УДК 621.396

В.В. МІЩУК
Національний аерокосмічний університет «Харківський авіаційний інститут», Харків,
Україна,  v.v.mishchuk@csn.khai.edu

Г.В. ФЕСЕНКО
Національний аерокосмічний університет «Харківський авіаційний інститут», Харків,
Україна,
 h.fesenko@csn.khai.edu

В.C. XAРЧЕНКО
Національний аерокосмічний університет «Харківський авіаційний інститут», Харків,
Україна,  v.kharchenko@csn.khai.edu

С.В. ЯКОВЛЕВ
Харківський національний університет ім. В.Н. Каразіна, Харків, Україна,
 s.yakovlev@karazin.ua

ДИФУЗІЙНІ МОДЕЛІ ДЛЯ СТВОРЕННЯ СИНТЕТИЧНИХ НАБОРІВ
ДАНИХ В ІНТЕЛЕКТУАЛЬНИХ СИСТЕМАХ ДЕТЕКТУВАННЯ
ВИБУХОНЕБЕЗПЕЧНИХ ПРЕДМЕТІВ

СПИСОК ЛІТЕРАТУРИ

• 1. Mishchuk V., Fesenko H., Kharchenko V. Deep learning models for detection of explosive ordnance using autonomous robotic systems: trade-off between accuracy and real-time processing speed. Radioelectronic and Computer Systems. 2024. N. 4. Р. 99–111. https://doi.org/10.32620/reks.2024.4.09.
• 2. Hu E.J., Shen Y., Wallis P., et al. LoRA: low-rank adaptation of large language models. arXiv:2106.09685v2 [cs.CL] 16 Oct 2021. https://doi.org/10.48550/arXiv.2106.09685.
• 3. Podell D., English Z., Lacey K., et al. SDXL: improving latent diffusion models for high-resolution image synthesis. arXiv:2307.01952v1 [cs.CV] 4 Jul 2023. https://doi.org/10.48550/arXiv.2307.01952.
• 4. Goodfellow I., Pouget-Abadie J., Mirza M., et al. Generative adversarial networks. Communications of the ACM. 2020. Vol. 63, N 11. P. 139–144. https://doi.org/10.1145/3422622.
• 5. Ho J., Jain A., Abbeel P. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems. 2020. Vol. 33. P. 6840–6851. URL:https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf.
• 6. Rombach R., Blattmann A., Lorenz D., Esser P., Ommer B. High-resolution image synthesis with latent diffusion models. Proc. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (18–24 June 2022, New Orleans, LA, USA) New Orleans, 2022. P. 10674–10685. https://doi.org/10.1109/CVPR52688.2022.01042.
• 7. Dhariwal P., Nichol A. Diffusion models beat GANs on image synthesis. arXiv:2105. 05233v4 [cs.LG] 1 Jun 2021. https://doi.org/10.48550/arXiv.2105.05233.
• 8. Zhou M., Bai Y., Yang Q., Zhao T. StyleInject: parameter efficient tuning of text-to-image diffusion models. ACM Transactions on Multimedia Computing, Communications and Applications. 2025. Vol. 21, Iss. 5. Article number 152. https://doi.org/10.1145/3730403.
• 9. Yang M., Chen J., Tao J., et al. Low-rank adaptation for foundation models: a comprehensive review. arXiv:2501.00365v2 [cs.LG] 3 Nov 2025. https://doi.org/10.48550/arXiv.2501.00365.
• 10. Lu Y., Chen L., Zhang Y., et al. Machine learning for synthetic data generation: a review. arXiv:2302.04062v10 [cs.LG] 4 Apr 2025. https://doi.org/10.48550/arXiv.2302.04062.
• 11. Mumuni A., Mumuni F., Gerrar N.K. A survey of synthetic data augmentation methods in machine vision. Machine Intelligence Research. 2024. Vol 21, Iss. 5. P. 831–869. https://doi.org/10.1007/s11633-022-1411-7.
• 12. Heusel M., Ramsauer H., Unterthiner T., Nessler B., Hochreiter S. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017. P. 6629–6640. URL: https://proceedings.neurips.cc/paper_files/paper/2017//file/8a1d694707eb0fefe5871369074926d-Paper.pdf.
• 13. Salimans T., Goodfellow I., Zaremba W., Cheung V., Radford A., Chen X. Improved techniques for training GANs. Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016. P. 2234–2242. URL: https://papers.nips.cc/paper_files/paper/2016/file/8a3363abe792db2d8761d6403605aeb7-Paper.pdf.
• 14. Otani M., Togashi R., Sawai Y. et al. Toward verifiable and reproducible human evaluation for text-to-image generation. Proc. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (17–24 June 2023, Vancouver, BC, Canada). Vancouver, 2023. P. 14277–14286. https://doi.org/10.1109/CVPR52729.2023.01372.
• 15. Hartwig S., Engel D., Sick L., et al. A survey on quality metrics for text-to-image generation. IEEE Transactions on Visualization and Computer Graphics. 2025. Vol. 31, Iss. 10. P. 9464–9483. https://doi.org/10.1109/TVCG.2025.3585077.
• 16. LoRA training parameters. URL: https://github.com/bmaltais/kohya_ss/wiki/LoRA-training-parameters.
• 17. A comprehensive guide to training a Stable Diffusion XL LoRA: optimal settings & dataset building. URL: https://medium.com/@guillaume.bieler/a-comprehensive-guide-to-training-a-stable-diffusion-xl-lora-optimal-settings-dataset-building-844113a6d5b3.
• 18. THE OTHER LoRA TRAINING RENTRY. URL: https://rentry.org/59xed3.
• 19. Scaling Parameter Alpha. URL: https://apxml.com/courses/lora-peft-efficient-llm-training/chapter-2-lora-in-depth/lora-scaling-alpha.
• 20. Batifol S., Blattmann A., Boesel F., et al. FLUX.1 kontext: flow matching for in-context image generation and editing in latent space. arXiv:2506.15742v2 [cs.GR] 24 Jun 2025. https:// doi.org/10.48550/arXiv.2506.15742.
• 21. Fedorenko G., Fesenko H., Kharchenko V., Kliushnikov I., Tolkunov I. Robotic-biological systems for detection and identification of explosive ordnance: concept, general structure, and models. Radioelectronic and Computer Systems. 2023. N 2. Р. 143–159. https://doi.org/10.32620/reks.2023.2.12.