diff --git a/README.md b/README.md
index 9675da1..d47f0aa 100644
--- a/README.md
+++ b/README.md
@@ -44,6 +44,7 @@ LCS calibrates per-VAE, so it should work with any model using a compatible VAE.
 - **Color Steering** — Push colors toward any target color
 - **Batch Multi-Color** — Different colors per batch item
 - **Tone Adjustment** — Contrast, brightness, saturation, temperature with one-click presets
+- **Color Anchor** — Zero-config color drift correction: self-anchor, reference-based, or spatial smoothing with auto mode
 - **Sharpness Control** — Sharpen or blur during generation via a discovered sharpness subspace (PC1 explains ~97% variance)
 - **Localized Control** — Optional mask for region-specific changes
 - **Latent Color Preview** — Visualize color structure without VAE decoding
@@ -111,6 +112,28 @@ LCS Load Data → LCS Color Batch → KSampler
 
 Enter comma-separated hex colors (e.g., `#FF0000,#00FF00,#0000FF`). Each color applies to one batch item.
 
+### Color Anchor (Zero-Config Drift Correction)
+
+```
+LCS Load Data → LCS Color Anchor → KSampler
+```
+
+1. **LCS Load Data** → **LCS Color Anchor** — connect MODEL and LCS_DATA
+2. Set mode to **auto** (default) and leave intensity at default
+3. Connect the output MODEL to KSampler
+
+That's it. In `auto` mode, the node automatically selects the correction strategy based on which optional inputs are connected:
+
+| Connected Inputs | Resolved Mode | Behavior |
+|---|---|---|
+| Nothing | self_anchor | Learns the image's color patterns early on, then prevents sudden color shifts |
+| reference_image + vae | reference | Keeps generated colors close to your reference image |
+| mask (no reference) | smooth | Smooths out color seams (great for inpainting) |
+
+Intensity is also derived automatically from measured drift — no manual tuning needed.
+
+> **When to use manual mode:** If you want full control, set mode to `smooth`, `reference`, or `self_anchor` explicitly and adjust the `intensity` slider (0–1). Auto mode is designed for zero-config "just works" usage.
+
 ## Nodes
 
 ### Calibration
@@ -129,6 +152,7 @@ Calibration runs once per VAE and caches automatically. Subsequent runs load ins
 | **LCS Color Intervene** | Steer colors toward a target. Supports Type I (LCS shift), Type II (HSL shift), or interpolated mode. |
 | **LCS Color Batch** | Different target colors per batch item. Outputs `batch_size` for EmptyLatentImage. |
 | **LCS Tone Adjust** | Contrast, brightness, saturation, temperature. Preset dropdown with real-time slider sync. |
+| **LCS Color Anchor** | Correct color drift during sampling. Auto mode infers strategy and intensity from connected inputs. |
 | **LCS Sharpness Intervene** | Control sharpness during generation. Positive = sharper, negative = blurrier. |
 
 ### Observation
@@ -160,6 +184,45 @@ Calibration runs once per VAE and caches automatically. Subsequent runs load ins
 
 > **Tip for distilled models**: Step-distilled models (e.g., z-image-turbo) use far fewer steps, so intervention should start earlier — even from step 0.
 
+### Color Anchor
+
+Sometimes diffusion models produce unexpected color shifts during sampling — a blue sky suddenly turns purple, or inpainting leaves visible color seams. The Color Anchor node fixes these problems by monitoring and correcting colors as the image is being generated.
+
+**Modes:**
+
+| Mode | What it does | When to use |
+|------|-------------|----------|
+| **auto** (default) | Looks at what you connected and picks the best strategy for you | Just want it to work, no config needed |
+| **self_anchor** | Watches how colors evolve in early steps, then prevents sudden color jumps in later steps | General color stability, no reference needed |
+| **reference** | Keeps the generated image's colors close to a reference image you provide | "Make it look like this photo's color palette" |
+| **smooth** | Smooths out abrupt color boundaries between regions | Fixing visible seams after inpainting |
+
+**How auto mode picks for you:**
+
+1. **Which strategy?** Based on what you plugged in:
+   - Connected a reference image + VAE → uses `reference`
+   - Connected a mask (but no reference) → uses `smooth`
+   - Connected nothing extra → uses `self_anchor`
+2. **How strong?** The node measures how much color drift is actually happening, then sets the correction strength accordingly. Big drift → stronger fix. Small drift → gentle touch. The range is 0.15–0.6, so it never over-corrects or does nothing.
+
+**What happens during sampling:**
+
+The node runs at every sampling step but doesn't always intervene. It automatically figures out which steps are safe to correct:
+
+1. **Early steps** (image is mostly noise) — Too early to fix colors without creating artifacts. Skipped. In self_anchor mode, the node uses these steps to *learn* the image's color patterns.
+2. **Middle steps** (image is taking shape) — The sweet spot. The node applies corrections here, ramping smoothly in and out to avoid sudden changes.
+3. **Late steps** (fine details) — Corrections would disturb fine detail. Skipped.
+
+Only colors are modified — structure, texture, and detail are never touched.
+
+**Parameters:**
+
+- **mode**: `auto`, `smooth`, `reference`, or `self_anchor`
+- **intensity** (0.0–1.0): How strong the correction is. In `auto` mode this is determined automatically. Set to 0 to disable the node entirely.
+- **vae** (optional): Needed for `reference` mode to encode the reference image
+- **reference_image** (optional): The image whose colors you want to match
+- **mask** (optional): Only correct colors inside the masked area
+
 ## Tone Presets
 
