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
-
Dual-Stream Contrastive Learning for Medical Visual Representations Using Synthetic Images Generated by Latent Diffusion Model
by: Weitao Ye, et al.
Published: (2025-01-01) -
AVINet: adaptive variational iteration network for low light image enhancement
by: Tao Chen, et al.
Published: (2025-08-01) -
Latent profiles and associated factors of body image in pregnant women
by: Yunmei Guo, et al.
Published: (2025-05-01) -
Multimodal diffusion framework for collaborative text image audio generation and applications
by: Junhua Wang, et al.
Published: (2025-07-01) -
Impact of Normalization Techniques on Synthetic Load Profile Generation Using Deep Generative Models
by: Luis H. T. Bandoria, et al.
Published: (2025-01-01)