Initial release

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BIAS.md

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+## Bias
+
+| Field | Response |
+| :---- | :---- |
+| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing | None. |
+| Measures taken to mitigate against unwanted bias | Training, evaluation, and testing data are curated before release to filter restricted content, including content relating to protected classes. Model behavior is evaluated across Physical AI domains — robotics, autonomous vehicles, human-centric scenes, common scenes, industry, miscellaneous, and physics-oriented benchmarks — with attention to coverage across diverse demographic and contextual characteristics that affect protected-class outcomes. |
+| Which characteristic (feature) show(s) the greatest difference in performance?: | Greatest performance differences are observed in tasks requiring long-horizon temporal consistency, fine-grained physical interactions, and embodiment-specific action generation. Performance is generally stronger on common visual reasoning and world-generation tasks than on complex multi-agent, robotics-control, or tightly synchronized multimodal generation scenarios. |
+| Which feature(s) have the worst performance overall? | Performance is generally weakest in tasks requiring long-horizon temporal consistency, precise physical interactions, embodiment-specific action control, and strict audio-visual synchronization. |
+| If using internal data, description of methods implemented in data acquisition or processing, if any, to address the prevalence of identifiable biases in the training, testing, and validation data: | Bias-specific methods applied during data processing include person-presence screening, demographic-taxonomy classification (age, gender, ethnicity), embedding-based diversity analysis, and dataset balancing across sources. Internal analysis surfaced: non-person scenes are more prevalent than person-centric content; demographic-taxonomy outputs on person-present samples are most frequently "uncertain" across age, gender, and ethnicity dimensions; and source-type variation, with people-centric image and video datasets showing higher demographic signal than document-, object-, robotics-, or scene-focused datasets. *(Quantitative details in the row below.)* Downstream deployments should add bias audits, fairness evaluation, red-teaming, demographically balanced fine-tuning, or counterfactual augmentation as mitigations. |
+| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | Dataset analytics pipelines, metadata distribution analysis, heuristic quality checks, embedding-based clustering, model-assisted filtering systems, and benchmark evaluation suites are used to assess statistical imbalances and identify patterns that may introduce bias into model behavior. |
+| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | These datasets, such as OpenImages-derived detection-to-NLP datasets, visual grounding and VQA datasets, document/image understanding datasets, video/action understanding datasets, and NVIDIA-created or curated visual datasets, do not collectively or exhaustively represent all demographic groups (and proportionally therein). For instance, automated person-presence screening did not identify a person in approximately 58% of visual samples analyzed across approximately 400 datasets, while person-present signals were identified in approximately 42% of analyzed samples. In the subset where person-present signals were identified, these datasets contain uneven representation splits across the measured visual taxonomies: age outputs were most frequently uncertain, followed by child and adult; gender outputs were most frequently uncertain, followed by male and female; and ethnicity outputs were most frequently uncertain, followed by Hispanic and White as the most frequent identified categories. Dataset-level results vary by source type, with people-centric image and video datasets containing higher person-present and demographic-taxonomy signals than document-, object-, robotics-, or scene-focused datasets. To mitigate these imbalances, we recommend considering evaluation techniques such as bias audits, task-specific fairness evaluation, and red-teaming, along with fine-tuning with demographically balanced datasets and counterfactual data augmentation to align with the desired model behavior. This evaluation used a baseline of 200 samples across all datasets, with larger subsets of up to 3,000 samples utilized for certain in-depth analyses, identified as optimal thresholds for maximizing embedder accuracy. |

EXPLAINABILITY.md

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+## Explainability
+
+| Field | Response |
+| :---- | :---- |
+| Intended Application & Domain | World reasoning and generation for Physical AI. |
+| Model Type | Mixture-of-Transformers architecture with two towers. One is an autoregressive model for Physical AI reasoning; the other is a diffusion model for Physical AI generation. |
+| Intended Users | Physical AI developers, researchers, and practitioners building or evaluating autonomous vehicle, robotics, and world-generation workflows. |
+| Output | Images, videos, audio, and action commands. |
+| Tools used to evaluate datasets to identify synthetic data and ensure data authenticity. | Dataset provenance analysis, metadata validation, watermark and artifact detection, embedding-based clustering, heuristic quality checks, and model-assisted data validation pipelines are used to identify synthetic content patterns, assess dataset authenticity, and improve data quality during dataset curation. |
+| Describe how the model works | Cosmos3 is an Omni world foundation model that generates texts, images, videos, audio, and action commands from combinations of text, images, videos, and action trajectory inputs. Input tokens from multiple modalities are packed into a shared sequence and processed by our mixture-of-transformer backbone with modality-specific output heads. |
+| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | None. |
+| Technical Limitations | The model may not follow text, image, video, audio, or action trajectory inputs accurately in challenging cases, especially where the input contains complex scene composition, unusual camera motion, multiple interacting agents, low lighting, high motion blur, or fine-grained physical interactions. Generated outputs may contain temporal inconsistency, object morphing, inaccurate 3D structure, or implausible physical dynamics. Generated audio may not accurately render intelligible speech, or maintain strict temporal and semantic alignment with the visual context. |
+| Verified to have met prescribed NVIDIA quality standards | Yes. |
+| Performance Metrics | Video generation is measured using PAIBench-G, RBench, PhysicsIQ, and Artifical Analysis Image2Video benchmark. Image generation uses UniGenBench and Artifical Analysis Text2Image benchmark. For transfer evaluation, we use PAIBench-C and AVBench-C. Audio generation uses internal benchmarks. Action prediction uses metrics such as action MSE, Absolute Translation Error, Relative Translation Error, Relative Rotation Error, PSNR, and robotic task completion success rate. |
+| Potential Known Risks | This model can generate synthetic media and may produce content that is offensive, unsafe, misleading, indecent, or unsuitable for a target deployment. Users should implement robust safety guardrails — including content filtering, abuse monitoring, and access controls — to reduce the risk of harmful outputs. Users are responsible for ensuring that their use of the model complies with all applicable laws and regulations, and for regularly reviewing and updating their guardrails as risks evolve. |
+| Licensing | [OpenMDW1.1](https://openmdw.ai/)  |

