ChopGrad truncates backpropagation to local frame windows in video diffusion models, reducing memory from linear in frame count to constant while enabling pixel-wise loss fine-tuning.
Star: Spatial-temporal augmentation with text-to-video models for real-world video super-resolution.arXiv preprint arXiv:2501.02976
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DiffST delivers state-of-the-art real-world space-time video super-resolution with 17x faster inference than prior diffusion methods by using one-step sampling, cross-frame context aggregation, and video representation guidance.
DVFace uses a spatio-temporal dual-codebook and asymmetric fusion in a one-step diffusion model to deliver better video face restoration quality, temporal consistency, and identity preservation than recent methods.
OASIS reduces redundancy in diffusion models for real-world video super-resolution via attention specialization routing and progressive training, delivering state-of-the-art quality with 6.2x faster inference than prior one-step baselines.
citing papers explorer
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ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation
ChopGrad truncates backpropagation to local frame windows in video diffusion models, reducing memory from linear in frame count to constant while enabling pixel-wise loss fine-tuning.
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DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution
DiffST delivers state-of-the-art real-world space-time video super-resolution with 17x faster inference than prior diffusion methods by using one-step sampling, cross-frame context aggregation, and video representation guidance.
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DVFace: Spatio-Temporal Dual-Prior Diffusion for Video Face Restoration
DVFace uses a spatio-temporal dual-codebook and asymmetric fusion in a one-step diffusion model to deliver better video face restoration quality, temporal consistency, and identity preservation than recent methods.
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Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution
OASIS reduces redundancy in diffusion models for real-world video super-resolution via attention specialization routing and progressive training, delivering state-of-the-art quality with 6.2x faster inference than prior one-step baselines.