SelfLift: Accelerating Few-Step Diffusion via Self-Recovering Resolution Transition

arXiv:2609.02036 · cs.CV · Submitted 2026-09-02 · Read on arXiv

cs.CV

Submitted: 2026-09-02

Updated: 2026-09-16

Code: https://github.com/sgl-project/sglang

Project page: https://happygirlty.github.io/SelfLift_res

Terminology

• Consistency — 27× in this paper · explained in 3 episodes
• Distillation — 24× in this paper · explained in 2 episodes
• Diffusion Model — 17× in this paper · explained in 8 episodes
• Ablation — 16× in this paper · explained in 1 episode
• Diffusion Models — 12× in this paper · explained in 6 episodes
• Adaptation — 9× in this paper · explained in 1 episode
• Alignment — 9× in this paper · explained in 1 episode
• Flow Matching — 6× in this paper · explained in 8 episodes
• Distribution Matching — 5× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• On-Policy Distillation — 4× in this paper · explained in 3 episodes
• Core — 3× in this paper · explained in 2 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Over-smoothing — 3× in this paper · explained in 4 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Flow Matching Model — 2× in this paper · explained in 2 episodes
• GRPO — 2× in this paper · explained in 6 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• LoRA Adaptation — 1× in this paper · explained in 2 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Flow-OPD: On-Policy Distillation for Flow Matching Models
• D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
• Pyramidal Flow Matching for Efficient Video Generative Modeling
• DiffusionOPD: A Unified Perspective of On-Policy Distillation in Diffusion Models
• ERTACache: Error Rectification and Timesteps Adjustment for Efficient Diffusion
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
• Motion-Aware Caching for Efficient Autoregressive Video Generation
• Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

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