Black Forest Labs FLUX 3深度解析：统一多模态基础模型，原生音频+视频+机器人动作预测的"视觉智能"新范式

import torch
import torch.nn as nn
import torch.nn.functional as F

class FlowMatchingLoss(nn.Module):
    """流匹配基础损失函数"""
    def __init__(self):
        super().__init__()
    
    def forward(self, velocity_pred, velocity_target, t):
        """
        Args:
            velocity_pred: 模型预测的速度场, shape [B, C, H, W]
            velocity_target: 真实速度场 (dx/dt), shape [B, C, H, W]
            t: 时间步, shape [B, 1]
        Returns:
            loss: 流匹配损失
        """
        # 标准流匹配: 直接匹配速度场
        loss = F.mse_loss(velocity_pred, velocity_target, reduction='mean')
        return loss


class FlowMatchingSampler:
    """流匹配采样器"""
    def __init__(self, model, num_steps=50):
        self.model = model
        self.num_steps = num_steps
    
    def sample(self, z_shape, device, cond=None):
        """
        ODE求解: 从噪声到数据的流
        """
        # 从标准高斯分布采样
        z = torch.randn(z_shape, device=device)
        
        dt = 1.0 / self.num_steps
        t = 0.0
        
        for i in range(self.num_steps):
            t_tensor = torch.full((z.shape[0], 1), t, device=device)
            # 预测速度场
            v = self.model(z, t_tensor, cond)
            # Euler法积分
            z = z + v * dt
            t += dt
        
        return z

class SelfFlowLoss(nn.Module):
    """Self-Flow: 统一生成与表示学习的损失函数"""
    def __init__(self, lambda_repr=0.1):
        super().__init__()
        self.lambda_repr = lambda_repr
        self.flow_loss = FlowMatchingLoss()
    
    def forward(self, model_output, target):
        """
        model_output: {
            'velocity': 预测速度场,
            'features': 中间层特征表示,
            'reconstructed': 自重建输出
        }
        """
        # 1. 生成损失: 流匹配
        gen_loss = self.flow_loss(
            model_output['velocity'], 
            target['velocity'],
            target['t']
        )
        
        # 2. 表示损失: 自监督对比学习
        # 确保不同模态的表示在潜在空间中对齐
        repr_loss = self._contrastive_representation_loss(
            model_output['features'],
            target['modality_labels']
        )
        
        # 3. 自重建损失: 确保表示包含完整信息
        recon_loss = F.mse_loss(
            model_output['reconstructed'],
            target['input'],
            reduction='mean'
        )
        
        total_loss = gen_loss + self.lambda_repr * repr_loss + recon_loss
        return {
            'total': total_loss,
            'gen_loss': gen_loss.item(),
            'repr_loss': repr_loss.item(),
            'recon_loss': recon_loss.item()
        }
    
    def _contrastive_representation_loss(self, features, modality_labels):
        """
        跨模态对比学习损失
        同一场景不同模态的表示应相似
        """
        # 简化实现: 使用InfoNCE损失
        features = F.normalize(features, dim=-1)
        similarity = torch.mm(features, features.t())
        
        # 正样本: 同一场景的不同模态
        mask = modality_labels.unsqueeze(0) == modality_labels.unsqueeze(1)
        mask = mask.float() - torch.eye(mask.shape[0], device=mask.device)
        
        # 温度参数
        tau = 0.07
        logits = similarity / tau
        exp_logits = torch.exp(logits) * (1 - torch.eye(logits.shape[0], device=logits.device))
        
        pos_sum = (exp_logits * mask).sum(dim=1)
        all_sum = exp_logits.sum(dim=1)
        
        loss = -torch.log(pos_sum / (all_sum + 1e-8)).mean()
        return loss

class MultimodalTokenProcessor:
    """
    多模态Token处理与比率计算
    """
    def __init__(self, image_size=256, video_frames=240, 
                 audio_sample_rate=16000, audio_duration=10):
        # 图像Token: 16x16 patches
        self.image_tokens = (image_size // 16) ** 2
        
        # 视频Token: 每帧图像Token × 帧数
        self.video_tokens = self.image_tokens * video_frames
        
        # 音频Token: 使用Mel频谱编码
        n_mels = 80
        hop_length = 160
        self.audio_tokens = (audio_sample_rate * audio_duration) // hop_length * n_mels // 64
        
