Stable Diffusion Model Customization

Problem Statement
To enhance the Stable Diffusion model’s capability to generate domain-specific images by fine-tuning it on a custom dataset, improving the model’s understanding of specific contexts and image styles while maintaining generation quality.

Approach

1. Dataset Preparation
   • Created a structured dataset pairing image paths with corresponding descriptive captions.
   • Implemented data validation and cleaning procedures to ensure dataset quality.
   • Organized data in a format compatible with the Stable Diffusion training pipeline.
2. Text Augmentation Pipeline
   • Implemented multiple NLP augmentation techniques:
     • Random text augmentation for diversity.
     • Spelling augmentation to improve model robustness.
   • Enhanced caption variety while maintaining semantic meaning.
3. Custom Tokenization Development
   • Created a hybrid tokenization approach by:
     • Combining WikiText vocabulary with Twitter-100d embeddings.
     • Developing a master vocabulary for comprehensive text representation.
     • Implementing custom tokenization rules for domain-specific terms.
4. Model Fine-tuning
   • Configured the trainer class with optimized parameters.
   • Fed tokenized text encodings into the training pipeline.
   • Implemented checkpoint saving and validation steps.
   • Monitored training metrics for performance evaluation.
5. Inference Pipeline Development
   • Designed a streamlined inference system for prompt-to-image generation.
   • Implemented pre-processing and post-processing steps.
   • Optimized the pipeline for efficient image generation.

Observations

• The hybrid tokenization approach showed improved handling of domain-specific terminology.
• Text augmentations contributed to better prompt understanding and generation diversity.
• The model showed enhanced performance in generating domain-specific images while maintaining general capabilities.

Optimization Steps and Results

Optimization Techniques

1. Fine-tuned learning rate scheduling.
2. Gradient accumulation for stability.
3. Mixed precision training for efficiency.
4. Regular validation checks to prevent overfitting.

Results

• Improved generation quality for domain-specific prompts.
• Reduced training time through optimized tokenization.
• Better handling of complex prompts with specialized vocabulary.
• Maintained generation quality while improving domain specificity.