generative_ai_advanced.mbt

// 生成式AI高级（v54.0.0）
// 扩散模型、GAN、VAE、流模型、自回归模型、能量模型

///|
/// 生成式AI任务类型
pub(all) enum GenerativeAITaskType {
  DiffusionModel
  GAN
  VAE
  FlowModel
  AutoregressiveModel
  EnergyBasedModel
  TextToImage
  ImageToImage
  Unknown
} derive(Eq)

///|
/// 扩散模型类型
pub(all) enum DiffusionModelType {
  DDPM
  DDIM
  ScoreBased
  LatentDiffusion
  StableDiffusion
  ControlNet
  InstructPix2Pix
  Unknown
} derive(Eq)

///|
/// GAN类型
pub(all) enum GANType {
  VanillaGAN
  DCGAN
  WGAN
  WGANGP
  StyleGAN
  StyleGAN2
  StyleGAN3
  CycleGAN
  Pix2Pix
  Unknown
} derive(Eq)

///|
/// VAE类型
pub(all) enum VAEType {
  VanillaVAE
  BetaVAE
  CVAE
  VQVAE
  VQVAE2
  GumbelVAE
  AdversarialAE
  Unknown
} derive(Eq)

///|
/// 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
  image_size : Int
  in_channels : Int
  out_channels : Int
  latent_dim : Int
  num_res_blocks : Int
  base_channels : Int
  channel_mults : Array[Int]
  attention_resolutions : Array[Int]
  dropout : Double
  use_ema : Bool
  ema_decay : Double
  use_cfg : Bool
  cfg_scale : Double
  classifier_free_guidance : Bool
}

///|
/// GAN配置
pub(all) struct GANConfig {
  gan_type : GANType
  latent_dim : Int
  image_size : Int
  in_channels : Int
  generator_channels : Int
  discriminator_channels : Int
  num_layers : Int
  learning_rate_g : Double
  learning_rate_d : Double
  beta1 : Double
  beta2 : Double
  use_wasserstein : Bool
  gradient_penalty : Double
  use_spectral_norm : Bool
  use_ema : Bool
  ema_decay : Double
  style_mixing_prob : Double
  truncation_psi : Double
}

///|
/// VAE配置
pub(all) struct VAEConfig {
  vae_type : VAEType
  latent_dim : Int
  image_size : Int
  in_channels : Int
  hidden_dims : Array[Int]
  learning_rate : Double
  beta : Double
  gamma : Double
  use_kl_annealing : Bool
  kl_annealing_steps : Int
  num_embeddings : Int
  commitment_cost : Double
  decay : Double
  use_adversarial_loss : Bool
  adversarial_weight : Double
  perceptual_weight : Double
}

///|
/// 生成结果
pub(all) struct GenerationResult {
  generated_samples : Array[Array[Double]]
  num_samples : Int
  image_size : Int
  num_channels : Int
  model_type : String
  inference_time_ms : Double
  fid_score : Double
  is_score : Double
  precision : Double
  recall : Double
}

///|
/// 训练状态
pub(all) struct TrainingState {
  epoch : Int
  step : Int
  generator_loss : Double
  discriminator_loss : Double
  reconstruction_loss : Double
  kl_loss : Double
  diffusion_loss : Double
  learning_rate : Double
  elapsed_time_s : Double
  best_metric : Double
  is_best : Bool
}

///|
/// 采样配置
pub(all) struct SamplingConfig {
  num_samples : Int
  num_inference_steps : Int
  guidance_scale : Double
  seed : Int
  use_ddim : Bool
  eta : Double
  use_parallel : Bool
  batch_size : Int
  output_dir : String
  save_intermediate : Bool
  intermediate_steps : Array[Int]
}

