SILVA

SigLIP-based Illustration Visual Aesthetic Scorer — learns one person's taste from their own 1–5 ratings and scores any illustration on a continuous scale.

It is not a universal quality model. You feed it your ratings, it fits an ordinal-regression head on top of frozen google/siglip2-so400m-patch14-384 embeddings, and the score reflects your preferences — nobody else's. The backbone stays frozen, so a run only trains a ~7 MB head over precomputed embeddings: minutes on a GPU, and it runs on CPU too.

• Input: 1152-d SigLIP2-SO400M-384 embeddings, computed upstream by an adapter — so the training library has no transformers dependency.
• Head: learnable monotone ordinal thresholds, with optional per-threshold class balancing.
• Output: a single score ∈ [0, 1] (fraction of quality bars cleared). Training metrics (MAE / RMSE / Spearman / QWK) are computed in the 1–5 label space.

Packages

This is a uv workspace of two packages:

Package	Install	Role
silva	pip install silva-scorer	Inference library + silva CLI. Published to PyPI (imports as silva).
silva-train	workspace-only	Training, evaluation, manifest tooling. Private.

Already have a trained head on the Hub? You only need silva — see its README.

Setup (development)

uv sync                 # GPU-ready: torch is pinned to a CUDA build (cu132)
uv sync --extra export  # + sqlite-vec, to read embeddings from the pictoria adapter

torch is pinned to cu132, so uv sync is GPU-ready out of the box (NVIDIA driver must support CUDA ≥ 13.2). Training also runs on CPU.

End-to-end workflow

1. Build a manifest — a columnar parquet matching the contract in silva_train.data.manifest:

column	type	required	notes
embedding	list[1152]	yes	SigLIP2 image embedding
personal_score	int 1..5	yes	your rating
split	train/val/test	yes	content-keyed by embedding (leakage-free, id-free)
post_id	int	no	optional provenance label (unused by splitting/merging)

An adapter for the pictoria SQLite DB ships in scripts/:

uv run python scripts/export_manifest.py \
    --db /path/to/pictoria.sqlite \
    --output data/manifest.parquet

Any other source works the same way — call build_manifest / write_manifest to emit the columns above. validate_manifest enforces the contract on load.

2. Train the ordinal head:

uv run accelerate launch -m silva_train.train --config configs/v1_stage1_head.yaml

Saves the best checkpoint (by Spearman) to outputs/v1_stage1_head/ as best.safetensors + best.json.

3. Evaluate on a split:

uv run python -m silva_train.evaluate --checkpoint outputs/v1_stage1_head --split val

Reports MAE, RMSE, Pearson, Spearman, QWK, Top-1% / Top-5% precision.

4. Score images once a head is published to the Hub:

pip install "silva-scorer[backbone]"
silva score image.jpg --repo-id Jannchie/silva-aesthetic

See packages/silva for the Python API.

Tests

uv run pytest

Covers pure logic (ordinal targets, threshold monotonicity, score reconstruction, all metrics, leakage-free splits, manifest contract) plus a CPU-only end-to-end training smoke test.

Scope (v1)

Personal score only. External AI scorers, LoRA / full fine-tune, distribution head, and serving are deferred. Design spec: docs/superpowers/specs/2026-05-24-silva-design.md.