- 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 data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.
By the end of this training, participants will be able to:
- Understand the principles of Stable Diffusion and how it works for image generation.
- Build and train Stable Diffusion models for image generation tasks.
- Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
- Optimize the performance and stability of Stable Diffusion models.
Introduction to Stable Diffusion
- Overview of Stable Diffusion and its applications
- How Stable Diffusion compares to other image generation models (e.g., GANs, VAEs)
- Advanced features and architecture of Stable Diffusion
- Beyond the basics: Stable Diffusion for complex image generation tasks
Building Stable Diffusion Models
- Setting up the development environment
- Data preparation and pre-processing
- Training Stable Diffusion models
- Stable Diffusion hyperparameter tuning
Advanced Stable Diffusion Techniques
- Inpainting and outpainting with Stable Diffusion
- Image-to-image translation with Stable Diffusion
- Using Stable Diffusion for data augmentation and style transfer
- Working with other deep learning models alongside Stable Diffusion
Optimizing Stable Diffusion Models
- Improving performance and stability
- Handling large-scale image datasets
- Diagnosing and resolving issues with Stable Diffusion models
- Advanced Stable Diffusion visualization techniques
Case Studies and Best Practices
- Real-world applications of Stable Diffusion
- Best practices for Stable Diffusion image generation
- Evaluation metrics for Stable Diffusion models
- Future directions for Stable Diffusion research
Summary and Next Steps
- Review of key concepts and topics
- Q&A session
- Next steps for advanced Stable Diffusion users
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