feat: add style-lock shared infrastructure (#642)

Shared style-lock module for consistent visual style across video generation.
Extracts style embeddings from reference images and provides conditioning
for IP-Adapter, ControlNet, and style token injection.

Used by Video Forge (#52) and LPM 1.0 (#641):
- Color palette extraction (k-means, temperature, saturation)
- Lighting profile (histogram, contrast, directional analysis)
- Texture features (Gabor filter bank)
- CLIP embedding (graceful degradation when unavailable)
- Conditioning output for SDXL/FLUX/ComfyUI backends
- Multi-reference merging for identity photo sets
- Comprehensive test suite (14 tests)

Branch: fix/issue-642-1

skills/creative/shared/style-lock/SKILL.md

@@ -0,0 +1,120 @@
+---
+name: style-lock
+description: "Shared style-lock infrastructure for consistent visual style across multi-frame video generation. Extracts style embeddings from a reference image and injects as conditioning (IP-Adapter, ControlNet, style tokens) into all subsequent generations. Used by Video Forge (playground #52) and LPM 1.0 (#641). Use when generating multiple images/clips that need visual coherence — consistent color palette, brush strokes, lighting, and aesthetic across scenes or frames."
+---
+
+# Style Lock — Consistent Visual Style Across Video Generation
+
+## Overview
+
+When generating multiple images/clips that compose a video, each generation is independent. Without conditioning, visual style drifts wildly between frames/scenes. Style Lock solves this by extracting a style embedding from a reference image and injecting it as conditioning into all subsequent generations.
+
+## Quick Start
+
+```python
+from scripts.style_lock import StyleLock
+
+# Initialize with a reference image
+lock = StyleLock("reference.png")
+
+# Get conditioning for Stable Diffusion XL
+conditioning = lock.get_conditioning(
+    backend="sdxl",       # "sdxl", "flux", "comfyui"
+    method="ip_adapter",   # "ip_adapter", "controlnet", "style_tokens", "hybrid"
+    strength=0.75          # Style adherence (0.0-1.0)
+)
+
+# Use in generation pipeline
+result = generate(prompt="a sunset over mountains", **conditioning)
+```
+
+## Architecture
+
+```
+Reference Image
+      │
+      ▼
+┌─────────────────┐
+│ Style Extractor  │──→ CLIP embedding
+│                  │──→ Color palette (dominant colors)
+│                  │──→ Texture features (Gabor filters)
+│                  │──→ Lighting analysis (histogram)
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│ Conditioning     │──→ IP-Adapter (reference image injection)
+│ Router           │──→ ControlNet (structural conditioning)
+│                  │──→ Style tokens (text conditioning)
+│                  │──→ Color palette constraint
+└────────┬────────┘
+         │
+         ▼
+   Generation Pipeline
+```
+
+## Methods
+
+| Method | Best For | Requires | Quality |
+|--------|----------|----------|---------|
+| `ip_adapter` | Reference-guided style transfer | SD XL, IP-Adapter model | ★★★★★ |
+| `controlnet` | Structural + style conditioning | ControlNet models | ★★★★ |
+| `style_tokens` | Text-prompt-based style | Any model | ★★★ |
+| `hybrid` | Maximum consistency | All of the above | ★★★★★ |
+
+## Cross-Project Integration
+
+### Video Forge (Playground #52)
+- Extract style from seed image or first scene
+- Apply across all scenes in music video generation
+- Scene-to-scene temporal coherence via shared style embedding
+
+### LPM 1.0 (Issue #641)
+- Extract style from 8 identity reference photos
+- Frame-to-frame consistency in real-time video generation
+- Style tokens for HeartMuLa audio style consistency
+
+## Configuration
+
+```yaml
+style_lock:
+  reference_image: "path/to/reference.png"
+  backend: "sdxl"
+  method: "hybrid"
+  strength: 0.75
+  color_palette:
+    enabled: true
+    num_colors: 5
+    tolerance: 0.15
+  lighting:
+    enabled: true
+    match_histogram: true
+  texture:
+    enabled: true
+    gabor_orientations: 8
+    gabor_frequencies: [0.1, 0.2, 0.3, 0.4]
+```
+
+## Reference Documents
+
+- `references/ip-adapter-setup.md` — IP-Adapter installation and model requirements
+- `references/controlnet-conditioning.md` — ControlNet configuration for style
+- `references/color-palette-extraction.md` — Color palette analysis and matching
+- `references/texture-analysis.md` — Gabor filter texture feature extraction
+
+## Dependencies
+
+```
+torch>=2.0
+Pillow>=10.0
+numpy>=1.24
+opencv-python>=4.8
+scikit-image>=0.22
+```
+
+Optional (for specific backends):
+```
+diffusers>=0.25          # SD XL / IP-Adapter
+transformers>=4.36       # CLIP embeddings
+safetensors>=0.4         # Model loading
+```

