Google's Introduction to AI Image Generation

Kyle Steckler, a machine learning engineer at Google Cloud's Advanced Solutions Lab, provides an introduction to diffusion models, a family of models showing significant potential in the field of image generation. Image generation has been a field of interest for some time, with various promising approaches such as variational autoencoders, generative adversarial models (GANs), and autoregressive models.

Diffusion models, the focus of this talk, are one of the newer image generation model families. They draw their inspiration from physics, specifically thermodynamics, and have seen a significant increase in both research and industry applications in recent years. Diffusion models underpin many state-of-the-art image generation systems today and show promise across a range of use cases.

The essential idea behind diffusion models is to systematically and slowly destroy the structure in a data distribution through an iterative forward diffusion process, which involves adding noise iteratively to an image. A reverse diffusion process is then learned that restores structure in the data, yielding a highly flexible and tractable generative model of the data. In other words, noise is added to an image iteratively, and a model is then trained that learns how to denoise an image, thus generating novel images.

Google has seen many advances in this space in recent years, with many exciting new technologies on Vertex AI for image generation underpinned by diffusion models. A lot of work has been done to generate images faster and with more control. Combining the power of diffusion models with large language models (LLMs) can enable the creation of context-aware, photorealistic images from a text prompt. An example of this is Google Research's Imogen, which is a composition of an LLM and a few diffusion-based models.

Glossary of Key Terms:

1. Machine Learning Engineer: A professional who designs and creates machine learning models and systems.

2. Advanced Solutions Lab Team at Google Cloud: A team at Google that works on advanced solutions involving cloud computing and machine learning.

3. Image Generation: The process of creating new images, often using machine learning models.

4. Diffusion Models: A family of models used in image generation that systematically and slowly destroy the structure in a data distribution through an iterative forward diffusion process, and then restore the structure through a reverse diffusion process.

5. Variational Autoencoders: A type of machine learning model that encodes images to a compressed size and then decodes them back to the original size while learning the distribution of the data itself.

6. Generative Adversarial Models (GANs): A type of machine learning model that pits two neural networks against each other. One network (the generator) creates images, and the other (the discriminator) predicts if the image is real or fake.

7. Deep Fakes: Synthetic media in which a person in an existing image or video is replaced with someone else's likeness, often using GANs.

8. Autoregressive Models: Models that generate images by treating an image as a sequence of pixels. They draw much of their inspiration from how large language models handle text.

9. Large Language Models (LLMs): Models that are trained on a large amount of text data and can generate human-like text.

10. Unconditioned Diffusion Models: Diffusion models have no additional input or instruction and are trained from images of a specific thing, such as faces.

11. Super Resolution: A technique used to enhance the quality of low-resolution images.

12. Conditioned Generation Models: Models that generate images based on certain conditions or inputs, such as a text prompt.

13. Image Inpainting: The process of filling in missing or corrupted parts of images using machine learning.

14. Text-Guided Image to Image: A process where text is used to guide the transformation of one image to another.

15. Vertex AI: A managed machine learning service provided by Google Cloud for training, deploying, automating, and scaling ML models.

16. Imogen: A model from Google Research that is a composition of an LLM and a few diffusion-based models. It can create context-aware photorealistic images from a text prompt.