Program Outline
Generative AI With Diffusion Models
This NVIDIA DLI course helps teams get started with generative AI application development by building a text-to-image solution with diffusion models. It combines from-scratch understanding with enterprise use of pretrained approaches to accelerate application delivery.
Delivery
Virtual, On-site, or Hybrid
Duration
8 hours
Product
PyTorch, CLIP
Role
AI Engineer, Developer
NVIDIA
Generative ImagingText-to-image and diffusion workflows
NVIDIA Diffusion
Best Fit
Audience Profile
Who This Program Is For
Built for practitioners with solid deep learning foundations who want to expand into enterprise image-generative AI workflows using diffusion models.
Overview
Program Summary
Official NVIDIA DLI generative AI course focused on building text-to-image applications with diffusion models.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Understand diffusion-based generation
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Module 1
Understand diffusion-based generation
Learn the core ideas behind diffusion models and how they support modern image-generative AI applications.
- Diffusion model foundations
- Text-to-image generation workflows
2Module 2
Accelerate and refine image generation solutions
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Module 2
Accelerate and refine image generation solutions
Use pretrained approaches and optimization techniques to improve output quality, speed development, and support more controlled generation.
- Pretrained model acceleration
- Optimization and output control
Coverage Areas
Topic Coverage
Coverage Item 1
Diffusion model foundations
Coverage Item 2
Text-to-image generation workflows
Coverage Item 3
Pretrained model acceleration
Coverage Item 4
Optimization and output control
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Use your visual content use case as the workshop context
- •Add multimodal and evaluation follow-on topics
- •Extend into deployment and product-integration considerations