PRIVACY.md

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+## Privacy
+| Privacy Information |
+|---|
+| The model was trained on large-scale publicly available data that may contain images, audio-video, and text relating to people. NVIDIA collected and used this data in compliance with applicable data protection and privacy laws. This model was not designed to derive insights or otherwise learn from any personal data contained in the datasets. |
+| NVIDIA uses a combination of filters, data minimization techniques, and other guardrails to help prevent personal data from being recited by our models. We employ automated tools and data processing techniques during pre-training or training to identify and filter certain categories of personal data. For example, for text-bearing source and document components, our automated tools identified potential personal data such as person names, locations, and possible business or public-facing contact information such as email addresses and phone numbers.  We reviewed and removed any verified instances of personal data through a combination of automated filtering and human-in-the-loop validation. |
+| Please review NVIDIA's [Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) for more information. |

README.md

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+---
+license: other
+license_name: openmdw1.1-license
+license_link: >-
+  https://openmdw.ai/license/1-1/
+library_name: cosmos
+tags:
+  - nvidia
+  - cosmos
+  - cosmos3
+  - vllm-omni
+  - text-to-image
+  - image-generation
+---
+
+# **Cosmos 3: Omnimodal World Models for Physical AI**
+**[Model Collection](https://huggingface.co/collections/nvidia/cosmos3)** | **[Code](https://github.com/nvidia/cosmos)** | **[White Paper](https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf)** | **[Website](https://research.nvidia.com/labs/cosmos-lab/cosmos3/)**
+
+[NVIDIA Cosmos™](https://github.com/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-Text2Image-4Step
+
+## 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
+
+**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)](https://arxiv.org/abs/2405.14867), 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)](https://arxiv.org/abs/2405.14867), 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](https://openmdw.ai/license/1-1/)
+
+### 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](https://huggingface.co/collections/nvidia/cosmos3).
+GitHub 07/20/2026 via [https://github.com/nvidia/cosmos](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 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:**
+
+**Released on: 07/20/2026**
+- Cosmos3-Edge: 4B
+- Cosmos3-Edge-Policy-DROID: 4B
+- Cosmos3-Super-Image2Video-4Step: 64B
+- Cosmos3-Super-Text2Image-4Step: 64B
+
+**Released on: 05/31/2026**
+- Cosmos3-Nano: 16B
+- Cosmos3-Super: 64B
+- Cosmos3-Nano-Policy-DROID: 16B
+- Cosmos3-Super-Image2Video: 64B
+- Cosmos3-Super-Text2Image: 64B
+
+## Input/Output Specifications
+
+- **Generator Input**
+  - **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.
+
+## Software Integration
+
+**Runtime Engine(s):**
+
+- [PyTorch](https://github.com/nvidia/cosmos3)
+- [vLLM-Omni](https://github.com/vllm-project/vllm-omni)
+
+**Supported Hardware Microarchitecture Compatibility:**
+
+- NVIDIA Ampere
+- NVIDIA Blackwell
+- NVIDIA Hopper
+
+**Operating System(s):**
+
+- 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      | 7M                   |
+
+**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
+
+### Text-to-Image Benchmark Results
+
+![Text-to-Image benchmark results](assets/benchmark-text2image.png)
+
+### Artificial Analysis Leaderboard
+
+#### Open-Source Models [2026/07/17]
+
+![Artificial Analysis Text-to-Image leaderboard — open-source models](images/aa-t2i-open-models-2026-07-17.png)
+
+#### All Models [2026/07/17] (Including Closed-Source)
+
+![Artificial Analysis Text-to-Image leaderboard — all models including closed-source](images/aa-t2i-all-models-2026-07-17.png)
+
+### PBR (Performance Benchmark Reporting)
+
+The following tables report estimated inference latency for **Cosmos3-Super-Text2Image-4Step** using the available Cosmos3-Super Text-to-Image generator benchmarks. The source benchmarks were collected using BF16 precision and batch size 1.
+
+The original Cosmos3-Super measurements used 50 denoising steps with classifier-free guidance (CFG), requiring two model evaluations per denoising step. Cosmos3-Super-Text2Image-4Step uses a fixed 4-step distilled schedule without CFG (`guidance_scale=1.0`). The latency estimates below therefore apply a theoretical **25× speedup**:
+
+`50 denoising steps × 2 CFG evaluations ÷ 4 distilled steps = 25×`
+
+These values are theoretical estimates rather than direct measurements of the 4-step checkpoint. Actual end-to-end latency may differ because fixed overheads such as text encoding, VAE processing, GPU communication, and output encoding may not scale linearly with the number of denoising steps.
+
+#### Text-to-Image Estimated Inference Latency
+
+Latency is reported in seconds. The suffix in each column indicates the number of GPUs used; for example, `480p/4` represents 480p generation using four GPUs. `N/A` means the configuration is unavailable because the model does not fit in GPU memory. `Not tested` means no source measurement was reported.
+
+##### vLLM-Omni
+
+| GPU | 480p/1 | 480p/4 | 480p/8 |
+|---|---:|---:|---:|
+| **RTX PRO 6000 Blackwell** | N/A | Not tested | Not tested |
+| **H20** | N/A | Not tested | Not tested |
+| **H100 NVL** | N/A | Not tested | Not tested |
+| **H200 NVL** | 0.251 | 0.129 | 0.708 |
+| **H200 141GB HBM3** | 0.228 | 0.150 | 0.286 |
+| **B200** | 0.132 | 0.124 | 0.364 |
+
+##### PyTorch
+
+| GPU | 480p/1 | 480p/4 | 480p/8 |
+|---|---:|---:|---:|
+| **RTX PRO 6000 Blackwell** | N/A | 3.689 | 3.736 |
+| **H20** | N/A | 0.797 | 0.658 |
+| **H100 NVL** | N/A | 0.791 | 0.792 |
+| **H200 NVL** | N/A | 1.301 | 1.322 |
+| **H200 141GB HBM3** | N/A | 0.533 | 0.541 |
+| **B200** | 0.191 | 0.165 | 0.179 |
+
+<sub>Benchmark notes:
+
+1. Results use BF16 precision and batch size 1.
+2. Configurations labeled `/1`, `/4`, and `/8` use one, four, and eight GPUs, respectively.
+3. PyTorch source results report average generation or sampling time and use automatic sharding for multi-GPU execution.
