CrossDistill: Balancing Quality and Diversity via Trajectory-Level Hybrid Few-Step Distillation

arXiv:2609.14725 · cs.CV · Submitted 2026-09-13 · Read on arXiv

cs.CV

Submitted: 2026-09-13

Updated: 2026-09-20

Terminology

• Distillation — 52× in this paper · explained in 2 episodes
• Distribution Matching — 25× in this paper · explained in 2 episodes
• Consistency — 20× in this paper · explained in 3 episodes
• Fidelity — 16× in this paper · explained in 2 episodes
• Diffusion Model — 13× in this paper · explained in 8 episodes
• Diffusion Models — 11× in this paper · explained in 6 episodes
• Flow Matching — 4× in this paper · explained in 8 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Ablation — 2× in this paper · explained in 1 episode
• Additivity — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Flow Matching Model — 2× in this paper · explained in 2 episodes
• Gradient Flow — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Dynamical regimes — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• On the Importance of Noise Scheduling for Diffusion Models
• From Structure to Detail: Hierarchical Distillation for Efficient Diffusion Model
• SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation
• AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation
• Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield
• Continuous-Time Distribution Matching for Few-Step Diffusion Distillation
• Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models
• Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
• Progressive Distillation for Fast Sampling of Diffusion Models
• Parallel Decoding Distillation for Fast Image and Video Generation
• VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
• Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency

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