NVIDIA Cosmos™ is a world foundation model platform designed to accelerate the development of Physical AI by enabling machines to understand, simulate, and interact with the physical world across robotics, autonomous driving, and smart space environments, including industrial and factory-scale applications.
Model Overview: Cosmos3-Super
Description
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
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.
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.
Input Type(s): Text, Image, Video (with audio or without audio), Action Trajectory
Input Format(s):
Text: String
Image: jpg, png, jpeg, webp
Video (with or without audio): mp4
Action: json (1D list)
Input Parameters:
Text: One-dimensional (1D)
Image: Two-dimensional (2D)
Video: Three-dimensional (3D)
Audio: One-dimensional (1D)
Action trajectory: One-dimensional (1D)
Other Properties Related to Input:
For video inputs, we accept various resolutions, including 720p, 480p, and 256p.
When using input video with audio muxed into the video MP4 file, the audio should have 2 channels (stereo) and a 48 kHz sample rate.
Image and video inputs are RGB color (8 bits per channel, sRGB color space); grayscale inputs are not supported.
Action input is a per-frame sequence of robot/agent state or control values (e.g., joint positions, gripper state, camera pose). The full input is a 2D array shaped (T, D), where T is the number of frames and D is the embodiment-specific dimensionality listed below.
Input action is only supported for compatible embodiments, including general camera motion (9D), autonomous vehicle (9D), egocentric motion (57D), single Franka Panda arm with RobotiQ gripper (10D), dual Franka Panda arm with RobotiQ gripper (20D), Agibot (29D), UR (10D), Google robot (10D), WidowX 250 (10D), UMI (9D).
Input Size and Length limits:
Text: 4096 tokens
Image: 256p, 480p, and 720p resolution at one of these aspect ratios (16:9, 4:3, 1:1, 3:4, 9:16)
Video: 256p, 480p, and 720p resolution at one of these aspect ratios (16:9, 4:3, 1:1, 3:4, 9:16). Max number of frames = 5.
Audio: Max 0.5 second
Action: 16 – 400 video frames
Generator Output
Output Type(s): Image, video, audio, action, text
Output Format(s):
Image: JPG
Video: MP4
Audio: Advanced Audio Coding (AAC) stream (muxed within the MP4)
Action: 1D list (.json)
Text: string
Output Parameters:
Image: Two-dimensional (2D)
Video: Three-dimensional (3D)
Audio: One-dimensional (1D)
Action: One-dimensional (1D)
Text: One-dimensional (1D)
Other Properties Related to Output:
The generated video is an MP4 file, with the resolution, frame rate, and duration specified in the input. The generated audio is encoded in AAC format, muxed into the video MP4 file with 2 channels (stereo) and a 48 kHz sample rate.
Video generation supports durations from 5 to 400 frames, with 189 frames as the default generation duration.
The generated action is only supported for compatible embodiments, including general camera motion (9D), autonomous vehicle (9D), egocentric motion (57D), single Franka Panda arm with RobotiQ gripper (10D), dual Franka Panda arm with RobotiQ gripper (20D), Agibot (29D), UR (10D), Google robot (10D), WidowX 250 (10D), UMI (9D).
Audio: 48 kHz stereo AAC stream muxed into video mp4
Video: mp4 at the FPS specified in input
Image: JPEG
Reasoner Input
Input Type(s): Text, Text+Image, Text+Video
Input Format(s):
Text: String
Image: jpg, png, jpeg, webp
Video: mp4
Input Parameters:
Text: One-dimensional (1D)
Image: Two-dimensional (2D)
Video: Three-dimensional (3D)
Other Properties Related to Input:
Video inputs are recommended at a frame rate of 4 fps.
Long-context inputs supported up to 256K tokens.
Input Size and Length limits:
Text: Up to 256K tokens (context window).
Image: Standard input image formats; passed as file or URL.
Video: mp4 at the recommended 4 fps.
