Generative AI with Diffusion Models: NVIDIA Certification Workshop for Academia

https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-FX-08+V1

Thanks to improvements in computing power and scientific theory, generative AI is more accessible than ever before. Generative AI plays a significant role across industries due to its numerous applications, such as creative content generation, data augmentation, simulation and planning, anomaly detection, drug discovery, personalized recommendations, and more.

In this course, learners will take a deeper dive into denoising diffusion models, which are a popular choice for text-to-image pipelines.

This course is online and offered free of charge to academic participants (students and staff). If you are in industry, and would like to take the training, please send an email to info@kineto.ai

Important
Access links to the NVIDIA DLI environment required to complete the graded assessment and obtain a certification will be sent to your academic email shortly before the event, please be sure to fill the access form to gain access to it.

Learning Objectives

• Build a U-Net to generate images from pure noise
• Improve the quality of generated images with the denoising diffusion process
• Control the image output with context embeddings
• Generate images from English text prompts using the Contrastive Language–Image Pretraining (CLIP) neural network

Topics Covered

• U-Nets
• Diffusion
• CLIP
• Text-to-image Models

From U-net to Diffusion

• Build a U-Net architecture
• Train a model to remove noise from an image

Diffusion Models

• Define the forward diffusion function
• Update the U-Net architecture to accommodate a timestep
• Define a reverse diffusion function

Optimizations

• Implement Group Normalization
• Implement GELU
• Implement Rearrange Pooling
• Implement Sinusoidal Position Embeddings

Classifier-free Diffusion Guidance

• Add categorical embeddings to a U-Net
• Train a model with a Bernoulli mask

CLIP

• Learn how to use CLIP Encodings
• Use CLIP to create a text-to-image neural network