///|
/// 创建默认扩散模型配置
pub fn create_default_diffusion_config() -> DiffusionConfig {
  DiffusionConfig::{
    model_type: DiffusionModelType::DDPM,
    num_timesteps: 1000,
    beta_start: 0.0001,
    beta_end: 0.02,
    beta_schedule: "linear",
    image_size: 64,
    in_channels: 3,
    out_channels: 3,
    latent_dim: 128,
    num_res_blocks: 2,
    base_channels: 128,
    channel_mults: [1, 2, 2, 2],
    attention_resolutions: [16],
    dropout: 0.1,
    use_ema: true,
    ema_decay: 0.9999,
    use_cfg: false,
    cfg_scale: 7.5,
    classifier_free_guidance: false,
  }
}

///|
/// 创建默认GAN配置
pub fn create_default_gan_config() -> GANConfig {
  GANConfig::{
    gan_type: GANType::StyleGAN2,
    latent_dim: 512,
    image_size: 256,
    in_channels: 3,
    generator_channels: 512,
    discriminator_channels: 512,
    num_layers: 8,
    learning_rate_g: 0.002,
    learning_rate_d: 0.002,
    beta1: 0.0,
    beta2: 0.99,
    use_wasserstein: true,
    gradient_penalty: 10.0,
    use_spectral_norm: true,
    use_ema: true,
    ema_decay: 0.999,
    style_mixing_prob: 0.9,
    truncation_psi: 0.7,
  }
}

///|
/// 创建默认VAE配置
pub fn create_default_vae_config() -> VAEConfig {
  VAEConfig::{
    vae_type: VAEType::BetaVAE,
    latent_dim: 128,
    image_size: 64,
    in_channels: 3,
    hidden_dims: [32, 64, 128, 256],
    learning_rate: 0.001,
    beta: 4.0,
    gamma: 1000.0,
    use_kl_annealing: true,
    kl_annealing_steps: 10000,
    num_embeddings: 512,
    commitment_cost: 0.25,
    decay: 0.99,
    use_adversarial_loss: false,
    adversarial_weight: 0.01,
    perceptual_weight: 10.0,
  }
}

///|
/// 创建默认采样配置
pub fn create_default_sampling_config() -> SamplingConfig {
  SamplingConfig::{
    num_samples: 16,
    num_inference_steps: 50,
    guidance_scale: 7.5,
    seed: 42,
    use_ddim: true,
    eta: 0.0,
    use_parallel: false,
    batch_size: 4,
    output_dir: "./output",
    save_intermediate: false,
    intermediate_steps: [],
  }
}

///|
/// 生成随机噪声
pub fn generate_noise(
  num_samples : Int,
  latent_dim : Int,
  seed : Int,
) -> Array[Array[Double]] {
  let samples : Array[Array[Double]] = []
  let mut state = seed

  for _ in 0..<num_samples {
    let sample : Array[Double] = []
    for _ in 0..<latent_dim {
      state = (state * 11035 + 12345) % 1000000
      let noise = (state % 1000).to_double() / 1000.0 * 2.0 - 1.0
      sample.push(noise)
    }
    samples.push(sample)
  }

  samples
}

///|
/// 模拟扩散采样（简化版）
pub fn simulate_diffusion_sampling(
  config : DiffusionConfig,
  sampling_config : SamplingConfig,
) -> GenerationResult {
  let samples : Array[Array[Double]] = []
  let image_size = config.image_size
  let num_channels = config.in_channels
  let pixels_per_image = image_size * image_size * num_channels

  let mut state = sampling_config.seed
  for _ in 0..<sampling_config.num_samples {
    let sample : Array[Double] = []
    for _ in 0..<pixels_per_image {
      state = (state * 11035 + 12345) % 1000000
      let pixel = (state % 1000).to_double() / 1000.0
      sample.push(pixel)
    }
    samples.push(sample)
  }

  GenerationResult::{
    generated_samples: samples,
    num_samples: sampling_config.num_samples,
    image_size,
    num_channels,
    model_type: "diffusion",
    inference_time_ms: sampling_config.num_inference_steps.to_double() * 10.0,
    fid_score: 25.0,
    is_score: 150.0,
    precision: 0.7,
    recall: 0.65,
  }
}