        # 动作Token: 机器人关节状态
        self.action_tokens = 32  # 假设32维动作空间
    
    def compute_token_ratios(self):
        """计算各模态Token占比"""
        video_plus_audio = self.video_tokens + self.audio_tokens
        
        ratios = {
            'image': self.image_tokens / video_plus_audio * 100,
            'video': self.video_tokens / video_plus_audio * 100,
            'audio': self.audio_tokens / video_plus_audio * 100,
        }
        
        print(f"=== 模态Token占比分析 (720p, 10s) ===")
        print(f"图像Token: {self.image_tokens:,} ({ratios['image']:.2f}%)")
        print(f"视频Token: {self.video_tokens:,} ({ratios['video']:.2f}%)")
        print(f"音频Token: {self.audio_tokens:,} ({ratios['audio']:.2f}%)")
        print(f"动作Token: {self.action_tokens}")
        print(f"================================")
        
        return ratios

# 模拟
processor = MultimodalTokenProcessor()
ratios = processor.compute_token_ratios()

import torch
import torch.nn as nn
import torch.nn.functional as F

class AudioVisualSyncModule(nn.Module):
    """
    音视频同步模块: 确保音频事件与视觉事件在时间上对齐
    """
    def __init__(self, latent_dim=1024, num_frames=480, 
                 audio_samples=160000):
        super().__init__()
        self.num_frames = num_frames
        
        # 跨模态时间注意力
        self.cross_modal_attn = nn.MultiheadAttention(
            embed_dim=latent_dim, 
            num_heads=16, 
            batch_first=True
        )
        
        # 时间对齐预测器
        self.sync_predictor = nn.Sequential(
            nn.Linear(latent_dim * 2, 512),
            nn.GELU(),
            nn.Linear(512, 1),
            nn.Sigmoid()
        )
    
    def forward(self, video_features, audio_features):
        """
        Args:
            video_features: [B, T_v, D] 视频帧特征
            audio_features: [B, T_a, D] 音频帧特征
        Returns:
            synced_video: [B, T, D] 对齐后的视频特征
            synced_audio: [B, T, D] 对齐后的音频特征
            sync_scores: [B, T] 各帧同步置信度
        """
        B, T_v, D = video_features.shape
        _, T_a, _ = audio_features.shape
        
        # 统一时间维度: 将音频特征插值到视频帧数
        audio_aligned = audio_features.transpose(1, 2)
        audio_aligned = F.interpolate(
            audio_aligned, 
            size=T_v, 
            mode='linear', 
            align_corners=False
        )
        audio_aligned = audio_aligned.transpose(1, 2)
        
        # 跨模态注意力: 视频查询音频
        synced_video, _ = self.cross_modal_attn(
            query=video_features,
            key=audio_aligned,
            value=audio_aligned
        )
        
        # 跨模态注意力: 音频查询视频
        synced_audio, _ = self.cross_modal_attn(
            query=audio_aligned,
            key=video_features,
            value=video_features
        )
        
        # 计算同步置信度
        combined = torch.cat([synced_video, synced_audio], dim=-1)
        sync_scores = self.sync_predictor(combined).squeeze(-1)
        
        return synced_video, synced_audio, sync_scores


class AudioVisualFlowMatching(nn.Module):
    """
    FLUX 3风格音视频联合流匹配模型
    """
    def __init__(self, latent_dim=1024, num_layers=24):
        super().__init__()
        self.video_encoder = nn.Linear(3 * 256 * 256, latent_dim)
        self.audio_encoder = nn.Linear(80 * 100, latent_dim)  # Mel频谱
        
        # Self-Flow Transformer层
        self.layers = nn.ModuleList([
            nn.TransformerEncoderLayer(
                d_model=latent_dim,
                nhead=16,
                dim_feedforward=4096,
                activation='gelu',
                batch_first=True
            ) for _ in range(num_layers)
        ])
        
        self.video_decoder = nn.Linear(latent_dim, 3 * 256 * 256)
        self.audio_decoder = nn.Linear(latent_dim, 80 * 100)
        self.sync_module = AudioVisualSyncModule(latent_dim)
    
    def forward(self, z, t, text_cond):
        """
        联合预测视频和音频的速度场
        """
        # 文本条件注入
        cond = text_cond.unsqueeze(1).expand(-1, z.shape[1], -1)
        h = z + cond
        
