Quick start

from hybrid_sample_generator.configuration.root import Configuration
from hybrid_sample_generator.evaluation.service import evaluate_study
from hybrid_sample_generator.pipeline.hybrid_data_generator import HybridDataGenerator
from hybrid_sample_generator.visualization import run_hybrid_visualizer

config = Configuration(
    "study-01",
    study_folder="results/study-01",
)
config.extraction.anomaly_size = (1, 32, 32)
config.model.set_model("cVAE_ConvNeXt_2D")

config.generation.variants_per_real_anomaly = 5
config.training.num_trials = 5
config.training.trial_selection = "best"
config.matching.hybrids_per_original = 3
config.matching.anomalies_per_hybrid = 2
config.matching.reuse_synthetic_across_hybrids = True
config.matching.allow_sibling_variants_in_same_hybrid = False
config.matching.routine = "local"

generator = HybridDataGenerator(config)
summary = generator.ingest_dataset(all_samples_dataloader)
generator.extract_anomalies()
generator.train_generator()
generator.generate_synthetic_anomalies()

# Planning writes HybridSample/Placement records and updates the matching cache.
generator.plan_hybrid_samples()

# Fusion consumes the stored plan and writes generated payloads.
generator.materialize_hybrid_samples()

hybrid_dataset = generator.datasets.hybrid_samples(
    load_to_ram=False,
    numpy_mode=True,
)
evaluation = evaluate_study(config)
config.save_config_file()
run_hybrid_visualizer(config)

Ingest is the only phase that accepts the source dataloader. Extraction and planning select anomalous or normal originals by database fields. Repository-backed phases need no load step: a new HybridDataGenerator(config) can immediately continue from persisted records. Only the generator model has to be loaded explicitly before producing new variants, because it is an in-memory runtime component. The classical fusion backend is created on demand from the validated fusion parameters. Save the configuration before opening the visualizer: its configuration view reads the saved JSON, and the GUI call blocks until the window closes.

Run the bundled examples from the repository root so their package imports are resolved consistently:

python -m examples.image_2d.main
python -m examples.nifti_3d.main
python -m examples.mvtec_ad2.categories.can

The MVTec AD 2 guide describes the category recipes, persisted data splits, hybrid generation, and downstream DRAEM evaluation.