Decoupled Latent Diffusion Model for Enhancing Image Generation
Latent Diffusion Models have emerged as an efficient alternative to conventional diffusion approaches by compressing high-dimensional images into a lower-dimensional latent space using a Variational Autoencoder (VAE) and performing diffusion in that space. In standard Latent Diffusion Model (LDM), t...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11091282/ |
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| author | Hyun-Tae Choi Kensuke Nakamura Byung-Woo Hong |
| author_facet | Hyun-Tae Choi Kensuke Nakamura Byung-Woo Hong |
| author_sort | Hyun-Tae Choi |
| collection | DOAJ |
| description | Latent Diffusion Models have emerged as an efficient alternative to conventional diffusion approaches by compressing high-dimensional images into a lower-dimensional latent space using a Variational Autoencoder (VAE) and performing diffusion in that space. In standard Latent Diffusion Model (LDM), the latent code is formed by sampling from a Gaussian distribution (i.e., combining both the mean and the standard deviation), which helps regularize the latent space but appears to contribute little beyond the deterministic component. Motivated by recent empirical observations that the decoder relies primarily on the latent mean, our work reexamines this paradigm and proposes a decoupled latent diffusion model that focuses on a simplified latent representation. Specifically, we compare three configurations: (i) the standard latent code, (ii) a concatenated representation that explicitly preserves both mean and variance, and (iii) a deterministic mean-only representation. Our extensive experiments on multiple benchmark datasets demonstrate that, when compared to the standard approach, the mean-only configuration not only maintains but in many cases improves synthesis quality by producing sharper and more coherent images while reducing unnecessary noise. These findings suggest that a simplified, deterministic latent representation can yield more stable and efficient generative models, challenging the conventional reliance on latent sampling in diffusion-based image synthesis. |
| format | Article |
| id | doaj-art-0754ed6ec3f041b4abd4a58ebccf0d34 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0754ed6ec3f041b4abd4a58ebccf0d342025-08-20T03:58:40ZengIEEEIEEE Access2169-35362025-01-011313050513051610.1109/ACCESS.2025.359216311091282Decoupled Latent Diffusion Model for Enhancing Image GenerationHyun-Tae Choi0https://orcid.org/0000-0001-8268-0705Kensuke Nakamura1https://orcid.org/0000-0002-6858-3551Byung-Woo Hong2https://orcid.org/0000-0003-2752-3939Department of Artificial Intelligence, Chung-Ang University, Seoul, South KoreaDepartment of Artificial Intelligence, Chung-Ang University, Seoul, South KoreaDepartment of Artificial Intelligence, Chung-Ang University, Seoul, South KoreaLatent Diffusion Models have emerged as an efficient alternative to conventional diffusion approaches by compressing high-dimensional images into a lower-dimensional latent space using a Variational Autoencoder (VAE) and performing diffusion in that space. In standard Latent Diffusion Model (LDM), the latent code is formed by sampling from a Gaussian distribution (i.e., combining both the mean and the standard deviation), which helps regularize the latent space but appears to contribute little beyond the deterministic component. Motivated by recent empirical observations that the decoder relies primarily on the latent mean, our work reexamines this paradigm and proposes a decoupled latent diffusion model that focuses on a simplified latent representation. Specifically, we compare three configurations: (i) the standard latent code, (ii) a concatenated representation that explicitly preserves both mean and variance, and (iii) a deterministic mean-only representation. Our extensive experiments on multiple benchmark datasets demonstrate that, when compared to the standard approach, the mean-only configuration not only maintains but in many cases improves synthesis quality by producing sharper and more coherent images while reducing unnecessary noise. These findings suggest that a simplified, deterministic latent representation can yield more stable and efficient generative models, challenging the conventional reliance on latent sampling in diffusion-based image synthesis.https://ieeexplore.ieee.org/document/11091282/Denoising diffusion modellatent representationimage generation |
| spellingShingle | Hyun-Tae Choi Kensuke Nakamura Byung-Woo Hong Decoupled Latent Diffusion Model for Enhancing Image Generation IEEE Access Denoising diffusion model latent representation image generation |
| title | Decoupled Latent Diffusion Model for Enhancing Image Generation |
| title_full | Decoupled Latent Diffusion Model for Enhancing Image Generation |
| title_fullStr | Decoupled Latent Diffusion Model for Enhancing Image Generation |
| title_full_unstemmed | Decoupled Latent Diffusion Model for Enhancing Image Generation |
| title_short | Decoupled Latent Diffusion Model for Enhancing Image Generation |
| title_sort | decoupled latent diffusion model for enhancing image generation |
| topic | Denoising diffusion model latent representation image generation |
| url | https://ieeexplore.ieee.org/document/11091282/ |
| work_keys_str_mv | AT hyuntaechoi decoupledlatentdiffusionmodelforenhancingimagegeneration AT kensukenakamura decoupledlatentdiffusionmodelforenhancingimagegeneration AT byungwoohong decoupledlatentdiffusionmodelforenhancingimagegeneration |