Course Outline

Introduction to Advanced Stable Diffusion

  • Overview of Stable Diffusion architecture and components
  • Deep learning for text-to-image generation: review of state-of-the-art models and techniques
  • Advanced Stable Diffusion scenarios and use cases

Advanced Text-to-Image Generation Techniques with Stable Diffusion

  • Generative models for image synthesis: GANs, VAEs, and their variations
  • Conditional image generation with text inputs: models and techniques
  • Multi-modal generation with multiple inputs: models and techniques
  • Fine-grained control of image generation: models and techniques

Performance Optimization and Scaling for Stable Diffusion

  • Optimizing and scaling Stable Diffusion for large datasets
  • Model parallelism and data parallelism for high-performance training
  • Techniques for reducing memory consumption during training and inference
  • Quantization and pruning techniques for efficient model deployment

Hyperparameter Tuning and Generalization with Stable Diffusion

  • Hyperparameter tuning techniques for Stable Diffusion models
  • Regularization techniques for improving model generalization
  • Advanced techniques for handling bias and fairness in Stable Diffusion models

Integrating Stable Diffusion with Other Deep Learning Frameworks and Tools

  • Integrating Stable Diffusion with PyTorch, TensorFlow, and other deep learning frameworks
  • Advanced deployment techniques for Stable Diffusion models
  • Advanced inference techniques for Stable Diffusion models

Debugging and Troubleshooting Stable Diffusion Models

  • Techniques for diagnosing and resolving issues in Stable Diffusion models
  • Debugging Stable Diffusion models: tips and best practices
  • Monitoring and analyzing Stable Diffusion models

Summary and Next Steps

  • Review of key concepts and topics
  • Q&A session
  • Next steps for advanced Stable Diffusion users.

Requirements

  • Good understanding of deep learning concepts and architectures
  • Familiarity with Stable Diffusion and text-to-image generation
  • Experience with PyTorch and Python programming

Audience

  • Data scientists and machine learning engineers
  • Deep learning researchers
  • Computer vision experts.
 21 Hours

Number of participants


Price per participant