+4. vLLM-Omni source results report “Avg. Generation Time” and use HSDP/Ulysses for multi-GPU execution.
+5. `N/A` means the configuration is unavailable because the model does not fit in GPU memory.
+6. `Not tested` means no source measurement was reported.
+
+</sub>
+
+
+
+## Usage: Prepare Prompts with Prompt Upsampling
+
+`Cosmos3-Super-Text2Image-4Step` supports dense natural-language prompts directly. It also supports JSON-format upsampled prompts, which can further improve generation quality. Description and code can be found [here](https://github.com/nvidia/cosmos-framework/blob/main/docs/prompt_upsampling.md).
+
+To create a text-to-image upsampled prompt using Opus-4.7:
+
+```bash
+git clone https://github.com/NVIDIA/cosmos-framework.git packages/cosmos-framework
+pip install -e packages/cosmos-framework
+
+export PROMPT_UPSAMPLER_ENDPOINT_URL="https://api.anthropic.com/v1/"
+export PROMPT_UPSAMPLER_MODEL_NAME="claude-opus-4-7"
+export PROMPT_UPSAMPLER_API_TOKEN="<your_token>"
+
+python -m cosmos_framework.inference.prompt_upsampling \
+    --input assets/original_prompt.txt \
+    --output /tmp/upsampled_t2i_opus/ \
+    --mode text2image \
+    --endpoint-url "${PROMPT_UPSAMPLER_ENDPOINT_URL}" \
+    --model "${PROMPT_UPSAMPLER_MODEL_NAME}" \
+    --api-token "${PROMPT_UPSAMPLER_API_TOKEN}" \
+    --resolution 768 \
+    --aspect-ratio "1,1"
+```
+
+The JSON-upsampled version of `assets/original_prompt.txt` is saved in `assets/example_caption.json` for convenience, and is used for the image generation examples below.
+
+## Usage: Run Inference with PyTorch
+
+For PyTorch inference with Cosmos Framework, see [Run with Cosmos Framework](https://github.com/NVIDIA/cosmos/tree/main/cookbooks/cosmos3/generator/audiovisual#notebook-walkthrough).
+
+## Usage: Run Inference with vLLM-Omni
+
+### Container
+
+```bash
+docker pull vllm/vllm-omni:cosmos3
+```
+
+### Multi-GPU Serving
+
+You can use the release-tested `vllm-omni` package for deploying an OpenAI-compatible API inference endpoint.
+The following configuration uses 4 GPUs and shards the 64B model with HSDP. This configuration is suitable for H100/H200-class multi-GPU nodes when sufficient memory is available:
+
+```bash
+vllm serve nvidia/Cosmos3-Super-Text2Image-4Step \
+  --omni \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --cfg-parallel-size 2 \
+  --ulysses-degree 2 \
+  --tensor-parallel-size 1 \
+  --use-hsdp \
+  --hsdp-shard-size 4 \
+  --init-timeout 1800
+```
+
+For an 8-GPU node, use `--ulysses-degree 4 --hsdp-shard-size 8`. Setting `--enable-layerwise-offload` can help with memory usage on GPUs with less available memory; however, please note that for text-to-image generation, this may incur a significant performance penalty.
+
+### Single-GPU Serving
+
+On a single GPU with sufficient memory, such as B200, serve the model without distributed parallelism or HSDP flags:
+
+```bash
+vllm serve nvidia/Cosmos3-Super-Text2Image-4Step \
+  --omni \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --init-timeout 1800
+```
+
+Single-GPU serving requires enough device memory for the 64B model. GPUs with less memory, such as H100/H200-class devices, may run out of memory; use the multi-GPU configuration above for those cases.
+
+`Cosmos3-Super-Text2Image-4Step` uses a fixed 4-step distilled schedule. vLLM-Omni detects the distilled checkpoint from `scheduler/scheduler_config.json` and reads the schedule from `fixed_step_sampler_config.t_list`. `num_inference_steps` and `flow_shift` are not user-configurable request fields for this model. Use `guidance_scale=1.0`.
+
+### Download Example Prompts and Scripts
+
+The inference scripts (`scripts/`) and example inputs (`assets/`) live in this model repo. Download just those folders with the Hugging Face CLI:
+
+```bash
+pip install -U "huggingface_hub[cli]"
+hf download nvidia/Cosmos3-Super-Text2Image-4Step scripts/ assets/ \
+    --local-dir Cosmos3-Super-Text2Image-4Step
+cd Cosmos3-Super-Text2Image-4Step
+```
+
+Run all commands below from the downloaded repo root.
+
+### Example: Text to Image Generation
+
+Generate an image from a JSON-format prompt by calling the vLLM-Omni endpoint. By default, the script uses `http://localhost:8000`, which matches the serving commands above. If the server runs on another host, pass `--endpoint http://<server-host>:8000`.
+
+```bash
+python scripts/gen_image.py \
+    --prompt-file assets/example_caption.json \
+    --output-path scripts/output.png
+```
+
+Or, as a minimal standalone script:
+
+```python
+import base64
+import json
+import requests
+
+# 1. Read JSON-upsampled prompt.
+json_prompt = json.load(open("assets/example_caption.json"))
+json_prompt["resolution"] = {"H": 768, "W": 768}
+json_prompt["aspect_ratio"] = "1,1"
+
+# 2. Build the request body.
+request_body = {
+    "prompt": json.dumps(json_prompt),
+    "size": "768x768",
+    "n": 1,
+    "guidance_scale": 1.0,
+    "negative_prompt": "",
+    "seed": 1143,
+    "extra_args": {
+      "use_resolution_template": False,
+      "guardrails": True,
+    },
+}
+
+# 3. Send the POST request.
+url = "http://localhost:8000/v1/images/generations"
+print("Sending request to server...")
+response = requests.post(url, json=request_body, headers={"Content-Type": "application/json"})
+response.raise_for_status()
+
+# 4. Extract the base64 data and decode it into an image.
+response_json = response.json()
+b64_data = response_json["data"][0]["b64_json"]
+image_bytes = base64.b64decode(b64_data)
+
+# 5. Save the final PNG file.
+with open("/tmp/cosmos3_t2i_4step.png", "wb") as image_file:
+    image_file.write(image_bytes)
+print("Saved image to /tmp/cosmos3_t2i_4step.png")
+```
+
+Example output generated from `assets/example_caption.json`:
+
+![example_image](assets/example_image.png)
+
+## Limitations
+
+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.
+
+## Inference
+
+**Acceleration Engine:** [PyTorch](https://pytorch.org/), [vLLM-Omni](https://github.com/vllm-project/vllm-omni)
+
+**Test Hardware:** RTX PRO 6000, H20, H100, H200, B200
+
+## Ethical Considerations
+
+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](EXPLAINABILITY.md), [Bias](BIAS.md), [Safety & Security](SAFETY.md), and [Privacy](PRIVACY.md) subcards. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

