Image Generation Models
DALL·E 2
Introduced by Ramesh et al. in Hierarchical Text-Conditional Image Generation with CLIP LatentsDALL·E 2 is a generative text-to-image model made up of two main components: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding.
Source: Hierarchical Text-Conditional Image Generation with CLIP Latents
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Conditional Image Generation | 1 | 25.00% |
| Image Generation | 1 | 25.00% |
| Text-to-Image Generation | 1 | 25.00% |
| Zero-Shot Text-to-Image Generation | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
