Asymmetric CUDA-stream pipelining, a compile-friendly LLLite reformulation, and periodic conditioning refresh sustain 27-30 fps video stylization on a consumer GPU with a 2.13B MLLM text encoder and 0.39B distilled U-Net.
Streaming video diffusion: Online video editing with diffusion models
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
Self Forcing trains autoregressive video diffusion models by performing autoregressive rollout with KV caching during training to close the exposure bias gap, using a holistic video-level loss and few-step diffusion for efficiency.
citing papers explorer
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Inverting the Streaming-Diffusion Bottleneck: Video-Rate MLLM-Conditioned Edit Diffusion on a Consumer GPU
Asymmetric CUDA-stream pipelining, a compile-friendly LLLite reformulation, and periodic conditioning refresh sustain 27-30 fps video stylization on a consumer GPU with a 2.13B MLLM text encoder and 0.39B distilled U-Net.
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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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Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
Self Forcing trains autoregressive video diffusion models by performing autoregressive rollout with KV caching during training to close the exposure bias gap, using a holistic video-level loss and few-step diffusion for efficiency.