skills/creative/shared/style-lock/references/color-palette-extraction.md

@@ -0,0 +1,106 @@
+# Color Palette Extraction for Style Lock
+
+## Overview
+
+Color palette is the most immediately visible aspect of visual style. Two scenes
+with matching palettes feel related even if content differs entirely. The StyleLock
+extracts a dominant palette from the reference and provides it as conditioning.
+
+## Extraction Method
+
+1. Downsample reference to 150x150 for speed
+2. Convert to RGB pixel array
+3. K-means clustering (k=5 by default) to find dominant colors
+4. Sort by frequency (most dominant first)
+5. Derive metadata: temperature, saturation, brightness
+
+## Color Temperature
+
+Derived from average R vs B channel:
+
+| Temperature | Condition | Visual Feel |
+|-------------|-----------|-------------|
+| Warm | avg_R > avg_B + 20 | Golden, orange, amber, cozy |
+| Cool | avg_B > avg_R + 20 | Blue, teal, steel, clinical |
+| Neutral | Neither | Balanced, natural |
+
+## Usage in Conditioning
+
+### Text Prompt Injection
+
+Convert palette to style descriptors:
+
+```python
+def palette_to_prompt(palette: ColorPalette) -> str:
+    parts = [f"{palette.temperature} color palette"]
+    if palette.saturation_mean > 0.6:
+        parts.append("vibrant saturated colors")
+    elif palette.saturation_mean < 0.3:
+        parts.append("muted desaturated tones")
+    return ", ".join(parts)
+```
+
+### Color Grading (Post-Processing)
+
+Match output colors to reference palette:
+
+```python
+import cv2
+import numpy as np
+
+def color_grade_to_palette(image: np.ndarray, palette: ColorPalette,
+                           strength: float = 0.5) -> np.ndarray:
+    """Shift image colors toward reference palette."""
+    result = image.astype(np.float32)
+    target_colors = np.array(palette.colors, dtype=np.float32)
+    target_weights = np.array(palette.weights)
+
+    # For each pixel, find nearest palette color and blend toward it
+    flat = result.reshape(-1, 3)
+    for i, pixel in enumerate(flat):
+        dists = np.linalg.norm(target_colors - pixel, axis=1)
+        nearest = target_colors[np.argmin(dists)]
+        flat[i] = pixel * (1 - strength) + nearest * strength
+
+    return np.clip(flat.reshape(image.shape), 0, 255).astype(np.uint8)
+```
+
+### ComfyUI Color Palette Node
+
+For ComfyUI integration, expose palette as a conditioning node:
+
+```python
+class StyleLockColorPalette:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+            "palette_json": ("STRING", {"multiline": True}),
+            "strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0}),
+        }}
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "apply_palette"
+    CATEGORY = "style-lock"
+
+    def apply_palette(self, palette_json, strength):
+        palette = json.loads(palette_json)
+        # Convert to CLIP text conditioning
+        # ...
+```
+
+## Palette Matching Between Frames
+
+To detect style drift, compare palettes across frames:
+
+```python
+def palette_distance(p1: ColorPalette, p2: ColorPalette) -> float:
+    """Earth Mover's Distance between two palettes."""
+    from scipy.spatial.distance import cdist
+    cost = cdist(p1.colors, p2.colors, metric='euclidean')
+    weights1 = np.array(p1.weights)
+    weights2 = np.array(p2.weights)
+    # Simplified EMD (full implementation requires optimization)
+    return float(np.sum(cost * np.outer(weights1, weights2)))
+```
+
+Threshold: distance > 50 indicates significant style drift.

skills/creative/shared/style-lock/references/controlnet-conditioning.md

@@ -0,0 +1,102 @@
+# ControlNet Conditioning for Style Lock
+
+## Overview
+
+ControlNet provides structural conditioning — edges, depth, pose, segmentation —
+that complements IP-Adapter's style-only conditioning. Used together in `hybrid`
+mode for maximum consistency.
+
+## Supported Control Types
+
+| Type | Preprocessor | Best For |
+|------|-------------|----------|
+| Canny Edge | `cv2.Canny` | Clean line art, geometric scenes |
+| Depth | MiDaS / DPT | Spatial consistency, 3D scenes |
+| Lineart | Anime/Realistic lineart | Anime, illustration |
+| Soft Edge | HED | Organic shapes, portraits |
+| Segmentation | SAM / OneFormer | Scene layout consistency |
+
+## Style Lock Approach
+
+For style consistency (not structural control), use ControlNet **softly**:
+
+1. Extract edges from the reference image (Canny, threshold 50-150)
+2. Use as ControlNet input with low conditioning scale (0.3-0.5)
+3. This preserves the compositional structure while allowing style variation
+
+```python
+import cv2
+import numpy as np
+from PIL import Image
+
+def extract_control_image(ref_path: str, method: str = "canny") -> np.ndarray:
+    img = np.array(Image.open(ref_path).convert("RGB"))
+    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+    if method == "canny":
+        edges = cv2.Canny(gray, 50, 150)
+        return cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
+    elif method == "soft_edge":
+        # HED-like approximation using Gaussian blur + Canny
+        blurred = cv2.GaussianBlur(gray, (5, 5), 1.5)
+        edges = cv2.Canny(blurred, 30, 100)
+        return cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
+    else:
+        raise ValueError(f"Unknown method: {method}")
+```
+
+## Diffusers Integration
+
+```python
+from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
+
+controlnet = ControlNetModel.from_pretrained(
+    "diffusers/controlnet-canny-sdxl-1.0",
+    torch_dtype=torch.float16,
+)
+
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+)
+
+result = pipe(
+    prompt="a sunset over mountains",
+    image=control_image,
+    controlnet_conditioning_scale=0.5,
+).images[0]
+```
+
+## Hybrid Mode (IP-Adapter + ControlNet)
+
+For maximum consistency, combine both:
+
+```python
+# IP-Adapter for style
+pipe.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models",
+                     weight_name="ip-adapter_sdxl.bin")
+
+# ControlNet for structure
+# Already loaded in pipeline construction
+
+result = pipe(
+    prompt="a sunset over mountains",
+    ip_adapter_image=reference_image,      # Style
+    image=control_image,                    # Structure
+    ip_adapter_scale=0.75,
+    controlnet_conditioning_scale=0.4,
+).images[0]
+```
+
+## Style Lock Output
+
+When using `method="controlnet"` or `method="hybrid"`, the StyleLock class
+preprocesses the reference image through Canny edge detection and provides
+it as `controlnet_image` in the ConditioningOutput.
+
+Adjust `controlnet_conditioning_scale` via the strength parameter:
+
+```
+effective_scale = controlnet_conditioning_scale * style_lock_strength
+```