Reasoner Output
Output Type(s): Text
Output Format(s):
Text: string
Output Parameters:
Text: One-dimensional (1D)
Other Properties Related to Output:
Default max_tokens=4096+ is recommended for reasoning outputs; longer outputs may be requested.
Reasoning outputs may include structured chain-of-thought, 2D/3D point localization, and bounding-box coordinates for vision-based tasks.
The video content visualizes the input text description as a short animated scene, capturing key elements within the specified time constraints.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g., GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Linux (We have not tested on other operating systems.)
Note: Only BF16 precision is tested. Other precisions like FP4, FP8, and FP16 are not officially supported.
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Training, Testing, and Evaluation Datasets
Dataset Overview
Total Size: 1.3B data points
Total Number of Datasets: 393 dataset entries
Dataset partition: Training [100%], Testing [N/A — evaluation benchmarks used separately], Validation [N/A — evaluation benchmarks used separately]
Time period for training data collection: 2024–2026
Time period for testing data collection: N/A (standard public benchmarks)
Time period for validation data collection: N/A (standard public benchmarks)
Raw data from internal and external sources is transformed into training-ready data through multiple stages of curation, filtering, and quality review. Data acquisition spans diverse multimodal sources — robotics, autonomous driving, industrial environments, indoor and outdoor scenes, varied lighting and weather conditions, camera viewpoints, object categories, and human activities — to broaden coverage across Physical AI operating environments. Automated filtering pipelines remove corrupted, duplicate, low-quality, and restricted content. Metadata analysis, heuristic rules, and model-assisted classifiers are applied during preprocessing to flag anomalous distributions and low-diversity subsets. Human review supplements automated filtering for selected datasets, benchmark construction, and targeted quality analysis. Datasets are balanced across modalities and task categories — visual reasoning, text-to-image, text-to-video, image-to-video, audio generation, video transfer, action-conditioned generation, and action command generation — to reduce overrepresentation of narrow domains. Synthetic and simulation-based augmentation supplements coverage of rare physical interactions and edge-case scenarios. Deduplication and provenance tracking are applied across the corpus. The resulting processed data is converted into model-ready tokenized or encoded representations through modality-specific preprocessors before training begins.
Training datasets passed through multiple layers of automated and manual safeguards designed to reduce the presence of harmful or policy-violating content across categories including weapons and weapons-related instructional content, criminal planning, child sexual abuse material (CSAM), non-consensual intimate imagery (NCII), sexual content involving minors, harassment, hate speech, profanity, threats and incitement to violence, self-harm or suicide-related content, and graphic violence. Data sources are reviewed for licensing compatibility, provenance, and alignment with internal data governance and safety policies before admission into training corpora. Automated filtering pipelines combine multiple detection strategies: hash-matching against known CSAM and NCII reference databases; classifier-based moderation models trained for explicit sexual content, hate speech, violence, weapons imagery, and other restricted categories; keyword and regex-based screening for criminal-planning, threats, and self-harm phrases in text data; metadata and provenance heuristics for source-level risk signals; and embedding-based anomaly detection to surface samples that fall outside expected distributions. Human review and targeted audits supplement automated filtering for selected datasets, benchmark construction, and safety-sensitive evaluation. For multimodal Physical AI data (robotics, autonomous driving, industrial scenes), additional filtering targets invalid action trajectories, physically implausible interactions, and unsafe control sequences. Synthetic and simulation-generated data are evaluated through internal validation before inclusion. Benchmark evaluations and red-team testing are applied post-training to surface remaining safety gaps across world generation, reasoning, audio, and action tasks. No large-scale data-filtering process can guarantee complete removal of all harmful content; residual risks may remain, particularly in rare edge cases or open-world deployment settings. Ongoing monitoring and dataset review continue post-release.