 Select a preset — sliders update in real-time. Tweak after selecting for fine-tuning. Select **Custom** to set values manually.
@@ -196,6 +259,16 @@ Sharpness lives in a separate subspace orthogonal to color:
 1. **Calibrate** — Generate grayscale noise images at multiple blur levels, VAE-encode, PCA on color-removed patch vectors. PC1 captures ~97% of sharpness variance.
 2. **Intervene** — Add `strength * pc1_direction` to each patch. Since pc1_direction is orthogonal to color (calibrated with LCS removal) and DC-free (per-vector zero-mean before PCA), this modifies only spatial frequency content without affecting color or brightness.
 
+### Color Anchor
+
+The Color Anchor stabilizes colors without pushing them toward a specific target — it prevents drift from what the model is already generating:
+
+1. **Decide when to act** — The node checks each sampling step: is the image still mostly noise (too early), taking shape (good time to correct), or nearly finished (too late)? It only corrects during the safe middle window.
+2. **Learn the color pattern** (self_anchor) — During early noisy steps, the node watches how colors relate to their neighbors and builds a running average of these relationships. This is more reliable than tracking absolute colors, which shift naturally as the image forms.
+3. **Measure drift** — On the first correction step, the node measures how much the colors have actually drifted (varies by mode: step-to-step jumps, distance from reference, or spatial roughness). This sets the correction strength in auto mode.
+4. **Apply gentle corrections** — Corrections ramp smoothly in and out (no sudden jumps). Each mode corrects differently: self_anchor fixes patches that deviate from learned patterns, reference pulls toward the reference image's colors, smooth blurs out sharp color boundaries.
+5. **Preserve everything else** — As with all LCS operations, only the 3D color coordinates change. Structure, texture, and detail are untouched.
+
 ## File Structure
 
 ```
@@ -203,15 +276,19 @@ ComfyUI-LCS/
 ├── __init__.py           # Entry point (V3 + V2 compat)
 ├── requirements.txt
 ├── core/
+│   ├── adaptive.py       # Adaptive scheduling (phases, envelopes, drift estimation)
+│   ├── bilateral.py      # Bilateral filter for LCS color smoothing
 │   ├── calibration.py    # PCA calibration pipeline (color)
 │   ├── color_space.py    # Bicone LCS ↔ HSL mapping
 │   ├── defaults.py       # Alpha/beta tables from paper
 │   ├── lcs_data.py       # LCSData dataclass
 │   ├── patchify.py       # Patch ↔ latent conversion
+│   ├── relationships.py  # Local color relationship analysis & anomaly detection
 │   ├── sampling.py       # Shared constants & step utilities
 │   ├── sharpness.py      # Sharpness subspace calibration
 │   └── timestep.py       # Sigma/timestep utilities
 ├── nodes/
+│   ├── anchor.py         # LCSColorAnchor (adaptive color drift correction)
 │   ├── calibrate.py      # LCSLoadData (auto-calibrate + cache)
 │   ├── intervene.py      # LCSColorIntervene, LCSColorBatch, LCSToneAdjust
 │   ├── observe.py        # LCSPreviewColors, LCSStepObserver
diff --git a/README_zh.md b/README_zh.md
index de04833..10c89c9 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -43,6 +43,7 @@ LCS 按 VAE 校准，理论上适用于任何使用兼容 VAE 架构的模型。
 - **颜色引导** — 将颜色推向任意目标色
 - **批量多色** — 为批次中每张图像指定不同颜色
 - **色调调整** — 对比度、亮度、饱和度、色温，支持一键预设
