- Model description
- P-TON is hybrid model for advance garment transfer with all intricate design, patterns, motif preserves.
- Benchmarked capabilities
- P-TON is evaluated on internal fashion benchmarks covering garment-transfer fidelity, silhouette consistency across poses, pattern and motif preservation, drape realism, and color accuracy on reference-based generation.
- Acceptable uses
- See our Usage Policy.
- Release date
- November 2025
- Access surfaces
- PatternAI Web App
- PatternAI API
- AWS deployment
- Google Cloud deployment
- Azure deployment
- Software integration guidance
- See our developer documentation.
- Modalities
- P-TON accepts image, reference, and text inputs and produces high-resolution image outputs. It supports garment transfer across a wide range of silhouettes with consistent pattern, motif, and drape preservation.
- Knowledge cutoff date
- May 2025. The model is highly reliable for information and events up to this date.
- Software and hardware used in development
- Cloud resources from AWS and GCP with frameworks such as PyTorch.
- Model architecture and training methodology
- Pretrained on proprietary mixed datasets and post-trained using safety-alignment methods including human and AI feedback loops.
- Training data
- A proprietary mix of publicly available web data (up to cutoff), licensed third-party data, contractor-labeled data, opted-in user data, and internal synthetic data.
- Testing methods and results
- Based on our assessments, we deployed P-TON equivalent safeguards with additional post-deployment monitoring.
