Dimensionality Reduction
Latent Diffusion Model
Introduced by Rombach et al. in High-Resolution Image Synthesis with Latent Diffusion ModelsDiffusion models applied to latent spaces, which are normally built with (Variational) Autoencoders.
Source: High-Resolution Image Synthesis with Latent Diffusion ModelsPapers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Generation | 89 | 12.71% |
| Denoising | 51 | 7.29% |
| Decoder | 21 | 3.00% |
| Diversity | 17 | 2.43% |
| Video Generation | 14 | 2.00% |
| Text-to-Image Generation | 13 | 1.86% |
| Computational Efficiency | 12 | 1.71% |
| Image Reconstruction | 12 | 1.71% |
| Super-Resolution | 12 | 1.71% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
