Learning Representations to Guide Pixel Diffusion Transformers
Motivated by these insights, we introduce Representation Guidance (RG), which directly forms and exploits internal representations while retaining a plain Transformer architecture.
ProofPaper ↗
Key points
- Visual representations are largely developed for understanding, yet what makes them useful for generation remains unclear.
- We study this question in pixel diffusion transformers and uncover two key insights.
- Second, architectural separation with specialized pixel decoders mainly redistributes semantic and spatial modeling, without necessarily yielding representations better suited to generation.
- RG learns clearer semantic and spatial structure while scaling efficiently in both parameters and computation.
Sources (2)
- [1]Learning Representations to Guide Pixel Diffusion TransformersarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:23 AM
Motivated by these insights, we introduce Representation Guidance (RG), which directly forms and exploits internal representations while retaining a plain Transformer architecture.
Visual representations are largely developed for understanding, yet what makes them useful for generation remains unclear.
- [2]Learning Representations to Guide Pixel Diffusion TransformersHugging Face Daily Papers · Oct 9, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
Before this
- Oct 8, 2026LeWAM: A JEPA World Action Model with Diffusion-Steering-Based MPC
- Oct 8, 2026MCL: Meta Convolution Layer
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