SAFETY.md

@@ -0,0 +1,11 @@
+## Safety & Security
+
+| Field | Response |
+| :---- | :---- |
+| Model Application(s) | World reasoning and generation for Physical AI. |
+| Describe the life critical impact: | This model is not a safety-certified component and must not be used as the sole basis for life-critical decisions or control without additional system-level validation, safety analysis, and safeguards. The model is not designed or tested by NVIDIA for use in any system or application where the use of or failure of such system or application developed with the model could result in injury, death, or catastrophic damage. NVIDIA is not liable to any party, in whole or in part, for any claims or damages arising from those uses. Any system or application developed with the model must include sufficient safety and redundancy features and comply with applicable legal and regulatory standards and requirements. |
+| Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data:  | Training, evaluation, and validation datasets pass through multi-stage automated and manual filtering to reduce harmful, unsafe, restricted, or policy-violating content. Pipelines include source-licensing review, deduplication, metadata-based and classifier-based moderation, embedding-based anomaly detection, and human audits on selected datasets. For Physical AI data (robotics, autonomous driving, industrial scenes), filtering also targets invalid action trajectories, physically implausible interactions, and unsafe control sequences. Synthetic and simulation-generated data are evaluated through internal validation before inclusion. Benchmark and red-team testing surface remaining safety gaps across world generation, reasoning, audio, and action tasks. No data-filtering process can guarantee complete removal; developers are responsible for application-specific safeguards and validation before deployment. |
+| Description of any methods implemented in data acquisition or processing, if any, to address illegal or harmful content in the training data, including, but not limited to, child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII)  | In addition to the general unsafe-content filtering described above, training data acquisition and preprocessing apply CSAM- and NCII-specific safeguards: hash-matching systems against known CSAM databases, classifier-based moderation models trained specifically for explicit content and NCII detection, and provenance and licensing review for sources containing human imagery. Identified content is removed at ingest, with human review and targeted audits supplementing automated filtering for selected datasets. Despite these safeguards, no large-scale data-filtering system can guarantee complete detection. Ongoing monitoring and dataset review continue post-release. |
+| Use Case Restrictions | Use is governed by the [OpenMDW1.1](https://openmdw.ai/) |
+| Model and dataset restrictions | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |
+| Responsible Data Handling | This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been scanned for harmful content and illegal content, consistent with our policies including scanning for Child Sexual Abuse Material (CSAM). Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. |

assets/example_caption.json

@@ -0,0 +1,88 @@
+{
+  "subjects": [
+    {
+      "description": "Two damp human hands working a spinning cylinder of wet gray clay on a pottery wheel, fingers gently pinching and pulling the walls upward to form a narrow neck and rounded belly",
+      "appearance_details": "Hands coated with a thin sheen of slip, glistening with water; fingertips leave subtle ridges and fingerprints in the clay; knuckles slightly creased, nails short and rimmed with gray clay; small splatters of clay on the back of the wrists",
+      "relationship": "The primary actor shaping the central spinning clay form on the wheel",
+      "location": "Center foreground, framing the clay column",
+      "relative_size": "Hands and clay occupy roughly the central 60 percent of the frame",
+      "orientation": "Hands angled inward from left and right, palms cupping the clay symmetrically",
+      "pose": "Both hands curled around the clay, thumbs and index fingers pinched at the upper neck while remaining fingers support the rounded belly below",
+      "clothing": "",
+      "expression": "",
+      "gender": "Unknown",
+      "age": "Adult",
+      "skin_tone_and_texture": "Light to medium skin tone, slick with watery slip giving a glossy sheen, fine pores and small wrinkles visible at knuckles",
+      "facial_features": "",
+      "number_of_subjects": 1,
+      "number_of_arms": 2,
+      "number_of_legs": 0,
+      "number_of_hands": 2,
+      "number_of_fingers": 10
+    },
+    {
+      "description": "A spinning cylinder of wet gray stoneware clay being formed into a vase shape with a narrow neck and rounded belly",
+      "appearance_details": "Concentric horizontal rings spiral around the surface from the rotation; glistening film of water; subtle fingerprint impressions; small drips of slip running down the lower belly onto the wheel head",
+      "relationship": "The object being shaped by the hands; central focal point of the scene",
+      "location": "Absolute center of the frame, rising vertically from the wheel head",
+      "relative_size": "Approximately one third of the frame height, dominant central form",
+      "orientation": "Vertical axis, rotating",
+      "pose": "Upright cylindrical-to-vase form mid-throw, neck tapering, belly bulging",
+      "clothing": "",
+      "expression": "",
+      "gender": "N/A",
+      "age": "N/A",
+      "skin_tone_and_texture": "",
+      "facial_features": "",
+      "number_of_subjects": 1,
+      "number_of_arms": 0,
+      "number_of_legs": 0,
+      "number_of_hands": 0,
+      "number_of_fingers": 0
+    }
+  ],
+  "subject_details": {
+    "clay_state": "Wet gray stoneware, plastic and pliable, glistening with slip",
+    "wheel_motion": "Visible motion blur in concentric ring patterns indicating rotation",
+    "forming_stage": "Mid-throw, transitioning from cylinder to vase form with narrow neck and rounded belly",
+    "slip_splatter": "Splattered clay droplets dot the black apron and the wheel tray",
+    "tools_in_background": "Wooden ribs, wire cutter, sponge, and metal trimming tools softly blurred behind"
+  },
+  "background_setting": "A dim pottery studio with a matte black wheel tray and splash pan surrounding the spinning wheel head. Behind the potter, softly blurred, sit a row of throwing tools: wooden ribs, a metal kidney, a wire cutter coiled on a small shelf, and a sponge in a shallow water bowl. The black apron worn by the potter forms part of the lower background, dotted with dried clay flecks. The studio walls are deep charcoal, allowing the warm directional light to sculpt the central action.",
+  "lighting": {
+    "conditions": "Warm, controlled studio lighting with a single key source",
+    "direction": "From the right side of the frame, slightly elevated",
+    "shadows": "Soft, defined shadows falling to the left of the clay form and beneath the hands; subtle contact shadow on the wheel head",
+    "illumination_effect": "Highlights the wet sheen on the clay, accentuates concentric ring textures and fingerprint detail, and creates a moody chiaroscuro that isolates the hands and clay from the darker background"
+  },
+  "aesthetics": {
+    "composition": "Centered symmetrical composition with the clay form on the vertical axis, hands framing it from both sides; rule-of-thirds intersections align with the neck and belly of the vase",
+    "color_scheme": "Muted earth tones dominated by cool grays of the clay, warm amber highlights from the studio light, deep blacks of the apron and wheel tray, and natural skin tones",
+    "mood_atmosphere": "Meditative, tactile, focused craftsmanship",
+    "patterns": "Concentric horizontal rings spiraling around the clay surface from the wheel's rotation"
+  },
+  "cinematography": {
+    "framing": "Medium close-up",
+    "camera_angle": "Slightly above rim height, looking gently down onto the hands and clay",
+    "depth_of_field": "Shallow",
+    "focus": "Crisp focus on the hands and the wet clay surface",
+    "lens_focal_length": "35mm"
+  },
+  "style_medium": "Photography",
+  "artistic_style": "Photorealistic studio product/process photography",
+  "context": "Editorial or artisanal documentation of a hand-thrown ceramic vase being formed on a pottery wheel",
+  "text_and_signage_elements": [],
+  "quadrant_scan": {
+    "top_left": "Softly blurred dark background with faint warm rim light catching the edge of a wooden rib tool on a shelf",
+    "top_right": "Warm directional studio light source area; brightest highlights spill across the upper right, illuminating the narrow neck of the vase",
+    "bottom_left": "Edge of the black apron speckled with dried clay flecks and the curved rim of the splash pan in shadow",
+    "bottom_right": "Wheel tray dotted with splattered clay droplets and trickles of slip; partial view of the rotating wheel head",
+    "absolute_center": "The wet gray clay vase form mid-throw, cradled by two slip-coated hands pinching the neck and supporting the rounded belly, concentric rings glistening under warm light"
+  },
+  "comprehensive_t2i_caption": "A photorealistic studio photograph captured at slightly above rim height with a 35mm lens shows a pottery wheel in motion at the center of the frame. A cylinder of wet gray stoneware clay spins, its surface scored with fine concentric rings from rotation and glistening with a thin film of water and slip. Two damp human hands, coated in pale gray slip, gently pinch and pull the walls upward, the thumbs and index fingers narrowing the neck while the lower fingers cradle and shape a rounded belly, transforming the cylinder into a vase. Warm directional studio light from the right rakes across the scene, accentuating the sheen of water, the subtle ridges of fingerprints, and the soft modeling of the clay's curves, while casting gentle shadows to the left. Splattered droplets of clay dot a black apron worn by the potter and the matte black wheel tray and splash pan beneath. In the softly blurred background, a row of pottery tools — wooden ribs, a metal kidney, a wire cutter, and a damp sponge in a water bowl — sit on a charcoal-toned shelf. The depth of field is shallow, holding crisp focus on the hands and clay while the surroundings dissolve into a moody, warm-toned haze. The atmosphere is meditative and tactile, celebrating the intimate craftsmanship of hand-thrown ceramics.",
+  "resolution": {
+    "H": 1024,
+    "W": 1024
+  },
+  "aspect_ratio": "1,1"
+}

