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Course Outline
1. Introduction to Advanced Stable Diffusion
- Course objectives and learning path
- Review of diffusion models
- Stable Diffusion architecture overview
- 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
- Diffusion process fundamentals
- Forward and reverse diffusion
- Noise prediction
- 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
- Token emphasis
- Prompt interpolation
- Prompt optimization strategies
- Reproducible image generation
5. Advanced Image Generation Techniques
- Image-to-Image generation
- Inpainting
- Outpainting
- High-resolution generation
- Multi-stage refinement
- Batch image generation
- Controlled randomization 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
- LoRA fundamentals
- DreamBooth training
- Textual Inversion
- Custom embeddings
- Fine-tuning datasets
- Evaluating custom models
8. Advanced Model Training
- Dataset preparation
- Data augmentation
- Caption generation
- Training pipelines
- Distributed training
- Mixed precision training
- Checkpoint management
9. Hyperparameter Optimization
- Learning rate selection
- Batch size optimization
- Scheduler selection
- CFG Scale optimization
- Sampling steps
- Regularization techniques
- Model evaluation metrics
10. Performance Optimization
- GPU optimization
- CUDA optimization
- Memory-efficient attention
- xFormers optimization
- Quantization techniques
- FP16 and BF16 inference
- Efficient batching
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
- PyTorch integration
- TensorFlow interoperability
- ONNX Runtime
- TensorRT optimization
- Accelerate library
- Pipeline customization
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
- 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
- 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 visualization
- Marketing content generation
- Character design
- Architectural visualization
- Medical imaging research
- Scientific visualization
- 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 optimization exercises
- Model evaluation and comparison
- Review of key concepts
- Questions and answers
- Next steps and further learning resources
Requirements
- Solid understanding of deep learning concepts and architectures.
- Familiarity with Stable Diffusion and text-to-image generation.
- Experience with Python programming and PyTorch.
Audience
- Data scientists and machine learning engineers.
- Deep learning researchers.
- Computer vision experts.
21 Hours
