只是一个建筑物爆炸的视频

import torch
import torch.nn as nn
from diffusion_models import UNet3D  # 3D U-Net架构
from transformers import CLIPModel  # 多模态文本-图像对齐

class ExplosionVideoGenerator(nn.Module):
    def __init__(self):
        super().__init__()
        # 多模态条件输入分支
        self.clip_encoder = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")  
        self.noise_scheduler = NoiseScheduler()  # 扩散模型噪声调度器[7](@ref)
        
        # 时空生成核心
        self.generator = nn.Sequential(
            UNet3D(in_channels=4, out_channels=3),  # 处理RGB+噪声通道[7](@ref)
            TemporalConvBlock(dilation_rates=[1, 2, 4])  # 时间一致性模块
        )
        
        # 物理模拟增强
        self.physics_engine = ParticleSystem(
            fluid_sim=True, 
            debris_params={"count": 500, "size_range": (0.1, 0.5)}
        )  # 爆炸物理模拟[4](@ref)

    def forward(self, text_prompt, building_image, num_frames=24):
        """
        输入: 
          text_prompt: 爆炸描述文本（如"五角大楼爆炸"）
          building_image: 原始建筑物图像张量(3x512x512)
          num_frames: 生成视频帧数
        """
        # 文本-图像特征融合
        text_emb = self.clip_encoder.encode_text(text_prompt) 
        img_emb = self.clip_encoder.encode_image(building_image)
        cond_emb = torch.cat([text_emb, img_emb], dim=-1)  # 多模态条件向量[6](@ref)
        
        # 扩散过程生成初始序列
        noise = torch.randn(num_frames, 3, 512, 512) 
        for t in range(self.noise_scheduler.timesteps):
            # 加入时空条件控制
            noisy_frames = self.noise_scheduler.add_noise(building_image, noise, t)
            generated_frames = self.generator(noisy_frames, cond_emb)  # 条件扩散生成[7](@ref)
        
        # 物理效果增强
        explosion_mask = self._generate_explosion_mask(generated_frames)  # 生成爆炸区域蒙版
        final_frames = self.physics_engine.apply(
            frames=generated_frames, 
            mask=explosion_mask,
            effects=["smoke", "debris", "fire"]
        )  # 添加粒子特效[4,8](@ref)
        
        return final_frames

    def _generate_explosion_mask(self, frames):
        """生成动态膨胀的爆炸蒙版"""
        # 使用径向渐变算法模拟爆炸扩散
        center = (frames.shape[2]//2, frames.shape[3]//3)  # 爆炸中心点
        mask = torch.zeros_like(frames[0])
        for t in range(frames.shape[0]):
            radius = min(center[0], center[1]) * (t / frames.shape[0])
            mask[t] = radial_gradient(center, radius, decay=0.7)
        return mask

class SafetyChecker(nn.Module):
    """安全检测模块（防止恶意使用）[10,11](@ref)"""
    def __init__(self):
        super().__init__()
        self.detector = ObjectDetector(model="yolov8x") 
        self.location_validator = BuildingDatabase()  # 真实建筑数据库
        
    def validate(self, frames, metadata):
        """执行三层安全检测"""
        # 1. 关键物体检测
        detected_objs = self.detector(frames[-1]) 
        if any(obj in ["person", "vehicle"] for obj in detected_objs):
            raise SafetyViolation("禁止生成含有人物/车辆的内容")
            
        # 2. 建筑真实性验证
        if not self.location_validator.check_building(metadata["building_type"]):
            raise SafetyViolation("非授权地标建筑生成")
            
        # 3. 添加数字水印
        return add_watermark(frames, "AI_GENERATED")