A survey that groups efficient video diffusion methods into four paradigms—step distillation, efficient attention, model compression, and cache/trajectory optimization—and outlines open challenges for practical use.
Live2Diff: Live stream translation via uni-directional attention in video diffusion models,
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AAD-1 uses a causal generator with a bidirectional holistic discriminator plus phased distribution matching before adversarial training to reach state-of-the-art one-step autoregressive video generation on VBench.
Reports a streaming pipeline with asymmetric CUDA pipelining and batched MLLM amortization that sustains 27.4 fps at 512x512 on RTX 3090 Ti for oil-painting stylization.
citing papers explorer
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Efficient Video Diffusion Models: Advancements and Challenges
A survey that groups efficient video diffusion methods into four paradigms—step distillation, efficient attention, model compression, and cache/trajectory optimization—and outlines open challenges for practical use.
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AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation
AAD-1 uses a causal generator with a bidirectional holistic discriminator plus phased distribution matching before adversarial training to reach state-of-the-art one-step autoregressive video generation on VBench.
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Video-Rate Streaming Stylization on a Vision-Aware MLLM-Conditioned Edit Diffusion: Asymmetric Batched Inference on a Distilled UNet + MLLM Text Encoder
Reports a streaming pipeline with asymmetric CUDA pipelining and batched MLLM amortization that sustains 27.4 fps at 512x512 on RTX 3090 Ti for oil-painting stylization.