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:
- 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
Prerequisites:
- Good understanding of PyTorch
- Good understanding of deep learning
Technologies: PyTorch, CLIP
Hardware: 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