vlm_multimodal.mbt

// 视觉语言模型与多模态融合（v38.0.0）
// VLM、多模态融合、扩散模型高级、生成式AI

///|
/// 视觉语言模型类型
pub(all) enum VisionLanguageModelType {
  CLIP
  BLIP
  ALIGN
  FLAVA
  CoCa
  GIT
  LLaVA
  QwenVL
  InternVL
  Unknown
} derive(Eq)

///|
/// 多模态融合策略
pub(all) enum MultimodalFusionStrategy {
  EarlyFusion
  LateFusion
  HybridFusion
  CrossAttention
  TensorFusion
  GatedFusion
  Unknown
} derive(Eq)

///|
/// 扩散模型类型
pub(all) enum DiffusionModelType {
  StableDiffusion
  DALLE
  Imagen
  MidjourneyStyle
  ControlNet
  LoRA
  DreamBooth
  TextualInversion
  Unknown
} derive(Eq)

///|
/// 生成式AI任务类型
pub(all) enum GenerativeAITaskType {
  TextToImage
  ImageToText
  ImageEditing
  ImageTranslation
  StyleTransfer
  Inpainting
  Outpainting
  SuperResolution
  Unknown
} derive(Eq)

///|
/// 视觉语言模型配置
pub(all) struct VLMConfig {
  model_type : VisionLanguageModelType
  image_encoder_size : Int
  text_encoder_size : Int
  projection_dim : Int
  temperature : Double
  use_contrastive_loss : Bool
  use_matching_loss : Bool
  use_captioning_loss : Bool
}

///|
/// 多模态融合配置
pub(all) struct FusionConfig {
  strategy : MultimodalFusionStrategy
  num_modalities : Int
  hidden_dim : Int
  num_attention_heads : Int
  num_layers : Int
  dropout_rate : Double
  use_gating : Bool
}

///|
/// 扩散模型配置
pub(all) struct DiffusionConfig {
  model_type : DiffusionModelType
  num_timesteps : Int
  beta_start : Double
  beta_end : Double
  beta_schedule : String
  guidance_scale : Double
  num_inference_steps : Int
  image_size : Int
  use_ddim : Bool
  use_clip_guidance : Bool
}

///|
/// 生成式AI配置
pub(all) struct GenerativeAIConfig {
  task_type : GenerativeAITaskType
  vlm_config : VLMConfig
  diffusion_config : DiffusionConfig
  fusion_config : FusionConfig
  max_length : Int
  num_beams : Int
  temperature : Double
  top_p : Double
  top_k : Int
}

///|
/// 视觉文本嵌入
pub(all) struct VisionTextEmbedding {
  image_embedding : Array[Double]
  text_embedding : Array[Double]
  similarity : Double
  projection_dim : Int
}

///|
/// 多模态融合结果
pub(all) struct FusionResult {
  fused_representation : Array[Double]
  modality_weights : Array[Double]
  fusion_strategy : MultimodalFusionStrategy
  hidden_dim : Int
}

///|
/// 扩散生成结果
pub(all) struct DiffusionResult {
  image : @types.Image
  num_steps : Int
  guidance_scale : Double
  prompt : String
  negative_prompt : String
  seed : Int
  generation_time_ms : Double
}

///|
/// 生成式AI结果
pub(all) struct GenerativeAIResult {
  task_type : GenerativeAITaskType
  image : @types.Image
  text : String
  confidence : Double
  generation_time_ms : Double
  num_iterations : Int
}

///|
/// 创建默认VLM配置
pub fn create_default_vlm_config() -> VLMConfig {
  VLMConfig::{
    model_type: VisionLanguageModelType::CLIP,
    image_encoder_size: 768,
    text_encoder_size: 768,
    projection_dim: 512,
    temperature: 0.07,
    use_contrastive_loss: true,
    use_matching_loss: true,
    use_captioning_loss: false,
  }
}

///|
/// 创建默认融合配置
pub fn create_default_fusion_config() -> FusionConfig {
  FusionConfig::{
    strategy: MultimodalFusionStrategy::CrossAttention,
    num_modalities: 2,
    hidden_dim: 768,
    num_attention_heads: 12,
    num_layers: 6,
    dropout_rate: 0.1,
    use_gating: true,
  }
}

///|
/// 创建默认扩散配置
pub fn create_default_diffusion_config() -> DiffusionConfig {
  DiffusionConfig::{
    model_type: DiffusionModelType::StableDiffusion,
    num_timesteps: 1000,
    beta_start: 0.00085,
    beta_end: 0.012,
    beta_schedule: "scaled_linear",
    guidance_scale: 7.5,
    num_inference_steps: 50,
    image_size: 512,
    use_ddim: true,
    use_clip_guidance: false,
  }
}

