Paidf Anomalygen Agent Skill

PAIDF AnomalyGen

Multi-phase pipeline (0–7); the mode flag selects which phases run.

| Phase | What runs | Mode(s) |

|---|---|---|

| 0 | Verify / download pretrained checkpoints | all |

| 1 | Fine-tune on dataset_dir | full, finetune_only |

| 2 | Prepare inference JSONL (AMP routing) | full, inference_only |

| 3 | SDG — generate synthetic anomaly images → original/ | full, inference_only |

| 4 | Eval original/ — emit per_sample.csv + eval.log, merge nn_score into SDG_result.csv | full, inference_only |

| 5 | Per-sample (guidance, crop_ratio) search rounds → rounds/round_NN/ (each round runs SDG + eval) | full, inference_only |

| 6 | Assemble best-of-rounds into searched/ (stitch only), plus rounds/search_summary.csv | full, inference_only |

| 7 | Filter searched/ by nn_threshold (default 0.4), regen dropped samples, then canonical bucket eval → searched/{per_sample.csv, eval.log} | full, inference_only |

Run every phase through to completion without mid-run pauses. Collect all

required parameters up front, and run every command from the repo root.

Shell setup. All ${ANOMALYGEN_SCRIPTS} references resolve to the packaged

helper-script directory. Inside the container this is preset (`ENV

ANOMALYGEN_SCRIPTS=<dir>/scripts/utilities`); on the host, export it once per

shell:

export ANOMALYGEN_SCRIPTS="$(git rev-parse --show-toplevel)/scripts/utilities"

python3 -m scripts.utilities.<name> invocations work from any CWD inside the

container (PYTHONPATH is preset) and from the repo root on the host. When inside

a product container (ANOMALYGEN_PRODUCT_MODE=1), invoke anomalygen-guard

before any GPU work; if it reports BLOCKED, fix the listed issues before

continuing.

Quick Start

The pipeline runs inside the metropolis_sdg.paidf_anomalygen container

(declared in versions.yaml) or any host with the cosmos-predict2 conda env

active. All phase commands assume that environment, at the repo root, with

ANOMALYGEN_SCRIPTS exported.

Minimal end-to-end run (mode=full):

# 1. Set the shared variables (see "Shared variables" for the full set).
export ANOMALYGEN_SCRIPTS="$(git rev-parse --show-toplevel)/scripts/utilities"
MODE=full
NAME=my_exp
DATASET_DIR=/data/uc1
DEFECT_DESC=assets/defect_spec_template.jsonl
NUM_SDG=20
MODEL_SIZE=2b

# 2. Phase 0 — verify / download checkpoints (~140 GB; needs HF_TOKEN).
${ANOMALYGEN_SCRIPTS}/check.sh || ${ANOMALYGEN_SCRIPTS}/download_checkpoints.sh

# 3. Walk Phases 1→7 in order (see each Phase section).

For mode=inference_only (reuse a checkpoint) also set CKPT/STEP and skip

Phase 1. For mode=finetune_only run only Phases 0–1.

Running in Docker — container launch, mounts & permissions

The paidf-anomalygen image runs as a non-root baked-in user (`USER

anomalygen, uid=10000`), independent of your host uid. Docker does not remap

uids on bind mounts, so a host directory owned by your uid is not writable by

uid 10000 and the container fails the instant it tries to create a file there.

Run as your host uid with --user "$(id -u):$(id -g)" plus the mandatory

/etc/passwd+/etc/group and HOME/cache-redirect companions, and run the

fail-fast write preflight before Phase 0. See references/docker.md for the

full docker run command, the load-bearing-flag table, the preflight snippet,

and the uid-10000 chown/chmod fallback.

Reference files — read before executing phases

Read references/finetune.md before Phase 0/1 and references/inference.md

before any of Phases 2–7; for mode=full read both before starting. The

remaining references below are on-demand — read when troubleshooting or needing

full detail for a specific phase.

