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2023

Fast Sampling Through The Reuse Of Attention Maps In Diffusion Models

Hunter, Rosco, Dudziak, Łukasz, Abdelfattah, Mohamed S. et al.

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Text-to-image diffusion models have demonstrated unprecedented capabilities for flexible and realistic image synthesis.

Nevertheless, these models rely on a time-consuming sampling procedure, which has motivated attempts to reduce their latency.
When improving efficiency, researchers often use the original diffusion model to train an additional network designed specifically for fast image generation.
In contrast, our approach seeks to reduce latency directly, without any retraining, fine-tuning, or knowledge distillation.

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Similar

Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (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, 36479–36494 (2022)
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Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512 (2022)
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Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models (2023)
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Gu, J., Zhai, S., Zhang, Y., Liu, L., Susskind, J.: Boot: Data-free distillation of denoising diffusion models with bootstrapping. arXiv preprint arXiv:2306.05544 (2023)
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Berthelot, D., Autef, A., Lin, J., Yap, D.A., Zhai, S., Hu, S., Zheng, D., Talbot, W., Gu, E.: Tract: Denoising diffusion models with transitive closure time-distillation. arXiv preprint arXiv:2303.04248 (2023)
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Liu, X., Zhang, X., Ma, J., Peng, J., Liu, Q.: Instaflow: One step is enough for high-quality diffusion-based text-to-image generation. arXiv preprint arXiv:2309.06380 (2023)
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Kim, B.-K., Song, H.-K., Castells, T., Choi, S.: On architectural compression of text-to-image diffusion models. arXiv preprint arXiv:2305.15798 (2023)

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