Kandinsky 2.2 Prior Model
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Architecture | CLIP-based Prior Model |
| Training Data | LAION Improved Aesthetics, LAION HighRes |
| Primary Use | Text-to-Image Generation |
What is kandinsky-2-2-prior?
Kandinsky 2.2 Prior is a sophisticated image prior model that forms a crucial component of the Kandinsky 2.2 text-to-image generation ecosystem. It leverages CLIP-ViT-G architecture to bridge the gap between text and image modalities, enabling high-quality image generation from textual descriptions.
Implementation Details
The model implements a transformer-based architecture trained on CLIP text and image embeddings. It utilizes a pre-trained CLIP-ViT-G model and incorporates advanced diffusion techniques alongside MoVQGAN for final image decoding.
- Trained on LAION Improved Aesthetics dataset with fine-tuning on LAION HighRes
- Supports resolution from 512x512 to 1536x1536
- Enables various aspect ratios for flexible image generation
Core Capabilities
- Text-to-image generation with high aesthetic quality
- Image interpolation between multiple conditions
- Support for image-to-image generation
- Integration with ControlNet for enhanced control
Frequently Asked Questions
Q: What makes this model unique?
The model's integration of CLIP-ViT-G significantly enhances aesthetic quality and text understanding compared to previous versions, achieving competitive FID scores of 8.21 on COCO_30k dataset.
Q: What are the recommended use cases?
The model excels in creative applications requiring high-quality image generation, including artistic rendering, content creation, and professional design work where precise control over image generation is needed.
