CusEnhancer - A Zero-Shot Scene and Controllability Enhancement Method for Photo Customization via ResInversion: Evaluation Design and Construct Validity

Authors

  • Wyatt Hayes Author
  • Alec Chapman Author
  • Brett Marshall Author

Keywords:

controllable image personalization, evaluation design and construct validity, evidence synthesis, reproducibility, research evaluation

Abstract

Evaluation is persuasive only when the measured outcome corresponds to the construct claimed by the study and the comparison answers the stated research question. 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 evaluation design and construct validity, 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 aligns claims, outcomes, comparators, sampling, and uncertainty before any performance estimate is interpreted.

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
  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
  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
  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
  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
  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
  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
  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
  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
  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

Downloads

Published

2026-08-27

Issue

Section

Review Article