LD-FPG-main/conditional_generation/param_conditional_diffusion.yaml

# ===================================================================
# Conditional Diffusion Model Configuration
# ===================================================================

# --- Data Paths ---
# These paths point to the HDF5 files produced by the chebnet_conditional_setup.py script.
data:
  # Path to the HDF5 file containing the dynamic, frame-by-frame pooled embeddings.
  embeddings_h5_path: "latent_reps/pooled_embeddings.h5"
  
  # Path to the HDF5 file containing the static reference conditioners (z_ref).
  conditioners_h5_path: "latent_reps/reference_conditioners.h5"
  
  # Template for the group name within the HDF5 files. '{}' is replaced by the system ID.
  group_key_template: "system_{}"
  
  # Name of the dataset within each group for the embeddings.
  embedding_dataset_name: "embeddings"
  
  # Name of the dataset within each group for the single reference conditioner.
  conditioner_dataset_name: "z_ref"
  
  # Name of the dataset within each group for the ensemble of conditioners.
  conditioner_ensemble_dataset_name: "z_ref_ensemble"

# --- Output Directory ---
# Directory to save generated embeddings and model checkpoints.
output_dir: "conditional_diffusion_output"


# --- Model & Training Parameters ---
parameters:
  # --- Augmentation Control ---
  # If true, the script will load the conditioner ensemble and randomly sample from it during training.
  # This makes the model more robust to noise in the conditioner.
  use_conditioner_ensemble: true

  # --- Core Training ---
  num_epochs: 100
  batch_size: 128
  learning_rate: 1e-5
  
  # Number of new embedding samples to generate for each conditioner.
  num_gen: 1000
  
  # Frequency (in epochs) to save a model checkpoint.
  save_interval: 2000
  
  # --- Model Architecture ---
  model_type: "mlp_conditional_cnn_encoder" # For reference
  hidden_dim: 2048              # Width of the main MLP layers
  conditioner_encoded_dim: 256  # Dimension of the fixed-size vector from the CNN encoder
  
  # --- Diffusion Schedule ---
  scheduler: "linear"
  diffusion_steps: 1000
  beta_start: 0.0001
  beta_end: 0.02