assets/original_prompt.txt

@@ -0,0 +1 @@
+Photorealistic studio photograph of a pottery wheel in motion, a cylinder of wet gray clay spinning with concentric rings. Two hands, damp and coated with slip, gently pinch and pull the walls upward to form a narrow neck and rounded belly like a vase. Directional warm studio light from the right highlights the sheen of water on clay and the texture of fingerprints. Splattered clay dots the black apron and wheel tray. Camera slightly above rim height, 35mm, crisp focus on hands and clay, background tools softly blurred.

chat_template.json

@@ -0,0 +1,3 @@
+{
+    "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {%- if messages[0].content is string %}\n            {{- messages[0].content }}\n        {%- else %}\n            {%- for content in messages[0].content %}\n                {%- if 'text' in content %}\n                    {{- content.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {{- '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' }}\n        {%- if messages[0].content is string %}\n            {{- messages[0].content }}\n        {%- else %}\n            {%- for content in messages[0].content %}\n                {%- if 'text' in content %}\n                    {{- content.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- for message in messages %}\n    {%- if message.role == \"user\" %}\n        {{- '<|im_start|>' + message.role + '\\n' }}\n        {%- if message.content is string %}\n            {{- message.content }}\n        {%- else %}\n            {%- for content in message.content %}\n                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}\n                    {%- set image_count.value = image_count.value + 1 %}\n                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}\n                    <|vision_start|><|image_pad|><|vision_end|>\n                {%- elif content.type == 'video' or 'video' in content %}\n                    {%- set video_count.value = video_count.value + 1 %}\n                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}\n                    <|vision_start|><|video_pad|><|vision_end|>\n                {%- elif 'text' in content %}\n                    {{- content.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role + '\\n' }}\n        {%- if message.content is string %}\n            {{- message.content }}\n        {%- else %}\n            {%- for content_item in message.content %}\n                {%- if 'text' in content_item %}\n                    {{- content_item.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and message.content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {%- if message.content is string %}\n            {{- message.content }}\n        {%- else %}\n            {%- for content in message.content %}\n                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}\n                    {%- set image_count.value = image_count.value + 1 %}\n                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}\n                    <|vision_start|><|image_pad|><|vision_end|>\n                {%- elif content.type == 'video' or 'video' in content %}\n                    {%- set video_count.value = video_count.value + 1 %}\n                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}\n                    <|vision_start|><|video_pad|><|vision_end|>\n                {%- elif 'text' in content %}\n                    {{- content.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
+}

checkpoint.json

@@ -0,0 +1,5 @@
+{
+  "checkpoint_type": "hf",
+  "source_weights": "ema",
+  "student_only": true
+}