| File | Read when |

|---|---|

| references/finetune.md | Before Phase 0/1: env check, checkpoint download, dataset validation, config generation, training commands, best-checkpoint selection |

| references/finetune-commands.md | Exact Phase 1 Step 1–4 commands and CKPT/STEP derivation |

| references/inference-commands.md | Exact Phase 5 run_round.sh and Phase 7 filter_with_regen commands |

| references/inference.md | Before Phases 2–7: AMP routing, JSONL validation, SDG flags, eval interpretation, search loop, filtering |

| references/setup.md | Checkpoint download fails; first-time setup; HF_TOKEN / disk issues |

| references/datasets.md | User needs to prepare or obtain a UC1 / UC2 / UC3 dataset; dataset_dir doesn't exist yet |

| references/prep-testcase.md | AMP fails; need full param table, helper script descriptions, allocation invariant |

| references/sdg-inference.md | NCCL hang; checkpoint validation error; multi-GPU VRAM question; full step list |

| references/eval.md | Unexpected scores; FID column order confusion; eval output format reference |

| references/sdg-refine.md | draws.json alignment; re-AMP heuristics; search output layout |

| references/guard-and-custom-counts.md | Full guard preflight command; --per-defect-counts example |

| references/docker.md | Container launch command, mount-permission flags, write preflight, uid-10000 fallback |

| references/output-layout.md | Full results/<name>/ directory tree with per-file annotations; post-run Verification checklist |

| references/error-handling.md | Pipeline-level failure modes: missing mask dirs, short/empty AMP, mid-round resume, off-boundary step |

Required parameters

num_SDG allocation depends on prep_testcase.sh --mode: inference (default,

Phase 2) is uniform across defect types, override per-defect via

--per-defect-counts; validation (Phase 1's validation JSONL) is proportional

to training mask counts (largest-remainder rounding) and enforces ≥1 per defect.

See references/prep-testcase.md for the full mode table.

| Parameter | Description |

|---|---|

| mode | full (Phase 0→7), inference_only (skip Phase 1), or finetune_only (Phase 0→1 only). |

| name | Experiment label. |

| dataset_dir | Training/reference dataset root. Drives mask-count allocation, AMP submask templates, and holds semantic_segmentation_labels.json for cad defects. |

| defect_spec | JSONL tagging each defect spatial_dependency as free/text/cad. text entries need roi_prompt_defect_location. Template: assets/defect_spec_template.jsonl. |

| num_SDG | Total output samples per bucket. *(Ignored when mode=finetune_only.)* |

Conditionally required

| Parameter | Required when | Description |

|---|---|---|

| checkpoint_dir / step | mode=inference_only | Pre-existing fine-tuned model. In mode=full these are auto-derived after Phase 1; passing them is an error. In mode=finetune_only silently ignored — Phase 1 always trains from scratch (no resume-from-checkpoint support). Both must be present together — supplying only one is an error. |

Optional parameters

| Parameter | Default | Description |

|---|---|---|

| clean_dir | dataset_dir | Clean images. Set only when they live outside the training dataset. Forwarded as --clean-dir to prep-testcase and --clean-image-path to finetune. |

| validation_jsonl | auto-generated | Pre-built validation JSONL for Phase 1. When supplied, preflight verifies every defect_spec type appears and paths exist. |

| num_search_run | 3 | Per-sample search budget for Phase 5. 0 skips search (only original/). *(Ignored when mode=finetune_only.)* |

| nn_threshold | 0.4 | nn_score cutoff for Phase 7 (DINOv2 correspondence to real defects — key KPI). Samples below are regenerated; final searched/ always has num_SDG. 0 disables filtering. |

| max_iter | 75000 | Phase 1 only. Total fine-tune iterations. |

| save_iter | 5000 | Phase 1 only. Checkpoint save interval. |

| validation_iter | 5000 | Phase 1 only. Validation (nn_score) logging interval. |

| num_gpus | 1 | Forwarded to Phase 1 (finetune) and Phase 3 (SDG). Eval and search rounds stay single-GPU. |

| model_size | 2b | 2b or 14b. Used by finetune and SDG. On-disk checkpoint path encodes in upper-case (2b→2B, 14b→14B). |

| lr | 0.02 | Phase 1 only. Learning rate. |

| batch_size | 2 | Phase 1 only. Per-GPU batch size. |

| image_size | 512 | Phase 1 only. Training resolution (square). |

| guidance_range | 1.5 10.0 | Phase 5 search draw range for guidance. |

| crop_ratio_range | 1.5 10.0 | Phase 5 search draw range for crop_ratio. |

Mode validation (fail fast before any phase)

• mode unset → halt: *"mode is required (full | inference_only | finetune_only)."*
• mode=inference_only missing either checkpoint_dir or step → halt: *"inference_only requires both checkpoint_dir and step."*
• mode=full with checkpoint_dir or step supplied → halt: *"full mode runs finetune; use mode=inference_only to reuse an existing checkpoint."*

