Fine-tune AnomalyGenNext

This leaf prepares a user-owned dataset and optional recipe for task-specific AnomalyGenNext LoRA training. The pinned image is declared in references/skill_info.yaml; do not replace it with the older 1.0 release. Read references/input-contract.md when validating a dataset or adapting a recipe, and references/container-runtime.md before submission.

Inputs

DATASET/
  defect_spec.jsonl
  TEXTURE/
    clean_image/*
    anomaly_image/DEFECT/*
    mask/DEFECT/*
VALIDATION/testcase.jsonl

Also supply a DCP-format Cosmos3-Nano base checkpoint directory containing checkpoint.json, model/.metadata, and at least one model/*.distcp shard, Wan2.2_VAE.pth, and a complete checkpoint root containing the required Qwen tokenizer model assets under hf/ plus facebook/dinov2-large/ for validation. The hf/ directory name is part of the upstream image's fixed checkpoint layout. The validation JSONL must contain image_filename, mask_filename, and anomaly_type; each trained TEXTURE+DEFECT needs at least three rows. A separately stored defect spec is accepted with --defect-spec.

If the official Cosmos3-Nano checkpoint is not already staged in DCP format, run the converter shipped in the pinned AnomalyGenNext image and write its output to a mounted, persistent directory:

python -m cosmos_framework.scripts.convert_model_to_dcp \
  -o /models/Cosmos3-Nano-dcp \
  --checkpoint-path Cosmos3-Nano

The registered Cosmos3-Nano name resolves the official checkpoint. To convert an already-staged Hugging Face checkpoint instead, replace it with that directory's absolute container path. Use the converter output as --base-checkpoint; preparation verifies checkpoint.json, model/.metadata, and the generated model/*.distcp shards before GPU work.

Dataset and validation images and masks must use .jpg, .jpeg, or .png, matching the extensions supported by the AnomalyGenNext 1.1 runtime loader. Every mask pixel must be binary 0 or 255; palette or grayscale masks with intermediate values are rejected before training. Each mask must contain both values, so empty/all-zero and full/all-255 masks are also rejected.

An optional user recipe.yaml is a template. Custom training settings remain, while dataset, checkpoint, validation, type order, and iteration-zero validation are replaced by validated identities. Without a template, the packaged recipe uses the established 5000-step defaults.

Prepare

After the common launch review, run the prepare_recipe action:

scripts/prepare_finetune_recipe.py \
  --dataset-root /data/my_dataset \
  --validation-testcase /data/validation/testcase.jsonl \
  --base-checkpoint /models/Cosmos3-Nano \
  --vae-path /models/Wan2.2_VAE.pth \
  --checkpoint-root /models/anomalygen-checkpoints \
  --dataset-name my_dataset \
  --recipe-template /data/recipe.yaml \
  --output /results/canonical_recipe.yaml

The action freezes absolute validation paths, validates the DCP base-checkpoint shape, rejects image extensions the runtime cannot decode, validates anomaly/mask pairing and binary masks, checks type agreement with defect_spec.jsonl, refuses output reuse, and emits a recipe plus metadata. validation_iter must be a multiple of save_iter; max_iter must reach a post-baseline validation.

Train and accept

Invoke tao-launch-workflow, review the platform, image, mounts, GPU shape, runtime, and exact recipe, then submit the train action. Bind the entire checkpoint root read-only at the fixed container path declared in skill_info.yaml; the upstream trainer resolves the Qwen tokenizer model assets at checkpoints/hf and DINOv2 at checkpoints/facebook/dinov2-large beneath its image repository. Mounting only the DINOv2 directory or setting HF_HOME does not satisfy that contract.

scripts/finetune_anomalygennext.sh \
  --recipe /results/canonical_recipe.yaml \
  --results-dir /new/training_results \
  --checkpoint-root /workspace/paidf-anomalygen/checkpoints \
  --num-gpus 1

The wrapper uses the upstream trainer already inside the pinned container. It refuses existing publication outputs and, unless --resume is explicit, an existing runtime directory. Accept success only when training_handoff.json is COMPLETE: iteration zero and a later scored checkpoint must exist, best_checkpoint.txt must select the maximum Average.nn_score, and the score must improve strictly. The handoff hashes the copied adapter and recipe.

Read references/nn-score-contract.md before changing metric or acceptance settings. A 1000-step run is the smallest quality smoke; shorter runs prove wiring only.