config.json

@@ -0,0 +1,267 @@
+{
+  "allow_patterns_overrides": [
+    "*/*.safetensors"
+  ],
+  "architectures": [
+    "Cosmos3ForConditionalGeneration"
+  ],
+  "image_token_id": 151655,
+  "model": {
+    "_recursive_": false,
+    "_target": "omni_mot_model",
+    "config": {
+      "_type": "omni_mot_model_config",
+      "action_gen": false,
+      "activation_checkpointing": {
+        "_type": "activation_checkpointing_config",
+        "determinism_check": "default",
+        "mode": "full",
+        "preserve_rng_state": true,
+        "save_ops_regex": [
+          "fmha"
+        ]
+      },
+      "causal_training_strategy": "none",
+      "compile": {
+        "_type": "compile_config",
+        "compile_dynamic": true,
+        "compiled_region": "language",
+        "coordinate_descent_tuning": false,
+        "enabled": false,
+        "max_autotune_pointwise": false,
+        "use_cuda_graphs": false
+      },
+      "diffusion_expert_config": {
+        "_type": "diffusion_expert_config",
+        "base_fps": 24,
+        "enable_fps_modulation": true,
+        "load_weights_from_pretrained": false,
+        "max_vae_latent_side_after_patchify": 20,
+        "patch_spatial": 2,
+        "sound_base_temporal_compression_factor": null,
+        "timestep_range": 1.0,
+        "unified_3d_mrope_reset_spatial_ids": true,
+        "unified_3d_mrope_temporal_modality_margin": 15000,
+        "vision_temporal_position_mode": "latent_index"
+      },
+      "ema": {
+        "_type": "ema_config",
+        "enabled": false,
+        "iteration_shift": 0,
+        "rate": 0.1
+      },
+      "enable_input_bias": true,
+      "exclude_reasoner_weights_from_checkpoint": false,
+      "fixed_step_sampler_config": {
+        "_type": "fixed_step_sampler_config",
+        "sample_type": "sde",
+        "t_list": [
+          1.0,
+          0.9375,
+          0.8333333333333334,
+          0.625
+        ]
+      },
+      "input_caption_key": "ai_caption",
+      "input_image_key": "images",
+      "input_video_key": "video",
+      "joint_attn_implementation": "two_way",
+      "latent_downsample_factor": 16,
+      "lbl": {
+        "_type": "lbl_config",
+        "coeff_gen": null,
+        "coeff_und": null,
+        "method": "local"
+      },
+      "log_enc_time_every_n": 100,
+      "lora_alpha": 32,
+      "lora_enabled": false,
+      "lora_rank": 16,
+      "lora_target_modules": "q_proj_moe_gen,k_proj_moe_gen,v_proj_moe_gen,o_proj_moe_gen",
+      "max_action_dim": 32,
+      "max_num_tokens_after_packing": 69632,
+      "natten_parameter_list": null,
+      "net": null,
+      "num_embodiment_domains": 32,
+      "parallelism": {
+        "_type": "parallelism_config",
+        "attention_io_layout": "sequence_sharded",
+        "cfg_parallel_shard_degree": 1,
+        "context_parallel_shard_degree": 1,
+        "data_parallel_replicate_degree": 1,
+        "data_parallel_shard_degree": 128,
+        "enable_inference_mode": false,
+        "fsdp_master_dtype": "float32"
+      },
+      "precision": "bfloat16",
+      "rectified_flow_inference_config": {
+        "_type": "rectified_flow_inference_config",
+        "num_train_timesteps": 1000,
+        "scheduler_type": "unipc",
+        "shift": 1,
+        "use_dynamic_shifting": false
+      },
+      "rectified_flow_training_config": {
+        "_type": "rectified_flow_training_config",
+        "action_loss_weight": 10.0,
+        "image_loss_scale": null,
+        "independent_action_schedule": false,
+        "independent_sound_schedule": false,
+        "loss_scale": 10.0,
+        "normalize_loss_by_active": false,
+        "shift": {
+          "256": 3,
+          "480": 5,
+          "720": 5,
+          "768": 5
+        },
+        "shift_action": null,
+        "shift_sound": null,
+        "sound_loss_scale": 2.0,
+        "train_time_action_distribution": "logitnormal",
+        "train_time_image_distribution": "logitnormal",
+        "train_time_sound_distribution": "logitnormal",
+        "train_time_video_distribution": "waver",
+        "train_time_weight": "uniform",
+        "use_discrete_rf": false,
+        "use_dynamic_shift": false
+      },
+      "resolution": "768",
+      "sound_dim": 64,
+      "sound_gen": true,
+      "sound_latent_fps": 25,
+      "sound_tokenizer": {
+        "_target": "avae_interface",
+        "audio_channels": 2,
+        "avae_config_path": "",
+        "avae_path": "pretrained/tokenizers/audio/avae/avae_48k_noncausal_25hz_64ch.ckpt",
+        "bucket_name": "bucket",
+        "hop_size": 1920,
+        "io_channels": 64,
+        "latent_mean": null,
+        "latent_std": null,
+        "normalization_type": "none",
+        "normalize_latents": false,
+        "object_store_credential_path_pretrained": "",
+        "sample_rate": 48000,
+        "tanh_clamp": 0.995,
+        "tanh_input_scale": 1.5,
+        "tanh_output_scale": 3.5
+      },
+      "state_ch": 48,
+      "state_t": 300,
+      "tokenizer": {
+        "_target": "wan2pt2_vae_interface",
+        "bucket_name": "bucket",
+        "causal": true,
+        "chunk_duration": 93,
+        "encode_bucket_multiple": 1,
+        "encode_chunk_frames": {
+          "256": 68,
+          "480": 24,
+          "720": 12,
+          "768": 12
+        },
+        "encode_exact_durations": null,
+        "keep_decoder_cache": false,
+        "object_store_credential_path_pretrained": "",
+        "spatial_compression_factor": 16,
+        "temporal_compression_factor": 4,
+        "temporal_window": null,
+        "use_streaming_encode": false,
+        "vae_path": "pretrained/tokenizers/video/wan2pt2/Wan2.2_VAE.pth"
+      },
+      "video_temporal_causal": false,
+      "vision_gen": true,
+      "vlm_config": {
+        "_type": "vlm_config",
+        "layer_module": null,
+        "model_instance": {
+          "_target": "qwen3_vl_text_for_causal_lm",
+          "config": {
+            "_target": "create_vlm_config",
+            "base_config": {
+              "_target": "qwen3_vl_mot_config_from_json_file",
+              "json_file": "cosmos3://vfm/models/vlm/qwen3_vl/configs/Qwen3-VL-32B-Instruct.json"
+            },
+            "qk_norm_for_text": true
+          }
+        },
+        "model_name": "nvidia/Cosmos3-Super-Reasoner",
+        "pretrained_weights": {
+          "_type": "pretrained_weights_config",
+          "backbone_path": "",
+          "checkpoint_format": null,
+          "credentials_path": "",
+          "enable_gcs_patch_in_boto3": false,
+          "enabled": false
+        },
+        "qk_norm": false,
+        "safetensors_path": "",
+        "tie_word_embeddings": false,
+        "tokenizer": {
+          "_target": "create_qwen2_tokenizer_with_download",
+          "config_variant": "hf",
+          "pretrained_model_name": "Qwen/Qwen3-VL-32B-Instruct"
+        },
+        "use_system_prompt": false
+      }
+    }
+  },
+  "model_type": "cosmos3_omni",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 5120,
+    "initializer_range": 0.02,
+    "intermediate_size": 25600,
+    "max_position_embeddings": 262144,
+    "model_type": "qwen3_vl_text",
+    "num_attention_heads": 64,
+    "num_hidden_layers": 64,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        24,
+        20,
+        20
+      ],
+      "rope_type": "default"
+    },
+    "rope_theta": 5000000,
+    "use_cache": true,
+    "vocab_size": 151936
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.0.dev0",
+  "video_token_id": 151656,
+  "vision_config": {
+    "deepstack_visual_indexes": [
+      8,
+      16,
+      24
+    ],
+    "depth": 27,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "model_type": "qwen3_vl",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 5120,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+    "bos_token_id": 151643,
+    "pad_token_id": 151643,
+    "do_sample": true,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "top_p": 0.8,
+    "top_k": 20,
+    "temperature": 0.7,
+    "repetition_penalty": 1.0,
+    "transformers_version": "4.56.0"
+}