skills/creative/shared/style-lock/references/ip-adapter-setup.md

@@ -0,0 +1,79 @@
+# IP-Adapter Setup for Style Lock
+
+## Overview
+
+IP-Adapter (Image Prompt Adapter) enables conditioning Stable Diffusion generation
+on a reference image without fine-tuning. It extracts a CLIP image embedding and
+injects it alongside text prompts via decoupled cross-attention.
+
+## Installation
+
+```bash
+pip install diffusers>=0.25 transformers>=4.36 accelerate safetensors
+```
+
+## Models
+
+| Model | Base | Use Case |
+|-------|------|----------|
+| `ip-adapter_sd15` | SD 1.5 | Fast, lower quality |
+| `ip-adapter_sd15_plus` | SD 1.5 | Better style fidelity |
+| `ip-adapter_sdxl_vit-h` | SDXL | High quality, recommended |
+| `ip-adapter_flux` | FLUX | Best quality, highest VRAM |
+
+Download from: `h94/IP-Adapter` on HuggingFace.
+
+## Usage with Diffusers
+
+```python
+from diffusers import StableDiffusionXLPipeline, DDIMScheduler
+from PIL import Image
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16,
+)
+pipe.load_ip_adapter(
+    "h94/IP-Adapter",
+    subfolder="sdxl_models",
+    weight_name="ip-adapter_sdxl.bin",
+)
+
+reference = Image.open("reference.png")
+pipe.set_ip_adapter_scale(0.75)  # Style adherence strength
+
+result = pipe(
+    prompt="a sunset over mountains",
+    ip_adapter_image=reference,
+    num_inference_steps=30,
+).images[0]
+```
+
+## Style Lock Integration
+
+The StyleLock class provides `ip_adapter_image` and `ip_adapter_scale` in the
+conditioning output. Pass these directly to the pipeline:
+
+```python
+lock = StyleLock("reference.png")
+cond = lock.get_conditioning(backend="sdxl", method="ip_adapter")
+kwargs = cond.to_api_kwargs()
+
+result = pipe(prompt="a sunset over mountains", **kwargs).images[0]
+```
+
+## Tuning Guide
+
+| Scale | Effect | Use When |
+|-------|--------|----------|
+| 0.3-0.5 | Loose style influence | Want style hints, not exact match |
+| 0.5-0.7 | Balanced | General video generation |
+| 0.7-0.9 | Strong adherence | Strict style consistency needed |
+| 0.9-1.0 | Near-except copy | Reference IS the target style |
+
+## Tips
+
+- First frame is the best reference — it has the exact lighting/mood you want
+- Use `ip_adapter_scale` 0.7+ for scene-to-scene consistency
+- Combine with ControlNet for both style AND structure
+- For LPM 1.0 frame-to-frame: use scale 0.85+, extract from best identity photo

skills/creative/shared/style-lock/references/texture-analysis.md

@@ -0,0 +1,105 @@
+# Texture Analysis for Style Lock
+
+## Overview
+
+Texture captures the "feel" of visual surfaces — smooth, rough, grainy, painterly,
+digital. Gabor filters extract frequency-orientation features that describe texture
+in a way similar to human visual cortex.
+
+## Gabor Filter Bank
+
+A Gabor filter is a sinusoidal wave modulated by a Gaussian envelope:
+
+```
+g(x, y) = exp(-(x'² + γ²y'²) / (2σ²)) * exp(2πi * x' * f)
+```
+
+Where:
+- `f` = spatial frequency (controls detail scale)
+- `θ` = orientation (controls direction)
+- `σ` = Gaussian standard deviation (controls bandwidth)
+- `γ` = aspect ratio (usually 0.5)
+
+## Default Configuration
+
+```python
+orientations = 8        # 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, 157.5°
+frequencies = [0.1, 0.2, 0.3, 0.4]  # Low to high spatial frequency
+```
+
+Total: 32 filter responses per image.
+
+## Feature Extraction
+
+```python
+from skimage.filters import gabor
+import numpy as np
+
+def extract_texture_features(gray_image, orientations=8,
+                             frequencies=[0.1, 0.2, 0.3, 0.4]):
+    theta_values = np.linspace(0, np.pi, orientations, endpoint=False)
+    features = []
+
+    for freq in frequencies:
+        for theta in theta_values:
+            magnitude, _ = gabor(gray_image, frequency=freq, theta=theta)
+            features.append(magnitude.mean())
+
+    return np.array(features)
+```
+
+## Derived Metrics
+
+| Metric | Formula | Meaning |
+|--------|---------|---------|
+| Energy | `sqrt(mean(features²))` | Overall texture strength |
+| Homogeneity | `1 / (1 + std(features))` | Texture uniformity |
+| Dominant orientation | `argmax(features per θ)` | Primary texture direction |
+| Dominant frequency | `argmax(features per f)` | Texture coarseness |
+
+## Style Matching
+
+Compare textures between reference and generated frames:
+
+```python
+def texture_similarity(ref_features, gen_features):
+    """Pearson correlation between feature vectors."""
+    return np.corrcoef(ref_features, gen_features)[0, 1]
+
+# Interpretation:
+# > 0.9  — Excellent match (same texture)
+# 0.7-0.9 — Good match (similar feel)
+# < 0.5  — Poor match (different texture, style drift)
+```
+
+## Practical Application
+
+### Painterly vs Photographic
+
+| Style | Energy | Homogeneity | Dominant Frequency |
+|-------|--------|-------------|-------------------|
+| Oil painting | High (>0.6) | Low (<0.5) | Low (0.1-0.2) |
+| Watercolor | Medium | Medium | Medium (0.2-0.3) |
+| Photography | Low-Medium | High (>0.7) | Variable |
+| Digital art | Variable | High (>0.8) | High (0.3-0.4) |
+| Sketch | Medium | Low | High (0.3-0.4) |
+
+Use these profiles to adjust generation parameters:
+
+```python
+def texture_to_guidance(texture: TextureFeatures) -> dict:
+    if texture.energy > 0.6 and texture.homogeneity < 0.5:
+        return {"prompt_suffix": "painterly brushstrokes, impasto texture",
+                "cfg_scale_boost": 0.5}
+    elif texture.homogeneity > 0.8:
+        return {"prompt_suffix": "smooth clean rendering, digital art",
+                "cfg_scale_boost": -0.5}
+    return {}
+```
+
+## Limitations
+
+- Gabor filters are rotation-sensitive; 8 orientations covers 180° at 22.5° intervals
+- Low-frequency textures (gradients, lighting) may not be well captured
+- Texture alone doesn't capture color — always combine with palette extraction
+- Computationally cheap but requires scikit-image (optional dependency)

skills/creative/shared/style-lock/scripts/__init__.py

@@ -0,0 +1 @@
+"""Style Lock — Shared infrastructure for consistent visual style."""

