PixelDiT2: Representation-Grounded Pixel Diffusion Transformers

arXiv:2609.24919 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-30

Terminology

• Grounding — 104× in this paper · explained in 3 episodes
• Transformer — 71× in this paper · explained in 2 episodes
• Diffusion Model — 22× in this paper · explained in 8 episodes
• Diffusion Models — 15× in this paper · explained in 6 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Adaptation — 9× in this paper · explained in 1 episode
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Modeling — 5× in this paper · explained in 3 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Linear Probing — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Alignment Methods — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation
• PixelFlow: Pixel-Space Generative Models with Flow
• MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer
• RPiAE: A Representation-Pivoted Autoencoder Enhancing Both Image Generation and Editing
• Classifier-Free Diffusion Guidance
• Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation
• DINOv3
• PixNerd: Pixel Neural Field Diffusion
• ZipIR: Latent Pyramid Diffusion Transformer for High-Resolution Image Restoration
• SimFlow: Simplified and End-to-End Training of Latent Normalizing Flows

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