Hands-On Generative AI with Diffusion Models: Building Real-World Applications
34mIntermediate2024-02-16
Authors
Nayan Saxena
Course details
As AI and machine learning applications become increasingly powerful and pervasive, it’s essential that developers know how to apply generative models, regardless of their industry or current role. Given the projected future demand for AI professionals, this hands-on, skills-based course is designed to equip you with the tools and technical know-how required to get you up to speed building real-world applications and stay apace with current and emerging trends.
Learn how to leverage some of the most recent advancements in generative AI with diffusion models by exploring the power of the Hugging Face diffusers library. Join instructor and generative AI expert Nayan Saxena as he dives into unconditional image generation, text-guided image generation, image-to-image translation, the art of image inpainting, image quality and efficiency improvements, music generation, and more. By the end of this course, you'll be adept at applying these models in real-world scenarios.
Learn how to leverage some of the most recent advancements in generative AI with diffusion models by exploring the power of the Hugging Face diffusers library. Join instructor and generative AI expert Nayan Saxena as he dives into unconditional image generation, text-guided image generation, image-to-image translation, the art of image inpainting, image quality and efficiency improvements, music generation, and more. By the end of this course, you'll be adept at applying these models in real-world scenarios.
Skills covered
OpenCVHugging FacePyTorchMobile Device ManagementFull-Stack Web DevelopmentGenerative AIArtificial Intelligence (AI)Web DevelopmentNetwork and System AdministrationOpen SourceOne-Off
Concepts
0. Introduction
- 01 - Introduction to generative AI and diffusion models
- 02 - What you should know
- 03 - Exercise files
1. Unconditional Image Generation
- 04 - Unconditional image generation with diffusion models
- 05 - Demo - Building an unconditional image generation model
2. Text-Guided Image Generation
- 06 - Text-guided image generation with diffusion models
- 07 - Demo - Building a text-guided image generation model
3. Image-to-Image Translation
- 08 - Image-to-image translation with diffusion models
- 09 - Demo - Building an image-to-image translation model
4. Image Inpainting
- 10 - Image inpainting with diffusion models
- 11 - Demo - Building an image inpainting model
5. Improving Image Quality and Efficiency
- 12 - Deterministic generation for image improvement
- 13 - Exploring different schedulers for diffusion models
- 14 - Demo - Deterministic generation in action
6. Reinforcement Learning (RL) and Music Applications
- 15 - Beyond images with diffusion models
- 16 - Demo - Music generation with diffusion models
Conclusion
- 17 - Next steps in building generation AI applications