skills/creative/shared/style-lock/scripts/style_lock.py

@@ -0,0 +1,631 @@
+"""
+Style Lock — Consistent Visual Style Across Video Generation
+
+Extracts style embeddings from a reference image and provides conditioning
+for Stable Diffusion, IP-Adapter, ControlNet, and style token injection.
+
+Used by:
+  - Video Forge (playground #52) — scene-to-scene style consistency
+  - LPM 1.0 (issue #641) — frame-to-frame temporal coherence
+
+Usage:
+    from style_lock import StyleLock
+
+    lock = StyleLock("reference.png")
+    conditioning = lock.get_conditioning(backend="sdxl", method="hybrid")
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass, field, asdict
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+from PIL import Image
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Data structures
+# ---------------------------------------------------------------------------
+
+@dataclass
+class ColorPalette:
+    """Dominant color palette extracted from reference image."""
+    colors: list[tuple[int, int, int]]  # RGB tuples
+    weights: list[float]                 # Proportion of each color
+    temperature: str                     # "warm", "cool", "neutral"
+    saturation_mean: float
+    brightness_mean: float
+
+
+@dataclass
+class LightingProfile:
+    """Lighting characteristics of reference image."""
+    histogram: np.ndarray               # Grayscale histogram (256 bins)
+    mean_brightness: float
+    contrast: float                     # Std dev of brightness
+    dynamic_range: tuple[float, float]  # (min, max) normalized
+    direction_hint: str                 # "even", "top", "left", "right", "bottom"
+
+
+@dataclass
+class TextureFeatures:
+    """Texture feature vector from Gabor filter responses."""
+    features: np.ndarray                # Shape: (orientations * frequencies,)
+    orientations: int
+    frequencies: list[float]
+    energy: float                       # Total texture energy
+    homogeneity: float                  # Texture uniformity
+
+
+@dataclass
+class StyleEmbedding:
+    """Complete style embedding extracted from a reference image."""
+    clip_embedding: Optional[np.ndarray] = None  # CLIP visual features
+    color_palette: Optional[ColorPalette] = None
+    lighting: Optional[LightingProfile] = None
+    texture: Optional[TextureFeatures] = None
+    source_path: str = ""
+
+    def to_dict(self) -> dict:
+        """Serialize to JSON-safe dict (excludes numpy arrays)."""
+        return {
+            "source_path": self.source_path,
+            "color_palette": asdict(self.color_palette) if self.color_palette else None,
+            "lighting": {
+                "mean_brightness": self.lighting.mean_brightness,
+                "contrast": self.lighting.contrast,
+                "dynamic_range": list(self.lighting.dynamic_range),
+                "direction_hint": self.lighting.direction_hint,
+            } if self.lighting else None,
+            "texture": {
+                "orientations": self.texture.orientations,
+                "frequencies": self.texture.frequencies,
+                "energy": self.texture.energy,
+                "homogeneity": self.texture.homogeneity,
+            } if self.texture else None,
+            "has_clip_embedding": self.clip_embedding is not None,
+        }
+
+
+@dataclass
+class ConditioningOutput:
+    """Conditioning parameters for a generation backend."""
+    method: str                          # ip_adapter, controlnet, style_tokens, hybrid
+    backend: str                         # sdxl, flux, comfyui
+    strength: float                      # Overall style adherence (0-1)
+    ip_adapter_image: Optional[str] = None   # Path to reference image
+    ip_adapter_scale: float = 0.75
+    controlnet_image: Optional[np.ndarray] = None  # Preprocessed control image
+    controlnet_conditioning_scale: float = 0.5
+    style_prompt: str = ""               # Text conditioning from style tokens
+    negative_prompt: str = ""            # Anti-style negatives
+    color_palette_guidance: Optional[dict] = None
+
+    def to_api_kwargs(self) -> dict:
+        """Convert to kwargs suitable for diffusers pipelines."""
+        kwargs = {}
+        if self.method in ("ip_adapter", "hybrid") and self.ip_adapter_image:
+            kwargs["ip_adapter_image"] = self.ip_adapter_image
+            kwargs["ip_adapter_scale"] = self.ip_adapter_scale * self.strength
+        if self.method in ("controlnet", "hybrid") and self.controlnet_image is not None:
+            kwargs["image"] = self.controlnet_image
+            kwargs["controlnet_conditioning_scale"] = (
+                self.controlnet_conditioning_scale * self.strength
+            )
+        if self.style_prompt:
+            kwargs["prompt_suffix"] = self.style_prompt
+        if self.negative_prompt:
+            kwargs["negative_prompt_suffix"] = self.negative_prompt
+        return kwargs
+
+
+# ---------------------------------------------------------------------------
+# Extractors
+# ---------------------------------------------------------------------------
+
+class ColorExtractor:
+    """Extract dominant color palette using k-means clustering."""
+
+    def __init__(self, num_colors: int = 5):
+        self.num_colors = num_colors
+
+    def extract(self, image: Image.Image) -> ColorPalette:
+        img = image.resize((150, 150)).convert("RGB")
+        pixels = np.array(img).reshape(-1, 3).astype(np.float32)
+
+        # Simple k-means (no sklearn dependency)
+        colors, weights = self._kmeans(pixels, self.num_colors)
+
+        # Analyze color temperature
+        avg_r, avg_g, avg_b = colors[:, 0].mean(), colors[:, 1].mean(), colors[:, 2].mean()
+        if avg_r > avg_b + 20:
+            temperature = "warm"
+        elif avg_b > avg_r + 20:
+            temperature = "cool"
+        else:
+            temperature = "neutral"
+
+        # Saturation and brightness
+        hsv = self._rgb_to_hsv(colors)
+        saturation_mean = float(hsv[:, 1].mean())
+        brightness_mean = float(hsv[:, 2].mean())
+
+        return ColorPalette(
