Cosmos-0.1-Tokenizer-CI8x8
| Property | Value |
|---|---|
| Developer | NVIDIA |
| Model Type | Continuous Image Tokenizer |
| Parameters | 77M |
| License | NVIDIA Open Model License |
| Compression Ratio | 8x8 spatial |
What is Cosmos-0.1-Tokenizer-CI8x8?
Cosmos-0.1-Tokenizer-CI8x8 is a state-of-the-art continuous image tokenizer that's part of NVIDIA's Cosmos Tokenizer suite. It provides efficient 8x8 spatial compression while maintaining exceptional image reconstruction quality. The model converts visual data into continuous latent embeddings, making it particularly suitable for diffusion-based models like Stable Diffusion.
Implementation Details
The model employs a lightweight and computationally efficient architecture with a symmetrical encoder-decoder design. It begins with a 2-level Haar wavelet transform layer for downsampling and uses a vanilla autoencoder formulation for the latent space. The model achieves impressive metrics with PSNR of 32.98 and SSIM of 0.836 on MS-COCO, significantly outperforming previous solutions.
- Processes images with resolutions from 256px up to 4K
- Outputs continuous value feature vectors with shape (B, 16, H/8, W/8)
- Runs 4x faster than comparable models like FLUX
- Supports BF16 precision on Ampere and Hopper GPUs
Core Capabilities
- High-quality image reconstruction with minimal information loss
- Efficient 8x8 spatial compression ratio
- Fast processing speed (62.7ms per 1024x1024 image)
- Seamless integration with diffusion-based models
- Compatible with both PyTorch and NeMo frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model offers an optimal balance between compression efficiency and reconstruction quality, achieving better performance metrics than previous SOTA models while requiring less computational resources. It's specifically designed for integration with modern AI image generation pipelines.
Q: What are the recommended use cases?
This tokenizer is ideal for applications requiring efficient image compression in AI pipelines, particularly in diffusion-based image generation models. It's well-suited for high-resolution image processing tasks where maintaining visual quality is crucial.
