2：LingBot-World 交互式世界模型：游戏环境学习物理规律的新范式

# 游戏环境学习核心代码示例
class GameEnvironmentLearner:
    def __init__(self, game_engine):
        self.game_engine = game_engine
        self.scene_generator = SceneGenerator()
        self.physical_learner = PhysicalLawLearner()
        self.causal_model = CausalModel()
    
    def learn_from_game(self, num_episodes=10000):
        for episode in range(num_episodes):
            # 生成随机场景
            scene = self.scene_generator.generate_scene()
            # 初始化游戏环境
            self.game_engine.init_scene(scene)
            # 收集交互数据
            trajectory = self.collect_trajectory()
            # 学习物理规律
            self.physical_learner.update(trajectory)
            # 更新因果模型
            self.causal_model.update(trajectory)
    
    def collect_trajectory(self, max_steps=1000):
        trajectory = []
        for step in range(max_steps):
            # 获取当前状态
            state = self.game_engine.get_state()
            # 执行随机动作
            action = self.sample_action()
            # 执行动作并观察结果
            next_state, reward, done = self.game_engine.step(action)
            # 记录轨迹
            trajectory.append((state, action, next_state, reward, done))
            if done:
                break
        return trajectory

# 连续生成核心代码示例
class ContinuousGenerator:
    def __init__(self, world_model, renderer):
        self.world_model = world_model
        self.renderer = renderer
        self.state_cache = []
    
    def generate(self, initial_state, duration=600, fps=16):
        """生成指定时长的连续场景"""
        frames = []
        current_state = initial_state
        
        for step in range(duration * fps // 60):
            # 预测下一状态
            next_state = self.world_model.predict(current_state)
            # 渲染当前帧
            frame = self.renderer.render(current_state)
            frames.append(frame)
            # 更新当前状态
            current_state = next_state
            # 更新状态缓存
            self.update_cache(current_state)
        
        return frames
    
    def update_cache(self, state):
        """更新状态缓存，加速后续预测"""
        self.state_cache.append(state)
        if len(self.state_cache) > 100:
            self.state_cache.pop(0)

# Zero-shot泛化核心代码示例
class ZeroShotGeneralizer:
    def __init__(self, world_model):
        self.world_model = world_model
        self.abstract_reasoner = AbstractReasoner()
    
    def generalize_to_new_scene(self, new_scene):
        """将模型泛化到新场景"""
        # 分析新场景的物理组成
        scene_analysis = self.abstract_reasoner.analyze_scene(new_scene)
        # 提取场景中的物理元素和关系
        physical_elements = scene_analysis['elements']
        relationships = scene_analysis['relationships']
        # 基于抽象物理知识预测场景演化
        predictions = self.world_model.predict_generic(
            physical_elements, relationships
        )
        return predictions

# 推理优化核心代码示例
class OptimizedInferenceEngine:
    def __init__(self, world_model):
        self.world_model = world_model
        self.quantize_model()
        self.setup_batch_processing()
    
    def quantize_model(self):
        """模型量化，减少计算开销"""
        self.world_model = self.quantize_weights(self.world_model)
    
    def setup_batch_processing(self):
        """设置批处理，提高吞吐量"""
        self.batch_size = 8
        self.batch_queue = []
    
    def infer(self, states):
        """高效推理接口"""
        if len(states) > 1:
            return self.batch_infer(states)
        return self.single_infer(states[0])
    
    def batch_infer(self, states):
        """批处理推理"""
        # 填充批次
        while len(states) % self.batch_size != 0:
            states.append(states[0])  # 填充重复数据
        
        # 批量预测
        predictions = []
        for i in range(0, len(states), self.batch_size):
            batch = states[i:i+self.batch_size]
            batch_pred = self.world_model.predict_batch(batch)
            predictions.extend(batch_pred)
        
        return predictions[:len(states)]

import gradio as gr
import numpy as np
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

# 加载模型
world_model_pipeline = pipeline(
    Tasks.video_generation,
    model='Robbyant/lingbot-world-base-cam'
)

# 处理函数
def generate_world(initial_image, duration=60, fps=16):
    """生成交互式世界模拟"""
    # 生成连续场景
    result = world_model_pipeline({
        'initial_image': initial_image,
        'duration': duration,
        'fps': fps
    })
    
    # 提取生成的视频帧
    video_frames = result['video_frames']
    
    # 转换为Gradio可显示的格式
    output_frames = []
    for frame in video_frames:
        # 确保帧是有效的RGB图像
        if isinstance(frame, np.ndarray) and len(frame.shape) == 3:
            output_frames.append(frame)
    
    # 生成统计信息
    stats = f"生成时长: {duration}秒\n"
    stats += f"生成帧率: {fps} FPS\n"
    stats += f"总帧数: {len(output_frames)}\n"
    stats += f"物理一致性评分: {result.get('physics_score', 'N/A')}\n"
    
    return output_frames, stats

# 创建Gradio界面
with gr.Blocks(title="LingBot-World 交互式世界模拟") as demo:
    gr.Markdown("# LingBot-World 交互式世界模拟")
    gr.Markdown("上传初始场景图像，生成连续的物理模拟")
    
    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(type="pil", label="初始场景")
            duration = gr.Slider(min=10, max=300, value=60, step=10, label="生成时长(秒)")
            fps = gr.Slider(min=8, max=30, value=16, step=2, label="帧率(FPS)")
            generate_btn = gr.Button("生成")
        
        with gr.Column(scale=2):
            video_output = gr.Video(label="生成结果")
            stats_output = gr.Textbox(label="生成统计", lines=5)
    
    # 绑定事件
    generate_btn.click(
        fn=generate_world,
        inputs=[image_input, duration, fps],
        outputs=[video_output, stats_output]
    )

if __name__ == "__main__":
    demo.launch(share=True)

pytorch==2.0.1
modelscope==1.9.1
gradio==4.14.0
Pillow==10.1.0
numpy==1.24.4
opencv-python==4.8.1.78

FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime

WORKDIR /app

COPY . /app

RUN pip install -r requirements.txt

EXPOSE 7860

CMD ["python", "app.py"]