+            colors=[tuple(int(c) for c in color) for color in colors],
+            weights=[float(w) for w in weights],
+            temperature=temperature,
+            saturation_mean=saturation_mean,
+            brightness_mean=brightness_mean,
+        )
+
+    def _kmeans(self, pixels: np.ndarray, k: int, max_iter: int = 20):
+        indices = np.random.choice(len(pixels), k, replace=False)
+        centroids = pixels[indices].copy()
+
+        for _ in range(max_iter):
+            dists = np.linalg.norm(pixels[:, None] - centroids[None, :], axis=2)
+            labels = np.argmin(dists, axis=1)
+            new_centroids = np.array([
+                pixels[labels == i].mean(axis=0) if np.any(labels == i) else centroids[i]
+                for i in range(k)
+            ])
+            if np.allclose(centroids, new_centroids, atol=1.0):
+                break
+            centroids = new_centroids
+
+        counts = np.bincount(labels, minlength=k)
+        weights = counts / counts.sum()
+        order = np.argsort(-weights)
+        return centroids[order], weights[order]
+
+    @staticmethod
+    def _rgb_to_hsv(colors: np.ndarray) -> np.ndarray:
+        """Convert RGB (0-255) to HSV (H: 0-360, S: 0-1, V: 0-1)."""
+        rgb = colors / 255.0
+        hsv = np.zeros_like(rgb)
+        for i, pixel in enumerate(rgb):
+            r, g, b = pixel
+            cmax, cmin = max(r, g, b), min(r, g, b)
+            delta = cmax - cmin
+            if delta == 0:
+                h = 0
+            elif cmax == r:
+                h = 60 * (((g - b) / delta) % 6)
+            elif cmax == g:
+                h = 60 * (((b - r) / delta) + 2)
+            else:
+                h = 60 * (((r - g) / delta) + 4)
+            s = 0 if cmax == 0 else delta / cmax
+            v = cmax
+            hsv[i] = [h, s, v]
+        return hsv
+
+
+class LightingExtractor:
+    """Analyze lighting characteristics from grayscale histogram."""
+
+    def extract(self, image: Image.Image) -> LightingProfile:
+        gray = np.array(image.convert("L"))
+        hist, _ = np.histogram(gray, bins=256, range=(0, 256))
+        hist_norm = hist.astype(np.float32) / hist.sum()
+
+        mean_brightness = float(gray.mean() / 255.0)
+        contrast = float(gray.std() / 255.0)
+
+        nonzero = np.where(hist_norm > 0.001)[0]
+        dynamic_range = (
+            float(nonzero[0] / 255.0) if len(nonzero) > 0 else 0.0,
+            float(nonzero[-1] / 255.0) if len(nonzero) > 0 else 1.0,
+        )
+
+        # Rough directional lighting estimate from quadrant brightness
+        h, w = gray.shape
+        quadrants = {
+            "top": gray[:h // 2, :].mean(),
+            "bottom": gray[h // 2:, :].mean(),
+            "left": gray[:, :w // 2].mean(),
+            "right": gray[:, w // 2:].mean(),
+        }
+        brightest = max(quadrants, key=quadrants.get)
+        delta = quadrants[brightest] - min(quadrants.values())
+        direction = brightest if delta > 15 else "even"
+
+        return LightingProfile(
+            histogram=hist_norm,
+            mean_brightness=mean_brightness,
+            contrast=contrast,
+            dynamic_range=dynamic_range,
+            direction_hint=direction,
+        )
+
+
+class TextureExtractor:
+    """Extract texture features using Gabor filter bank."""
+
+    def __init__(
+        self,
+        orientations: int = 8,
+        frequencies: Optional[list[float]] = None,
+    ):
+        self.orientations = orientations
+        self.frequencies = frequencies or [0.1, 0.2, 0.3, 0.4]
+
+    def extract(self, image: Image.Image) -> TextureFeatures:
+        try:
+            from skimage.filters import gabor
+            from skimage.color import rgb2gray
+            from skimage.transform import resize
+        except ImportError:
+            logger.warning("scikit-image not available, returning empty texture features")
+            return TextureFeatures(
+                features=np.zeros(len(self.frequencies) * self.orientations),
+                orientations=self.orientations,
+                frequencies=self.frequencies,
+                energy=0.0,
+                homogeneity=0.0,
+            )
+
+        gray = rgb2gray(np.array(image))
+        gray = resize(gray, (256, 256), anti_aliasing=True)
+
+        features = []
+        theta_values = np.linspace(0, np.pi, self.orientations, endpoint=False)
+
+        for freq in self.frequencies:
+            for theta in theta_values:
+                magnitude, _ = gabor(gray, frequency=freq, theta=theta)
+                features.append(float(magnitude.mean()))
+
+        features_arr = np.array(features)
+        energy = float(np.sqrt(np.mean(features_arr ** 2)))
+        homogeneity = float(1.0 / (1.0 + np.std(features_arr)))
+
+        return TextureFeatures(
+            features=features_arr,
+            orientations=self.orientations,
+            frequencies=self.frequencies,
+            energy=energy,
+            homogeneity=homogeneity,
+        )
+
+
+class CLIPEmbeddingExtractor:
+    """Extract CLIP visual embedding for IP-Adapter conditioning."""
+
+    def __init__(self, model_name: str = "openai/clip-vit-large-patch14"):
+        self.model_name = model_name
+        self._model = None
+        self._processor = None
+        self._load_attempted = False
+
+    def _load(self):
+        if self._model is not None:
+            return
+        if self._load_attempted:
+            return
+        self._load_attempted = True
+        try:
+            from transformers import CLIPModel, CLIPProcessor
+            from huggingface_hub import try_to_load_from_cache
+            import torch
+            # Only load if model is already cached locally — no network
+            import os
+            cache_dir = os.path.expanduser(
+                f"~/.cache/huggingface/hub/models--{self.model_name.replace('/', '--')}"
+            )
+            if not os.path.isdir(cache_dir):
+                logger.info("CLIP model not cached locally, embedding disabled")
+                self._model = None
+                return
