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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.17651 |
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| _version_ | 1866914050617638912 |
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| author | Botti, Filippo Ergasti, Alex Fontanini, Tomaso Ferrari, Claudio Bertozzi, Massimo Prati, Andrea |
| author_facet | Botti, Filippo Ergasti, Alex Fontanini, Tomaso Ferrari, Claudio Bertozzi, Massimo Prati, Andrea |
| contents | Diffusion Models have become very popular for Semantic Image Synthesis (SIS) of human faces. Nevertheless, their training and inference is computationally expensive and their computational requirements are high due to the quadratic complexity of attention layers. In this paper, we propose a novel architecture called SISMA, based on the recently proposed Mamba. SISMA generates high quality samples by controlling their shape using a semantic mask at a reduced computational demand. We validated our approach through comprehensive experiments with CelebAMask-HQ, revealing that our architecture not only achieves a better FID score yet also operates at three times the speed of state-of-the-art architectures. This indicates that the proposed design is a viable, lightweight substitute to transformer-based models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17651 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SISMA: Semantic Face Image Synthesis with Mamba Botti, Filippo Ergasti, Alex Fontanini, Tomaso Ferrari, Claudio Bertozzi, Massimo Prati, Andrea Computer Vision and Pattern Recognition Diffusion Models have become very popular for Semantic Image Synthesis (SIS) of human faces. Nevertheless, their training and inference is computationally expensive and their computational requirements are high due to the quadratic complexity of attention layers. In this paper, we propose a novel architecture called SISMA, based on the recently proposed Mamba. SISMA generates high quality samples by controlling their shape using a semantic mask at a reduced computational demand. We validated our approach through comprehensive experiments with CelebAMask-HQ, revealing that our architecture not only achieves a better FID score yet also operates at three times the speed of state-of-the-art architectures. This indicates that the proposed design is a viable, lightweight substitute to transformer-based models. |
| title | SISMA: Semantic Face Image Synthesis with Mamba |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.17651 |