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README.md

@@ -22,13 +22,7 @@ Cosmos AnomalyGen — PCB (UC1) generates synthetic printed-circuit-board anomal
 
 ### License/Terms of Use:
 
-Use of the AnomalyGen finetuned modules in this release is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).<br>
-
-Inference also requires the following components, which are **not** redistributed in this release and remain governed by their own terms:
-- Cosmos-Predict2-2B-Text2Image — [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)
-- NV-DINOv2 classification model — distributed via NVIDIA NGC under the NVIDIA TAO license
-- google-t5/t5-large text encoder — Apache 2.0
-- The Anomaly Diffusion pipeline concept (adopted as the framework) — MIT License<br>
+Governing Terms: Use of this model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).<br>
 
 ### Deployment Geography:
 Global<br>
@@ -181,7 +175,7 @@ Technical Limitations & Mitigation:
 Verified to have met prescribed NVIDIA quality standards:                                              |  Yes.
 Performance Metrics:                                                                                   |  FID (logged during training validation), nearest-neighbor metrics (`nn_score`, `mnn_score`), and visual inspection of `log_image` callback outputs.
 Potential Known Risks:                                                                                 |  Generated synthetic defects may not perfectly cover all real-world defect variations. Downstream detection models trained primarily on these synthetic samples should be validated against real defect data before deployment in production QA lines.<br><br>This model can generate synthetic images 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:                                                                                             |  [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)
+Licensing:                                                                                             |  Governing Terms: Use of this model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).<br>
 
 
 ## Privacy
@@ -205,5 +199,5 @@ Field                                               |  Response
 :---------------------------------------------------|:----------------------------------
 Model Application Field(s):                               |  Industrial/Machinery and Robotics — specifically synthetic data generation for manufacturing QA / visual inspection of printed circuit boards.
 Describe the life critical impact (if present).   |  Not Applicable.
-Use Case Restrictions:                              |  Abide by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
+Use Case Restrictions:                              |  Governing Terms: Use of this model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).<br>
 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.