+            self._model = CLIPModel.from_pretrained(self.model_name, local_files_only=True)
+            self._processor = CLIPProcessor.from_pretrained(self.model_name, local_files_only=True)
+            self._model.eval()
+            logger.info(f"Loaded cached CLIP model: {self.model_name}")
+        except ImportError:
+            logger.warning("transformers not available, CLIP embedding disabled")
+        except Exception as e:
+            logger.warning(f"CLIP model load failed ({e}), embedding disabled")
+            self._model = None
+
+    def extract(self, image: Image.Image) -> Optional[np.ndarray]:
+        self._load()
+        if self._model is None:
+            return None
+
+        import torch
+        inputs = self._processor(images=image, return_tensors="pt")
+        with torch.no_grad():
+            features = self._model.get_image_features(**inputs)
+        return features.squeeze().numpy()
+
+
+# ---------------------------------------------------------------------------
+# Style Lock — main class
+# ---------------------------------------------------------------------------
+
+class StyleLock:
+    """
+    Style Lock: extract and apply consistent visual style across generations.
+
+    Args:
+        reference_image: Path to reference image or PIL Image.
+        num_colors: Number of dominant colors to extract (default 5).
+        texture_orientations: Gabor filter orientations (default 8).
+        texture_frequencies: Gabor filter frequencies (default [0.1, 0.2, 0.3, 0.4]).
+        clip_model: CLIP model name for embedding extraction.
+    """
+
+    def __init__(
+        self,
+        reference_image: str | Image.Image,
+        num_colors: int = 5,
+        texture_orientations: int = 8,
+        texture_frequencies: Optional[list[float]] = None,
+        clip_model: str = "openai/clip-vit-large-patch14",
+    ):
+        if isinstance(reference_image, str):
+            self._ref_path = reference_image
+            self._ref_image = Image.open(reference_image).convert("RGB")
+        else:
+            self._ref_path = ""
+            self._ref_image = reference_image
+
+        self._color_ext = ColorExtractor(num_colors=num_colors)
+        self._lighting_ext = LightingExtractor()
+        self._texture_ext = TextureExtractor(
+            orientations=texture_orientations,
+            frequencies=texture_frequencies,
+        )
+        self._clip_ext = CLIPEmbeddingExtractor(model_name=clip_model)
+        self._embedding: Optional[StyleEmbedding] = None
+
+    @property
+    def embedding(self) -> StyleEmbedding:
+        """Lazy-computed full style embedding."""
+        if self._embedding is None:
+            self._embedding = self._extract_all()
+        return self._embedding
+
+    def _extract_all(self) -> StyleEmbedding:
+        logger.info("Extracting style embedding from reference image...")
+        return StyleEmbedding(
+            clip_embedding=self._clip_ext.extract(self._ref_image),
+            color_palette=self._color_ext.extract(self._ref_image),
+            lighting=self._lighting_ext.extract(self._ref_image),
+            texture=self._texture_ext.extract(self._ref_image),
+            source_path=self._ref_path,
+        )
+
+    def get_conditioning(
+        self,
+        backend: str = "sdxl",
+        method: str = "hybrid",
+        strength: float = 0.75,
+    ) -> ConditioningOutput:
+        """
+        Generate conditioning output for a generation backend.
+
+        Args:
+            backend: Target backend — "sdxl", "flux", "comfyui".
+            method: Conditioning method — "ip_adapter", "controlnet",
+                    "style_tokens", "hybrid".
+            strength: Overall style adherence 0.0 (loose) to 1.0 (strict).
+
+        Returns:
+            ConditioningOutput with all parameters for the pipeline.
+        """
+        emb = self.embedding
+        style_prompt = self._build_style_prompt(emb)
+        negative_prompt = self._build_negative_prompt(emb)
+        controlnet_img = self._build_controlnet_image(emb)
+
+        palette_guidance = None
+        if emb.color_palette:
+            palette_guidance = {
+                "colors": emb.color_palette.colors,
+                "weights": emb.color_palette.weights,
+                "temperature": emb.color_palette.temperature,
+            }
+
+        return ConditioningOutput(
+            method=method,
+            backend=backend,
+            strength=strength,
+            ip_adapter_image=self._ref_path if self._ref_path else None,
+            ip_adapter_scale=0.75,
+            controlnet_image=controlnet_img,
+            controlnet_conditioning_scale=0.5,
+            style_prompt=style_prompt,
+            negative_prompt=negative_prompt,
+            color_palette_guidance=palette_guidance,
+        )
+
+    def _build_style_prompt(self, emb: StyleEmbedding) -> str:
+        """Generate text conditioning from extracted style features."""
+        parts = []
+
+        if emb.color_palette:
+            palette = emb.color_palette
+            parts.append(f"{palette.temperature} color palette")
+            if palette.saturation_mean > 0.6:
+                parts.append("vibrant saturated colors")
+            elif palette.saturation_mean < 0.3:
+                parts.append("muted desaturated tones")
+            if palette.brightness_mean > 0.65:
+                parts.append("bright luminous lighting")
+            elif palette.brightness_mean < 0.35:
+                parts.append("dark moody atmosphere")
+
+        if emb.lighting:
+            if emb.lighting.contrast > 0.3:
+                parts.append("high contrast dramatic lighting")
+            elif emb.lighting.contrast < 0.15:
+                parts.append("soft even lighting")
+            if emb.lighting.direction_hint != "even":
+                parts.append(f"light from {emb.lighting.direction_hint}")
+
+        if emb.texture:
+            if emb.texture.energy > 0.5:
+                parts.append("rich textured surface")
+            if emb.texture.homogeneity > 0.8:
+                parts.append("smooth uniform texture")
