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# 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
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