VDFP uses degradation field modeling based on rolling shutter and continuous prior perception with a flicker-aware loss to deflicker videos while preserving spatial-temporal details via zero-initialized pre-trained priors.
Align your latents: High-resolution video synthesis with latent diffusion models
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FIS-DiT achieves 2.11-2.41x speedup on video DiT models in few-step regimes with negligible quality loss by exploiting frame-wise sparsity and consistency through a training-free interleaved execution strategy.
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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VDFP: Video Deflickering with Flicker-banding Priors
VDFP uses degradation field modeling based on rolling shutter and continuous prior perception with a flicker-aware loss to deflicker videos while preserving spatial-temporal details via zero-initialized pre-trained priors.
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FIS-DiT: Breaking the Few-Step Video Inference Barrier via Training-Free Frame Interleaved Sparsity
FIS-DiT achieves 2.11-2.41x speedup on video DiT models in few-step regimes with negligible quality loss by exploiting frame-wise sparsity and consistency through a training-free interleaved execution strategy.
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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.