PRX Part 4: Our Data Strategy

Part 4 of the PRX series highlights the crucial role of the data pipeline in shaping the quality of PRX, a text-to-image model. The team assembled training data from a blend of public and internal datasets, emphasizing diversity over per-image perfection to teach the model about the visual world. They used long, detailed captions generated by a Visual Language Model (VLM) to enhance output quality and adopted formats like Mosaic Data Shards (MDS) for distributed training, balancing the flexibility of Lance for feature engineering. The approach included pragmatic data curation practices, such as deduplication using perceptual hashes, filtering based on captions, and using JPEG for image encoding, to efficiently prepare a robust pre-training corpus. The article also discusses the ongoing development of curation tools to refine datasets for fine-tuning, signaling future exploration into aligning model preferences and quality-focused training.

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