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DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations (2403.06951v2)
Published 11 Mar 2024 in cs.CV
Abstract: The diffusion-based text-to-image model harbors immense potential in transferring reference style. However, current encoder-based approaches significantly impair the text controllability of text-to-image models while transferring styles. In this paper, we introduce DEADiff to address this issue using the following two strategies: 1) a mechanism to decouple the style and semantics of reference images. The decoupled feature representations are first extracted by Q-Formers which are instructed by different text descriptions. Then they are injected into mutually exclusive subsets of cross-attention layers for better disentanglement. 2) A non-reconstructive learning method. The Q-Formers are trained using paired images rather than the identical target, in which the reference image and the ground-truth image are with the same style or semantics. We show that DEADiff attains the best visual stylization results and optimal balance between the text controllability inherent in the text-to-image model and style similarity to the reference image, as demonstrated both quantitatively and qualitatively. Our project page is https://tianhao-qi.github.io/DEADiff/.
- Artflow: Unbiased image style transfer via reversible neural flows. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 862–871, 2021.
- Artistic style transfer with internal-external learning and contrastive learning. Advances in Neural Information Processing Systems, 34:26561–26573, 2021.
- Stytr2: Image style transfer with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11326–11336, 2022.
- Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780–8794, 2021.
- An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022.
- Image style transfer using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2414–2423, 2016.
- Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022.
- Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
- Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
- Composer: Creative and controllable image synthesis with composable conditions. arXiv preprint arXiv:2302.09778, 2023.
- Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
- Style transfer by relaxed optimal transport and self-similarity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10051–10060, 2019.
- Multi-concept customization of text-to-image diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1931–1941, 2023.
- Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing. arXiv preprint arXiv:2305.14720, 2023a.
- Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023b.
- Decoupled weight decay regularization. In International Conference on Learning Representations, 2018.
- T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv preprint arXiv:2302.08453, 2023.
- Arbitrary style transfer with style-attentional networks. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5880–5888, 2019.
- Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
- Online and linear-time attention by enforcing monotonic alignments. In International Conference on Machine Learning, pages 2837–2846, 2017.
- Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022.
- High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022.
- U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, 2015.
- Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22500–22510, 2023.
- Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479–36494, 2022.
- Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256–2265, 2015.
- Styledrop: Text-to-image generation in any style. arXiv preprint arXiv:2306.00983, 2023.
- Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.
- Improved artgan for conditional synthesis of natural image and artwork. IEEE Transactions on Image Processing, 28(1):394–409, 2018.
- Attention is all you need. Advances in neural information processing systems, 30, 2017.
- p+limit-from𝑝p+italic_p +: Extended textual conditioning in text-to-image generation. arXiv preprint arXiv:2303.09522, 2023.
- Styleadapter: A single-pass lora-free model for stylized image generation. arXiv preprint arXiv:2309.01770, 2023.
- Styleformer: Real-time arbitrary style transfer via parametric style composition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14618–14627, 2021.
- Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint arXiv:2308.06721, 2023.
- Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023a.
- Domain enhanced arbitrary image style transfer via contrastive learning. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–8, 2022.
- Inversion-based style transfer with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10146–10156, 2023b.
- Uni-controlnet: All-in-one control to text-to-image diffusion models. arXiv preprint arXiv:2305.16322, 2023.
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