GeoShrink: Accelerating Diffusion Transformers with Two Lines of Code

arXiv:2609.33723 · cs.CV · Submitted 2026-09-27 · Read on arXiv

cs.CV

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Diffusion Model — 20× in this paper · explained in 8 episodes
• Transformer — 18× in this paper · explained in 2 episodes
• Diffusion Models — 16× in this paper · explained in 6 episodes
• Trace — 16× in this paper · explained in 2 episodes
• Fidelity — 11× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Regularization — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 2× in this paper
• Calibration — 2× in this paper · explained in 7 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• MusicLM: Generating Music From Text
• All are Worth Words: A ViT Backbone for Diffusion Models
• LESA: Learnable Stage-Aware Predictors for Diffusion Model Acceleration
• $\Delta$-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers
• POLARIS: Projection-Orthogonal Least Squares for Robust and Adaptive Inversion in Diffusion Models
• MemoGen: Can Past Experience Improve Future Text-to-Image Generation?
• A Point Set Generation Network for 3D Object Reconstruction from a Single Image
• ACE-Step: A Step Towards Music Generation Foundation Model
• T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation
• Denoising Diffusion Probabilistic Models
• HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer Acceleration
• VBench: Comprehensive Benchmark Suite for Video Generative Models
• TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization
• Adaptive Caching for Faster Video Generation with Diffusion Transformers
• Elucidating the Design Space of Diffusion-Based Generative Models
• Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• $Z^2$-Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion Models
• Delta Score Matters! Spatial Adaptive Multi Guidance in Diffusion Models
• Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference

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