Compare model versions

Diff

Before

- Cosmos3-Nano:
  - Given multimodal inputs including text, images, video, audio, and action trajectories, generate coherent text, images, video, audio, and action outputs for multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications.

- Cosmos3-Super:
  - Given multimodal inputs including text, images, video, audio, and action trajectories, generate coherent text, images, video, audio, and action outputs for multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications.

- Cosmos3-Nano-Policy-DROID:
  - Given language instructions and visual observations from the DROID robot platform, generate robot action trajectories for manipulation and control tasks.

- Cosmos3-Super-Image2Video:
  - Given one input image and text instructions, generate temporally coherent video sequences that are consistent with the provided visual content.

- Cosmos3-Super-Text2Image:
  - Given text input, generate high-fidelity images that are consistent with the provided description.

After

- Cosmos3-Super:
  - Given multimodal inputs including text, images, video, audio, and action trajectories, generate coherent text, images, video, audio, and action outputs for multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications.

Diff

Before

**Architecture Type:** Transformer

**Network Architecture:** Mixture-of-Transformers (MoT)

Cosmos3 is an Omni-modal foundation model built on a Mixture-of-Transformers (MoT) architecture consisting of two complementary transformer towers: an autoregressive transformer for discrete token generation and a diffusion transformer for continuous multimodal generation. During inference, text is generated through standard next-token autoregressive decoding, while non-text modalities, such as images, video, audio, and actions, are synthesized through iterative denoising. This unified architecture enables Cosmos3 to model heterogeneous modalities within a single framework while preserving generation mechanisms best suited to each modality.

**This model was developed based on:**  [Cosmos Framework](https://github.com/nvidia/cosmos-framework)

**Number of trainable model parameters:**

- Cosmos3-Nano: 16B
- Cosmos3-Super: 64B
- Cosmos3-Nano-Policy-DROID: 16B
- Cosmos3-Super-Image2Video: 64B
- Cosmos3-Super-Text2Image: 64B

After

**Architecture Type:** Transformer

**Network Architecture:** Mixture-of-Transformers (MoT)

Cosmos3 is an Omni-modal foundation model built on a Mixture-of-Transformers (MoT) architecture consisting of two complementary transformer towers: an autoregressive transformer for discrete token generation and a diffusion transformer for continuous multimodal generation. During inference, text is generated through standard next-token autoregressive decoding, while non-text modalities, such as images, video, audio, and actions, are synthesized through iterative denoising. This unified architecture enables Cosmos3 to model heterogeneous modalities within a single framework while preserving generation mechanisms best suited to each modality.

**This model was developed based on:**  [Cosmos Framework](https://github.com/nvidia/cosmos-framework)

**Number of trainable model parameters:**

- Cosmos3-Super: 64B