library_name: pytorch
pipeline_tag: feature-extraction
license: other
license_name: circlestone-labs-non-commercial-license
license_link: LICENSE.md
base_model:
- circlestone-labs/Anima
- timm/vit_so400m_patch16_siglip_gap_512.v2_webli
tags:
- anime
- style-embedding
- image-retrieval
- reference-image
- siglip2
- anima
- pytorch
Anima Style Embedding v1.0
A multi-reference style encoder for person-centric anime and illustration images.
Anima Style Embedding converts one to eight reference images into a normalized 512-dimensional style vector. It combines multi-level SigLIP2 features from the full image and face with internal Anima features, then grounds the result across the complete reference set. The embedding is designed for open-set style retrieval and reference-driven style transfer systems rather than a fixed bank of known artists.
| Model size | References | Embedding | Modalities |
|---|---|---|---|
| 17.49M parameters | 1–8 images | 512-D, L2-normalized | Full image + face + Anima internals |
Highlights
- Open-set style representation: unseen artists can be embedded directly from reference images.
- Native multi-reference grounding: Reference Consensus Re-reading lets each image be interpreted in the context of the complete 1–8 image set.
- Full-image and face awareness: the encoder combines global composition and rendering cues with face-specific style information.
- Anima-aware features: three internal Anima blocks complement SigLIP2 visual features with representations aligned to the target generator.
- Strong retrieval gains: unseen synthetic-style MRR improves from 32.0% with the frozen SigLIP2 Full+Face baseline to 60.8% with the learned encoder on the 4,500-way validation gallery.
Performance
Retrieval uses cosine similarity between normalized query embeddings and five-image style prototypes.
4,500-way validation — unseen identities
The unseen subset contains 500 identities retrieved against a gallery of 4,500 styles.
| Source | Top-1 | Top-5 | MRR |
|---|---|---|---|
| Synthetic | 51.8% | 71.7% | 60.8% |
| Human | 56.4% | 75.3% | 65.0% |
500-way sealed test — unseen identities
| Source | Top-1 | Top-5 | MRR |
|---|---|---|---|
| Synthetic | 73.8% | 90.3% | 81.1% |
| Human | 76.1% | 92.0% | 83.0% |
The sealed test uses a smaller gallery than validation, so its absolute scores should be read independently.
Reference count
More references consistently improve human-style grounding. Synthetic K=5 uses the locked controlled/matched/cross-content protocol and is shown separately from the generic K diagnostics.
Architecture
The encoder projects full-image, face, and Anima tokens into a shared 512-wide space and reads them with four eight-head mixer blocks. Functional Style Factorization introduces four auxiliary style queries during training. Reference Consensus Re-reading computes a consensus across the reference set, re-reads every image under that context, and forms an agreement-weighted prototype.
| Input | Shape per batch |
|---|---|
| Full-image SigLIP2 features | [B, R, 30, 1152] |
| Face SigLIP2 features | [B, R, 30, 1152] |
| Face validity mask | [B, R] |
| Anima internal features | [B, R, 3, 4096] |
| Reference mask | [B, R] |
SigLIP2 features use timm/vit_so400m_patch16_siglip_gap_512.v2_webli at 512×512 and three backbone depths. Anima features use 768×768 inputs and blocks 8, 18, and 26 at sigma 0.1.
Quick start
import json
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from modeling_anima_style_embedding import UnifiedStyleEncoder
repo_id = "Baragi-AI/Anima-Style-Embedding"
config = json.load(open(hf_hub_download(repo_id, "config.json"), encoding="utf-8"))
cfg = config["architecture"]
encoder = UnifiedStyleEncoder(
width=cfg["width"],
blocks=cfg["blocks"],
heads=cfg["heads"],
embedding_dim=cfg["embedding_dim"],
dropout=cfg["dropout"],
use_anima=True,
reference_grounding=cfg["reference_grounding"],
functional_factors=cfg["functional_factors"],
)
encoder.load_state_dict(
load_file(hf_hub_download(repo_id, "model.safetensors")),
strict=True,
)
encoder.eval()
with torch.inference_mode():
style_embedding, per_image_embeddings = encoder(
full_features,
reference_mask,
face_features=face_features,
face_mask=face_mask,
anima_features=anima_features,
)
assert style_embedding.shape == (full_features.shape[0], 512)
modeling_anima_style_embedding.py contains the matching encoder definition. The checkpoint expects precomputed SigLIP2 and Anima features; RGB and Anima feature extraction are supplied by downstream integrations.
Training
The model was trained on 320,000 person-centric style records: 160,000 Anima-generated images and 160,000 human-created illustrations across 8,000 training identities. Synthetic and human examples are mixed within each batch. Training uses four RTX 5090 GPUs, BF16 DistributedDataParallel, 16-way episodes, two queries per prototype, variable 1–8 image support, global negatives, and weak hard-negative sampling. The 4,500-step checkpoint produced the best full-validation score.
The training record is available in Weights & Biases. Machine-readable evaluation results are included under metrics/.
Applications
- reference-driven style transfer for Anima;
- artist-style retrieval and clustering;
- aggregation of multiple references into a single style prototype;
- open-set style recognition for previously unseen artists;
- similarity scoring and reference-set curation.
Files
| File | Contents |
|---|---|
model.safetensors |
Encoder weights |
config.json |
Architecture and feature contract |
modeling_anima_style_embedding.py |
PyTorch model definition |
metrics/ |
Training curves and retrieval results |
SHA256SUMS.json |
Release checksums |
Limitations
- The model is optimized for person-centric anime and illustration images; other domains are not evaluated.
- Retrieval metrics measure representation quality, not generated-image fidelity.
- Style and artist-associated content preferences are not completely disentangled.
- Anima features must follow the documented model, block, resolution, and sigma configuration.
License
The model weights are distributed under the CircleStone Labs Non-Commercial License v1.2. Commercial or production use requires appropriate authorization from CircleStone Labs.
The original source code supplied with this release is available under the Apache License 2.0. Third-party components remain subject to their respective licenses; attribution details are listed in NOTICE and THIRD_PARTY_NOTICES.md.
Built on NVIDIA Cosmos.
Citation
@misc{baragi2026animastyleembedding,
title = {Anima Style Embedding v1.0},
author = {{Baragi AI}},
year = {2026},
howpublished = {Hugging Face model repository},
url = {https://huggingface.co/Baragi-AI/Anima-Style-Embedding}
}