        # Self-Flow Transformer
        for layer in self.layers:
            h = layer(h)
        
        # 拆分视频和音频潜在表示
        video_h = h[:, :480, :]   # 480帧
        audio_h = h[:, 480:, :]   # 音频帧
        
        # 音视频同步
        video_h, audio_h, sync_scores = self.sync_module(video_h, audio_h)
        
        # 预测速度场
        video_velocity = self.video_decoder(video_h)
        audio_velocity = self.audio_decoder(audio_h)
        
        return {
            'video_velocity': video_velocity,
            'audio_velocity': audio_velocity,
            'sync_scores': sync_scores
        }

class ActionDecoder(nn.Module):
    """
    FLUX-mimic动作解码器
    从FLUX 3 backbone的特征表示中解码机器人动作
    """
    def __init__(self, latent_dim=1024, action_dim=32, 
                 chunk_size=16):
        super().__init__()
        self.chunk_size = chunk_size
        
        # 从中间特征提取动作表示
        self.feature_proj = nn.Sequential(
            nn.Linear(latent_dim, 512),
            nn.GELU(),
            nn.Linear(512, 256),
            nn.GELU()
        )
        
        # 时序动作预测(使用因果卷积)
        self.temporal_conv = nn.Conv1d(
            in_channels=256,
            out_channels=256,
            kernel_size=5,
            padding=2,
            padding_mode='replicate'
        )
        
        # 动作头: 输出关节位置和力矩
        self.action_head = nn.Sequential(
            nn.Linear(256, 128),
            nn.GELU(),
            nn.Linear(128, action_dim)
        )
        
        # 结束标志预测
        self.termination_head = nn.Linear(256, 1)
    
    def forward(self, backbone_features):
        """
        Args:
            backbone_features: [B, T, D] FLUX 3 backbone中间特征
        Returns:
            action_chunk: [B, chunk_size, action_dim] 动作序列
            termination: [B, 1] 任务结束概率
        """
        # 特征投影
        h = self.feature_proj(backbone_features)
        
        # 时序建模
        h = h.transpose(1, 2)  # [B, D, T]
        h = self.temporal_conv(h)
        h = h.transpose(1, 2)  # [B, T, D]
        
        # 预测动作序列
        action_chunk = self.action_head(h[:, :self.chunk_size, :])
        termination = torch.sigmoid(self.termination_head(h[:, 0, :]))
        
        return action_chunk, termination


class FLUXMimicPipeline:
    """
    FLUX-mimic完整推理管线
    """
    def __init__(self, flux_backbone, action_decoder, 
                 rt_optimizer=True):
        self.backbone = flux_backbone
        self.action_decoder = action_decoder
        self.rt_optimizer = rt_optimizer
    
    def predict_action(self, camera_input, text_instruction):
        """
        从视觉输入预测机器人动作
        
        Args:
            camera_input: [H, W, 3] 当前帧
            text_instruction: str 任务指令
        Returns:
            action_chunk: [chunk_size, action_dim]
        """
        # 1. 编码视觉输入
        visual_tokens = self.backbone.encode_image(camera_input)
        
        # 2. 文本编码
        text_tokens = self.backbone.encode_text(text_instruction)
        
        # 3. 融合推理 (仅中间特征)
        with torch.no_grad():
            backbone_features = self.backbone.extract_intermediate_features(
                visual_tokens, text_tokens
            )
        
        # 4. 动作解码
        action_chunk, _ = self.action_decoder(backbone_features)
        
        return action_chunk
    
    def optimize_latency(self):
        """
        优化延迟: 使用TensorRT或量化
        """
        if self.rt_optimizer:
            print("Enabling real-time optimization...")
            print(f"  - Backbone → representation: <80ms")
            print(f"  - Action decoder: <15ms")
            print(f"  - Inter-process latency: <6ms")
            print(f"  - End-to-end: ~101ms")


# 使用示例
backbone = load_flux3_backbone()
decoder = ActionDecoder(latent_dim=1024, action_dim=32, chunk_size=16)
pipeline = FLUXMimicPipeline(backbone, decoder)

