physical-ai-defect-image-generation-v1-1
Physical AI Defect Image Generation
Table of Contents
- Supported Flows
- Disambiguation (full table in
references/disambiguation.md) - Step 0: Select Flow, Cookbook, and Gather Inputs
- Common Preconditions (long-form in
references/preconditions.md) - Flow walkthroughs (one entry per flow; details in
references/flows/) - OSMO Monitoring
- Supporting files
End-to-end orchestration of defect image generation, augmentation, and labeling pipelines for AOI (Automated Optical Inspection) datasets. AnomalyGen 1.1 uses a frozen Cosmos3 Nano or Edge base plus a per-use-case LoRA adapter. No use-case-specific pretrained checkpoints are released; fresh runs finetune from datasets/<usecase>/raw, while Day 0 and Day 1 manual ROI may reuse a completed run through existing_finetune_url. Every flow has a canonical OSMO workflow YAML in assets/configs/ that chains all steps non-interactively. Use-case cookbooks in assets/cookbooks/ provide PCBA usd2roi/image-edit configs and AnomalyGen training configs for PCBA, metal surface, and glass inspection. This skill governs flow selection, data handoffs, and submit commands; component internals live in each component's SKILL.md.