Cosmos3-Super-Image2Video-4Step

Model Overview: Cosmos3-Super-Image2Video-4Step

Description

Cosmos3-Super-Image2Video-4Step is a 4-step distilled version of the base Cosmos3-Super-Image2Video model. Given an input image plus an optional text prompt, it generates a temporally coherent video.

• What's different from the base model: Distilled into a fixed 4-step sampling schedule without the need for classifier-free guidance. At the recommended 50-step + CFG setting for the Image2Video model, this replaces 50 × 2 = 100 model evaluations with 4, leading up to 25× fewer diffusion-model evaluations per video (see PBR for latency estimates).
• Quality: Ranked #1 among open-source models on the Artificial Analysis Image-to-Video leaderboard (as of 2026/07/17; see Benchmarks section).
• Input / Output: Image(s) in → MP4 video out. Recommended 480p (832×480, 16:9); 189 frames by default.
• Example usage and output: See Usage section.
• Hardware: The 64B model needs a multi-GPU H100/H200 node (4–8 GPUs) or a single B200; it does not fit on a single smaller GPU. See Usage. Full model details below.

Cosmos3-Super-Image2Video-4Step was developed by NVIDIA as a part of Cosmos3.

Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs. It serves as a foundational building block for a broad range of Physical AI applications and research spanning world understanding, world generation, simulation, and embodied policy learning.

This model is ready for commercial and non-commercial use.

Model Developer: NVIDIA

Model Versions

Released on: 07/20/2026

• Cosmos3-Edge:

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

• Cosmos3-Edge-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-4Step:

  • Given one or more input images and optional text instructions, generate temporally coherent video sequences that are consistent with the provided visual content. Distilled from Cosmos3-Super-Image2Video using Improved Distribution Matching Distillation (DMD2), enabling high-quality generation in 4 steps.

• Cosmos3-Super-Text2Image-4Step:

  • Given text input, generate high-fidelity images that are consistent with the provided description. Distilled from Cosmos3-Super-Text2Image using Improved Distribution Matching Distillation (DMD2), enabling high-quality generation in 4 steps.

Released on: 05/31/2026

• 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 or more input images and optional 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.

License

This model is released under the OpenMDW1.1

Deployment Geography

Global

Use Case

Physical AI: Encompassing robotics, autonomous vehicles (AV), and smart space environments, including industrial and factory-scale applications.

Release Date

Hugging Face 07/20/2026 via https://huggingface.co/collections/nvidia/cosmos3. GitHub 07/20/2026 via https://github.com/nvidia/cosmos.

Model Architecture

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 inferen