Design axis
Architecture
What backbone turns the objective into a trainable network?
DiT with diffusion head (DiT^DH)
A DiT variant whose final block is replaced by a small per-token diffusion head, used to decode high-dimensional semantic latents where a single linear projection is insufficient.
Diffusion Transformer (DiT)
A plain Vision-Transformer backbone for diffusion/flow models, operating on a grid of latent patch tokens with adaptive layer norm for timestep/class conditioning.
MM-DiT (dual-stream joint attention)
Separate weights for text and image token streams, joined only through shared self-attention layers, so each modality keeps its own representation while still attending jointly.
Single-stream (unified-weight) DiT
Give text and image tokens one shared set of transformer weights (rather than MM-DiT's two parallel weight sets) and rely purely on the joint sequence concatenation to let self-attention mix modalities, trading modality-specific capacity for a smaller, simpler backbone.
UNet backbone
A convolutional encoder-decoder with skip connections; the dominant diffusion backbone before diffusion transformers.