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Diffusion Models (DMs) have become powerful image generation tools, especially for few-shot fine-tuning where a pretrained DM is fine-tuned on a small image set to capture specific styles or objects.
The worst-case time complexity for generating all maximal cliques and computational experiments
Tomita, E., Tanaka, A., and Takahashi, H · 2006
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Principal component analysis
Abdi, H. and Williams, L. J · 2010
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Deep Unsupervised Learning Using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Painter by Numbers, WikiArt
Nichol, K · 2016
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Improved Regularization of Convolutional Neural Networks with Cutout
DeVries, T. and Taylor, G. W · 2017
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Randaugment: Practical Automated Data Augmentation with a Reduced Search Space
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V · 2020
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Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., and Abbeel, P · 2020
Earlier work this paper cites.
Score-based Generative Modeling through Stochastic Differential Equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
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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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An Image Is Worth One Word: Personalizing Text-to-Image Generation Using Textual Inversion
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., and Cohen-Or, D · 2022
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
Cited alongside, same era.
Imagic: Text-Based Real Image Editing With Diffusion Models
Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., and Irani, M · 2022
Cited alongside, same era.
A self-supervised descriptor for image copy detection
Pizzi, E., Roy, S. D., Ravindra, S. N., Goyal, P., and Douze, 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.
Diffusion Probabilistic Modeling for Video Generation
Yang, R., Srivastava, P., and Mandt, S · 2022
Cited alongside, same era.
White-box Membership Inference Attacks against Diffusion Models
Pang, Y., Wang, T., Kang, X., Huai, M., and Zhang, Y · 2023
Later among the works it cites.
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 · 2023
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Controlling Text-to-Image Diffusion by Orthogonal Finetuning
Qiu, Z., Liu, W., Feng, H., Xue, Y., Feng, Y., Liu, Z., Zhang, D., Weller, A., and Schölkopf, B · 2023
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DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K · 2023
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Glaze: Protecting artists from style mimicry by text-to-image models
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Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., and Wallace, E · 2023
Cited alongside, same era.
Are Diffusion Models Vulnerable to Membership Inference Attacks?
Duan, J., Kong, F., Wang, S., Shi, X., and Xu, K · 2023
Cited alongside, same era.
Erasing concepts from diffusion models
Gandikota, R., Materzynska, J., Fiotto-Kaufman, J., and Bau, D · 2023
Cited alongside, same era.
Ai art and its impact on artists
Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., Hanna, A., Flowers, J., and Gebru, T · 2023
Cited alongside, same era.
An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization
Kong, F., Duan, J., Ma, R., Shen, H., Zhu, X., Shi, X., and Xu, K · 2023
Cited alongside, same era.
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
Liang, C., Wu, X., Hua, Y., Zhang, J., Xue, Y., Song, T., Zhengui, X., Ma, R., and Guan, H · 2023
Cited alongside, same era.
https://www.civitai.com
Civitai: The Home of Open-Source Generative AI
Cited in the paper.
Shan, S., Cryan, J., Wenger, E., Zheng, H., Hanocka, R., and Zhao, B. Y · 2023
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Exploring Clip for Assessing the Look and Feel of Images
Wang, J., Chan, K. C., and Loy, C. C · 2023
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AltDiffusion: A Multilingual Text-to-Image Diffusion Model
Ye, F., Liu, G., Wu, X., and Wu, L · 2023
Later among the works it cites.
Reconstruction attacks on machine unlearning: Simple models are vulnerable
Bertran, M., Tang, S., Kearns, M., Morgenstern, J., Roth, A., and Wu, Z. S · 2024
Closest in time.
Extracting training data from unconditional diffusion models
Chen, Y., Ma, X., Zou, D., and Jiang, Y.-G · 2024
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Unveiling and mitigating memorization in text-to-image diffusion models through cross attention
Ren, J., Li, Y., Zeng, S., Xu, H., Lyu, L., Xing, Y., and Tang, J · 2024
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Cgi-dm: Digital copyright authentication for diffusion models via contrasting gradient inversion
Wu, X., Hua, Y., Liang, C., Zhang, J., Wang, H., Song, T., and Guan, H · 2024
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