Cosmos-Predict2.5 是 Cosmos World Foundation Models (WFMs) 系列的最新版本,专门用于模拟和预测世界未来状态。
Cosmos-Predict2.5 是 Cosmos World Foundation Models (WFMs) 系列的最新版本,专门用于模拟和预测世界未来状态。
[!IMPORTANT]
Cosmos 3 Has Arrived
Cosmos 3 is NVIDIA's next-generation foundation model platform for Physical AI. Compared with Cosmos-Predict2.5, Cosmos 3 delivers significantly stronger world prediction capabilities, producing more accurate, coherent, and physically grounded future-state predictions across a wide range of environments and embodiments.
Beyond improving prediction quality, Cosmos 3 unifies capabilities that previously required multiple specialized models. A single Cosmos 3 model can reason, predict future world states, transfer across domains and modalities, and generate actions and policies for embodied agents within one unified architecture.
This repository is no longer under active development and will receive only limited maintenance updates. Future model releases, features, documentation, and community support will be focused on Cosmos 3.
Visit the new Cosmos home: https://github.com/NVIDIA/Cosmos
There you will find the latest Cosmos 3 models, technical reports, tutorials, benchmarks, and ecosystem updates.
Thank you for your support of Cosmos-Predict2.5. We encourage all users to migrate to Cosmos 3 for the latest state-of-the-art Physical AI capabilities.
Product Website  | Hugging Face  | Paper  | Paper Website | Cosmos Cookbook
NVIDIA Cosmos™ is a platform purpose-built for physical AI, featuring state-of-the-art generative world foundation models (WFMs), robust guardrails, and an accelerated data processing and curation pipeline. Designed specifically for real-world systems, Cosmos enables developers to rapidly advance physical AI applications such as autonomous vehicles (AVs), robots, and video analytics AI agents.
Cosmos World Foundation Models come in three model types which can all be customized in post-training: cosmos-predict, cosmos-transfer, and cosmos-reason.
We introduce Cosmos-Predict2.5, the latest version of the Cosmos World Foundation Models (WFMs) family, specialized for simulating and predicting the future state of the world in the form of video. Cosmos-Predict2.5 is a flow based model that unifies Text2World, Image2World, and Video2World into a single model and utilizes Cosmos-Reason1, a Physical AI reasoning vision language model (VLM), as the text encoder. Cosmos-Predict2.5 significantly improves upon Cosmos-Predict1 in both quality and prompt alignment.
Input prompt A nighttime city bus terminal gradually shifts from stillness to subtle movement. At first, multiple double-decker buses are parked under the glow of overhead lights, with a central bus labeled '87D' facing forward and stationary. As the video progresses, the bus in the middle moves ahead slowly, its headlights brightening the surrounding area and casting reflections onto adjacent vehicles. The motion creates space in the lineup, signaling activity within the otherwise quiet station. It then comes to a smooth stop, resuming its position in line. Overhead signage in Chinese characters remains illuminated, enhancing the vibrant, urban night scene.
| Input image | Output video |
|---|---|
Input prompt A robotic arm, primarily white with black joints and cables, is shown in a clean, modern indoor setting with a white tabletop. The arm, equipped with a gripper holding a small, light green pitcher, is positioned above a clear glass containing a reddish-brown liquid and a spoon. The robotic arm is in the process of pouring a transparent liquid into the glass. To the left of the pitcher, there is an opened jar with a similar reddish-brown substance visible through its transparent body. In the background, a vase with white flowers and a brown couch are partially visible, adding to the contemporary ambiance. The lighting is bright, casting soft shadows on the table. The robotic arm's movements are smooth and controlled, demonstrating precision in its task. As the video progresses, the robotic arm completes the pour, leaving the glass half-filled with the reddish-brown liquid. The jar remains untouched throughout the sequence, and the spoon inside the glass remains stationary. The other robotic arm on the right side also stays stationary throughout the video. The final frame captures the robotic arm with the pitcher finishing the pour, with the glass now filled to a higher level, while the pitcher is slightly tilted but still held securely by the gripper.
| Input Video | Output Video |
|---|---|
Our world simulation models, Cosmos-Predict's fundamental capability is predicting future world states in video form supporting multimodal inputs. We have open sourced both pre-trained foundation models as well as post-trained models accelerating multiple domains. Please check back as we continue to add more specialized models and capabilities to the Predict family!
Cosmos-Predict2.5: Base 2B checkpoints and 14B checkpoints, trained from the ground up for Physical AI and robotics.
Cosmos-Predict2.5/auto/multiview: Specialized checkpoints, post-trained for Autonomous Vehicle applications.
| Model Name | Capability | Input |
|---|---|---|
| Cosmos-Predict2.5 base | ||
| Cosmos-Predict2.5-2B/pre-trained | pre-trained base | text + image or video |
| Cosmos-Predict2.5-2B/post-trained | post-trained base | text + image or video |
| Cosmos-Predict2.5-2B/distilled | distilled base | text |
| Cosmos-Predict2.5-14B/pre-trained | pre-trained base | text + image or video |
| Cosmos-Predict2.5-14B/post-trained | post-trained base | text + image or video |
| Cosmos-Predict2.5 auto | ||
| Cosmos-Predict2.5-2B/auto/multiview | driving, 7-camera view | text + image or video |
| Cosmos-Predict2.5-2B robot | ||
| Cosmos-Predict2.5-2B/robot/action-cond | robotic, action-conditioned | action |
| Cosmos-Predict2.5-2B/robot/multiview-agibot | robotic, AgiBot data, 3-camera view | text + image |
| Cosmos-Predict2.5-2B/robot/policy | post-trained on Libero and RoboCasa | action + image |
We thrive on community collaboration! NVIDIA-Cosmos wouldn't be where it is without contributions from developers like you. Check out our Contributing Guide to get started, and share your feedback through issues.
Big thanks to everyone helping us push the boundaries of open-source physical AI!
This project will download and install additional third-party open source software projects. Review the license terms of these open sourc
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