Cross-attention reads another representation, DiT is a diffusion-backbone choice, and Q-Former uses learned queries to extract or bridge information. They can coexist rather than being three competing products. The Lecture 14 lesson handles Prompt-to-Prompt and classifier-free guidance in detail.
Use an original numerical test to check gradients, data, and evaluation alongside the original assignment specification.
Lab 3 builds a conditional latent diffusion model on MNIST from scratch, in four stages: CFG training with label dropout (checked on a three-component Gaussian mixture), a diffusion transformer built piece by piece (Fourier time embedding, patchify, multi-head attention, adaLN-Zero, depatchify), a VAE, and finally the DiT trained inside the VAE's latent space. Problems and official solutions are public; submission goes through Gradescope on Canvas, which only enrolled MIT students can use.
The algorithms are complete by Lecture 3B; Lecture 4 tackles two engineering problems that show up at scale. First, the network must take an image, a time t, and a prompt and output a vector field of the same size, so we use a U-Net or a diffusion transformer (DiT), embedding time with Fourier features and text with frozen CLIP/T5 encoders. Second, pixel space is too big, so we first train a VAE to compress images into a latent space, run flow matching there, and decode at the end. Stable Diffusion 3 and Meta Movie Gen Video both follow this recipe: flow matching in latent space, a DiT variant, and CFG.
Sora's February 2024 preview shocked the industry, but the landscape reversed in two and a half years: OpenAI closed the consumer Sora app in April 2026 and scheduled its API for retirement on September 24; Veo 3.1 became the narrative default with native audio and Flow; Kling 3.0 became a unified multimodal model with $240M annualized revenue; and Runway Gen-4.5 briefly led Artificial Analysis in a November 2025 snapshot while defending the professional market through enterprise workflows. This guide compares four families by generation, specifications, pricing, and use case.
Every serious image-to-video model in 2026 runs latent diffusion on a DiT backbone, so visual quality is no longer a useful axis for choosing one. The real axes are native audio, self-hostability, and dollars per second. Three widely-repeated errors worth correcting: Sora's app shut down on April 26 and its API goes on September 24; Wan 2.7 is described everywhere as Apache 2.0 open weights but no first-party source has them; Veo 3.1 officially costs $0.40/s, not the $0.75/s that circulates on review sites.