Freestyle Layout-to-Image Synthesis
Han Xue, Zhiwu Huang, Qianru Sun, Li Song, Wenjun Zhang
摘要
Layout "train bush grass railroad" "lego train bush grass railroad" "an ink painting of train bush grass railroad" "warehouse bush grass railroad" Layout "bench cat building bush furniture grass ground pavement roof sky tree window" "bench tabby cat building bush furniture grass ground pavement roof sky tree window" "a sketch of bench cat building bush furniture grass ground pavement roof sky tree window" "bench unicorn building bush furniture grass ground pavement roof sky tree window" Figure 1 . Freestyle Layout-to-Image Synthesis (FLIS) results generated by using our model. Each has two kinds of inputs: a layout of semantic masks (on the 1st column), and a text (on the top of each result). For each layout, we show three example results with edited texts (3rd-5th columns). They validate that our model is able to introduce new attributes (3rd column), styles (4th column), and objects (5th column), which are all unseen during training, in the synthesized images. The generated hornless unicorn is due to the layout constraint.
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引用它的顶会 Paper33
- FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation ModelsLihe Yang, Xiaogang Xu, Bingyi Kang, Yinghuan Shi 等NeurIPS 2023 · 被引用 94 次
- SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-Form Layout-to-Image GenerationChengyou Jia, Minnan Luo, Zhuohang Dang, Guang Dai 等AAAI 2024 · 被引用 30 次
- SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified FlowChaoyang Wang, Xiangtai Li, Lu Qi, Henghui Ding 等NeurIPS 2024 · 被引用 25 次
- SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion ModelTao Wu, Xuewei Li, Zhongang Qi, Di Hu 等AAAI 2024 · 被引用 24 次
- Adversarial Supervision Makes Layout-to-Image Diffusion Models ThriveYumeng Li, Margret Keuper, Dan Zhang, Anna KhorevaICLR 2024 · 被引用 20 次
它引用的顶会 Paper28
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 被引用 13,211 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 被引用 11,743 次
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