Data Modality and Training Data Size
Modality
Reasoning Data Sample Count
Generation Data Sample Count
Text
22M
Not Applicable
Image
19M
767M
Video
1M
348M
Audio
Not Applicable
139M
Action
Not Applicable
8M
Data Collection Method by dataset
Hybrid: Automatic/Sensors, Synthetic, Automated
Labeling Method by dataset
Hybrid: Human, Automated
Properties: The training, testing, and evaluation datasets consist of diverse multimodal video, image, audio, action, synthetic, and sensor-conditioned data sourced from NVIDIA-owned data and publicly available, commercially permissive datasets. These datasets are curated to exclude known restricted content and to support building an Omni model that learns to generate and reason about dynamic physical environments across world reasoning and generation tasks.
Public Datasets
Dataset
Samples
OpenImage
1.2M
Coyo700M
100M
YouTube Video
340M
UMI
4.5M
Private Datasets
Dataset
Samples
Egocentric
7M
Nexar
0.6M
AgiBot
0.2M
HOI
0.3M
Synthetic Datasets
Dataset
Samples
synthetic images generated using HiDream-I1
15M
synthetic images generated using Qwen-Image-2512
14M
synthetic captions generated using Qwen3-VL
1115M
Evaluation Datasets
Data Collection Method by dataset
Hybrid: Automatic/Sensors, Synthetic, Automated
Labeling Method by dataset
Hybrid: Human, Automated
Properties: The training, testing, and evaluation datasets consist of diverse multimodal video, image, audio, action, synthetic, and sensor-conditioned data sourced from NVIDIA-owned data and publicly available, commercially permissive datasets. These datasets are curated to exclude known restricted content and to support building an Omni model that learns to generate and reason about dynamic physical environments across world reasoning and generation tasks.
Benchmarks
Please see our technical paper for detailed evaluations of the base model.
The JSON-upsampled version of assets/example_t2v_prompt_short.txt is saved in assets/example_t2v_prompt.json for convenience, and is used for the video generation examples below.
vLLM-Omni
Container
docker pull vllm/vllm-omni:cosmos3
General Invocation
You can use the release-tested vllm-omni package for deploying an OpenAI-compatible API inference endpoint.
The recommended vLLM-Omni serving configuration for nvidia/Cosmos3-Super on 8xH200, 8xH100, or 8xA100 is:
With this configuration, video generation with 50 steps should take approximately 55 seconds on H200 GPUs. For 2xH200, one can simply use --cfg-parallel-size 2 --use-hsdp --hsdp-shard-size 2, and a video should take approximately 3 minutes to generate. Tensor parallelism is also supported by setting --tensor-parallel-size. Setting --enable-layerwise-offload can help reduce memory usage on GPUs with less available memory.
Examples
Download example prompts
The example inputs (assets/) live in this model repo. Download just this folder with the Hugging Face CLI:
Run all commands below from the downloaded repo root.
Image to video generation
python
1import json
2import mimetypes
3from pathlib import Path
45import requests
67# 1. Read JSON-upsampled prompt and negative prompt8json_prompt = json.load(open("assets/example_i2v_prompt.json"))9negative_prompt = json.load(open("assets/negative_prompt.json"))1011# 2. Build and send the multipart API request12url ="http://localhost:8000/v1/videos/sync"13image_path = Path("assets/example_i2v_input.jpg")14mime_type = mimetypes.guess_type(image_path)[0]or"image/png"15data ={16"prompt": json.dumps(json_prompt),17"negative_prompt": json.dumps(negative_prompt),18"size":"1280x720",19"num_frames":"189",20"fps":"24",21"num_inference_steps":"35",22"guidance_scale":"6.0",23"max_sequence_length":"4096",24"flow_shift":"10.0",25"extra_params": json.dumps(26{27"use_resolution_template":False,28"use_duration_template":False,29"guardrails":True,30}31),32"seed":"17",33}3435with image_path.open("rb")as image_file:36 files ={37"input_reference":(image_path.name, image_file, mime_type),38}39print("Sending request to server...")40 response = requests.post(41 url,42 data=data,43 files=files,44 headers={"Accept":"video/mp4"},45)46 response.raise_for_status()4748# 3. Save the generated video49output_path = Path("/tmp/cosmos3_super_i2v.mp4")50output_path.write_bytes(response.content)51print(f"Saved video to {output_path}")
Example output:
Text to video generation
python
1import json
2from pathlib import Path
34import requests
