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Instructor-Led Workshop
Generative AI with Diffusion Models

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Thanks to improvements in computing power and scientific theory, Generative AI is more accessible than ever before.Generative AI will play a significant role across industries and will gain significant importance due to its numerous applications such as Creative Content Generation, Data Augmentation, Simulation and Planning, Anomaly Detection, Drug Discovery, and Personalized Recommendations etc. In this course we will take a deeper dive on denoising diffusion models, which are a popular choice for text-to-image pipelines, disrupting several industries.

Learning Objectives

By participating in this workshop, you’ll learn:

Build a U-Net to generate images from pure noise
Improve the quality of generated images with the Denoising Diffusion process
- Compare Denoising Diffusion Probabilistic Models (DDPMs) with Denoising Diffusion Implicit Models (DDIMs)
Control the image output with context embeddings
Generate images from English text-prompts using CLIP

Download workshop datasheet (PDF 242 KB)

Workshop Outline

Introduction (15 mins)	Meet the instructor. Create an account at courses.nvidia.com/join
From U-Nets to Diffusion (60 mins)	Build a U-Net, a type of autoencoder for images. Learn about transposed convolution to increase the size of an image. Learn about non-sequential neural networks and residual connections. Experiment with feeding noise through the U-Net to generate new images
Break (10 mins)
Control with Context (60 minutes)	Learn how to alter the output of the diffusion process by adding context embeddings Add additional model optimizations such as Sinusoidal Position Embeddings The GELU activation function Attention
Text-to-Image with CLIP (60 minutes)	Walk through the CLIP architecture to learn how it associates image embeddings with text embeddings Use CLIP to train a text-to-image diffusion model
Break (60 mins)
State-of-the-art Models (60 mins)	Review various state-of-the-art generative ai models and connect them to the concepts learned in class Discuss prompt engineering and how to better influence the output of generative AI models Learn about content authenticity and how to build trustworthy models
Final Review (60 mins)	Review key learnings and answer questions. Complete the assessment and earn a certificate. Complete the workshop survey. Learn how to set up your own AI application development environment.
Next Steps	Continue learning with these DLI trainings: Building Conversational AI Applications Data Parallelism: How to Train Deep Learning Models with Multiple GPUs Building Transformer-Based Natural Language Processing Applications

Workshop Details

Duration: 8 hours

Price: Coming soon

Prerequisites:

Good understanding of PyTorch
Good understanding of deep learning

Technologies: PyTorch, CLIP

Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated workstation in the cloud.

Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Languages: English

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Instructor-Led Workshop Generative AI with Diffusion Models

Learning Objectives

Workshop Outline

Workshop Details

Upcoming Public Workshops

Continue Your Learning with These DLI Trainings

Getting Started with Image Segmentation

Modeling Time-Series Data with Recurrent Neural Networks in Keras

Building Transformer-Based Natural Language Processing Applications

Fundamentals of Deep Learning for Multi-GPUs

Questions?

Read Our FAQs

Inquire about NVIDIA Deep Learning Institute services.

For technical questions, check out the NVIDIA Developer Forums.

Contact Us for Questions on Deep Learning Training

NVIDIA DLI Certificate

Building Intelligent Recommender Systems

Instructor-Led Workshop
Generative AI with Diffusion Models

Contact Us for Questions on
Deep Learning Training