EP 26 | CS25: Transformers in Diffusion Models

Transformers aren't just for text anymore. This episode unpacks the massive shift in visual AI: merging the power of Transformers with Diffusion models. We break down how the architecture behind text generation is now the engine driving state-of-the-art image and video creation.Key Topics:The Evolution of Visual AI: Moving away from traditional U-Nets and fully embracing Diffusion Transformers (DiTs).Patching Images: How a model chops an image into "patches" and treats them exactly like words in a sentence to apply the Attention mechanism.Scaling Visuals: Why putting Transformers inside diffusion models makes them significantly more scalable and predictable when training on massive visual datasets.Note: This is an AI-generated study resource created via NotebookLM based on Stanford’s CS25 curriculum and personal study notes.