56# 1. Read JSON-upsampled prompt and negative prompt7json_prompt = json.load(open("assets/example_t2v_prompt.json"))8negative_prompt = json.load(open("assets/negative_prompt.json"))910# 2. Build your API payload11data ={12"prompt": json.dumps(json_prompt),13"negative_prompt": json.dumps(negative_prompt),14"size":"1280x720",15"num_frames":"189",16"fps":"24",17"num_inference_steps":"35",18"guidance_scale":"6.0",19"max_sequence_length":"4096",20"flow_shift":"10.0",21"extra_params": json.dumps(22{23"use_resolution_template":False,24"use_duration_template":False,25"guardrails":True,26}27),28"seed":"17",29}3031# 3. Send the POST request32url ="http://localhost:8000/v1/videos/sync"33print("Sending request to server...")34response = requests.post(35 url,36 data=data,37 headers={"Accept":"video/mp4"},38)39response.raise_for_status()4041# 4. Save the generated video42output_path = Path("/tmp/cosmos3_super_t2v.mp4")43output_path.write_bytes(response.content)44print(f"Saved video to {output_path}")
Example output:
Image to Video + Audio generation
python
1import json
2import mimetypes
3from pathlib import Path
45import requests
67# 1. Read JSON-upsampled prompt and negative prompt8json_prompt = json.load(open("assets/example_i2v_prompt.json"))9negative_prompt = json.load(open("assets/negative_prompt.json"))1011# 2. Build and send the multipart API request12url ="http://localhost:8000/v1/videos/sync"13image_path = Path("assets/example_i2v_input.jpg")14mime_type = mimetypes.guess_type(image_path)[0]or"image/png"15data ={16"prompt": json.dumps(json_prompt),17"negative_prompt": json.dumps(negative_prompt),18"size":"1280x720",19"num_frames":"189",20"fps":"24",21"num_inference_steps":"35",22"guidance_scale":"6.0",23"max_sequence_length":"4096",24"generate_sound":"true",25"sound_duration":"7.875",26"flow_shift":"10.0",27"extra_params": json.dumps(28{29"use_resolution_template":False,30"use_duration_template":False,31"guardrails":True,32}33),34"seed":"17",35}3637with image_path.open("rb")as image_file:38 files ={39"input_reference":(image_path.name, image_file, mime_type),40}41print("Sending request to server...")42 response = requests.post(43 url,44 data=data,45 files=files,46 headers={"Accept":"video/mp4"},47)48 response.raise_for_status()4950# 3. Save the generated video51output_path = Path("/tmp/cosmos3_super_i2vs.mp4")52output_path.write_bytes(response.content)53print(f"Saved video to {output_path}")
Example output:
Text to Video + Audio generation
python
1import json
2from pathlib import Path
34import requests
56# 1. Read JSON-upsampled prompt and negative prompt7json_prompt = json.load(open("assets/example_t2vs_prompt.json"))8negative_prompt = json.load(open("assets/negative_prompt.json"))910# 2. Build your API payload11data ={12"prompt": json.dumps(json_prompt),13"negative_prompt": json.dumps(negative_prompt),14"size":"1280x720",15"num_frames":"189",16"fps":"24",17"num_inference_steps":"35",18"guidance_scale":"6.0",19"max_sequence_length":"4096",20"generate_sound":"true",21"sound_duration":"7.875",22"flow_shift":"10.0",23"extra_params": json.dumps(24{25"use_resolution_template":False,26"use_duration_template":False,27"guardrails":True,28}29),30"seed":"17",31}3233# 3. Send the POST request34url ="http://localhost:8000/v1/videos/sync"35print("Sending request to server...")36response = requests.post(37 url,38 data=data,39 headers={"Accept":"video/mp4"},40)41response.raise_for_status()4243# 4. Save the generated video44output_path = Path("/tmp/cosmos3_super_t2vs.mp4")45output_path.write_bytes(response.content)46print(f"Saved video to {output_path}")
Example output:
Action generation
The forward-dynamics example uses AgiBotWorld-Beta robotics action trajectories, and the inverse-dynamics examples use autonomous-vehicle (AV) action trajectories. Source files:
Forward dynamics first frame: assets/example_action_fd_agibotworld_first_frame.png
The example below performs a 4-chunk AgiBotWorld-Beta robotics rollout with the vLLM-Omni /v1/videos/sync inference endpoint. Each request sends one conditioning frame through input_reference and one 16-step normalized 29-D action chunk through extra_params["action"]. The request also sets the top-level size field to the input image resolution, so vLLM-Omni returns each chunk at the same resolution as the conditioning image without reflection padding. The stitched output drops each chunk's conditioning frame, producing 64 generated frames. The script extracts the last generated frame from each chunk and uses it as the next chunk's conditioning frame.
