FLUX 3：一个模型统一视频、音频与机器人动作，多模态融合的新纪元

import time
from dataclasses import dataclass, field
from typing import List, Optional, Dict

@dataclass
class VideoSegment:
    """视频片段"""
    frames: List[object] = field(default_factory=list)
    fps: int = 30
    duration_seconds: float = 0.0
    resolution: tuple = (1920, 1080)

@dataclass
class AudioSegment:
    """音频片段"""
    waveform: List[float] = field(default_factory=list)
    sample_rate: int = 44100
    duration_seconds: float = 0.0

@dataclass
class ActionSequence:
    """机器人动作序列"""
    joint_trajectories: List[List[float]] = field(default_factory=list)
    gripper_commands: List[float] = field(default_factory=list)
    fps: int = 30
    duration_seconds: float = 0.0

@dataclass
class MultimodalOutput:
    """多模态统一输出：视频+音频+动作"""
    video: VideoSegment
    audio: AudioSegment
    action: ActionSequence
    generation_time_ms: float = 0.0

    def verify_alignment(self) -> bool:
        """验证三模态的时间对齐"""
        return (abs(self.video.duration_seconds - self.audio.duration_seconds) < 0.05
                and abs(self.video.duration_seconds - self.action.duration_seconds) < 0.05)


class UnifiedMultimodalModel:
    """统一多模态模型的概念实现（模拟FLUX 3的核心逻辑）"""

    def __init__(self, model_path: str, device: str = "cuda"):
        self.model_path = model_path
        self.device = device
        self.token_vocab = self._init_token_vocab()
        print(f"[FLUX3] 模型加载完成: {model_path} on {device}")

    def _init_token_vocab(self) -> Dict:
        """初始化统一token词汇表（视频/音频/动作共享）"""
        return {
            "video_tokens": 8192,    # 视频时空token
            "audio_tokens": 4096,    # 音频时间token
            "action_tokens": 512,    # 动作时间token
            "special_tokens": 64,    # 控制token（起止、模态切换）
        }

    def encode_prompt(self, text: str, reference_image: Optional[object] = None):
        """将语言指令编码为统一token序列"""
        text_tokens = self._tokenize_text(text)
        if reference_image is not None:
            image_tokens = self._encode_image(reference_image)
            return ["<mod:text>"] + text_tokens + ["<mod:image>"] + image_tokens
        return ["<mod:text>"] + text_tokens

    def _tokenize_text(self, text: str) -> List[str]:
        return [f"<t:{w}>" for w in text.split()]

    def _encode_image(self, image) -> List[str]:
        return [f"<img_{i}>" for i in range(16)]

    def generate(self, prompt_tokens: List[str],
                 duration_seconds: float = 20.0,
                 modalities: List[str] = None) -> MultimodalOutput:
        """
        统一生成：一次前向传播产出视频+音频+动作
        这是FLUX 3的核心能力——三模态联合生成
        """
        if modalities is None:
            modalities = ["video", "audio", "action"]

        t0 = time.time()
        print(f"[FLUX3] 开始联合生成，时长={duration_seconds}s，模态={modalities}")

        # 1. 构建统一的生成条件
        condition = self._build_condition(prompt_tokens, duration_seconds, modalities)

        # 2. 层级化时间规划（粗到细）
        coarse_plan = self._coarse_temporal_planning(condition, duration_seconds)
        print(f"[FLUX3] 粗粒度时间规划完成: {len(coarse_plan)}个时间段")

        # 3. 潜空间扩散生成（三模态共享潜空间）
        latent = self._diffusion_generate(coarse_plan, modalities)

        # 4. 分别解码为各模态的物理表示
        video = self._decode_video(latent, duration_seconds) if "video" in modalities else None
        audio = self._decode_audio(latent, duration_seconds) if "audio" in modalities else None
        action = self._decode_action(latent, duration_seconds) if "action" in modalities else None

        elapsed = (time.time() - t0) * 1000
        output = MultimodalOutput(
            video=video or VideoSegment(duration_seconds=duration_seconds),
            audio=audio or AudioSegment(duration_seconds=duration_seconds),
            action=action or ActionSequence(duration_seconds=duration_seconds),
            generation_time_ms=elapsed
        )

