Cosmos-Tokenize1-DI16x16-360p

Cosmos-Tokenize1: A suite of image and video tokenizers

Cosmos | Code | Paper | Paper Website

Model Overview

Description:

Cosmos-Tokenize1 is a suite of visual tokenizers for images and videos that delivers various compression rates while maintaining high reconstruction quality. Cosmos Tokenizer can serve as an effective and efficient building block in both diffusion-based and autoregressive models for image and video generation. The models are ready for commercial use.

Our tokenizers come in two types: Continuous (C) and Discrete (D), each with Image (I) and Video (V) variants:

• Continuous tokenizers encode visual data into continuous latent embeddings, as shown in latent diffusion models like Stable Diffusion. These embeddings are suitable for models that generate data by sampling from continuous distributions.
• Discrete tokenizers encode visual data into discrete latent codes, mapping them into quantized indices, as seen in autoregressive transformers such as VideoPoet. This discretization is required for models that generate data by optimizing the cross-entropy loss, such as the GPT models.

Model: Cosmos-Tokenize1-DI16x16-360p, a discrete image tokenizer with 16x16 spatial compression rate.

Model Developer: NVIDIA

Model Versions

The Cosmos-Tokenize1 includes the following tokenizers:

• Continuous Tokenizers

  • Cosmos-Tokenize1-CI8x8-360p (8x8 spatial compression, 360p and above)
  • Cosmos-Tokenize1-CI16x16-360p (16x16 spatial compression, 360p and above)
  • Cosmos-Tokenize1-CV4x8x8-360p (4x temporal compression, 8x8 spatial compression, 360p and above, 49 frames context)
  • Cosmos-Tokenize1-CV8x8x8-720p (8x temporal compression, 8x8 spatial compression, 720p and above, 121 frames context)

• Discrete Tokenizers

  • Cosmos-Tokenize1-DI8x8-360p (8x8 spatial compression, 360p and above)
  • Cosmos-Tokenize1-DI16x16-360p (16x16 spatial compression, 360p and above)
  • Cosmos-Tokenize1-DV4x8x8-360p (4x temporal compression, 8x8 spatial compression, 360p and above, 49 frames context)
  • Cosmos-Tokenize1-DV8x16x16-720p (8x temporal compression, 16x16 spatial compression, 720p and above, 49 frames context)

License:

This model is released under the NVIDIA Open Model License. For a custom license, please contact [email protected].

Under the NVIDIA Open Model License, NVIDIA confirms:

• Models are commercially usable.
• You are free to create and distribute Derivative Models.
• NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.

Model Architecture:

Cosmos-Tokenize1-DI16x16-360p is a lightweight and computationally efficient architecture. The encoder starts with a 1-level Haar wavelet transform layer, which down-samples inputs by a factor of 2 in both spatial dimension. Likewise, the decoder ends with an inverse wavelet transform. We employ the vanilla autoencoder (AE) formulation to model the latent space for continuous tokenizers.

Input/Output Specifications

Encoder

• Input

  • Type: Images
  • Format: RGB (Red, Green, Blue)
  • Parameters: Two-dimensional (2D)
  • Properties:
    • Resolution: Minimum: 256px (shorter side). Maximum: Up to 4K

• Output

  • Type: Tokens
  • Format: 16-Channel Vector
  • Parameters: Two-dimensional (2D)
  • Properties:
    • Continuous-valued feature vectors with a dimensionality of 16

Decoder

• Input

  • Type: Tokens
  • Format: 16-Channel Vector
  • Parameters: Two-dimensional (2D)
  • Properties:
    • Continuous-valued feature vectors with a dimensionality of 16

• Output

  • Type Images (matching input type)
  • Format: RGB (Red, Green, Blue)
  • Parameters: Two-dimensional (2D)
  • Properties:
    • Resolution: Same as input resolution. The output image is a reconstruction of the input image.

Software Integration:

Runtime Engine(s):

• Cosmos-Predict1

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere (e.g., A100)
• NVIDIA Hopper (e.g., H100)

Note: We have only tested Cosmos Tokenizer with BF16 precision on Ampere and Hopper GPUs. If you are using older versions of NVIDIA GPUs (e.g., NVIDIA Volta GPUs), you may need to switch to FP32 precision.

Operating System(s):

• Linux (We have not tested on other operating systems.)

Usage

Inference Engines:

• See Cosmos-Predict1 (PyTorch) for details.

Evaluation

Tokenization Comparison

We have extensively evaluated the Cosmos Tokenizer suite on various image and video benchmark datasets. For the evaluation of image tokenizers, we follow prior art to evaluate on MS-COCO 2017, ImageNet-1K, FFHQ, and CelebA-HQ. We use the MS-COCO 2017 validation subset of 5,000 images, ImageNet-1K validation subset of 50,000 images, FFHQ subset of 10,000 images, and CelebA-HQ subset of 14,645 images as image evaluation benchmark.

• We compare with the state-of-the-art discrete image tokenizers, Open-MAGVIT2 and LlamaGen.
• Evaluation metrics:
  • Peak Signal-to-Noise Ratio (PSNR)
  • Structural Similarity (SSIM)
  • Reconstruction Fréchet Inception Distance (rFID)

Runtime Comparison

The following table shows the number of parameters and the averaged encoding and decoding times per image or video frame, measured on a single A100 80GB GPU. For comparison, we also list the parameters and average speeds of prior state-of-the-art tokenizer(s) with the same compression ratio.

Note: We benchmarked the runtime for images under the 8x8 compression and videos under the 4×8×8 compression. Tokenizers with different compression ratios are not included in this comparison.

Ethical Considerations

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For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns here.

Plus Plus (++) Promise

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• Verified to comply with current applicable disclosure laws, regulations, and industry standards.
• Verified to comply with applicable privacy labeling requirements.
• Annotated to describe the collector/source (NVIDIA or a third-party).
• Characterized for technical limitations.
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• Reviewed before release.
• Tagged for known restrictions and potential safety implications.

Bias

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