- Presencial
- 21 horas
- Aula Virtual
- No disponible
Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions.
This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.
By the end of this training, participants will be able to:
- Understand advanced deep learning architectures and techniques for text-to-image generation.
- Implement complex models and optimizations for high-quality image synthesis.
- Optimize performance and scalability for large datasets and complex models.
- Tune hyperparameters for better model performance and generalization.
- Integrate Stable Diffusion with other deep learning frameworks and tools.
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
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