docs: align model card with current Stability legal format

README.md

@@ -6,72 +6,70 @@ pipeline_tag: image-to-3d
 tags:
 - image-to-3d
 - checkpoint
+inference: false
 ---
 
 # ReLi3D
+![](reli3d_teaser.jpg)
 
-<img src="reli3d_teaser.jpg" alt="ReLi3D Teaser" />
+**ReLi3D** is a multi-view image-to-3D reconstruction model that takes object images and camera poses and generates a textured, UV-unwrapped, relightable 3D mesh asset.
 
-ReLi3D is a large reconstruction model that takes in a multiple images and corresponding camera poses of an object and generates a textured UV-unwrapped 3D relightable mesh asset.
+Please note: For individuals or organizations generating annual revenue of US $1,000,000 (or local currency equivalent) or more, regardless of the source of that revenue, you must obtain an enterprise commercial license directly from Stability AI before commercially using ReLi3D, or any derivative work of ReLi3D or its outputs, such as fine-tuned models. You may submit a request for an Enterprise License at https://stability.ai/enterprise. Please refer to Stability AI's Community License, available at https://stability.ai/license, for more information.
 
-Please note: For individuals or organizations generating annual revenue of USD 1,000,000 (or local currency equivalent) or more, regardless of the source of that revenue, you must obtain an enterprise commercial license directly from Stability AI before commercially using ReLi3D or any derivative work of ReLi3D or its outputs, such as fine-tuned models. You may submit a request for an Enterprise License at https://stability.ai/enterprise. Please refer to Stability AI Community License, available at https://stability.ai/license, for more information.
+### Model Description
 
-## Model Description
+* **Developed by**: [Stability AI](https://stability.ai/)
+* **Model type**: Transformer multi-view image-to-3D model
+* **Model details**: ReLi3D is trained to reconstruct a relightable 3D mesh from multiple 512x512 object images with known camera poses. The model outputs UV-unwrapped geometry and texture, and predicts material properties such as roughness and metallic values, together with an estimated illumination representation for downstream rendering workflows.
 
-- Developed by: Stability AI
-- Model type: Transformer multi-view image-to-3D model
-- Input size: 512x512xN (N is the number of images)
+### License
 
-This model is trained to create a 3D model from multiple images in under one second. The output asset is UV-unwrapped and textured with low polygon count, and the system predicts per-object material spatially varying parameters (roughness, metallic) for downstream rendering workflows. Additionally the model predicts the illumination environment for the object. 
+- **Community License:** Free for research, non-commercial, and commercial use by organizations and individuals generating annual revenue of US $1,000,000 (or local currency equivalent) or less, regardless of the source of that revenue. If your annual revenue exceeds US $1M, any commercial use of this model or derivative works thereof requires obtaining an Enterprise License directly from Stability AI. You may submit a request for an Enterprise License at https://stability.ai/enterprise. Please refer to Stability AI's Community License, available at https://stability.ai/license, for more information.
 
-## License
+### Model Sources
 
-- Community License: Free for research, non-commercial, and commercial use by organizations and individuals generating annual revenue of USD 1,000,000 (or local currency equivalent) or less, regardless of source.
-- If annual revenue exceeds USD 1M, any commercial use of this model or derivative works requires an Enterprise License directly from Stability AI.
-- Enterprise licensing: https://stability.ai/enterprise
-- Full license terms: https://stability.ai/license and LICENSE.md in this repository.
+* **Repository**: https://github.com/Stability-AI/ReLi3D
+* **Project page**: https://reli3d.jdihlmann.com/
+* **arXiv page**: Coming soon
 
-## Model Sources
+### Files
 
-- Usage repository: https://github.com/Stability-AI/ReLi3D
-- Project page: https://reli3d.jdihlmann.com/
-- arXiv page: Coming Soon
+* **config.yaml**: ReLi3D inference config
+* **reli3d_final.ckpt**: ReLi3D model checkpoint
 
-## Files
-
-- config.yaml: ReLi3D inference config
-- reli3d_final.ckpt: ReLi3D checkpoint
-
-## Training Dataset
+### Training Dataset
 
 The training process uses renders from Objaverse and curated subsets of additional sources with license review and filtering for training suitability.
 
 ## Usage
 
-For usage instructions, please refer to the ReLi3D GitHub repository.
+For usage instructions, please refer to the [ReLi3D GitHub repository](https://github.com/Stability-AI/ReLi3D).
 
-## Intended Uses
+### Intended Uses
 
-Intended uses include:
+Intended uses include the following:
 
-- Generation of artworks and use in design and other artistic processes.
-- Applications in educational or creative tools.
-- Research on reconstruction models, including understanding model limitations.
+* Generation of artworks and use in design and other artistic processes.
+* Applications in educational or creative tools.
+* Research on reconstruction models, including understanding model limitations.
 
-All uses must follow Stability AI Acceptable Use Policy.
+All uses of the model should be in accordance with our [Acceptable Use Policy](https://stability.ai/use-policy).
 
-## Out-of-Scope Uses
+### Out-of-Scope Uses
 
-The model is not trained to be factual or true representations of people or events. Using the model for such claims is out of scope.
+The model was not trained to be factual or true representations of people or events. As such, using the model to generate such content is out-of-scope of the abilities of this model.
 
 ## Safety
 
-Stability AI applies safety-by-design mitigations throughout model development and deployment. Developers should conduct additional testing and apply safeguards for their specific use cases.
+As part of our safety-by-design and responsible AI deployment approach, we implement safety measures throughout the development of our models, from the time we begin pre-training a model to the ongoing development, fine-tuning, and deployment of each model. We have implemented a number of safety mitigations that are intended to reduce the risk of severe harms. However, we recommend that developers conduct their own testing and apply additional mitigations based on their specific use cases.
+For more about our approach to Safety, please visit our [Safety page](https://stability.ai/safety).
+
+### Contact
 
-## Contact
+Please report any issues with the model or contact us:
 
-- Safety issues: safety@stability.ai
-- Security issues: security@stability.ai
-- Privacy issues: privacy@stability.ai
-- License and general: https://stability.ai/license
-- Enterprise license: https://stability.ai/enterprise
+* Safety issues: safety@stability.ai
+* Security issues: security@stability.ai
+* Privacy issues: privacy@stability.ai
+* License and general: https://stability.ai/license
+* Enterprise license: https://stability.ai/enterprise