+- **颜色锚定** — 零配置颜色漂移校正：自锚定、参考图锚定、空间平滑，支持全自动模式
 - **锐度控制** — 在生成过程中增强或减弱锐度，基于发现的锐度子空间（PC1 解释 ~97% 方差）
 - **局部控制** — 可选遮罩，实现区域性变化
 - **潜在颜色预览** — 无需 VAE 解码即可可视化颜色结构
@@ -110,6 +111,28 @@ LCS Load Data → LCS Color Batch → KSampler
 
 输入逗号分隔的十六进制颜色（如 `#FF0000,#00FF00,#0000FF`），每个颜色对应一个批次项。
 
+### 颜色锚定（零配置漂移校正）
+
+```
+LCS Load Data → LCS Color Anchor → KSampler
+```
+
+1. **LCS Load Data** → **LCS Color Anchor** — 连接 MODEL 和 LCS_DATA
+2. 模式设为 **auto**（默认），intensity 保持默认值
+3. 将输出 MODEL 连接到 KSampler
+
+完成。在 `auto` 模式下，节点根据连接的可选输入自动选择校正策略：
+
+| 已连接输入 | 解析模式 | 行为 |
+|---|---|---|
+| 无 | self_anchor | 在早期学习图像的颜色规律，然后防止突然的颜色偏移 |
+| reference_image + vae | reference | 让生成的颜色贴近你的参考图 |
+| mask（无参考图） | smooth | 平滑颜色接缝（很适合修复/补绘） |
+
+intensity 也会根据实测漂移自动推导——无需手动调参。
+
+> **手动模式：** 如果需要完全控制，可以将模式设为 `smooth`、`reference` 或 `self_anchor`，并手动调节 `intensity` 滑条（0–1）。auto 模式适合零配置「开箱即用」场景。
+
 ## 节点一览
 
 ### 校准
@@ -128,6 +151,7 @@ LCS Load Data → LCS Color Batch → KSampler
 | **LCS Color Intervene** | 将颜色引导至目标色。支持 Type I（LCS 平移）、Type II（HSL 偏移）或插值模式。 |
 | **LCS Color Batch** | 每个批次项施加不同目标颜色。输出 `batch_size` 可连接 EmptyLatentImage。 |
 | **LCS Tone Adjust** | 对比度、亮度、饱和度、色温调整。预设下拉菜单，滑条实时同步。 |
+| **LCS Color Anchor** | 采样过程中校正颜色漂移。auto 模式根据连接输入自动推断策略和强度。 |
 | **LCS Sharpness Intervene** | 在生成过程中控制锐度。正值 = 更锐利，负值 = 更模糊。 |
 
 ### 观察
@@ -159,6 +183,45 @@ LCS Load Data → LCS Color Batch → KSampler
 
 > **步数蒸馏模型提示**：对于步数蒸馏模型（如 z-image-turbo），总步数很少，干预应从更早的步骤开始——甚至可以从第 0 步就开始干预。
 
+### 颜色锚定
+
+扩散模型在采样过程中有时会出现意想不到的颜色偏移——蓝天突然变紫，或者修复/补绘后留下明显的颜色接缝。颜色锚定节点在图像生成过程中监控和修正这些问题。
+
+**模式：**
+
+| 模式 | 功能 | 适用场景 |
+|------|------|----------|
+| **auto**（默认） | 根据你连接的输入自动选最合适的策略 | 不想调参，开箱即用 |
+| **self_anchor** | 在早期步骤观察颜色变化规律，在后续步骤防止突然的颜色跳变 | 通用颜色稳定，不需要参考图 |
+| **reference** | 让生成图像的颜色贴近你提供的参考图 | 「我想要这张照片的配色风格」 |
+| **smooth** | 平滑区域之间的突兀颜色边界 | 修复/补绘后消除接缝 |
+
+**auto 模式如何自动选择：**
+
+1. **用哪种策略？** 看你连了什么：
+   - 连了参考图 + VAE → 用 `reference`
+   - 连了遮罩（没有参考图）→ 用 `smooth`
+   - 什么额外输入都没连 → 用 `self_anchor`
+2. **修正多强？** 节点会测量实际的颜色漂移幅度，据此自动设置校正强度。漂移大 → 修正更强；漂移小 → 轻轻一碰。范围是 0.15–0.6，既不会矫枉过正，也不会毫无作用。
+
+**采样过程中发生了什么：**
+
+节点在每个采样步都会运行，但不会每步都干预。它自动判断哪些步骤适合校正：
+
+1. **早期步骤**（图像基本是噪声）— 太早修正颜色会产生伪影，跳过。在 self_anchor 模式下，节点利用这些步骤*学习*图像的颜色规律。
+2. **中间步骤**（图像逐渐成形）— 最佳校正时机。节点在这里施加校正，平滑地渐入渐出，避免突变。
+3. **后期步骤**（精细细节）— 校正会干扰细节，跳过。
+
+只修改颜色——结构、纹理、细节始终不受影响。
+
+**参数：**
+
+- **mode**：`auto`、`smooth`、`reference` 或 `self_anchor`
+- **intensity**（0.0–1.0）：校正强度。auto 模式下自动决定。设为 0 可完全禁用此节点。
+- **vae**（可选）：reference 模式需要用它来编码参考图
+- **reference_image**（可选）：你想匹配其颜色的参考图
+- **mask**（可选）：只在遮罩区域内校正颜色
+
 ## 色调预设
 
 选择预设后滑条实时更新。可在预设基础上微调。选择 **Custom** 可完全手动设置。
@@ -195,6 +258,16 @@ LCS Load Data → LCS Color Batch → KSampler
 1. **校准** — 生成灰度噪声图像，应用多级高斯模糊，VAE 编码后对去除颜色分量的 patch 向量做 PCA。PC1 捕获 ~97% 的锐度方差。
 2. **干预** — 在每个 patch 上沿 `strength * pc1_direction` 方向添加偏移。由于 pc1_direction 与颜色正交（校准时已移除 LCS 分量）且无直流分量（PCA 前做了逐向量零均值化），因此只改变空间频率内容，不影响颜色或亮度。
 
+### 颜色锚定
+
+颜色锚定的作用是稳定颜色，而不是把颜色推向某个特定目标——它防止模型已经在生成的颜色发生偏移：
+
+1. **判断何时介入** — 节点检查每个采样步：图像还是一片噪声（太早）、正在成形（适合校正）、还是快完成了（太晚）？只在安全的中间窗口进行校正。
+2. **学习颜色规律**（self_anchor）— 在早期噪声较大的步骤中，节点观察每个区域的颜色与邻居之间的关系，建立一个动态平均值。比起追踪绝对颜色值，这种「相对关系」更可靠，因为绝对颜色在图像成形过程中本来就会自然变化。
+3. **测量漂移** — 在第一个校正步，节点测量颜色实际漂移了多少（根据模式不同：步间跳变幅度、与参考图的差距、或空间粗糙程度）。这决定了 auto 模式下的校正强度。
+4. **温和地修正** — 校正平滑地渐入渐出（不会突变）。每种模式的修正方式不同：self_anchor 修复偏离已学规律的区域，reference 拉近与参考图的颜色，smooth 模糊掉尖锐的颜色边界。
+5. **保留其他一切** — 与所有 LCS 操作一样，只修改 3D 颜色坐标，结构、纹理、细节完全不受影响。
+
 ## 文件结构
 
