References: Transformers in diffusion models for image generation

Source material

Source material:
• Stanford CS25 V5 (May 27, 2025):
  "Transformers in Diffusion Models for Image Generation and Beyond"
  Speaker: Sayak Paul (Hugging Face)
  YouTube: https://www.youtube.com/watch?v=vXtapCFctTI
  Course site: https://web.stanford.edu/class/cs25/past/cs25-v5/
  License (lecture video): as published on Stanford's public CS25 YouTube
                           channel (link-out only)

Clawdemy provides original notes, summaries, and quizzes derived from this
material for educational purposes. All rights to the original lecture remain
with Stanford and the speaker.

What this lesson draws from each source

• Sayak Paul’s CS25 V5 lecture anchors the topic and the structural framing: the U-Net to DiT shift, MM-DiT as the modern conditioning approach, and the practical tradeoffs at scale. The lecture also covers diffusion preliminaries and the broader landscape of transformer-based generation.
• The explicit “where this lesson stops” enumeration of out-of-scope conversations (use-case policy, provenance/watermarking, sector-specific standards, training-data licensing, likeness rights), and the recurring tie-back to L3’s native multimodal pattern as the same architectural family applied on the generative side, are Clawdemy’s own connective tissue.

Going deeper

• “Scalable Diffusion Models with Transformers” (Peebles & Xie, 2023). The DiT paper. Section 3 is the architectural specification (patchification, adaLN-Zero conditioning, scaling experiments). Reading it alongside this lesson gives the technical detail behind the structural framing.
• “Stable Diffusion 3” technical report (Stability AI, 2024). The MM-DiT architecture and Stable Diffusion 3’s design rationale; the reference account for how text+image-token fusion via one transformer is used in a frontier production system.
• Stanford CS25 V5 schedule. The full V5 lineup for readers who want the broader context.

Adjacent topics

• Transformers for video generation (the next lesson). Same DiT-family architecture extended with a temporal axis; what changes when frames become a sequence and the dataset is many orders of magnitude larger.
• Flow matching and rectified flow. Modern variants that reduce diffusion’s many-step inference cost; pair cleanly with DiT backbones. The current frontier of inference efficiency for diffusion.
• Latent diffusion and modern VAEs. The compressed latent space that makes high-resolution DiT practical; the encoder/decoder quality bounds the whole system.
• Provenance and watermarking (deliberately out of scope here): C2PA, SynthID, and related technical standards for marking generated images. Worth knowing exists; lives in its own track or external reading.

Community discussion

None selected for this lesson at the present time. The DiT paper, Stable Diffusion 3 technical report, and Sayak Paul’s lecture together are the strongest public account of this material. If a canonical secondary discussion surfaces, it will be added at the next review.