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...
Saved in:
| Main Authors: | Hyun-Tae Choi, Kensuke Nakamura, Byung-Woo Hong |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11091282/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Remote Sensing Image Semantic Segmentation Sample Generation Using a Decoupled Latent Diffusion Framework
by: Yue Xu, et al.
Published: (2025-06-01) -
Spatial Compression Methods for Latent Diffusion Models
by: Vladimir Abramov, et al.
Published: (2025-04-01) -
Denoising Diffusion-Based Image Generation Model Using Principal Component Analysis
by: Myung Keun Song, et al.
Published: (2024-01-01) -
Spread Spectrum Image Watermarking Through Latent Diffusion Model
by: Hongfei Wu, et al.
Published: (2025-04-01) -
On Denoising Diffusion Probabilistic Models for Synthetic Aperture Radar Despeckling
by: Alec Paul, et al.
Published: (2025-03-01)