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In this paper, we aim to enhance the performance of SwiftBrush, a prominent one-step text-to-image diffusion model, to be competitive with its multi-step Stable Diffusion counterpart.
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Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In: International Conference on Machine Learning (2021)
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 10684–10695 (2022)
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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.: Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems 35
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Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., Jitsev, J.: LAION-5B: An open large-scale dataset for training next generation image-text models. Adv. Neural Inform. Process. Syst. (2022)
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2022
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Wortsman, M., Ilharco, G., Gadre, S.Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A.S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al.: Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In: International Conference on Machine Learning. pp. 23965–23998. PMLR (2022)
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Agarwal, A., Karanam, S., Joseph, K., Saxena, A., Goswami, K., Srinivasan, B.V.: A-star: Test-time attention segregation and retention for text-to-image synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2283–2293 (2023)
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Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., et al.: Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf 2
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Check, F.: Images appearing to show Donald Trump arrest created by AI. Reuters (Mar 2023), https://www.reuters.com/article/idUSL1N35T2TU
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Wang, Z., Lu, C., Wang, Y., Bao, F., Li, C., Su, H., Zhu, J.: ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation. Adv. Neural Inform. Process. Syst. (2023)
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Bohan, O.B.: Madebyollin/taesd (Mar 2024), https://github.com/madebyollin/taesd
2024
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Chen, J., Wu, Y., Luo, S., Xie, E., Paul, S., Luo, P., Zhao, H., Li, Z.: Pixart- δ \delta : Fast and controllable image generation with latent consistency models (2024)
2024
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Epstein, D., Jabri, A., Poole, B., Efros, A., Holynski, A.: Diffusion self-guidance for controllable image generation. Advances in Neural Information Processing Systems 36
2024
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Lin, S., Wang, A., Yang, X.: Sdxl-lightning: Progressive adversarial diffusion distillation (2024)
2024
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Nguyen, T.H., Tran, A.: Swiftbrush: One-step text-to-image diffusion model with variational score distillation. IEEE Conf. Comput. Vis. Pattern Recog. (2024)
2024
Closest in time.
Yin, T., Gharbi, M., Zhang, R., Shechtman, E., Durand, F., Freeman, W.T., Park, T.: One-step diffusion with distribution matching distillation. IEEE Conf. Comput. Vis. Pattern Recog. (2024)
2024
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