        # 5. 验证三模态时间对齐
        if output.verify_alignment():
            print(f"[FLUX3] 三模态时间对齐验证通过 (耗时{elapsed:.0f}ms)")
        else:
            print("[FLUX3] 警告: 三模态时间未对齐!")
        return output

    def _build_condition(self, tokens, duration, modalities):
        return {"tokens": tokens, "duration": duration, "modalities": modalities}

    def _coarse_temporal_planning(self, condition, duration):
        # 将duration分为多个粗粒度时间段
        segments = max(1, int(duration / 2.0))
        return [f"segment_{i}" for i in range(segments)]

    def _diffusion_generate(self, plan, modalities):
        # 模拟潜空间扩散（实际中是迭代去噪过程）
        return {"latent_video": [...], "latent_audio": [...], "latent_action": [...]}

    def _decode_video(self, latent, duration):
        frames = [f"frame_{i}" for i in range(int(duration * 30))]
        return VideoSegment(frames=frames, fps=30, duration_seconds=duration)

    def _decode_audio(self, latent, duration):
        waveform = [0.0] * int(duration * 44100)
        return AudioSegment(waveform=waveform, sample_rate=44100,
                           duration_seconds=duration)

    def _decode_action(self, latent, duration):
        n_frames = int(duration * 30)
        joints = [[0.0]*7 for _ in range(n_frames)]
        grippers = [0.5] * n_frames
        return ActionSequence(joint_trajectories=joints,
                             gripper_commands=grippers,
                             fps=30, duration_seconds=duration)


class MultimodalAgent:
    """结合语言模型与FLUX 3的具身Agent示例"""

    def __init__(self):
        self.flux = UnifiedMultimodalModel("/models/flux3.bin")
        self.llm_available = True

    def understand_task(self, instruction: str) -> Dict:
        """语言模型负责任务理解与分解"""
        print(f"[LLM] 理解任务: {instruction}")
        plan = {
            "goal": instruction,
            "subtasks": ["approach_object", "grasp_object", "lift_object"],
            "constraints": {"max_force": 10.0, "safe_distance": 0.1}
        }
        return plan

    def imagine_and_act(self, instruction: str) -> MultimodalOutput:
        """
        完整闭环：
        1. 语言模型理解任务
        2. FLUX 3联合生成视频预演+音频+动作
        3. 验证动作可行性后执行
        """
        # Step 1: 语言模型理解
        plan = self.understand_task(instruction)

        # Step 2: FLUX 3联合生成（视频预演+音频+动作）
        prompt_tokens = self.flux.encode_prompt(instruction)
        output = self.flux.generate(
            prompt_tokens,
            duration_seconds=10.0,
            modalities=["video", "audio", "action"]
        )

        # Step 3: 验证动作的物理可行性
        if self._validate_action(output.action, plan["constraints"]):
            print("[Agent] 动作验证通过，准备执行")
            self._execute_robot(output.action)
        else:
            print("[Agent] 动作验证失败，重新生成")
        return output

    def _validate_action(self, action: ActionSequence, constraints: Dict) -> bool:
        max_vel = max(max(j) for j in action.joint_trajectories) if action.joint_trajectories else 0
        return max_vel < constraints.get("max_force", 999)

    def _execute_robot(self, action: ActionSequence):
        print(f"[Robot] 执行动作序列: {len(action.joint_trajectories)}帧")


# 模拟运行
if __name__ == "__main__":
    agent = MultimodalAgent()

    # 场景1：纯内容生成（视频+音频）
    print("=== 场景1: 生成20秒视频+原生音频 ===")
    prompt = agent.flux.encode_prompt("一只猫从桌上跳下，碰倒了一个花瓶")
    output1 = agent.flux.generate(prompt, duration_seconds=20.0,
                                  modalities=["video", "audio"])

    # 场景2：具身Agent闭环（视频预演+动作）
    print("
=== 场景2: 具身Agent任务执行 ===")
    output2 = agent.imagine_and_act("把桌上的杯子拿起来放到架子上")