Asset fingerprinting assigns each visual asset (reference sheet, pose, outfit, prop, panel) a compact signature you can match later. Two common layers:

• Perceptual hashes (pHash/aHash/dHash): fast, robust to minor changes; great for dedupe and near-duplicate detection.
• Visual embeddings (e.g., CLIP/OpenCLIP): semantic vectors that capture identity, outfit, and style; ideal for retrieving the right reference across angles and lighting.

Compared to watermarking, fingerprinting is non-invasive (stored as metadata/index), supports fuzzy matches, and scales to large libraries.

Consistency fails when the model drifts on hair color, outfit details, or facial proportions. Fingerprints let you:

• Retrieve the closest on-model reference before generation and feed it back via image-conditioning (e.g., IP-Adapter/ControlNet/LoRA) to lock identity.
• Auto-check new panels against a character’s registry; flag off-model panels when similarity drops below a threshold.
• Track variants (season, outfit, prop) so the correct look is enforced per scene.

1. Collect references

• For each character, store: front/side 3/4 heads, full-body turnarounds, key expressions, primary outfit, alt outfits.

2. Fingerprint the set

• Compute CLIP embeddings (e.g., ViT-L/14, OpenCLIP H/14) for each reference.
• Compute pHash for fast near-duplicate checks.
• Store in a vector index (FAISS/Milvus/Qdrant) with metadata: character_id, appearance_id (outfit/season), shot_type, colorway, notes.

3. Preflight retrieval

• For a new panel/shot, query the index using your storyboard frame or nearest existing reference.
• Select top-k matches per character and feed the best reference to your model via image conditioning.

4. Generation + QA

• Generate panel/shot with the retrieved reference(s).
• Post-check: compute embedding of the output; verify cosine similarity against the intended appearance_id. If below threshold, retry with stronger conditioning or updated prompt.

5. Registry upkeep

• Save final panels and their fingerprints back into the index to improve future retrieval.
• Version on costume/props; merge or split clusters as the design evolves.

• Embeddings: OpenCLIP ViT-H/14 (if available) or ViT-L/14 for balanced speed/quality.
• Similarity metric: cosine similarity in [0–1].
• Top-k retrieval: 5–10 per character; re-rank by metadata (scene, outfit).
• Cosine thresholds (starting points):
  • 0.32–0.38: likely same character (looser, handles angle/lighting changes).
  • 0.38–0.45: strong match for close-up faces.
  • Use higher thresholds for face crops, lower for full-body/action frames.
• pHash Hamming distance: 0–10 = near duplicate; 11–20 = similar; tune per asset size and compression.
• Conditioning strength: start moderate; raise if the post-check similarity falls short.

• Image-conditioning: IP-Adapter, ControlNet reference-only, or similar image encoders.
• Identity modules: character-specific LoRAs or Textual Inversion tokens, selected by appearance_id.
• Pose guidance: retrieve pose-matched references (via keypoints) to stabilize anatomy while preserving identity.

Define a consistent schema:

• character_id: e.g., ch_akira
• appearance_id: e.g., akira_s1_school_uniform
• components: hair_color=navy, eye_color=amber, accessory=ribbon_red, prop=katana_v2
• shot_type: headshot, half, full, dynamic
• notes: palette hex codes, must-keep marks (scar, emblem) This metadata enables precise retrieval and automated QA per scene.

• Preflight: ensure a high-similarity reference is available for the planned shot_type.
• Post-gen: compute embedding on the output; compare to the target appearance cluster centroid.
• Flagging: trigger review if similarity < threshold or if pHash suggests an unintended duplicate.
• Drift watch: track rolling average similarity across pages/scenes; investigate drops (new prompts, lighting, or LoRA conflicts).

• Embeddings: OpenCLIP, LAION CLIP; Face embeddings: ArcFace/InsightFace for human-like faces.
• Hashing: pHash, imagededup.
• Vector DB: FAISS, Milvus, Qdrant, Annoy.
• Pose/landmarks: MediaPipe, OpenPose.
• Orchestrate with your existing pipeline (Automatic1111/ComfyUI) by inserting retrieval and QA nodes.

• Angle/occlusion mismatches: crop to face or torso and compare again; use pose-aware retrieval.
• Style jumps between chapters: maintain per-chapter appearance_id clusters or retrain LoRA per arc.
• Over-conditioning (stiff results): blend reference strength; keep identity reference constant but vary pose/lighting guides.
• Palette drift in B&W comics: store tone-mapped fingerprints (grayscale) and rely more on geometry/embedding than color.

Use this to get production-ready fast.