Conditional Residual Prediction: Improving Autoregressive Video Diffusion without a Bidirectional Teacher

arXiv:2610.11479 · cs.CV, cs.AI, cs.LG · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Conditional Residual Prediction — 35× in this paper
• Diffusion Model — 27× in this paper · explained in 8 episodes
• Diffusion Models — 21× in this paper · explained in 6 episodes
• Distillation — 20× in this paper · explained in 2 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Generative Model — 7× in this paper · explained in 2 episodes
• Exposure Bias — 6× in this paper · explained in 2 episodes
• Latent Space — 6× in this paper · explained in 7 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Decode — 4× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 4 episodes
• Causal Reasoning — 2× in this paper · explained in 2 episodes
• Consistency Distillation — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Video Diffusion Models
• Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Wan: Open and Advanced Large-Scale Video Generative Models
• SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer
• From Slow Bidirectional to Fast Autoregressive Video Diffusion Models
• Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
• LongLive: Real-time Interactive Long Video Generation
• Rolling Forcing: Autoregressive Long Video Diffusion in Real Time
• SkyReels-V2: Infinite-length Film Generative Model
• MAGI-1: Autoregressive Video Generation at Scale
• World Models
• DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models
• Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation
• Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation
• Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation
• Score-Based Generative Modeling through Stochastic Differential Equations
• Scalable Diffusion Models with Transformers

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