///|
/// 模拟GAN生成（简化版）
pub fn simulate_gan_generation(
  config : GANConfig,
  sampling_config : SamplingConfig,
) -> GenerationResult {
  let noise = generate_noise(
    sampling_config.num_samples,
    config.latent_dim,
    sampling_config.seed,
  )
  let samples : Array[Array[Double]] = []
  let image_size = config.image_size
  let num_channels = config.in_channels
  let pixels_per_image = image_size * image_size * num_channels

  for n in noise {
    let sample : Array[Double] = []
    for i in 0..<pixels_per_image {
      let latent_idx = i % config.latent_dim
      let pixel = (n[latent_idx] + 1.0) / 2.0
      sample.push(pixel)
    }
    samples.push(sample)
  }

  GenerationResult::{
    generated_samples: samples,
    num_samples: sampling_config.num_samples,
    image_size,
    num_channels,
    model_type: "gan",
    inference_time_ms: 50.0,
    fid_score: 15.0,
    is_score: 200.0,
    precision: 0.75,
    recall: 0.7,
  }
}

///|
/// 模拟VAE重构（简化版）
pub fn simulate_vae_reconstruction(
  config : VAEConfig,
  input_samples : Array[Array[Double]],
) -> GenerationResult {
  let samples : Array[Array[Double]] = []
  let image_size = config.image_size
  let num_channels = config.in_channels

  for input in input_samples {
    let sample : Array[Double] = []
    for i in 0..<input.length() {
      // 简化的重构：添加噪声
      let noise = 0.05
      let reconstructed = input[i] + noise
      let clamped = if reconstructed > 1.0 {
        1.0
      } else if reconstructed < 0.0 {
        0.0
      } else {
        reconstructed
      }
      sample.push(clamped)
    }
    samples.push(sample)
  }

  GenerationResult::{
    generated_samples: samples,
    num_samples: input_samples.length(),
    image_size,
    num_channels,
    model_type: "vae",
    inference_time_ms: 20.0,
    fid_score: 35.0,
    is_score: 100.0,
    precision: 0.6,
    recall: 0.55,
  }
}

///|
/// 生成生成式AI报告
pub fn generative_ai_report_to_string(
  task_type : GenerativeAITaskType,
  result : GenerationResult,
  config_description : String,
) -> String {
  let mut report = "## 生成式AI高级报告\n\n"

  report = report + "### 任务信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 任务类型 | " +
    (match task_type {
      GenerativeAITaskType::DiffusionModel => "扩散模型"
      GenerativeAITaskType::GAN => "GAN"
      GenerativeAITaskType::VAE => "VAE"
      GenerativeAITaskType::FlowModel => "流模型"
      GenerativeAITaskType::AutoregressiveModel => "自回归模型"
      GenerativeAITaskType::EnergyBasedModel => "能量模型"
      GenerativeAITaskType::TextToImage => "文生图"
      GenerativeAITaskType::ImageToImage => "图生图"
      _ => "未知"
    }) +
    " |\n"
  report = report + "| 模型类型 | " + result.model_type + " |\n"
  report = report +
    "| 生成样本数 | " +
    result.num_samples.to_string() +
    " |\n"
  report = report +
    "| 图像尺寸 | " +
    result.image_size.to_string() +
    "x" +
    result.image_size.to_string() +
    " |\n"
  report = report + "| 通道数 | " + result.num_channels.to_string() + " |\n"
  report = report +
    "| 推理时间 | " +
    result.inference_time_ms.to_string() +
    " ms |\n"

  report = report + "\n### 质量指标\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report + "| FID分数 | " + result.fid_score.to_string() + " |\n"
  report = report + "| IS分数 | " + result.is_score.to_string() + " |\n"
  report = report +
    "| 精度 | " +
    (result.precision * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 召回率 | " +
    (result.recall * 100.0).to_string() +
    "% |\n"

  report = report + "\n### 配置\n\n"
  report = report + config_description + "\n"

  report
}
src\process\generative_ai_advanced\generative_ai_advanced.mbt

81.48%
