DiffuseVAE++: Mitigating training-sampling mismatch based on additional noise for higher fidelity image generation
Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated remarkable results in image generation. However, there exist a mismatch between the training and sampling process in current diffusion models, in addition, the U-Net denoising network based on simple residual blocks cannot predict no...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 633; s. 129814 |
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| Jazyk: | English |
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Elsevier B.V
07.06.2025
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| ISSN: | 0925-2312 |
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| Abstract | Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated remarkable results in image generation. However, there exist a mismatch between the training and sampling process in current diffusion models, in addition, the U-Net denoising network based on simple residual blocks cannot predict noise information accurately, which affects the generated quality. To address these limitations, we present a novel image generation method that achieves higher fidelity. First, by additionally adding the standard Gaussian noise in the diffusion forward process, which does not disrupt the forward process, our method alleviates the mismatch. Subsequently, an important efficient denoising network based on U-Net is presented, where our proposed Simple Squeeze-Excitation and Simple GLU, combined with Depthwise Separable Convolution, enhance the ability of the model to predict real noise using the Simplified Nonlinear No Activation (SNNA) block. Furthermore, considering the structural characteristics of the baseline model, we introduce an additional cross-attention mechanism to enable DDPM to focus on VAE stage characteristics. Allowing the model to more accurately capture and learn the noise information. Finally, it is shown after extensive experiments the proposed DiffuseVAE++ obtains significant gains in FID scores, improving from 3.84 to 2.41 on CIFAR-10 and from 3.94 to 2.30 on CelebA-64. In particular, the IS scores on CIFAR-10 reaches 10.10, which is comparable to the current state-of-the-art methods competitively (e.g., U-ViT, StyleGAN2). |
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| AbstractList | Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated remarkable results in image generation. However, there exist a mismatch between the training and sampling process in current diffusion models, in addition, the U-Net denoising network based on simple residual blocks cannot predict noise information accurately, which affects the generated quality. To address these limitations, we present a novel image generation method that achieves higher fidelity. First, by additionally adding the standard Gaussian noise in the diffusion forward process, which does not disrupt the forward process, our method alleviates the mismatch. Subsequently, an important efficient denoising network based on U-Net is presented, where our proposed Simple Squeeze-Excitation and Simple GLU, combined with Depthwise Separable Convolution, enhance the ability of the model to predict real noise using the Simplified Nonlinear No Activation (SNNA) block. Furthermore, considering the structural characteristics of the baseline model, we introduce an additional cross-attention mechanism to enable DDPM to focus on VAE stage characteristics. Allowing the model to more accurately capture and learn the noise information. Finally, it is shown after extensive experiments the proposed DiffuseVAE++ obtains significant gains in FID scores, improving from 3.84 to 2.41 on CIFAR-10 and from 3.94 to 2.30 on CelebA-64. In particular, the IS scores on CIFAR-10 reaches 10.10, which is comparable to the current state-of-the-art methods competitively (e.g., U-ViT, StyleGAN2). |
| ArticleNumber | 129814 |
| Author | Sun, Wei Luo, Wei Yang, Xiaobao Ma, Sugang Ning, Hailong Zhang, Guorui |
| Author_xml | – sequence: 1 givenname: Xiaobao orcidid: 0000-0003-1515-8663 surname: Yang fullname: Yang, Xiaobao email: y78h11b09@xupt.edu.cn organization: School of Computer Science & Technology, Xi’an University of Posts and Telecommunications, Xi’an, China – sequence: 2 givenname: Wei surname: Luo fullname: Luo, Wei email: Backwards@stu.xupt.edu.cn organization: School of Computer Science & Technology, Xi’an University of Posts and Telecommunications, Xi’an, China – sequence: 3 givenname: Hailong surname: Ning fullname: Ning, Hailong email: ninghailong@xupt.edu.cn organization: School of Computer Science & Technology, Xi’an University of Posts and Telecommunications, Xi’an, China – sequence: 4 givenname: Guorui surname: Zhang fullname: Zhang, Guorui email: zgr777@stu.xupt.edu.cn organization: School of Computer Science & Technology, Xi’an University of Posts and Telecommunications, Xi’an, China – sequence: 5 givenname: Wei surname: Sun fullname: Sun, Wei email: sunwei@xupt.edu.cn organization: School of Computer Science & Technology, Xi’an University of Posts and Telecommunications, Xi’an, China – sequence: 6 givenname: Sugang surname: Ma fullname: Ma, Sugang email: msg@xupt.edu.cn organization: School of Computer Science & Technology, Xi’an University of Posts and Telecommunications, Xi’an, China |
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| Cites_doi | 10.1109/ICCV51070.2023.01816 10.1109/CVPR46437.2021.00088 10.1007/978-3-031-73242-3_3 10.1109/CVPR.2018.00745 10.1109/CVPR52688.2022.01117 10.1109/CVPR52729.2023.02171 10.1109/ICCV51070.2023.00387 10.9734/jamcs/2019/v33i330178 10.1109/CVPR52729.2023.01768 10.3389/fpls.2024.1352935 10.1109/CVPR52729.2023.02159 10.1109/CVPR46437.2021.01268 10.1016/j.neucom.2022.01.029 10.1016/j.neucom.2021.01.047 10.1109/ICCV.2019.00461 10.1109/CVPR42600.2020.00813 10.1186/s40537-024-00944-3 10.1109/CVPR52688.2022.01042 10.1109/CVPR.2019.00453 10.3390/s23156727 10.1109/ICCV.2015.425 10.1016/j.neucom.2023.126589 10.1109/CVPR52733.2024.00806 |
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