SAGE: Subspace Alignment for Classifier-Free Guidance in Mixture-of-Experts Diffusion Models

arXiv:2609.34525 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Alignment — 22× in this paper · explained in 1 episode
• Diffusion Model — 16× in this paper · explained in 8 episodes
• Diffusion Models — 14× in this paper · explained in 6 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Ablation — 11× in this paper · explained in 1 episode
• Classifier-Free Guidance — 11× in this paper
• Benchmark — 9× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Flow Matching — 4× in this paper · explained in 7 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Mixture-of-Experts (MoE) — 2× in this paper · explained in 7 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Diffusion Transformers with Mixture-of-Experts — 1× in this paper
• Frechet Inception Distance — 1× in this paper
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• CFG-Zero*: Improved Classifier-Free Guidance for Flow Matching Models
• Scaling Diffusion Transformers to 16 Billion Parameters
• Classifier-Free Diffusion Guidance
• ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
• Seedance 2.0: Advancing Video Generation for World Complexity
• ST-MoE: Designing Stable and Transferable Sparse Expert Models

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models