model_index.json

@@ -0,0 +1,28 @@
+{
+  "_class_name": "Cosmos3OmniPipeline",
+  "_diffusers_version": "0.39.0",
+  "safety_checker": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "sound_tokenizer": [
+    "diffusers",
+    "Cosmos3AVAEAudioTokenizer"
+  ],
+  "text_tokenizer": [
+    "transformers",
+    "Qwen2TokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "Cosmos3OmniTransformer"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLWan"
+  ]
+}

modular_model_index.json

@@ -0,0 +1,77 @@
+{
+  "_blocks_class_name": "Cosmos3DistilledBlocks",
+  "_class_name": "Cosmos3DistilledModularPipeline",
+  "_diffusers_version": "0.39.0",
+  "is_distilled": true,
+  "distilled_sigmas": [
+    1.0,
+    0.9375,
+    0.8333333333333334,
+    0.625
+  ],
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler",
+    {
+      "pretrained_model_name_or_path": "nvidia/Cosmos3-Super-Text2Image-4Step",
+      "subfolder": "scheduler",
+      "type_hint": [
+        "diffusers",
+        "FlowMatchEulerDiscreteScheduler"
+      ],
+      "variant": null
+    }
+  ],
+  "sound_tokenizer": [
+    "diffusers",
+    "Cosmos3AVAEAudioTokenizer",
+    {
+      "pretrained_model_name_or_path": "nvidia/Cosmos3-Super-Text2Image-4Step",
+      "subfolder": "sound_tokenizer",
+      "type_hint": [
+        "diffusers",
+        "Cosmos3AVAEAudioTokenizer"
+      ],
+      "variant": null
+    }
+  ],
+  "text_tokenizer": [
+    "transformers",
+    "Qwen2TokenizerFast",
+    {
+      "pretrained_model_name_or_path": "nvidia/Cosmos3-Super-Text2Image-4Step",
+      "subfolder": "text_tokenizer",
+      "type_hint": [
+        "transformers",
+        "Qwen2TokenizerFast"
+      ],
+      "variant": null
+    }
+  ],
+  "transformer": [
+    "diffusers",
+    "Cosmos3OmniTransformer",
+    {
+      "pretrained_model_name_or_path": "nvidia/Cosmos3-Super-Text2Image-4Step",
+      "subfolder": "transformer",
+      "type_hint": [
+        "diffusers",
+        "Cosmos3OmniTransformer"
+      ],
+      "variant": null
+    }
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLWan",
+    {
+      "pretrained_model_name_or_path": "nvidia/Cosmos3-Super-Text2Image-4Step",
+      "subfolder": "vae",
+      "type_hint": [
+        "diffusers",
+        "AutoencoderKLWan"
+      ],
+      "variant": null
+    }
+  ]
+}

preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 16777216,
+        "shortest_edge": 65536
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "image_processor_type": "Qwen2VLImageProcessorFast"
+}

scheduler/scheduler_config.json

@@ -0,0 +1,28 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.39.0",
+  "base_image_seq_len": 256,
+  "base_shift": 0.5,
+  "fixed_step_requires_explicit_sigmas": true,
+  "fixed_step_sampler_config": {
+    "sample_type": "sde",
+    "t_list": [
+      1.0,
+      0.9375,
+      0.8333333333333334,
+      0.625
+    ]
+  },
+  "invert_sigmas": false,
+  "max_image_seq_len": 4096,
+  "max_shift": 1.15,
+  "num_train_timesteps": 1000,
+  "shift": 1.0,
+  "shift_terminal": null,
+  "stochastic_sampling": true,
+  "time_shift_type": "exponential",
+  "use_beta_sigmas": false,
+  "use_dynamic_shifting": false,
+  "use_exponential_sigmas": false,
+  "use_karras_sigmas": false
+}

scripts/gen_image.py

@@ -0,0 +1,67 @@
+"""Minimal text-to-image generation against a vLLM-Omni endpoint.
+
+Run from the Cosmos3-Super-Text2Image-4Step repo root:
+
+    python scripts/gen_image.py \
+        --prompt-file assets/example_caption.json \
+        --output-path scripts/output.png
+
+Use --endpoint http://<server-host>:8000 if the vLLM-Omni server is not local.
+"""
+
+import argparse
+import base64
+import json
+from pathlib import Path
+
+import requests
+
+# Fixed generation settings: square 768px image.
+WIDTH = 768
+HEIGHT = 768
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Generate one T2I sample.")
+    parser.add_argument("--endpoint", default="http://localhost:8000", help="vLLM-Omni endpoint base URL.")
+    parser.add_argument("--prompt-file", type=Path, default=Path("assets/example_caption.json"))
+    parser.add_argument("--output-path", type=Path, default=Path("scripts/output.png"))
+    args = parser.parse_args()
+
+    json_prompt = json.loads(args.prompt_file.read_text(encoding="utf-8"))
+    json_prompt["resolution"] = {"H": HEIGHT, "W": WIDTH}
+    json_prompt["aspect_ratio"] = "1,1"
+
+    request_body = {
+        "prompt": json.dumps(json_prompt, ensure_ascii=False),
+        "size": f"{WIDTH}x{HEIGHT}",
+        "n": 1,
+        "guidance_scale": 1.0,
+        "negative_prompt": "",
+        "seed": 1143,
+        "extra_args": {
+            "guardrails": True,
+            "use_resolution_template": False,
+        },
+    }
+
+    endpoint = args.endpoint.rstrip("/")
+    response = requests.post(
+        f"{endpoint}/v1/images/generations",
+        json=request_body,
+        headers={"Content-Type": "application/json"},
+        timeout=(10, 600),
+    )
+    response.raise_for_status()
+
+    response_json = response.json()
+    b64_data = response_json["data"][0]["b64_json"]
+    image_bytes = base64.b64decode(b64_data)
+
+    args.output_path.parent.mkdir(parents=True, exist_ok=True)
+    args.output_path.write_bytes(image_bytes)
+    print(f"Saved image to {args.output_path} ({len(image_bytes) / (1024 * 1024):.1f} MB)")
+
+
+if __name__ == "__main__":
+    main()