 ```
@@ -202,15 +275,19 @@ ComfyUI-LCS/
 ├── __init__.py           # 入口（V3 + V2 兼容）
 ├── requirements.txt
 ├── core/
+│   ├── adaptive.py       # 自适应调度（阶段、包络、漂移估计）
+│   ├── bilateral.py      # LCS 颜色平滑的双边滤波
 │   ├── calibration.py    # PCA 校准流程（颜色）
 │   ├── color_space.py    # 双锥 LCS ↔ HSL 映射
 │   ├── defaults.py       # 论文中的 Alpha/beta 表
 │   ├── lcs_data.py       # LCSData 数据类
 │   ├── patchify.py       # Patch ↔ 潜在空间转换
+│   ├── relationships.py  # 局部颜色关系分析与异常检测
 │   ├── sampling.py       # 共享常量和步骤工具
 │   ├── sharpness.py      # 锐度子空间校准
 │   └── timestep.py       # Sigma/时间步工具
 ├── nodes/
+│   ├── anchor.py         # LCSColorAnchor（自适应颜色漂移校正）
 │   ├── calibrate.py      # LCSLoadData（自动校准 + 缓存）
 │   ├── intervene.py      # LCSColorIntervene, LCSColorBatch, LCSToneAdjust
 │   ├── observe.py        # LCSPreviewColors, LCSStepObserver