+            elif emb.texture.homogeneity < 0.4:
+                parts.append("complex varied texture")
+
+        return ", ".join(parts) if parts else "consistent visual style"
+
+    def _build_negative_prompt(self, emb: StyleEmbedding) -> str:
+        """Generate anti-style negatives to prevent style drift."""
+        parts = ["inconsistent style", "style variation", "color mismatch"]
+
+        if emb.color_palette:
+            if emb.color_palette.temperature == "warm":
+                parts.append("cold blue tones")
+            elif emb.color_palette.temperature == "cool":
+                parts.append("warm orange tones")
+
+        if emb.lighting:
+            if emb.lighting.contrast > 0.3:
+                parts.append("flat lighting")
+            else:
+                parts.append("harsh shadows")
+
+        return ", ".join(parts)
+
+    def _build_controlnet_image(self, emb: StyleEmbedding) -> Optional[np.ndarray]:
+        """Preprocess reference image for ControlNet input (edge/canny)."""
+        try:
+            import cv2
+        except ImportError:
+            return None
+
+        img = np.array(self._ref_image)
+        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+        edges = cv2.Canny(gray, 50, 150)
+        edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
+        return edges_rgb
+
+    def save_embedding(self, path: str) -> None:
+        """Save extracted embedding metadata to JSON (excludes raw arrays)."""
+        data = self.embedding.to_dict()
+        Path(path).write_text(json.dumps(data, indent=2))
+        logger.info(f"Style embedding saved to {path}")
+
+    def compare(self, other: "StyleLock") -> dict:
+        """
+        Compare style similarity between two StyleLock instances.
+
+        Returns:
+            Dict with similarity scores for each feature dimension.
+        """
+        scores = {}
+        a, b = self.embedding, other.embedding
+
+        # Color palette similarity
+        if a.color_palette and b.color_palette:
+            scores["color_temperature"] = (
+                1.0 if a.color_palette.temperature == b.color_palette.temperature else 0.0
+            )
+            scores["saturation_diff"] = abs(
+                a.color_palette.saturation_mean - b.color_palette.saturation_mean
+            )
+            scores["brightness_diff"] = abs(
+                a.color_palette.brightness_mean - b.color_palette.brightness_mean
+            )
+
+        # Lighting similarity
+        if a.lighting and b.lighting:
+            scores["brightness_diff"] = abs(
+                a.lighting.mean_brightness - b.lighting.mean_brightness
+            )
+            scores["contrast_diff"] = abs(a.lighting.contrast - b.lighting.contrast)
+
+        # Texture similarity
+        if a.texture and b.texture:
+            if a.texture.features.shape == b.texture.features.shape:
+                corr = np.corrcoef(a.texture.features, b.texture.features)[0, 1]
+                scores["texture_correlation"] = float(corr) if not np.isnan(corr) else 0.0
+
+        # CLIP embedding cosine similarity
+        if a.clip_embedding is not None and b.clip_embedding is not None:
+            cos_sim = np.dot(a.clip_embedding, b.clip_embedding) / (
+                np.linalg.norm(a.clip_embedding) * np.linalg.norm(b.clip_embedding)
+            )
+            scores["clip_cosine_similarity"] = float(cos_sim)
+
+        return scores
+
+
+# ---------------------------------------------------------------------------
+# Multi-reference style locking (for LPM 1.0 identity photos)
+# ---------------------------------------------------------------------------
+
+class MultiReferenceStyleLock:
+    """
+    Style Lock from multiple reference images (e.g., 8 identity photos).
+
+    Extracts style from each reference, then merges into a consensus style
+    that captures the common aesthetic across all references.
+
+    Args:
+        reference_paths: List of paths to reference images.
+        merge_strategy: How to combine styles — "average", "dominant", "first".
+    """
+
+    def __init__(
+        self,
+        reference_paths: list[str],
+        merge_strategy: str = "average",
+    ):
+        self.locks = [StyleLock(p) for p in reference_paths]
+        self.merge_strategy = merge_strategy
+
+    def get_conditioning(
+        self,
+        backend: str = "sdxl",
+        method: str = "hybrid",
+        strength: float = 0.75,
+    ) -> ConditioningOutput:
+        """Get merged conditioning from all reference images."""
+        if self.merge_strategy == "first":
+            return self.locks[0].get_conditioning(backend, method, strength)
+
+        # Use the first lock as the primary conditioning source,
+        # but adjust parameters based on consensus across all references
+        primary = self.locks[0]
+        conditioning = primary.get_conditioning(backend, method, strength)
+
+        if self.merge_strategy == "average":
+            # Average the conditioning scales across all locks
+            scales = []
+            for lock in self.locks:
+                emb = lock.embedding
+                if emb.color_palette:
+                    scales.append(emb.color_palette.saturation_mean)
+            if scales:
+                avg_sat = np.mean(scales)
+                # Adjust IP-Adapter scale based on average saturation agreement
+                conditioning.ip_adapter_scale *= (0.5 + 0.5 * avg_sat)
+
+        # Build a more comprehensive style prompt from all references
+        all_style_parts = []
+        for lock in self.locks:
+            prompt = lock._build_style_prompt(lock.embedding)
+            all_style_parts.append(prompt)
+        # Deduplicate style descriptors
+        seen = set()
+        unique_parts = []
+        for part in ", ".join(all_style_parts).split(", "):
+            stripped = part.strip()
+            if stripped and stripped not in seen:
+                seen.add(stripped)
+                unique_parts.append(stripped)
+        conditioning.style_prompt = ", ".join(unique_parts)
+
+        return conditioning

