Generate AnomalyGenNext OD Defects
This leaf uses the upstream generator and pseudo-labeler in the pinned public
container. It does not prepare detector gaps, run AMP, train AnomalyGenNext, or
append outputs to a detector training set.
Read references/execution-contract.md when adapting input or completion
behavior, and references/container-runtime.md before submission.
Inputs
Pass either:
--inputs-dirpointing to the completed output oftao-prepare-anomalygennext-inputs; or--input-data-path,--checkpoint, and matching--recipefor a native AnomalyGenNext testcase.
Also provide the Cosmos3-Nano base checkpoint, the complete AnomalyGenNext checkpoint root, and one or more GPUs. Every testcase image and aligned mask must exist, every anomaly type must occur in the recipe, and the requested row count must match the testcase. When a prepared integrity manifest exists, every recorded hash is verified before generation.
Run
Invoke tao-launch-workflow, review the exact image, mounts, GPU shape, runtime,
and output directory, then submit the generate action:
bashscripts/generate_od_defects.py \ --inputs-dir /results/prepared_inputs \ --base-checkpoint /models/Cosmos3-Nano \ --checkpoint-root /workspace/paidf-anomalygen/checkpoints \ --output-dir /temporary/generation \ --published-root /persistent/generation \ --no-guardrail \ --num-gpus 1
When execution uses temporary storage, --published-root records the
persistent locations that will contain the saved results.
Guardrails are enabled by default and preserve the native invocation without a
guardrail flag. The wrapper appends --no-guardrail only when explicitly
disabled.
--no-guardrail disables text screening, image content-safety screening, and
face blurring together. The checkpoint root is mounted over the image's
canonical /workspace/paidf-anomalygen/checkpoints tree and validated before
GPU work. The Nano tokenizer, Edge processor, and DINOv2 assets are always
required; guardrail-enabled runs additionally require the Qwen3Guard and Cosmos
Guardrail repositories. Generation is forced offline after validation. The
selected platform must honor the declared checkpoint_root.container_path;
the generation leaf rejects a different mount point. The base-checkpoint
argument remains separate: it is the parent containing
checkpoint.json and the model/ checkpoint directory.
Completion
For each dataset, generated + guardrail_blocked must equal requested rows.
With --no-guardrail, generated must equal requested rows and
guardrail_blocked remains zero. The validation summary records the selected
guardrail mode.
The pseudo-label count must match generated images, each image needs an
annotation, categories must be declared anomaly types, and every COCO bbox must
be positive and inside its image. The action emits:
textDATASET/raw/ DATASET/pseudo_labels/coco_annotations.json pseudo_labels/coco_annotations.json pseudo_labels/coco_annotations_od_defect.json validation_summary.json status.json
The native COCO preserves TEXTURE+DEFECT categories. The binary file maps all
annotations to defect. Only a calling application may admit these outputs to
training; this leaf always reports training_pool_mutated=false.
