Training Pipeline

Training Pipeline

This document outlines the training process flow and methodology implementation details.

Training Methods

DDPM Training Flow

The DDPM trainer (methods/ddpm_trainer.py) implements:

1. Velocity Prediction

   • v-prediction parameterization
   • Enhanced high-frequency details
   • Reduced color shifting

2. SNR Weighting

   • Signal-to-noise ratio gamma: 5.0
   • Better detail preservation
   • Improved stability

3. Zero Terminal SNR

   • Infinite noise approximation
   • High-frequency artifact reduction
   • Enhanced contrast

Flow Matching Implementation

The flow matching trainer (methods/flow_matching_trainer.py) offers:

1. Optimal Transport

   • Direct velocity field learning
   • Faster convergence rates
   • Lower memory overhead

2. Time Sampling

   • Logit-normal distribution
   • Better coverage of time steps
   • Reduced training variance

Pipeline Stages

1. Data Loading

graph TD
    A[Dataset Load] --> B[Cache Check]
    B -->|Cache Hit| C[Load Cached]
    B -->|Cache Miss| D[Process New]
    D --> E[Cache Results]
    C --> F[Batch Formation]
    E --> F

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2. Training Loop

graph TD
    A[Forward Pass] --> B[Loss Calculation]
    B --> C[Gradient Accumulation]
    C -->|Steps Complete| D[Optimizer Step]
    D --> E[Memory Cleanup]
    E --> F[Metrics Logging]

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3. Validation Pipeline

graph TD
    A[Generate Samples] --> B[Quality Metrics]
    B --> C[Log Images]
    C --> D[Archive Results]

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Performance Optimizations

Memory Management

• Dynamic tensor offloading
• Gradient checkpointing
• Activation caching
• Async memory transfers

Throughput Enhancement

• Mixed precision training
• Optimal batch formation
• Worker thread management
• Pipeline prefetching

Custom Training Extensions

Adding New Training Methods

1. Extend the base trainer:

from .base import BaseTrainer

class CustomTrainer(BaseTrainer):
    def training_step(self, batch):
        # Implement custom training logic

2. Register in configuration:

@dataclass
class CustomConfig:
    """Custom training configuration."""
    enabled: bool = False
    # Add custom parameters

Custom Loss Functions

1. Define in trainer:

def custom_loss(self, pred, target):
    # Implement custom loss

2. Update training step:

loss = self.custom_loss(predictions, targets)

Monitoring & Analysis

Key Metrics

• Loss curves
• Learning rate progression
• Memory utilization
• Sample quality metrics
• Training throughput

Validation Methods

• FID scores
• CLIP scores
• Perceptual metrics
• User feedback integration

Advanced Topics

Gradient Analysis

• Gradient flow monitoring
• Layer-wise updates
• Update magnitude tracking
• Convergence indicators

Training Stability

• Loss smoothing techniques
• Gradient clipping strategies
• Learning rate adaptation
• Batch size scaling

Dynamic Scheduling

• Noise schedule adaptation
• Learning rate modulation
• Batch size adjustment
• Memory optimization timing

Research Integration

Experiment Tracking

• Hypothesis validation
• Parameter sensitivity
• Performance correlation
• Quality metrics evolution

Methodology Comparison

• Training speed analysis
• Memory efficiency
• Quality benchmarks
• Stability metrics

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Next: See Research Guidelines for experimental protocols and Quality Metrics for evaluation methods.