Shared variables

Set once before Phase 0:

MODE=<full|inference_only|finetune_only>
NAME=<exp>
DATASET_DIR=<dataset_dir>
CLEAN_DIR=${clean_dir:-${DATASET_DIR}}
CKPT=<checkpoint_dir>      # required iff MODE=inference_only; auto-derived after Phase 1 when MODE=full
STEP=<iter>                # required iff MODE=inference_only; auto-derived after Phase 1 when MODE=full
NUM_SDG=<N>
DEFECT_DESC=<defect_spec.jsonl>
DEFECTS=(T+A T+B)          # TEXTURE+TYPE names. For mode=inference_only, derive from ${CKPT}/ag_config.yaml → dataloader_train.dataset.anomaly_types (also printed by validate_checkpoint.py in Phase 0). For mode=full, take from DEFECT_DESC entries. See references/inference.md §Phase 0.
NUM_SEARCH_RUN=${num_search_run:-3}
NN_THRESHOLD=${nn_threshold:-0.4}
MODEL_SIZE=<2b|14b>
NUM_GPUS=${num_gpus:-1}
MAX_ITER=${max_iter:-75000}
SAVE_ITER=${save_iter:-5000}
VALIDATION_ITER=${validation_iter:-5000}
LR=${lr:-0.02}
BATCH_SIZE=${batch_size:-2}
IMAGE_SIZE=${image_size:-512}
VALIDATION_JSONL=${validation_jsonl:-}  # optional; set by Phase 1 Step 2 if not user-supplied

BASE=results/${NAME}
JSONL=ag_inference/${NAME}/testcase.jsonl
ORIGINAL=${BASE}/original
SEARCHED=${BASE}/searched
ROUNDS=${BASE}/rounds
REGENS=${BASE}/regens

Guard preflight (product mode only)

When ANOMALYGEN_PRODUCT_MODE=1, run

.agents/skills/anomalygen-guard/scripts/preflight.py before any GPU work and

fix any BLOCKED issues. --validation-jsonl is forwarded only when the user

supplied one; for MODE=finetune_only omit --num-sdg if not supplied. See

references/guard-and-custom-counts.md for the full preflight command with all

forwarded flags and the validation-JSONL / allocate_samples.py 0-entry

checks.

Phase 0 — checkpoints

Read references/finetune.md §Phase 0 for HF_TOKEN requirements and what gets

downloaded (~140 GB). Verify first; download only what is missing.

${ANOMALYGEN_SCRIPTS}/check.sh \
    || ${ANOMALYGEN_SCRIPTS}/download_checkpoints.sh

Phase 1 — fine-tune (skip when MODE=inference_only)

Read references/finetune.md §Phase 1 for dataset structure, config template

details, and best-checkpoint selection. Four steps: (1) validate dataset /

derive anomaly types, (2) generate the validation JSONL (skip if user supplied

VALIDATION_JSONL), (3) generate the training config — **show it to the user

and confirm before writing** — (4) launch training in the background. Then

derive CKPT (path encodes upper-case MODEL_SIZE) and STEP (highest

nn_score step from validation logs). If MODE=finetune_only, stop after

training. See references/finetune-commands.md for the exact Step 1–4 commands

and the CKPT/STEP derivation snippet.

Phase 2 — prep-testcase (skip when MODE=finetune_only)

Read references/inference.md §Phase 2 for AMP routing detail and n_seeds

sizing. Do NOT pass --seeds — it is auto-computed and is not a recognized

flag. prep_testcase.sh defaults to --mode inference (uniform allocation

across defect types, no KPI floor), which Phase 2 always uses.

${ANOMALYGEN_SCRIPTS}/prep_testcase.sh \
    --name ${NAME} --num-sdg ${NUM_SDG} \
    --dataset-dir ${DATASET_DIR} \
    --clean-dir ${CLEAN_DIR} \
    --defect-spec ${DEFECT_DESC} \
    --amp-output-dir ag_inference/${NAME}/amp \
    --output-jsonl ${JSONL}

Custom per-defect counts: when the user specifies counts per defect type,

translate to --num-sdg plus a --per-defect-counts JSON dict (types absent

from the dict get 0; sum should equal --num-sdg, else the script warns on

stderr and uses the override sum). Confirm the allocation when intent is

ambiguous. See references/guard-and-custom-counts.md for the full

--per-defect-counts command example and the ambiguity-handling detail.