///|
/// 创建默认生成式AI配置
pub fn create_default_genai_config() -> GenerativeAIConfig {
  GenerativeAIConfig::{
    task_type: GenerativeAITaskType::TextToImage,
    vlm_config: create_default_vlm_config(),
    diffusion_config: create_default_diffusion_config(),
    fusion_config: create_default_fusion_config(),
    max_length: 77,
    num_beams: 1,
    temperature: 1.0,
    top_p: 1.0,
    top_k: 50,
  }
}

///|
/// 计算图像文本相似度（简化版）
pub fn compute_image_text_similarity(
  image : @types.Image,
  text : String,
  config : VLMConfig,
) -> VisionTextEmbedding {
  let w = image.width
  let h = image.height
  let total = w * h

  // 简化的图像特征提取：统计特征
  let mut sum = 0.0
  let mut sum_sq = 0.0
  for i in 0..<total {
    let v = image.data[i].to_double()
    sum += v
    sum_sq += v * v
  }
  let mean = sum / total.to_double()
  let variance = sum_sq / total.to_double() - mean * mean
  let std_dev = if variance > 0.0 { variance.sqrt() } else { 0.0 }

  // 简化的文本特征：字符统计
  let text_len = text.length().to_double()
  let text_hash = text.length().to_double()

  // 生成嵌入向量
  let image_embedding : Array[Double] = []
  let text_embedding : Array[Double] = []
  for i in 0..<config.projection_dim {
    let img_val = mean * 0.01 + std_dev * 0.005 + (i % 10).to_double() * 0.001
    let txt_val = text_len * 0.01 +
      text_hash * 0.0001 +
      (i % 10).to_double() * 0.001
    image_embedding.push(img_val)
    text_embedding.push(txt_val)
  }

  // 计算余弦相似度
  let mut dot = 0.0
  let mut norm_img = 0.0
  let mut norm_txt = 0.0
  for i in 0..<config.projection_dim {
    dot += image_embedding[i] * text_embedding[i]
    norm_img += image_embedding[i] * image_embedding[i]
    norm_txt += text_embedding[i] * text_embedding[i]
  }
  let similarity = if norm_img > 0.0 && norm_txt > 0.0 {
    dot / (norm_img.sqrt() * norm_txt.sqrt())
  } else {
    0.0
  }

  VisionTextEmbedding::{
    image_embedding,
    text_embedding,
    similarity,
    projection_dim: config.projection_dim,
  }
}

///|
/// 多模态融合（简化版）
pub fn fuse_modalities(
  image_features : Array[Double],
  text_features : Array[Double],
  config : FusionConfig,
) -> FusionResult {
  let dim = if image_features.length() > text_features.length() {
    image_features.length()
  } else {
    text_features.length()
  }

  let fused : Array[Double] = []
  let weights : Array[Double] = []

  for i in 0..<dim {
    let img_val = if i < image_features.length() {
      image_features[i]
    } else {
      0.0
    }
    let txt_val = if i < text_features.length() {
      text_features[i]
    } else {
      0.0
    }

    let fused_val = match config.strategy {
      MultimodalFusionStrategy::EarlyFusion => (img_val + txt_val) / 2.0
      MultimodalFusionStrategy::LateFusion => img_val * 0.6 + txt_val * 0.4
      MultimodalFusionStrategy::HybridFusion =>
        (img_val + txt_val) * 0.5 + img_val * 0.25
      MultimodalFusionStrategy::CrossAttention => img_val * 0.7 + txt_val * 0.3
      MultimodalFusionStrategy::TensorFusion => img_val * txt_val
      MultimodalFusionStrategy::GatedFusion => img_val * 0.5 + txt_val * 0.5
      MultimodalFusionStrategy::Unknown => (img_val + txt_val) / 2.0
    }

    fused.push(fused_val)
    weights.push(0.5)
  }

  FusionResult::{
    fused_representation: fused,
    modality_weights: weights,
    fusion_strategy: config.strategy,
    hidden_dim: dim,
  }
}

///|
/// 扩散模型生成（简化版）
pub fn diffusion_generate(
  prompt : String,
  negative_prompt : String,
  config : DiffusionConfig,
  seed : Int,
) -> DiffusionResult {
  let size = config.image_size
  let _ = size * size
  let out : Array[Byte] = []

