Kandinsky 2.1
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Total Parameters | ~3.27B |
| Architecture Type | Multi-modal Diffusion |
| Primary Components | CLIP, Latent Diffusion, Transformer |
What is Kandinsky_2.1?
Kandinsky 2.1 is an advanced text-to-image generation model that combines best practices from DALL-E 2 and Latent Diffusion while introducing innovative approaches to image generation. The model employs a sophisticated multi-component architecture that bridges text and image modalities through diffusion-based techniques.
Implementation Details
The model architecture consists of multiple specialized components: a 560M parameter text encoder (XLM-Roberta-Large-Vit-L-14), a 1B parameter Diffusion Image Prior, a 427M parameter CLIP image encoder (ViT-L/14), a 1.22B parameter Latent Diffusion U-Net, and a 67M parameter MoVQ encoder/decoder. The diffusion mapping between latent spaces utilizes a transformer with 20 layers, 32 heads, and 2048 hidden dimensions.
- Multi-modal CLIP-based architecture
- Innovative diffusion mapping between latent spaces
- Advanced transformer-based diffusion prior
- Efficient latent space representation
Core Capabilities
- High-quality text-to-image generation
- Advanced image manipulation
- Text-guided image editing
- Image and text blending
- Multi-lingual support through XLM-Roberta
Frequently Asked Questions
Q: What makes this model unique?
Kandinsky 2.1's uniqueness lies in its hybrid architecture that combines CLIP-based encoding with diffusion mapping between modalities, enabling more precise control over image generation while maintaining high visual quality.
Q: What are the recommended use cases?
The model excels in creative applications including digital art creation, design visualization, content generation, and image editing tasks that require precise text-based control.
