NV-Tesseract AD Diffusion — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune NV-Tesseract", "run AD diffusion inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune ad-diffusion", "use perform_anomaly_analysis_with_diffusion", "automl ad-diffusion", "hyperparameter search ad-diffusion", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "nv-tesseract-ad-diffusion", "ad_diffusion", or "TSDiffuser_Generic".
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NV-Tesseract AD Diffusion — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune NV-Tesseract", "run AD diffusion inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune ad-diffusion", "use perform_anomaly_analysis_with_diffusion", "automl ad-diffusion", "hyperparameter search ad-diffusion", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "nv-tesseract-ad-diffusion", "ad_diffusion", or "TSDiffuser_Generic".
license
Apache-2.0
compatibility
Requires Python 3.12+ and uv. CUDA GPU recommended; CPU-only supported.
Diffusion-based anomaly detection and fine-tuning for multivariate time series. The model
reconstructs randomly masked segments and scores each timestep by MAE between reconstruction
and original signal; adaptive thresholding (SCS or MACS) converts scores to binary labels.
nvidia/nv-tesseract-ad-diffusion is a public repo — no token required for downloading weights.
If you ever hit a 401/403 (gated access or private fork) or a 504 on first download, see the Known pitfalls section.
Quick start
git clone --branch main --single-branch https://github.com/NVIDIA/NV-Tesseract
cd NV-Tesseract/ad_diffusion
uv sync# install dependencies (one-time)
uv run python examples/quick_example.py
uv run python examples/quick_example.py \
--model-path final_model.pth \
--config-path curriculum_medium.yaml \
--dataset-path /path/to/data.csv
uv run python examples/quick_example.py --download-weights
uv run python examples/finetune_example.py \
--csv /path/to/normal_training_data.csv \
--timestamp-col timestamp \
--label-col is_anomaly \
--epochs 20 \
--output-dir artifacts/finetune_my_data
# Inference — synthetic data, auto-downloads weights from HF on first run
# Inference — your own CSV
# Pre-download weights only (warm the cache before going offline)
# Fine-tune on your own normal-behavior data
Inference
Use perform_anomaly_analysis_with_diffusion in sdk/anomaly_analysis.py. It validates
input, auto-dispatches across all visible GPUs, applies adaptive thresholding (SCS or MACS),
and returns the original DataFrame with Anomaly (0/1) and MAE columns appended.
import sys, pandas as pd
sys.path.append("/path/to/NV-Tesseract/ad_diffusion") # clone NV-Tesseract with --branch mainfrom sdk.anomaly_analysis import perform_anomaly_analysis_with_diffusion
df = pd.read_csv("your_data.csv")
# The API raises ValueError on non-numeric columns — drop timestamp, IDs, and labels first.
df = df.select_dtypes(include="number")
results = perform_anomaly_analysis_with_diffusion(
df=df,
threshold_strategy="scs", # "scs" (fast) or "macs" (adaptive)
model_path=None, # None → auto-download final_model.pth from HF
config_path=None, # None → auto-download curriculum_medium.yaml from HF
nsample=15, # diffusion samples per window; ↑ accuracy, ↑ latency
preprocess_model_dir=None, # optional preprocessing model directory
)
# results columns: Anomaly (0/1), MAE (float), plus all original columnsprint(results[["Anomaly", "MAE"]].describe())
Inference CLI reference
Argument
Default
Description
--dataset-path
synthetic
CSV with numeric feature columns
--model-path
auto-download
Path to .pth checkpoint
--config-path
auto-download
Path to curriculum_medium.yaml
--download-weights
—
Fetch weights from HF and exit
--skip-download
false
Require local weights; skip HF fetch
Fine-tuning
Fine-tune on your own data. The training CSV should contain mostly normal behavior. Validate the pretrained model on your domain before fine-tuning.
This skill supports AutoML for fine-tuning HPO and labeled inference HPO through tao-skill-bank:tao-run-automl with this model's skill_dir.
Read references/automl.md when the user asks for AutoML/HPO setup, tunable parameters, VirtualEnvSDK setup, config flow, window-length constraints, inference trial scripts, or AutoML result handoff details.
Data requirements
Property
Requirement
Rows
≥ window_length (default 100); ≥ target_dim (default 18) for PCA
Columns
Must be numeric — the API raises ValueError on non-numeric columns; drop timestamp, IDs, and labels before calling
Values
No NaN / ±Inf — fill before passing to the API
Feature count > target_dim
PCA reduction to target_dim; needs ≥ target_dim rows
Pass only numeric feature columns to the inference API — it raises ValueError on
non-numeric columns rather than dropping them. Use df.select_dtypes(include="number")
or drop by name before calling. Fine-tuning handles this via --timestamp-col,
--label-col, and --drop-cols CLI args.
Output structure
Inference (examples/quick_example.py):
examples/datasets/
└── anomaly_results.csv # original columns + Anomaly (0/1) + MAE
artifacts/finetune_my_data/
├── best_finetuned_model.pth # checkpoint with lowest validation loss
├── final_finetuned_model.pth # checkpoint from last epoch
├── metrics.json # scalar for AutoML: {"val_loss": <best>}
├── epoch_metrics.json # per-epoch log: [{"epoch": N, "train_loss": …, "val_loss": …}]
└── finetune_config.yaml # config used during training (for reproducibility)
Model configuration (curriculum_medium.yaml)
Field
Default
Description
model.target_dim
18
Internal feature dim; data is PCA'd/padded to this
dataset.window_length
100
Sliding window size in timesteps
dataset.split
10
Alternating mask segments per window
dataset.scale_factor
1
Scale multiplier after min-max normalization
diffusion.num_steps
500
Full diffusion steps (overridden by DPM-Solver)
diffusion.channels
128
Model hidden dimension
diffusion.layers
6
Transformer encoder layers
Hardware
Tier
Setup
Notes
Minimum
1× CPU
Functional; DPM-Solver reduces steps 500 → 20
Recommended
1× NVIDIA GPU (≥8 GB VRAM)
Strongly recommended for fine-tuning
Multi-GPU inference
2–8× NVIDIA GPUs
auto-dispatched by perform_anomaly_analysis_with_diffusion
Multi-GPU fine-tuning
2+× NVIDIA GPUs
Auto DDP via --num-gpus (defaults to all visible GPUs)
Known pitfalls
Symptom
Cause
Fix
HfHubHTTPError: 401
Repo gated or token missing
export HUGGINGFACE_HUB_TOKEN="hf_..." or huggingface-cli login
504 / timeout on first weight download
HF CDN throttles unauthenticated requests — public repos are still subject to this on first download
Set export HUGGINGFACE_HUB_TOKEN="$HF_TOKEN" before running; authenticated requests use a more reliable CDN path
ValueError: No numeric columns
All columns are strings/dates
Drop non-numeric columns before calling API
ValueError: PCA needs at least target_dim rows
Fewer rows than target_dim (18)
Provide a longer time series
ValueError: Need at least N rows (finetune)
Split shorter than window_length
Ensure each train/val split has ≥ 100 rows
RuntimeError: CUDA out of memory
Batch too large
Reduce --batch-size or nsample
All MAE scores identical
Constant-value columns
Drop zero-variance columns before calling API
ModuleNotFoundError: sdk
Wrong working directory
cd ad_diffusion/ before uv run, or add it to sys.path