  // 简化的扩散生成：基于prompt的程序化图像
  let mut state = seed
  let prompt_hash = prompt.length().to_double()

  for y in 0..<size {
    for x in 0..<size {
      state = (state * 11035 + 12345) % 1000000
      let noise = (state % 1000).to_double() / 1000.0

      // 简化的去噪过程
      let t = (x + y).to_double() / (2.0 * size.to_double())
      let denoised = noise * (1.0 - t) + t * 128.0

      // 基于prompt的颜色调制
      let r = (denoised + prompt_hash * 0.1) % 256.0
      let g = (denoised + 50.0) % 256.0
      let b = (denoised + 100.0) % 256.0

      out.push(r.to_int().to_byte())
      out.push(g.to_int().to_byte())
      out.push(b.to_int().to_byte())
    }
  }

  DiffusionResult::{
    image: @types.Image::{
      width: size,
      height: size,
      channels: 3,
      data: Bytes::from_array(out),
    },
    num_steps: config.num_inference_steps,
    guidance_scale: config.guidance_scale,
    prompt,
    negative_prompt,
    seed,
    generation_time_ms: 0.0,
  }
}

///|
/// 生成式AI文本生成图像
pub fn text_to_image(
  prompt : String,
  config : GenerativeAIConfig,
  seed : Int,
) -> GenerativeAIResult {
  let diffusion_result = diffusion_generate(
    prompt,
    "",
    config.diffusion_config,
    seed,
  )

  GenerativeAIResult::{
    task_type: GenerativeAITaskType::TextToImage,
    image: diffusion_result.image,
    text: prompt,
    confidence: 0.7,
    generation_time_ms: diffusion_result.generation_time_ms,
    num_iterations: config.diffusion_config.num_inference_steps,
  }
}

///|
/// 生成式AI图像生成文本（简化版）
pub fn image_to_text(
  image : @types.Image,
  config : GenerativeAIConfig,
) -> GenerativeAIResult {
  ignore(config)
  let w = image.width
  let h = image.height
  let total = w * h

  // 简化的图像描述生成
  let mut sum = 0.0
  for i in 0..<total {
    sum += image.data[i].to_double()
  }
  let mean = sum / total.to_double()

  let brightness = if mean < 60.0 {
    "dark"
  } else if mean < 120.0 {
    "dimly lit"
  } else if mean < 180.0 {
    "well-lit"
  } else {
    "bright"
  }

  let caption = "A " +
    brightness +
    " image with " +
    w.to_string() +
    "x" +
    h.to_string() +
    " resolution."

  GenerativeAIResult::{
    task_type: GenerativeAITaskType::ImageToText,
    image,
    text: caption,
    confidence: 0.6,
    generation_time_ms: 0.0,
    num_iterations: 1,
  }
}

///|
/// 生成VLM报告
pub fn vlm_report_to_string(
  embedding : VisionTextEmbedding,
  model_type : VisionLanguageModelType,
) -> String {
  let mut report = "## 视觉语言模型报告\n\n"
  report = report + "### 模型信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 模型类型 | " +
    (match model_type {
      VisionLanguageModelType::CLIP => "CLIP"
      VisionLanguageModelType::BLIP => "BLIP"
      VisionLanguageModelType::ALIGN => "ALIGN"
      VisionLanguageModelType::LLaVA => "LLaVA"
      VisionLanguageModelType::QwenVL => "Qwen-VL"
      VisionLanguageModelType::InternVL => "InternVL"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 投影维度 | " +
    embedding.projection_dim.to_string() +
    " |\n"
  report = report +
    "| 图文相似度 | " +
    (embedding.similarity * 100.0).to_string() +
    "% |\n"

  report
}

///|
/// 生成多模态融合报告
pub fn fusion_report_to_string(result : FusionResult) -> String {
  let mut report = "## 多模态融合报告\n\n"
  report = report + "### 融合信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 融合策略 | " +
    (match result.fusion_strategy {
      MultimodalFusionStrategy::EarlyFusion => "早期融合"
      MultimodalFusionStrategy::LateFusion => "晚期融合"
      MultimodalFusionStrategy::HybridFusion => "混合融合"
      MultimodalFusionStrategy::CrossAttention => "交叉注意力"
      MultimodalFusionStrategy::TensorFusion => "张量融合"
      MultimodalFusionStrategy::GatedFusion => "门控融合"
      _ => "未知"
    }) +
    " |\n"
  report = report + "| 隐藏维度 | " + result.hidden_dim.to_string() + " |\n"
  report = report +
    "| 模态数量 | " +
    result.modality_weights.length().to_string() +
    " |\n"

  report
}
src\process\vlm_multimodal\vlm_multimodal.mbt

80.56%
