C., Lu, Y., & Yu, F.-R. (2025). OmniStyle: Attention-Optimized Global and Local Image Stylization with Diffusion Model Inversion. 2025 IEEE International Conference on Multimedia and Expo (ICME), 1-6. https://doi.org/10.1109/icme59968.2025.11210103 DOI
Publication details
Journal
Convergence in Science and Society
Volume
1 (2026)
Article number
css20260029
License
CC BY 4.0