skills/creative/shared/style-lock/tests/test_style_lock.py

@@ -0,0 +1,251 @@
+"""
+Tests for Style Lock module.
+
+Validates:
+  - Color extraction from synthetic images
+  - Lighting profile extraction
+  - Texture feature extraction
+  - Style prompt generation
+  - Conditioning output format
+  - Multi-reference merging
+"""
+
+import numpy as np
+from PIL import Image
+
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from scripts.style_lock import (
+    StyleLock,
+    ColorExtractor,
+    LightingExtractor,
+    TextureExtractor,
+    MultiReferenceStyleLock,
+    ConditioningOutput,
+)
+
+
+def _make_test_image(width=256, height=256, color=(128, 64, 200)):
+    """Create a solid-color test image."""
+    return Image.new("RGB", (width, height), color)
+
+
+def _make_gradient_image(width=256, height=256):
+    """Create a gradient test image."""
+    arr = np.zeros((height, width, 3), dtype=np.uint8)
+    for y in range(height):
+        for x in range(width):
+            arr[y, x] = [int(255 * x / width), int(255 * y / height), 128]
+    return Image.fromarray(arr)
+
+
+def test_color_extractor_solid():
+    img = _make_test_image(color=(200, 100, 50))
+    ext = ColorExtractor(num_colors=3)
+    palette = ext.extract(img)
+
+    assert len(palette.colors) == 3
+    assert len(palette.weights) == 3
+    assert sum(palette.weights) > 0.99
+    assert palette.temperature == "warm"  # R > B
+    assert palette.saturation_mean > 0
+
+
+def test_color_extractor_cool():
+    img = _make_test_image(color=(50, 100, 200))
+    ext = ColorExtractor(num_colors=3)
+    palette = ext.extract(img)
+
+    assert palette.temperature == "cool"  # B > R
+
+
+def test_lighting_extractor():
+    img = _make_test_image(color=(128, 128, 128))
+    ext = LightingExtractor()
+    profile = ext.extract(img)
+
+    assert 0.4 < profile.mean_brightness < 0.6
+    assert profile.contrast < 0.1  # Uniform image, low contrast
+    assert profile.direction_hint == "even"
+
+
+def test_texture_extractor():
+    img = _make_test_image(color=(128, 128, 128))
+    ext = TextureExtractor(orientations=4, frequencies=[0.1, 0.2])
+    features = ext.extract(img)
+
+    assert features.features.shape == (8,)  # 4 orientations * 2 frequencies
+    assert features.orientations == 4
+    assert features.frequencies == [0.1, 0.2]
+
+
+def test_style_lock_embedding():
+    img = _make_test_image(color=(180, 90, 45))
+    lock = StyleLock(img)
+    emb = lock.embedding
+
+    assert emb.color_palette is not None
+    assert emb.lighting is not None
+    assert emb.texture is not None
+    assert emb.color_palette.temperature == "warm"
+
+
+def test_style_lock_conditioning_ip_adapter():
+    img = _make_test_image()
+    lock = StyleLock(img)
+    cond = lock.get_conditioning(backend="sdxl", method="ip_adapter", strength=0.8)
+
+    assert cond.method == "ip_adapter"
+    assert cond.backend == "sdxl"
+    assert cond.strength == 0.8
+    assert cond.style_prompt  # Non-empty
+    assert cond.negative_prompt  # Non-empty
+
+
+def test_style_lock_conditioning_hybrid():
+    img = _make_test_image()
+    lock = StyleLock(img)
+    cond = lock.get_conditioning(method="hybrid")
+
+    assert cond.method == "hybrid"
+    assert cond.controlnet_image is not None
+    assert cond.controlnet_image.shape[2] == 3  # RGB
+
+
+def test_style_lock_conditioning_to_api_kwargs():
+    img = _make_test_image()
+    lock = StyleLock(img)
+    cond = lock.get_conditioning(method="hybrid")
+    kwargs = cond.to_api_kwargs()
+
+    assert "prompt_suffix" in kwargs or "negative_prompt_suffix" in kwargs
+
+
+def test_style_lock_negative_prompt_warm():
+    img = _make_test_image(color=(220, 100, 30))
+    lock = StyleLock(img)
+    emb = lock.embedding
+    neg = lock._build_negative_prompt(emb)
+
+    assert "cold blue" in neg.lower() or "cold" in neg.lower()
+
+
+def test_style_lock_save_embedding(tmp_path):
+    img = _make_test_image()
+    lock = StyleLock(img)
+    path = str(tmp_path / "style.json")
+    lock.save_embedding(path)
+
+    import json
+    data = json.loads(open(path).read())
+    assert data["color_palette"] is not None
+    assert data["lighting"] is not None
+
+
+def test_style_lock_compare():
+    img1 = _make_test_image(color=(200, 50, 30))
+    img2 = _make_test_image(color=(200, 60, 40))
+    lock1 = StyleLock(img1)
+    lock2 = StyleLock(img2)
+
+    scores = lock1.compare(lock2)
+    assert "color_temperature" in scores
+    assert scores["color_temperature"] == 1.0  # Both warm
+
+
+def test_style_lock_compare_different_temps():
+    img1 = _make_test_image(color=(200, 50, 30))
+    img2 = _make_test_image(color=(30, 50, 200))
+    lock1 = StyleLock(img1)
+    lock2 = StyleLock(img2)
+
+    scores = lock1.compare(lock2)
+    assert scores["color_temperature"] == 0.0  # Warm vs cool
+
+
+def test_multi_reference_style_lock():
+    imgs = [_make_test_image(color=(180, 90, 45)) for _ in range(3)]
+    paths = []
+    import tempfile, os
+    for i, img in enumerate(imgs):
+        p = os.path.join(tempfile.gettempdir(), f"ref_{i}.png")
+        img.save(p)
+        paths.append(p)
+
+    mlock = MultiReferenceStyleLock(paths, merge_strategy="average")
+    cond = mlock.get_conditioning(backend="sdxl", method="hybrid")
+
+    assert cond.method == "hybrid"
+    assert cond.style_prompt  # Merged style prompt
+
+    for p in paths:
+        os.unlink(p)
+
+
+def test_multi_reference_first_strategy():
+    imgs = [_make_test_image(color=(200, 50, 30)) for _ in range(2)]
+    paths = []
+    import tempfile, os
+    for i, img in enumerate(imgs):
+        p = os.path.join(tempfile.gettempdir(), f"ref_first_{i}.png")
+        img.save(p)
+        paths.append(p)
+
+    mlock = MultiReferenceStyleLock(paths, merge_strategy="first")
+    cond = mlock.get_conditioning()
+    assert cond.method == "hybrid"
+
+    for p in paths:
+        os.unlink(p)
+
+
+if __name__ == "__main__":
+    import tempfile
+    print("Running Style Lock tests...")
+
+    test_color_extractor_solid()
+    print("  [PASS] color_extractor_solid")
+
+    test_color_extractor_cool()
+    print("  [PASS] color_extractor_cool")
+
+    test_lighting_extractor()
+    print("  [PASS] lighting_extractor")
+
+    test_texture_extractor()
+    print("  [PASS] texture_extractor")
+
+    test_style_lock_embedding()
+    print("  [PASS] style_lock_embedding")
+
+    test_style_lock_conditioning_ip_adapter()
+    print("  [PASS] style_lock_conditioning_ip_adapter")
+
+    test_style_lock_conditioning_hybrid()
+    print("  [PASS] style_lock_conditioning_hybrid")
+
+    test_style_lock_conditioning_to_api_kwargs()
+    print("  [PASS] style_lock_conditioning_to_api_kwargs")
+
+    test_style_lock_negative_prompt_warm()
+    print("  [PASS] style_lock_negative_prompt_warm")
+
+    td = tempfile.mkdtemp()
+    test_style_lock_save_embedding(type('X', (), {'__truediv__': lambda s, o: f"{td}/{o}"})())
+    print("  [PASS] style_lock_save_embedding")
+
+    test_style_lock_compare()
+    print("  [PASS] style_lock_compare")
+
+    test_style_lock_compare_different_temps()
+    print("  [PASS] style_lock_compare_different_temps")
+
+    test_multi_reference_style_lock()
+    print("  [PASS] multi_reference_style_lock")
+
+    test_multi_reference_first_strategy()
+    print("  [PASS] multi_reference_first_strategy")
+
+    print("\nAll 14 tests passed.")