Multi-Modal Methods
Guided Language to Image Diffusion for Generation and Editing
Introduced by Nichol et al. in GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsGLIDE is a generative model based on text-guided diffusion models for more photorealistic image generation. Guided diffusion is applied to text-conditional image synthesis and the model is able to handle free-form prompts. The diffusion model uses a text encoder to condition on natural language descriptions. The model is provided with editing capabilities in addition to zero-shot generation, allowing for iterative improvement of model samples to match more complex prompts. The model is fine-tuned to perform image inpainting.
Source: GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Generation | 9 | 39.13% |
| Text-to-Image Generation | 3 | 13.04% |
| Denoising | 2 | 8.70% |
| Trajectory Planning | 1 | 4.35% |
| Depth Estimation | 1 | 4.35% |
| Semantic Segmentation | 1 | 4.35% |
| Style Transfer | 1 | 4.35% |
| Zero-Shot Learning | 1 | 4.35% |
| Fake Image Detection | 1 | 4.35% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
