Image Generation Models
Blended Diffusion
Introduced by Avrahami et al. in Blended Diffusion for Text-driven Editing of Natural ImagesBlended Diffusion enables a zero-shot local text-guided image editing of natural images. Given an input image $x$, an input mask $m$ and a target guiding text $t$ - the method enables to change the masked area within the image corresponding the the guiding text s.t. the unmasked area is left unchanged.
Source: Blended Diffusion for Text-driven Editing of Natural Images
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Generation | 2 | 18.18% |
| Image Inpainting | 2 | 18.18% |
| text-guided-image-editing | 2 | 18.18% |
| Text-to-Image Generation | 2 | 18.18% |
| Zero-Shot Text-to-Image Generation | 2 | 18.18% |
| Super-Resolution | 1 | 9.09% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