# 工厂场景: 软体零件装配
camera_input = load_camera_frame("factory_kitting_scene.jpg")
action = pipeline.predict_action(
    camera_input=camera_input,
    text_instruction="Pick up the rubber seal and place it in the tray slot 3"
)
print(f"Predicted action chunk: {action.shape}")
print(f"  - Joint positions: {action[0, :7].tolist()}")
print(f"  - Gripper: {action[0, 7].item():.3f}")

import requests
import json
import base64
from pathlib import Path

class FLUX3VideoAPI:
    """
    FLUX 3 Video API客户端
    """
    def __init__(self, api_key, base_url="https://api.bfl.ai/v1"):
        self.api_key = api_key
        self.base_url = base_url
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
    
    def generate_video(self, prompt, mode="text-to-video", 
                       aspect_ratio="16:9", duration=10, 
                       resolution="720p", generate_audio=True,
                       safety_tolerance=2, draft=False):
        """
        生成带原生音频的视频
        
        Args:
            prompt: 文本描述
            mode: 生成模式
            aspect_ratio: 宽高比
            duration: 视频时长(秒), 最大20
            resolution: 分辨率
            generate_audio: 是否生成原生音频
            safety_tolerance: 安全过滤级别(0-5)
            draft: 是否草稿模式(更快但质量较低)
        """
        payload = {
            "prompt": prompt,
            "mode": mode,
            "aspect_ratio": aspect_ratio,
            "duration": duration,
            "resolution": resolution,
            "generate_audio": generate_audio,
            "safety_tolerance": safety_tolerance,
            "draft": draft
        }
        
        response = requests.post(
            f"{self.base_url}/flux-3-video",
            headers=self.headers,
            json=payload
        )
        response.raise_for_status()
        return response.json()["result"]
    
    def image_to_video(self, image_path, prompt=None, 
                       duration=10, **kwargs):
        """
        图像到视频生成
        """
        # 编码图像为base64
        with open(image_path, "rb") as f:
            image_b64 = base64.b64encode(f.read()).decode("utf-8")
        
        payload = {
            "mode": "image-to-video",
            "image_data": image_b64,
            "prompt": prompt or "Animate this image",
            "duration": duration,
            **kwargs
        }
        
        response = requests.post(
            f"{self.base_url}/flux-3-video",
            headers=self.headers,
            json=payload
        )
        response.raise_for_status()
        return response.json()["result"]
    
    def video_to_video(self, video_path, prompt, **kwargs):
        """
        视频到视频转换
        """
        with open(video_path, "rb") as f:
            video_b64 = base64.b64encode(f.read()).decode("utf-8")
        
        payload = {
            "mode": "video-to-video",
            "video_data": video_b64,
            "prompt": prompt,
            **kwargs
        }
        
        response = requests.post(
            f"{self.base_url}/flux-3-video",
            headers=self.headers,
            json=payload
        )
        response.raise_for_status()
        return response.json()["result"]
    
    def check_status(self, task_id):
        """检查生成任务状态"""
        response = requests.get(
            f"{self.base_url}/tasks/{task_id}",
            headers=self.headers
        )
        return response.json()


# 使用示例
api = FLUX3VideoAPI(api_key="your-api-key")

# 文本到视频(带原生音频)
result = api.generate_video(
    prompt="A majestic eagle soaring over a mountain range at sunset, "
           "wind rushing past its wings, with distant thunder rumbling",
    mode="text-to-video",
    duration=15,
    resolution="720p",
    generate_audio=True,
    aspect_ratio="16:9"
)
print(f"Task ID: {result['task_id']}")
print(f"Status: {result['status']}")
# 输出: Task ID: flux-3-xxxxxxxxxxxx
#       Status: pending

# 图像到视频
result = api.image_to_video(
    image_path="concept_art.png",
    prompt="The character comes to life, looking around curiously",
    duration=10
)

# 场景1: 多语言对话生成
def generate_multilingual_dialogue(api, text, language="zh"):
    """生成多语言对话视频"""
    prompts = {
        "zh": f"一个中国演员正在说: '{text}'，面部表情自然生动",
        "en": f"An actor speaking: '{text}' with natural facial expressions",
        "ja": f"俳優が '{text}' と言っている、自然な表情で",
        "de": f"Ein Schauspieler sagt: '{text}' mit natürlichen Gesichtsausdrücken"
    }
    
    result = api.generate_video(
        prompt=prompts[language],
        duration=8,
        generate_audio=True
    )
    return result

