The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation
Open accessAbstract
Large-scale text-to-image generative models have been a revolutionary breakthrough in the evolution of generative AI, synthesizing diverse images with highly complex visual concepts. However, a pivotal challenge in leveraging such models for real-world content creation is providing users with control over the generated content. In this paper, we present a new framework that takes text-to- image synthesis to the realm of image-to-image translation - given a guidance image and a target text prompt as input, our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text, while preserving the semantic layout of the guidance image. Specifically, we observe and empirically demonstrate that fine-grained control over the generated structure can be achieved by manipulating spatial features and their self-attention inside the model. This results in a simple and effective approach, where features extracted from the guidance image are directly injected into the generation process of the translated image, requiring no training or fine-tuning. We demonstrate high-quality results on versatile text-guided image translation tasks, including translating sketches, rough drawings and animations into realistic images, changing the class and appearance of objects in a given image, and modifying global qualities such as lighting and color.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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High-Resolution Image Synthesis with Latent Diffusion Models
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Learning Transferable Visual Models From Natural Language Supervision
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Denoising Diffusion Probabilistic Models
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Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P., & Nichol, A. (2021) · arXiv · doi:10.48550/arxiv.2105.05233 · Cited by 3,362
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
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Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
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Cited by
Adding Conditional Control to Text-to-Image Diffusion Models
Zhang, L., Rao, A., & Agrawala, M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00355 · Cited by 4,547
OmniGen: Unified Image Generation
Xiao, S., Wang, Y., Zhou, J., Yuan, H., Xing, X., Yan, R., Li, C., Wang, S., Huang, T., & Liu, Z. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.01241 · Cited by 75
Training-Free Consistent Text-to-Image Generation
Tewel, Y., Kaduri, O., Gal, R., Kasten, Y., Wolf, L., Chechik, G., & Atzmon, Y. (2024) · ACM Transactions on Graphics · doi:10.1145/3658157 · Cited by 68
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Avrahami, O., Hertz, A., Vinker, Y., Arar, M., Fruchter, S., Fried, O., Cohen-Or, D., & Lischinski, D. (2024) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers · doi:10.1145/3641519.3657430 · Cited by 29
UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics
Chen, X., Zhang, Z., Zhang, H., Zhou, Y., Kim, S. Y., Liu, Q., Li, Y., Zhang, J., Zhao, N., Wang, Y., et al. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.01166 · Cited by 23
Metadata
- Authors
- Tumanyan, N., Geyer, M., Bagon, S., & Dekel, T.
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 1921-1930
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386113271
- Cited by
- 609
- References
- 50
- FWCI
- 69.25
- Type
- conference paper
- Language
- en
- Open access
- green
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- Tumanyan, N., Geyer, M., Bagon, S., & Dekel, T. (2023). Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52729.2023.00191
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