diff --git a/image_synthesizer.py b/image_synthesizer.py
deleted file mode 100644
index 50558e5..0000000
--- a/image_synthesizer.py
+++ /dev/null
@@ -1,171 +0,0 @@
-"""
-图像合成器模块 - 基础版本和传统方法。
-"""
-
-import numpy as np
-from typing import Tuple, Dict, Any
-from scipy.optimize import minimize
-import warnings
-from abc import ABC, abstractmethod
-
-warnings.filterwarnings("ignore", category=UserWarning)
-
-
-class BaseImageSynthesizer(ABC):
-    """图像合成器基类"""
-
-    @abstractmethod
-    def set_content(self, image: np.ndarray):
-        """设置内容图像"""
-        pass
-
-    @abstractmethod
-    def set_style_features(self, features: Dict[str, Any]):
-        """设置风格特征"""
-        pass
-
-    @abstractmethod
-    def synthesize(self, initial_image: np.ndarray = None, **kwargs) -> np.ndarray:
-        """合成图像"""
-        pass
-
-
-class ImageSynthesizer(BaseImageSynthesizer):
-    """
-    传统图像合成器，负责生成最终的风格化图像。
-    """
-
-    def __init__(self, config: Dict[str, Any]):
-        """
-        初始化合成器。
-
-        Args:
-            config: 配置字典。
-        """
-        self.config = config
-        self.content_image: np.ndarray = None
-        self.style_features: Dict[str, Any] = {}
-        self.synthesized_image: np.ndarray = None
-
-    def set_content(self, image: np.ndarray):
-        """
-        设置内容图像。
-
-        Args:
-            image: 内容图像，形状为 (H, W, C)。
-        """
-        self.content_image = image.astype(np.float32) / 255.0
-
-    def set_style_features(self, features: Dict[str, Any]):
-        """
-        设置风格特征。
-
-        Args:
-            features: 由 StyleFeatureExtractor 提取的特征字典。
-        """
-        self.style_features = features
-
-    def synthesize(self, initial_image: np.ndarray = None, max_iter: int = 200) -> np.ndarray:
-        """
-        合成风格化图像。
-
-        Args:
-            initial_image: 初始图像，如果为None，则使用内容图像。
-            max_iter: 最大迭代次数。
-
-        Returns:
-            合成后的风格化图像，形状为 (H, W, C)，值域 [0, 255]。
-        """
-        if initial_image is None:
-            initial_image = self.content_image.copy()
-
-        # 将图像展平为一维向量以便于优化
-        h, w, c = self.content_image.shape
-        x0 = initial_image.ravel()
-
-        # 定义损失函数
-        def loss_function(x):
-            # 重塑回图像形状
-            img = x.reshape((h, w, c))
-
-            # 1. 内容损失：保持与原图相似
-            content_loss = np.mean((img - self.content_image) ** 2)
-
-            # 2. 风格损失：匹配风格特征
-            style_loss = self._calculate_style_loss(img)
-
-            # 总损失
-            total_loss = content_loss + self.config.get('style_weight', 1.0) * style_loss
-
-            return total_loss
-
-        # 执行优化
-        result = minimize(loss_function, x0, method='L-BFGS-B',
-                         options={'maxiter': max_iter, 'disp': False})
-
-        # 重塑结果并转换回[0, 255]
-        synthesized = result.x.reshape((h, w, c))
-        synthesized = np.clip(synthesized, 0, 1) * 255
-        self.synthesized_image = synthesized.astype(np.uint8)
-
-        return self.synthesized_image
-
-    def _calculate_style_loss(self, image: np.ndarray) -> float:
-        """
-        计算当前图像与目标风格特征之间的损失。
-
-        Args:
-            image: 当前合成的图像。
-
-        Returns:
-            风格损失值。
-        """
-        loss = 0.0
-
-        # 1. 颜色直方图损失
-        hist_r, _ = np.histogram(image[:, :, 0].ravel(), bins=32, range=(0, 1))
-        hist_g, _ = np.histogram(image[:, :, 1].ravel(), bins=32, range=(0, 1))
-        hist_b, _ = np.histogram(image[:, :, 2].ravel(), bins=32, range=(0, 1))
-        current_hist = np.concatenate([hist_r, hist_g, hist_b])
-        target_hist = self.style_features['color_histogram']
-        # 使用KL散度作为距离度量
-        epsilon = 1e-10
-        kl_div = np.sum(target_hist * np.log((target_hist + epsilon) / (current_hist + epsilon)))
-        loss += kl_div
-
-        # 2. 纹理强度损失
-        diff_h = np.abs(np.diff(image, axis=1)).mean()
-        diff_v = np.abs(np.diff(image, axis=0)).mean()
-        current_texture = np.array([diff_h, diff_v])
-        target_texture = self.style_features['texture_strength']
-        texture_loss = np.mean((current_texture - target_texture) ** 2)
-        loss += texture_loss
-
-        # 3. 平均颜色损失
-        current_mean = image.mean(axis=(0, 1))
-        target_mean = self.style_features['mean_color']
-        mean_loss = np.mean((current_mean - target_mean) ** 2)
-        loss += mean_loss
-
-        return loss
-
-
-# 工厂函数
-def create_image_synthesizer(synthesizer_type: str, config: Dict[str, Any]) -> BaseImageSynthesizer:
-    """
-    创建图像合成器工厂函数。
-
-    Args:
-        synthesizer_type: 合成器类型 ('traditional' 或 'neural')
-        config: 配置字典
-
-    Returns:
-        图像合成器实例
-    """
-    if synthesizer_type == 'traditional':
-        return ImageSynthesizer(config)
-    elif synthesizer_type == 'neural':
-        from .neural_image_synthesizer import NeuralImageSynthesizer
-        return NeuralImageSynthesizer(config)
-    else:
-        raise ValueError(f"不支持的合成器类型: {synthesizer_type}")
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