# 场景2: 视频续写
def video_continuation(api, input_video, continuation_prompt):
    """基于输入视频进行续写，保持角色和场景一致"""
    result = api.video_to_video(
        video_path=input_video,
        prompt=continuation_prompt,
        mode="video-to-video",
        duration=10
    )
    return result

# 场景3: 关键帧过渡
def keyframe_transition(api, keyframe_1, keyframe_2, 
                        transition_style="smooth"):
    """在两个关键帧之间生成平滑过渡"""
    result = api.generate_video(
        prompt=f"Generate a smooth {transition_style} transition "
               f"between these two keyframes",
        mode="keyframe-to-video",
        duration=5
    )
    return result

class Flux3TrainingConfig:
    """
    FLUX 3训练配置估算
    """
    def __init__(self):
        # 数据规模
        self.general_video_hours = 50_000_000  # 5000万小时
        self.manipulation_video_hours = 500_000  # 50万小时
        self.image_count = 2_000_000_000  # 20亿张图像
        self.audio_hours = 10_000_000  # 1000万小时
        
        # 模型架构
        self.num_parameters = 30_000_000_000  # 300亿参数
        self.num_layers = 48
        self.hidden_dim = 8192
        self.num_heads = 64
        
        # 训练配置
        self.batch_size = 2048
        self.learning_rate = 1e-4
        self.warmup_steps = 10_000
        self.total_steps = 2_000_000
        
        # 硬件配置
        self.num_gpus = 8192  # 假设使用H100
        self.training_days = 60
    
    def compute_cost_breakdown(self):
        """计算各模态训练成本占比"""
        # 视频: 主要成本
        video_cost = 95.0  # 占总计算95%
        image_cost = 4.5   # 图像
        audio_cost = 0.5   # 音频(不足0.5%的token)
        
        print("=== FLUX 3 Training Cost Analysis ===")
        print(f"Model Parameters: {self.num_parameters:,}")
        print(f"Training Data:")
        print(f"  - General Video: {self.general_video_hours:,} hours")
        print(f"  - Manipulation: {self.manipulation_video_hours:,} hours")
        print(f"  - Images: {self.image_count:,}")
        print(f"  - Audio: {self.audio_hours:,} hours")
        print(f"\nCompute Cost by Modality:")
        print(f"  - Video: {video_cost}%")
        print(f"  - Image: {image_cost}%")
        print(f"  - Audio: {audio_cost}%")
        print(f"\nEstimated Hardware: {self.num_gpus} GPUs")
        print(f"Estimated Duration: {self.training_days} days")


config = Flux3TrainingConfig()
config.compute_cost_breakdown()

def simulate_training_dynamics():
    """
    模拟加入动作预测后的训练动态
    """
    import numpy as np
    
    # 训练步骤
    steps = np.arange(5000)
    
    # 加入动作预测的时刻
    action_insertion_step = 1000
    
    # 视频生成质量(归一化评分)
    quality = np.ones_like(steps, dtype=float)
    
    # 加入动作预测前: 质量稳定
    quality[:action_insertion_step] = 1.0 + 0.01 * np.random.randn(action_insertion_step)
    
    # 加入动作预测后: 质量下降, 然后恢复
    initial_drop = 0.10  # 10%下降
    recovery_steps = 3500  # 恢复步数
    
    for i in range(action_insertion_step, len(steps)):
        progress = min(1.0, (i - action_insertion_step) / recovery_steps)
        quality[i] = 1.0 - initial_drop * (1 - progress) + 0.01 * np.random.randn()
    
    print("=== Training Dynamics: Action Insertion ===")
    print(f"Step {action_insertion_step}: Action prediction added")
    print(f"  - Quality drops by {initial_drop*100}%")
    print(f"Step {action_insertion_step + recovery_steps}:")
    print(f"  - Quality fully recovered")
    print(f"  - Model now predicts actions without quality loss")
    print(f"  - Key insight: Video generation and action prediction")
    print(f"    share the same backbone without permanent capacity cost")
    
    return steps, quality

steps, quality = simulate_training_dynamics()