Agent skill · nvidia
tao-finetune-anomalygennext
Validate a user AnomalyGenNext dataset and recipe, freeze its validation set, and prepare a canonical texture fine-tuning recipe. Use when AnomalyGenNext task weights do not yet exist. Do not use for ordinary defect generation.
What it needs
About 3k tokens when loaded.
What this skill does
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/skillinfo.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 Also supply a DCP-format Cosmos3-Nano base checkpoint directory containing checkpoint.json, model/.metadata, and at least one model/.distcp shard, Wan2.2VAE.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 imagefilename, maskfilename, and anomalytype; 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: 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-finetune-anomalygennext