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We introduce ProLoRA, enabling zero-shot adaptation of parameter-efficient fine-tuning in text-to-image diffusion models.
Maximum mean discrepancy
Smola, A. J., Gretton, A., and Borgwardt, K · 2006
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Distilling the knowledge in a neural network
Hinton, G · 2015
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Sequence-level knowledge distillation
Kim, Y. and Rush, A. M · 2016
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The e2e dataset: New challenges for end-to-end generation
Novikova, J., Dušek, O., and Rieser, V · 2017
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Towards understanding knowledge distillation
Bui Thi Mai, P. and Lampert, C · 2019
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Samsum corpus: A human-annotated dialogue dataset for abstractive summarization
Gliwa, B., Mochol, I., Biesek, M., and Wawer, A · 2019
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Relational knowledge distillation
Park, W., Kim, D., Lu, Y., and Cho, M · 2019
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Be your own teacher: Improve the performance of convolutional neural networks via self distillation
Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., and Ma, K · 2019
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Self-distillation as instance-specific label smoothing
Zhang, Z. and Sabuncu, M · 2020
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Distilling from professors: Enhancing the knowledge distillation of teachers
Bang, D., Lee, J., and Shim, H · 2021
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Knowledge distillation: A survey
Gou, J., Yu, B., Maybank, S. J., and Tao, D · 2021
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The power of scale for parameter-efficient prompt tuning, 2021
Lester, B., Al-Rfou, R., and Constant, N · 2021
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Self-distillation: Towards efficient and compact neural networks
Zhang, L., Bao, C., and Ma, K · 2021
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LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Cited alongside, same era.
Knowledge distillation: Bad models can be good role models
Kaplun, G., Malach, E., Nakkiran, P., and Shalev-Shwartz, S · 2022
Cited alongside, same era.
Sdedit: Guided image synthesis and editing with stochastic differential equations, 2022
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation, 2022
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K · 2022
Cited alongside, same era.
Xu, L., Xie, H., Qin, S.-Z. J., Tao, X., and Wang, F. L · 2023
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Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models, 2023
Ye, H., Zhang, J., Liu, S., Han, X., and Yang, W · 2023
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Adding conditional control to text-to-image diffusion models, 2023
Zhang, L., Rao, A., and Agrawala, M · 2023
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FouRA: Fourier low rank adaptation
Borse, S., Kadambi, S., Pandey, N. P., Bhardwaj, K., Ganapathy, V., Priyadarshi, S., Garrepalli, R., Esteves, R., Hayat, M., and Porikli, F · 2024
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Implicit style-content separation using b-lora, 2024
Frenkel, Y., Vinker, Y., Shamir, A., and Cohen-Or, D · 2024
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Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks
Sung, Y.-L., Cho, J., and Bansal, M · 2022
Cited alongside, same era.
Efficient knowledge distillation from model checkpoints
Wang, C., Yang, Q., Huang, R., Song, S., and Huang, G · 2022
Cited alongside, same era.
Svdiff: Compact parameter space for diffusion fine-tuning
Han, L., Li, Y., Zhang, H., Milanfar, P., Metaxas, D., and Yang, F · 2023
Cited alongside, same era.
VeRA: Vector-based random matrix adaptation
Kopiczko, D. J., Blankevoort, T., and Asano, Y. M · 2023
Cited alongside, same era.
Fatezero: Fusing attentions for zero-shot text-based video editing, 2023
Qi, C., Cun, X., Zhang, Y., Lei, C., Wang, X., Shan, Y., and Chen, Q · 2023
Cited alongside, same era.
X-adapter: Adding universal compatibility of plugins for upgraded diffusion model
Ran, L., Cun, X., Liu, J.-W., Zhao, R., Zijie, S., Wang, X., Keppo, J., and Shou, M. Z · 2023
Cited alongside, same era.
Weight subcloning: direct initialization of transformers using larger pretrained ones
Samragh, M., Farajtabar, M., Mehta, S., Vemulapalli, R., Faghri, F., Naik, D., Tuzel, O., and Rastegari, M · 2023
Cited alongside, same era.
Progressive knowledge distillation of stable diffusion xl using layer level loss, 2024
Gupta, Y., Jaddipal, V. V., Prabhala, H., Paul, S., and Platen, P. V · 2024
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Dora: Weight-decomposed low-rank adaptation
Liu, S.-Y., Wang, C.-Y., Yin, H., Molchanov, P., Wang, Y.-C. F., Cheng, K.-T., and Chen, M.-H · 2024
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DINOv2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H. V., Szafraniec, M., Khalidov, V., Fernandez, P., HAZIZA, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.-Y., Li, S.-W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., and Bojanowski, P · 2024
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SDXL: Improving latent diffusion models for high-resolution image synthesis
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2024
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Measuring style similarity in diffusion models
Somepalli, G., Gupta, A., Gupta, K., Palta, S., Goldblum, M., Geiping, J., Shrivastava, A., and Goldstein, T · 2024
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Trans-LoRA
Wang, R., Ghosh, S., Cox, D., Antognini, D., Oliva, A., Feris, R., and Karlinsky, L · 2024
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Tinyllama: An open-source small language model, 2024
Zhang, P., Zeng, G., Wang, T., and Lu, W · 2024
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Lora-x: Bridging foundation models with training-free cross-model adaptation, 2025
Farhadzadeh, F., Das, D., Borse, S., and Porikli, F · 2025
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