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In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitations due to their intensive fine-tuning demands and substantial parameter requirements.
Kronecker products in image restoration
Nagy, J. G. and Perrone, L · 2003
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Robust kronecker product pca for spatio-temporal covariance estimation
Greenewald, K. and Hero, A. O · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, 2015
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Fast orthogonal projection based on kronecker product
Zhang, X., Yu, F. X., Guo, R., Kumar, S., Wang, S., and Chang, S.-F · 2015
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Measuring the intrinsic dimension of objective landscapes
Li, C., Farkhoor, H., Liu, R., and Yosinski, J · 2018
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Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
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Kandinsky: Abstract art-inspired visualization of social discussions
Lui, C., Bhowmick, S. S., and Jatowt, A · 2019
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Compressing rnns for iot devices by 15-38x using kronecker products
Thakker, U., Beu, J., Gope, D., Zhou, C., Fedorov, I., Dasika, G., and Mattina, M · 2019
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Few-shot image generation with elastic weight consolidation
Li, Y., Zhang, R., Lu, J., and Shechtman, E · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
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Beyond fully-connected layers with quaternions: Parameterization of hypercomplex multiplications with 1 / n 1/n parameters
Zhang, A., Tay, Y., Zhang, S., Chan, A., Luu, A. T., Hui, S., and Fu, J · 2020
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Emerging properties in self-supervised vision transformers, 2021
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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stable-diffusion, 2021
CompVis · 2021
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Compacter: Efficient low-rank hypercomplex adapter layers
Edalati, A., Tahaei, M. S., Rashid, A., Nia, V. P., Clark, J. J., and Rezagholizadeh, M · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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ediffi: Text-to-image diffusion models with an ensemble of expert denoisers
Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., et al · 2022
Cited alongside, same era.
An image is worth one word: Personalizing text-to-image generation using textual inversion, 2022
Pixart- α \alpha : Fast training of diffusion transformer for photorealistic text-to-image synthesis, 2023
Chen, J., Yu, J., Ge, C., Yao, L., Xie, E., Wu, Y., Wang, Z., Kwok, J., Luo, P., Lu, H., and Li, Z · 2023
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Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models
Gu, Y., Wang, X., Wu, J. Z., Shi, Y., Chen, Y., Fan, Z., Xiao, W., Zhao, R., Chang, S., Wu, W., et al · 2023
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Convolutional neural network compression through generalized kronecker product decomposition
Hameed, M. G. A., Tahaei, M. S., Mosleh, A., Nia, V. P., Chen, H., Deng, L., Yan, T., and Li, G · 2023
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Svdiff: Compact parameter space for diffusion fine-tuning
Han, L., Li, Y., Zhang, H., Milanfar, P., Metaxas, D., and Yang, F · 2023
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Multi-concept customization of text-to-image diffusion
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Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., and Cohen-Or, D · 2022
Cited alongside, same era.
Vector quantized diffusion model for text-to-image synthesis
Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., and Guo, B · 2022
Cited alongside, same era.
Parameter-efficient model adaptation for vision transformers
He, X., Li, C., Zhang, P., Yang, J., and Wang, X. E · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 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.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al · 2022
Cited alongside, same era.
KroneckerBERT: Significant compression of pre-trained language models through kronecker decomposition and knowledge distillation
Tahaei, M., Charlaix, E., Nia, V., Ghodsi, A., and Rezagholizadeh, M · 2022
Cited alongside, same era.
Kroneckerbert: Significant compression of pre-trained language models through kronecker decomposition and knowledge distillation
Tahaei, M., Charlaix, E., Nia, V., Ghodsi, A., and Rezagholizadeh, M · 2022
Cited alongside, same era.
Kumari, N., Zhang, B., Zhang, R., Shechtman, E., and Zhu, J.-Y · 2023
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Subject-diffusion: Open domain personalized text-to-image generation without test-time fine-tuning
Ma, J., Liang, J., Chen, C., and Lu, H · 2023
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Mou, C., Wang, X., Xie, L., Zhang, J., Qi, Z., Shan, Y., and Qie, X · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2023
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Low-rank adaptation for fast text-to-image diffusion fine-tuning, 2023
Ryu, S · 2023
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Key-locked rank one editing for text-to-image personalization
Tewel, Y., Gal, R., Chechik, G., and Atzmon, Y · 2023
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Diffusers: State-of-the-art diffusion models, 2023
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., and Wolf, T · 2023
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Kronecker cp decomposition with fast multiplication for compressing rnns
Wang, D., Wu, B., Zhao, G., Yao, M., Chen, H., Deng, L., Yan, T., and Li, G · 2023
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Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models
Ye, H., Zhang, J., Liu, S., Han, X., and Yang, W · 2023
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Navigating text-to-image customization: From lycoris fine-tuning to model evaluation
Yeh, S.-Y., Hsieh, Y.-G., Gao, Z., Yang, B. B., Oh, G., and Gong, Y · 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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