physical-ai-defect-image-generation

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: physical-ai-defect-image-generation
description: >-
  Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment.

  Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint, cosmos defect generation, cosmos-predict2 defect, cosmos-anomalygen, cosmos predict2 finetune.
version: "1.0.1"
license: CC-BY-4.0 AND Apache-2.0
tools:
  - Read
  - Shell
metadata:
  owner: NVIDIA
  service: physical-ai-data-factory
  version: 1.0.1
  reviewed: 2026-06-23
  author: NVIDIA
  tags:
    - physical-ai
    - defect-image-generation
    - aoi
    - anomalygen
    - usd2roi
    - cosmos
    - cosmos-predict2
    - cosmos-anomalygen
---

# Physical AI Defect Image Generation


## Table of Contents

- [Supported Flows](#supported-flows)
- [Disambiguation](#disambiguation-handle-vague-requests-before-committing) (full table in `references/disambiguation.md`)
- [Step 0: Select Flow, Cookbook, and Gather Inputs](#step-0-select-flow-cookbook-and-gather-inputs)
- [Common Preconditions](#common-preconditions-all-flows) (long-form in `references/preconditions.md`)
- [Flow walkthroughs](#flow-walkthroughs) (one entry per flow; details in `references/flows/`)
- [OSMO Monitoring](#osmo-monitoring)
- [Supporting files](#supporting-files)

End-to-end orchestration of defect image generation, augmentation, and labeling pipelines for AOI (Automated Optical Inspection) datasets. **AnomalyGen = Cosmos-Predict2-2B finetuned per use case** (Cosmos-AnomalyGen-PCB-2B, -Metal-2B, -Glass-2B). 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`.

## Supported Flows

| Flow | Entry point | OSMO YAML | Steps | Use cases |
|------|-------------|-----------|-------|-----------|
| **Day 0 — Texture Defects** | CAD scene USD (`pcba_target.yaml` ships in the cookbook) | `texture_defect_generation_day0.yaml` | usd2roi (scan_grid + per-cell ROI crops) → image-edit augmentation (`nvidia/Qwen-Image-Edit-NVPCB-OVSL2SL`) → finetune-or-passthrough → infer (anomalygen labels inline, **including missing-component**) | PCBA |
| **Day 0 — Good Image** *(usd2roi + Image-Edit)* | CAD scene USD + per-board `pcba_target.yaml` / `day0_image.yaml` / `day0_crop.yaml` | `good_image_generation.yaml` | usd2roi-render (scan_grid + per-cell ROI crop) → Qwen Image-Edit (OVSL2SL appearance transfer) | PCBA clean-image set (ChangeNet golden halves, finetune positives, real-photo pairing) |
| **Day 0 — Structural Defects** | CAD scene USD + per-board `pcba_target.yaml` | `structural_defect_generation.yaml` | isaac-render (pose defects: shift / tombstone / sideflip) + per-component crop (single pod) → Qwen Image-Edit (OVSL2SL lighting transfer; pose geometry preserved) | PCBA pose-defect set; ChangeNet defect halves |
| **Day 1 — Infer + Label (real-photo alignment, DEFAULT)** | CAD-derived USD + real PCBA photo (both ship in `datasets/pcb/assets`) | `texture_defect_generation_day1_real_alignment.yaml` |