14-Step Hand-Calculated Walkthrough of Sora's Diffusion Transformer Architecture
ProfTomYeh · x · 2026-08-05
Although OpenAI's Sora is currently offline, its underlying Diffusion Transformer (DiT) architecture continues to revolutionize video generation. This walkthrough breaks down DiT into 14 explicit, hand-calculated steps:
- Input & Patching: Video pixels are divided into spacetime patches and compressed into latent vectors via a visual encoder.
- Diffusion & Conditioning: Noise is added to the latents. Text prompts and diffusion timesteps are encoded into scale and shift parameters, injected via adaptive layer norm.
- Self-Attention & Generation: Self-attention mixes spacetime features, and a pointwise FFN predicts the noise. Training uses MSE loss gradients for backprop, while generation subtracts predicted noise to reconstruct the video.
Takeaway: DiT is fundamentally a noise-predicting transformer. Video enters as latent patches, while prompts and timesteps interact solely through arithmetic scale and shift operations.
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