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Title & Speakers	Event
Generative AI with Diffusion Models: NVIDIA Certification Workshop for Academia 2025-12-03 · 08:30 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 [email protected] 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	Generative AI with Diffusion Models: NVIDIA Certification Workshop for Academia
Generative AI with Diffusion Models: NVIDIA Certification Workshop for Academia 2025-10-04 · 08:30 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 [email protected] 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	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 [email protected]

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