IIDM: Image-to-Image Diffusion Model for Semantic Image Synthesis
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916362354425856 |
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| author | Liu, Feng Chang, Xiaobin |
| author_facet | Liu, Feng Chang, Xiaobin |
| contents | Semantic image synthesis aims to generate high-quality images given semantic conditions, i.e. segmentation masks and style reference images. Existing methods widely adopt generative adversarial networks (GANs). GANs take all conditional inputs and directly synthesize images in a single forward step. In this paper, semantic image synthesis is treated as an image denoising task and is handled with a novel image-to-image diffusion model (IIDM). Specifically, the style reference is first contaminated with random noise and then progressively denoised by IIDM, guided by segmentation masks. Moreover, three techniques, refinement, color-transfer and model ensembles, are proposed to further boost the generation quality. They are plug-in inference modules and do not require additional training. Extensive experiments show that our IIDM outperforms existing state-of-the-art methods by clear margins. Further analysis is provided via detailed demonstrations. We have implemented IIDM based on the Jittor framework; code is available at https://github.com/ader47/jittor-jieke-semantic_images_synthesis. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_13378 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | IIDM: Image-to-Image Diffusion Model for Semantic Image Synthesis Liu, Feng Chang, Xiaobin Computer Vision and Pattern Recognition Semantic image synthesis aims to generate high-quality images given semantic conditions, i.e. segmentation masks and style reference images. Existing methods widely adopt generative adversarial networks (GANs). GANs take all conditional inputs and directly synthesize images in a single forward step. In this paper, semantic image synthesis is treated as an image denoising task and is handled with a novel image-to-image diffusion model (IIDM). Specifically, the style reference is first contaminated with random noise and then progressively denoised by IIDM, guided by segmentation masks. Moreover, three techniques, refinement, color-transfer and model ensembles, are proposed to further boost the generation quality. They are plug-in inference modules and do not require additional training. Extensive experiments show that our IIDM outperforms existing state-of-the-art methods by clear margins. Further analysis is provided via detailed demonstrations. We have implemented IIDM based on the Jittor framework; code is available at https://github.com/ader47/jittor-jieke-semantic_images_synthesis. |
| title | IIDM: Image-to-Image Diffusion Model for Semantic Image Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.13378 |