python
1import json
2import mimetypes
3from pathlib import Path
45import imageio.v3 as iio
6import numpy as np
7import requests
8from PIL import Image
910url ="http://localhost:8000/v1/videos/sync"11first_frame_path = Path("assets/example_action_fd_agibotworld_first_frame.png")12action_spec = json.loads(Path("assets/example_action_fd_agibotworld_action_chunks.json").read_text())13action_chunks = action_spec["action_chunks"]1415prompt = action_spec.get("prompt","Pickup items in the supermarket")16fps =int(action_spec.get("fps",10))17action_chunk_size =int(action_spec.get("action_chunk_size",16))18current_frame_path = first_frame_path
19input_width, input_height = Image.open(first_frame_path).size
20chunk_video_paths =[]21stitch_frames =[]2223for chunk_idx, action_chunk inenumerate(action_chunks):24 mime_type = mimetypes.guess_type(current_frame_path)[0]or"image/png"25 extra_params ={26"action_mode":"forward_dynamics",27"domain_name": action_spec.get("domain_name","agibotworld"),28"action_chunk_size": action_chunk_size,29"image_size": action_spec.get("image_size",480),30"view_point": action_spec.get("view_point","concat_view"),31"action": action_chunk,32"guardrails":True,33}34 data ={35"prompt": prompt,36"num_frames":str(action_chunk_size +1),# conditioning frame + generated frames37"fps":str(fps),38"size":f"{input_width}x{input_height}",# return chunks at input resolution39"num_inference_steps":"30",40"guidance_scale":"1.0",41"flow_shift":"10.0",42"seed":"0",43"extra_params": json.dumps(extra_params),44}4546with current_frame_path.open("rb")as image_file:47 files ={"input_reference":(current_frame_path.name, image_file, mime_type)}48print(f"Sending action FD chunk {chunk_idx} to vLLM-Omni...")49 response = requests.post(50 url,51 data=data,52 files=files,53 headers={"Accept":"video/mp4"},54 timeout=600,55)56 response.raise_for_status()5758 chunk_video_path = Path(f"/tmp/cosmos3_super_action_fd_chunk_{chunk_idx:02d}.mp4")59 chunk_video_path.write_bytes(response.content)60 chunk_video_paths.append(chunk_video_path)6162# The returned chunk contains the conditioning frame followed by generated frames.63# Drop the conditioning frame when stitching the generated-only rollout.64 frames = iio.imread(chunk_video_path)65 stitch_frames.extend(frames[1:])6667# Autoregressive conditioning: use the final generated frame from this chunk68# as the input image for the next vLLM-Omni request.69if chunk_idx +1<len(action_chunks):70 current_frame_path = Path(f"/tmp/cosmos3_super_action_fd_ar_frame_{chunk_idx +1:02d}.png")71 iio.imwrite(current_frame_path, frames[-1])7273stitched_path = Path("/tmp/cosmos3_super_action_fd_agibotworld_4chunk.mp4")74iio.imwrite(stitched_path, np.asarray(stitch_frames), fps=fps)75print("Generated chunk videos:", chunk_video_paths)76print("Saved stitched rollout:", stitched_path)77print("stitched resolution:",f"{input_width}x{input_height}")
Example output:
Action inverse dynamics
python
1import json
2import time
3from pathlib import Path
45import requests