sound_tokenizer/config.json

@@ -0,0 +1,67 @@
+{
+  "_class_name": "Cosmos3AVAEAudioTokenizer",
+  "_diffusers_version": "0.39.0",
+  "activation": "snakebeta",
+  "anti_aliasing": false,
+  "bottleneck": {
+    "type": "vae"
+  },
+  "bottleneck_type": "vae",
+  "causal": false,
+  "dec_anti_aliasing": false,
+  "dec_c_mults": [
+    1,
+    2,
+    4,
+    8,
+    16
+  ],
+  "dec_dim": 320,
+  "dec_final_tanh": false,
+  "dec_out_channels": 2,
+  "dec_strides": [
+    2,
+    4,
+    5,
+    6,
+    8
+  ],
+  "dec_type": "oobleck",
+  "dec_use_nearest_upsample": false,
+  "dec_use_snake": true,
+  "dec_use_tanh_at_final": false,
+  "enc_c_mults": [
+    1,
+    2,
+    4
+  ],
+  "enc_dim": 192,
+  "enc_hop_length": 16,
+  "enc_identity_init": false,
+  "enc_intermediate_dim": 768,
+  "enc_latent_dim": 128,
+  "enc_n_fft": 64,
+  "enc_num_blocks": 2,
+  "enc_num_layers": 12,
+  "enc_strides": [
+    4,
+    5,
+    6
+  ],
+  "enc_type": "spec_convnext",
+  "enc_use_snake": true,
+  "encoder_enabled": true,
+  "hop_size": 1920,
+  "input_channels": 1,
+  "latent_mean": null,
+  "latent_std": null,
+  "model_type": "autoencoder_v2",
+  "normalize_volume": true,
+  "padding_mode": "zeros",
+  "sampling_rate": 48000,
+  "snake_logscale": true,
+  "stereo": true,
+  "use_cuda_kernel": false,
+  "use_wav_as_input": true,
+  "vocoder_input_dim": 64
+}

sound_tokenizer/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a4b6da5975bf89f6853fe589ad4752d281ac79fbdfad52ea90537fa080b4b9c2
+size 1985176840

text_tokenizer/added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

text_tokenizer/chat_template.jinja

@@ -0,0 +1,120 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' }}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- for message in messages %}
+    {%- if message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content_item in message.content %}
+                {%- if 'text' in content_item %}
+                    {{- content_item.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and message.content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

text_tokenizer/special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

text_tokenizer/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

text_tokenizer/tokenizer_config.json

@@ -0,0 +1,239 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 262144,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

tokenizer_config.json

@@ -0,0 +1,239 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {%- if messages[0].content is string %}\n            {{- messages[0].content }}\n        {%- else %}\n            {%- for content in messages[0].content %}\n                {%- if 'text' in content %}\n                    {{- content.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {{- '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' }}\n        {%- if messages[0].content is string %}\n            {{- messages[0].content }}\n        {%- else %}\n            {%- for content in messages[0].content %}\n                {%- if 'text' in content %}\n                    {{- content.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- for message in messages %}\n    {%- if message.role == \"user\" %}\n        {{- '<|im_start|>' + message.role + '\\n' }}\n        {%- if message.content is string %}\n            {{- message.content }}\n        {%- else %}\n            {%- for content in message.content %}\n                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}\n                    {%- set image_count.value = image_count.value + 1 %}\n                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}\n                    <|vision_start|><|image_pad|><|vision_end|>\n                {%- elif content.type == 'video' or 'video' in content %}\n                    {%- set video_count.value = video_count.value + 1 %}\n                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}\n                    <|vision_start|><|video_pad|><|vision_end|>\n                {%- elif 'text' in content %}\n                    {{- content.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role + '\\n' }}\n        {%- if message.content is string %}\n            {{- message.content }}\n        {%- else %}\n            {%- for content_item in message.content %}\n                {%- if 'text' in content_item %}\n                    {{- content_item.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and message.content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {%- if message.content is string %}\n            {{- message.content }}\n        {%- else %}\n            {%- for content in message.content %}\n                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}\n                    {%- set image_count.value = image_count.value + 1 %}\n                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}\n                    <|vision_start|><|image_pad|><|vision_end|>\n                {%- elif content.type == 'video' or 'video' in content %}\n                    {%- set video_count.value = video_count.value + 1 %}\n                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}\n                    <|vision_start|><|video_pad|><|vision_end|>\n                {%- elif 'text' in content %}\n                    {{- content.text }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 262144,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

transformer/config.json

@@ -0,0 +1,43 @@
+{
+  "_class_name": "Cosmos3OmniTransformer",
+  "_diffusers_version": "0.39.0",
+  "action_dim": 32,
+  "action_gen": false,
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "base_fps": 24,
+  "enable_fps_modulation": true,
+  "head_dim": 128,
+  "hidden_size": 5120,
+  "intermediate_size": 25600,
+  "latent_channel": 48,
+  "latent_patch_size": 2,
+  "num_attention_heads": 64,
+  "num_embodiment_domains": 32,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 8,
+  "patch_latent_dim": 192,
+  "rms_norm_eps": 1e-06,
+  "rope_axes_dim": [
+    24,
+    20,
+    20
+  ],
+  "rope_scaling": {
+    "mrope_interleaved": true,
+    "mrope_section": [
+      24,
+      20,
+      20
+    ],
+    "rope_type": "default"
+  },
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