Phase 3 — SDG → original/

Read references/inference.md §Phase 3 for JSONL validation against the

checkpoint, multi-GPU caveats, and output verification.

python3 -m scripts.utilities.validate_checkpoint ${CKPT} --step ${STEP}
python3 -m scripts.utilities.validate_jsonl ${CKPT} ${JSONL}

${ANOMALYGEN_SCRIPTS}/run_sdg.sh \
    --checkpoint_dir ${CKPT} --step ${STEP} \
    --input_jsonl ${JSONL} --output_dir ${ORIGINAL} \
    --model_size ${MODEL_SIZE} --num_gpus ${NUM_GPUS}

${ANOMALYGEN_SCRIPTS}/verify_output.sh ${JSONL} ${ORIGINAL}

Phase 4 — eval original/

Read references/inference.md §Eval for score interpretation and feature-count

explanation. run_eval.sh writes per_sample.csv and eval.log inside

original/ and merges nn_score into SDG_result.csv.

${ANOMALYGEN_SCRIPTS}/run_eval.sh \
    --real-path ${DATASET_DIR} --generated-path ${ORIGINAL} \
    --anomaly-types ${DEFECTS[@]}

Phase 5 — per-sample search rounds

Read references/inference.md §Phase 5 for draw strategy, ranges, and re-AMP

guidance. For r in 1..NUM_SEARCH_RUN:

• Read prior round's per_sample.csv (or ${ORIGINAL}/per_sample.csv for r=1).
• Write ${ROUNDS}/round_${r}/draws.json with selected (guidance, crop_ratio) per sample.
• Run round via ${ANOMALYGEN_SCRIPTS}/run_round.sh (SDG + eval; the round dir

gets its own sdg/{SDG_result.csv, per_sample.csv, eval.log}). See

references/inference-commands.md §Phase 5 for the full command and flags.

NUM_SEARCH_RUN=0 is valid — skip this phase entirely and let Phase 6

clone original/ into searched/.

Phase 6 — assemble searched/ (stitch only)

Always run assemble (works with 0 rounds — searched/ clones original/, so

downstream always reads searched/ regardless of num_search_run). Stitch-only:

copies winning images per sample-index into searched/ and carries over

per-sample nn_score / mnn_score from each pick's source-round per_sample.csv.

No eval — Phase 7 emits the canonical searched/eval.log.

mkdir -p ${ROUNDS}
python3 -m scripts.utilities.assemble_searched \
    --original-dir ${ORIGINAL} --original-csv ${ORIGINAL}/per_sample.csv \
    --rounds-dir ${ROUNDS} --searched-dir ${SEARCHED}

Phase 7 — filter + regen + eval (default nn_threshold=0.4)

Phase 7 runs by default (nn_threshold=0.4) on every mode=full and

mode=inference_only invocation; pass nn_threshold=0 to skip it. It filters

searched/ by nn_threshold, regenerates dropped samples via re-AMP (fresh

(clean, submask) pairing in the same defect type) for up to 5 attempts, then

falls back to best-scoring non-passing regens and finally to dropped originals,

so the final bucket always equals num_SDG.

Run python3 -m scripts.utilities.filter_with_regen. It runs the final

run_eval.sh internally — the only eval against searched/. Read

references/inference.md §Phase 7 for regen mechanics, source-column tracing,

and the regens/regen_summary.csv schema; see

references/inference-commands.md §Phase 7 for the full command and flags.

Output layout

Every bucket that gets eval'd carries the same triad of files:

SDG_result.csv (generation params + nn_score), per_sample.csv

(per-sample nn + mnn), and eval.log (aggregate FID / per-defect avg).

Buckets live under results/<name>/ as original/ (Phase 3+4), searched/

(Phase 6 stitch + Phase 7 filter+regen+eval), rounds/round_NN/ (Phase 5,

plus search_summary.csv), and regens/regen_NN/ (Phase 7, plus

regen_summary.csv).

See references/output-layout.md for the full directory tree with per-file

annotations and the post-run Verification checklist (image counts per

bucket, search_summary.csv / regen_summary.csv row checks, and the per-type

nn_score / mnn_score / fid fields in each eval.log).

Error handling

Common pipeline failure modes (missing mask dirs, short/empty AMP output and

the 0 entries written halt, mid-round SDG failure resume, off-boundary

step) are covered in references/error-handling.md; see also

references/finetune.md and references/inference.md for phase-specific

error handling.