67base_url ="http://localhost:8000"8input_videos ={9"av_inverse_0": Path("assets/example_action_id_av_0_input.mp4"),10"av_inverse_1": Path("assets/example_action_id_av_1_input.mp4"),11}1213for name, video_path in input_videos.items():14 extra_params ={15"action_mode":"inverse_dynamics",16"domain_name":"av",17"action_chunk_size":60,18"image_size":480,19"view_point":"ego_view",20"raw_action_dim":9,21"guardrails":True,22}23 data ={24"prompt":"You are an autonomous vehicle planning system.",25"num_frames":"61",26"fps":"10",27"num_inference_steps":"30",28"guidance_scale":"1.0",29"flow_shift":"10.0",30"seed":"0",31"extra_params": json.dumps(extra_params),32}3334with video_path.open("rb")as video_file:35 files ={36"input_reference":(video_path.name, video_file,"video/mp4"),37}38print(f"Submitting {name} request to server...")39 response = requests.post(f"{base_url}/v1/videos", data=data, files=files)40 response.raise_for_status()41 initial = response.json()4243whileTrue:44 response = requests.get(f"{base_url}/v1/videos/{initial['id']}", timeout=30)45 response.raise_for_status()46 final = response.json()47print(initial["id"], final.get("status"),f"{final.get('progress',0)}%")48if final.get("status")=="completed":49break50if final.get("status")in{"failed","cancelled"}:51raise RuntimeError(json.dumps(final, indent=2))52 time.sleep(2)5354 action = final.get("action")55ifnot action or"data"notin action:56raise RuntimeError(f"Response did not include action data: {json.dumps(final, indent=2)}")5758 output_path = Path(f"/tmp/cosmos3_super_action_id_{name}.json")59 output_path.write_text(json.dumps(action, indent=2))60print(f"Saved predicted action to {output_path}")61print("action shape:", action.get("shape"),"dtype:", action.get("dtype"))
Run this example from the model repository root. It reads the robot planning prompt from assets/example_reasoning_prompt.json and sends assets/example_reasoning_input.png to the local vLLM server.
The task is to put flower into the red bottle. Generate a plan consisting of subtasks for accomplish the task.
Example output from the command above:
text
11. Move the arm to the left side of the table.
22. Pick up the flower.
33. Move the arm to the right side of the table.
44. Place the flower into the red bottle.
Run this example from the model repository root. It reads the JSON-upsampled prompt from assets/example_t2v_prompt.json and the negative prompt from assets/negative_prompt.json. It then loads the pipeline and generate the video, then save it to an MP4 file.
Cosmos3 may produce imperfect outputs in challenging scenarios. Generation artifacts include temporal inconsistency, unstable camera or object motion, imprecise physical interactions, inaccurate audio-video synchronization, and action-state drift — especially in long-horizon or high-resolution outputs. Reasoning may also be incorrect: object states, causal relationships, spatial geometry, temporal ordering, agent intent, and future outcomes can be misinferred, and complex or long-context inputs may yield hallucinated entities, inconsistent interpretations, or implausible predictions. Because the model lacks an explicit physics simulator, 3D geometry, 4D space-time evolution, object permanence, contact dynamics, and physical laws are only approximated — producing artifacts such as disappearing or morphing objects, unrealistic collisions, and physically implausible motions. Quality further degrades in out-of-distribution environments, safety-critical edge cases, and domains underrepresented in training.
Cosmos3 outputs should not be treated as physically accurate simulation, reliable ground-truth reasoning, or safety-certified decision making. Applications involving robotics control, autonomous systems, scientific simulation, or safety-critical planning require additional validation, external constraints, system-level safety analysis, and domain-specific guardrails before deployment.
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy subcards. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
