How Shap-E Changed How We Think About Diffusion Models

Diffusion models have revolutionized generative AI in recent years, with applications ranging from image synthesis to weather prediction. One of the most significant advancements in this field is Shap-E, a model that can generate 3D objects based on text or images. The journey towards creating Shap-E involved numerous developments and improvements in diffusion models, such as Classifier Guided Diffusion, Classifier-Free Guided Diffusion, and Latent Diffusion. These advancements allowed for the creation of more complex and diverse samples, ultimately leading to the development of 3D generation with Shap-E. The potential applications of diffusion models are vast, and their continued evolution promises even greater breakthroughs in various fields.