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Course Outline
1. Introduction to Advanced Stable Diffusion
- Course objectives and learning pathway.
- Review of diffusion models.
- Overview of the Stable Diffusion architecture.
- Latent Diffusion Models (LDMs).
- Evolution of Stable Diffusion models (SD 1.x, SDXL, and newer architectures).
- Enterprise use cases and applications.
2. Deep Learning Foundations for Diffusion Models
- Fundamentals of the diffusion process.
- Forward and reverse diffusion steps.
- Noise prediction mechanisms.
- Denoising U-Net architecture.
- Variational Autoencoders (VAE).
- CLIP text encoder.
- Cross-attention mechanisms.
3. Understanding Stable Diffusion Architecture
- Pipeline components.
- Text encoding process.
- Latent space representation.
- Scheduler algorithms.
- Sampling methods.
- Image decoding workflow.
4. Advanced Prompt Engineering
- Prompt structure and syntax.
- Positive and negative prompts.
- Prompt weighting techniques.
- Token emphasis strategies.
- Prompt interpolation.
- Prompt optimisation strategies.
- Reproducible image generation methods.
5. Advanced Image Generation Techniques
- Image-to-Image generation.
- Inpainting techniques.
- Outpainting techniques.
- High-resolution generation.
- Multi-stage refinement processes.
- Batch image generation.
- Controlled randomisation using seeds.
6. Conditional Image Generation
- ControlNet architecture.
- Pose-guided generation.
- Depth-guided generation.
- Edge detection conditioning.
- Segmentation guidance.
- Reference image conditioning.
- Multi-ControlNet workflows.
7. LoRA, DreamBooth and Model Fine-Tuning
- Transfer learning concepts.
- Fundamentals of LoRA.
- DreamBooth training procedures.
- Textual Inversion techniques.
- Custom embeddings.
- Fine-tuning datasets.
- Evaluating custom models.
8. Advanced Model Training
- Dataset preparation.
- Data augmentation strategies.
- Caption generation.
- Training pipelines.
- Distributed training methods.
- Mixed precision training.
- Checkpoint management.
9. Hyperparameter Optimization
- Learning rate selection.
- Batch size optimisation.
- Scheduler selection.
- CFG Scale optimisation.
- Sampling steps management.
- Regularisation techniques.
- Model evaluation metrics.
10. Performance Optimization
- GPU optimisation strategies.
- CUDA optimisation.
- Memory-efficient attention mechanisms.
- xFormers optimisation.
- Quantisation techniques.
- FP16 and BF16 inference.
- Efficient batching methods.
11. Scaling Stable Diffusion Workloads
- Multi-GPU training.
- Distributed inference.
- Large-scale dataset management.
- Cloud GPU deployment.
- Model serving strategies.
- Performance benchmarking.
12. Integrating Stable Diffusion with Deep Learning Frameworks
- Hugging Face Diffusers library.
- PyTorch integration.
- TensorFlow interoperability.
- ONNX Runtime.
- TensorRT optimisation.
- Accelerate library usage.
- Pipeline customisation.
13. Building Production Pipelines
- API development.
- Batch inference services.
- Workflow automation.
- Queue-based generation.
- Model versioning.
- Production deployment strategies.
14. Image Quality Enhancement
- Upscaling techniques.
- Super-resolution methods.
- Face restoration.
- Artifact reduction.
- Image refinement workflows.
- Post-processing pipelines.
15. Responsible AI and Model Safety
- Bias in generative models.
- Ethical image generation.
- Copyright considerations.
- AI-generated content disclosure.
- Safety filters implementation.
- Prompt moderation.
- Responsible deployment practices.
16. Troubleshooting and Debugging
- Diagnosing generation failures.
- Resolving CUDA errors.
- Memory management issues.
- Improving image consistency.
- Debugging custom pipelines.
- Performance troubleshooting.
17. Monitoring and Model Evaluation
- Measuring generation quality.
- Benchmarking models.
- Comparing checkpoints.
- Logging experiments.
- Experiment tracking.
- Model reproducibility.
18. Advanced Applications
- Product design visualisation.
- Marketing content generation.
- Character design.
- Architectural visualisation.
- Medical imaging research.
- Scientific visualisation.
- Creative AI workflows.
19. Integrating Stable Diffusion with Other AI Models
- Large Language Models (LLMs).
- Vision-Language Models (VLMs).
- Image captioning.
- Retrieval-Augmented Generation (RAG) for multimodal systems.
- AI agent workflows.
- Multi-model orchestration.
20. Best Practices for Enterprise Deployment
- Infrastructure planning.
- GPU resource management.
- Security considerations.
- Model governance.
- CI/CD for AI models.
- Maintenance and upgrades.
21. Hands-on Workshop and Summary
- Building a complete image generation pipeline.
- Fine-tuning a custom Stable Diffusion model.
- Creating an automated generation workflow.
- Performance optimisation exercises.
- Model evaluation and comparison.
- Review of key concepts.
- Questions and answers.
- Next steps and further learning resources.
Requirements
- Strong grasp of deep learning concepts and architectures.
- Familiarity with Stable Diffusion and text-to-image generation processes.
- Practical experience with Python programming and PyTorch.
Audience
- Data scientists and machine learning engineers.
- Deep learning researchers.
- Computer vision experts.
21 Hours
