Show-o and Discrete-Diffusion Unified Models

Transfusion mixes continuous and discrete representations. Show-o (Xie et al., August 2024) goes the other way: text tokens use causal next-token prediction, image tokens use masked discrete diffusion in the spirit of MaskGIT. Both sit inside one transformer with a hybrid attention mask. The result unifies VQA, text-to-image, inpainting, and mixed-modality generation on one backbone, one tokenizer per modality, one loss formulation (next-token extended to masked prediction). This lesson walks the Show-o design — why masked discrete diffusion is a parallel, few-step image generator — and contrasts with Transfusion and Emu3.

Learn 2 hr Python (stdlib, masked-discrete-diffusion sampler)

Prerequisites: Phase 12 · 13 (Transfusion)

#Learning Objectives

Explain masked discrete diffusion: the schedule that masks tokens uniformly then asks the transformer to recover them.
Compare parallel image decoding (Show-o, MaskGIT) to autoregressive image decoding (Chameleon, Emu3) on speed and quality.
Name the three tasks Show-o handles in one checkpoint: T2I, VQA, image inpainting.
Pick a masking schedule (cosine, linear, truncated) and reason about its effect on sample quality.

#Learning Objectives

Keep going. The rest of this lesson is one step away.