Masked-attention diffusion guidance for spatially controlling text-to-image generation
Author:
Funder
Japan Society for the Promotion of Science
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-03151-y.pdf
Reference61 articles.
1. Abdal, R., Zhu, P., Femiani, J., Mitra, N., Wonka, P.: Clip2StyleGAN: Unsupervised extraction of StyleGAN edit directions. In: ACM SIGGRAPH 2022 Conference Proceedings, pp. 1–9 (2022)
2. Abdal, R., Zhu, P., Mitra, N.J., Wonka, P.: (2021) StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows. ACM Trans. Gr. 403, 21:1–21:21 (2021)
3. Avrahami, O., Fried, O., Lischinski, D.: Blended Latent Diffusion. CoRR arxiv: 2206.02779 (2022)
4. Avrahami, O., Hayes, T., Gafni, O., Gupta, S., Taigman, Y., Parikh, D., Lischinski, D., Fried, O., Yin, X.: SpaText: Spatio-Textual Representation for Controllable Image Generation. CoRR arxiv:2211.14305 (2022)
5. Avrahami, O., Lischinski, D., Fried, O.: Blended diffusion for text-driven editing of natural images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022, 18208–18218 (2022)
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