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+# ==============================================================================
+# 本节点由 心寸 开发
+# Author: 心寸
+# Description: 基于 Lissajous 曲线的 ComfyUI 手持镜头运镜模拟节点
+# ==============================================================================
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+import json
+
+def ease_out_quadratic(t: float) -> float:
+    return 1 - (1 - t) ** 2
+
+def generate_lissajous_trajectory(
+    frame_count: int,
+    fps: float,
+    width: int,
+    height: int,
+    intensity: float = 1.0,
+    seed: int = 42
+) -> dict:
+    np_random = np.random.RandomState(seed)
+    dx_total = np.zeros(frame_count)
+    dy_total = np.zeros(frame_count)
+    frequencies =[0.033, 0.1, 0.2, 0.3]
+    
+    for freq in frequencies:
+        t = np.arange(frame_count) / fps
+        phase = 2 * np.pi * t * freq
+        amp = np_random.uniform(5.2, 15.6)
+        
+        if np_random.random() < 0.64:
+            angle_deg = np_random.uniform(0, 45)
+        else:
+            angle_deg = np_random.uniform(45, 90)
+            
+        if np_random.random() < 0.5:
+            angle_deg = -angle_deg
+        if np_random.random() < 0.5:
+            angle_deg = 180 - angle_deg
+            
+        angle_rad = np.deg2rad(angle_deg)
+        phase_normalized = (phase % (2 * np.pi)) / (2 * np.pi)
+        envelope = np.array([ease_out_quadratic(min(p, 1.0)) for p in phase_normalized])
+        
+        base_motion = np.sin(phase) * envelope * amp
+        arc_offset = np.sin(phase) * np.deg2rad(np_random.uniform(15, 30))
+        
+        dx_total += base_motion * np.cos(angle_rad + arc_offset)
+        dy_total += base_motion * np.sin(angle_rad + arc_offset)
+    
+    dx_total = dx_total * intensity
+    dy_total = dy_total * intensity
+    dtheta = np.zeros(frame_count)
+    
+    return {
+        "dx": dx_total.tolist(),
+        "dy": dy_total.tolist(),
+        "dtheta": dtheta.tolist(),
+        "frame_count": frame_count,
+        "fps": fps,
+        "width": width,
+        "height": height,
+        "intensity": intensity
+    }
+
+
+class DNode_CameraShakeLissajous:
+    DESCRIPTION = """🎥 Lissajous Camera Shake (V1)
+
+【核心特性】
+基于利萨茹曲线(Lissajous)算法，通过叠加多组低频正弦波，确保镜头始终围绕画面绝对中心点进行平滑的呼吸感往复漂移，从根本上解决传统噪波抖动带来的画面偏离问题。
+
+【参数说明】
+- intensity (强度): 控制晃动剧烈程度。0.40为最佳手持平衡点。
+- zoom_factor (缩放): 边缘防穿帮保护。1.05为默认安全区。
+- fps (帧速率): 用于计算正确的物理运动周期。
+- seed (随机种子): 决定频率叠加的初始形态。
+
+--------------------------------------------------[Core Features]
+Based on the Lissajous curve algorithm, this node superimposes multiple low-frequency sine waves to ensure the camera smoothly and rhythmically drifts around the absolute center of the frame. This fundamentally solves the frame deviation problem caused by traditional noise-based shakes.
+
+[Parameters]
+- intensity: Controls the intensity of the shake. 0.40 is the optimal balance for handheld camera simulation.
+- zoom_factor: Edge protection to prevent clipping. 1.05 is the default safe zone.
+- fps: Frame rate, used to calculate the correct physical motion cycle.
+- seed: Determines the initial state of the frequency superposition.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "intensity": ("FLOAT", {"default": 0.40, "min": 0.0, "max": 5.0, "step": 0.01}),
+                "zoom_factor": ("FLOAT", {"default": 1.05, "min": 1.0, "max": 2.0, "step": 0.01}),
+                "fps": ("FLOAT", {"default": 30.0, "min": 1.0, "max": 120.0, "step": 1.0}),
+                "seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffff}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "STRING")
+    RETURN_NAMES = ("IMAGE", "trajectory_json")
+    FUNCTION = "apply_shake"
+    CATEGORY = "DNode"
+
+    def apply_shake(self, images, intensity, zoom_factor, fps, seed):
+        B, H, W, C = images.shape
+        trajectory = generate_lissajous_trajectory(B, fps, W, H, intensity, seed)
+        dx_arr = trajectory["dx"]
+        dy_arr = trajectory["dy"]
+        
+        images_pt = images.permute(0, 3, 1, 2)
+        device = images_pt.device
+        theta = torch.zeros((B, 2, 3), dtype=torch.float32, device=device)
+        z = 1.0 / zoom_factor
+        
+        for i in range(B):
+            tx = (dx_arr[i]) / (W / 2)
+            ty = (dy_arr[i]) / (H / 2)
+            theta[i, 0, 0] = z     
+            theta[i, 1, 1] = z     
+            theta[i, 0, 2] = tx    
+            theta[i, 1, 2] = ty    
+
+        grid = F.affine_grid(theta, images_pt.size(), align_corners=False)
+        shaken_images = F.grid_sample(
+            images_pt, grid, mode='bilinear', padding_mode='border', align_corners=False
+        )
+        
+        shaken_images_out = shaken_images.permute(0, 2, 3, 1)
+        json_output = json.dumps(trajectory, indent=2, ensure_ascii=False)
+        
+        return (shaken_images_out, json_output)
+
+NODE_CLASS_MAPPINGS = {
+    "DNode_CameraShakeLissajous": DNode_CameraShakeLissajous
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DNode_CameraShakeLissajous": "🎥 Lissajous Camera Shake (V1)"
+}