ReCaVSR: One-Step Streaming Diffusion Video Super-Resolution with Recycled Latents and Learned Cache Routing

arXiv:2609.37831 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/kopperx/ReCaVSR

Terminology

• Transformer — 17× in this paper · explained in 2 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• KV cache — 11× in this paper · explained in 3 episodes
• Adaptation — 8× in this paper · explained in 1 episode
• Multi-Scope Query — 7× in this paper
• Consistency — 6× in this paper · explained in 3 episodes
• Diffusion Model — 5× in this paper · explained in 8 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
• Alignment — 3× in this paper · explained in 1 episode
• Distillation — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Sparse Attention — 3× in this paper · explained in 5 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Analyzer — 1× in this paper · explained in 1 episode
• Causal Inference — 1× in this paper · explained in 4 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• KV cache reuse — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Improved Adversarial Diffusion Compression for Real-World Video Super-Resolution
• DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
• TRaM-VSR: Importance-Aware Token Routing and Merging for One-Step Diffusion Video Super-Resolution
• End-to-End Training for Autoregressive Video Diffusion via Self-Resampling
• Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
• Diffusion Adversarial Post-Training for One-Step Video Generation
• Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation
• FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality
• DUO-VSR: Dual-Stream Distillation for One-Step Video Super-Resolution
• HeadCast: Casting Attention Heads for Efficient Autoregressive Video Generation
• Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion
• SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training
• SwiftVR: Real-Time One-Step Generative Video Restoration
• SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention
• VSA: Faster Video Diffusion with Trainable Sparse Attention
• InfVSR: Toward Consistency-Driven Streaming Generative Video Super-Resolution
• FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution

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