v0.1.0 to v0.1.0

---

name: tao-finetune-anomalygennext

description: >-

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.

license: Apache-2.0

compatibility: Requires the AnomalyGenNext 1.1 container and its model checkpoints.

metadata:

author: NVIDIA Corporation

version: "0.1.0"

allowed-tools: Read Bash

tags: [tao, data, anomalygen-next, fine-tuning, synthetic-data]

---

# 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.

## Inputs

```text

DATASET/

defect_spec.jsonl

TEXTURE/

clean_image/*

anomaly_image/DEFECT/*

mask/DEFECT/*

VALIDATION/testcase.jsonl

```

Also supply the Cosmos3-Nano base checkpoint directory, `Wan2.2_VAE.pth`, and

a local `facebook/dinov2-large` checkpoint directory. 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`.

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:

```bash

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 \

--nn-backbone /models/facebook/dinov2-large \

--dataset-name my_dataset \

--recipe-template /data/recipe.yaml \

--output /results/canonical_recipe.yaml

```

The action freezes absolute validation paths, validates anomaly images and

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.

The training action and strict `Average.nn_score` completion gate are added by

the next review slice. Preparation alone is not evidence of successful model

training.