ActDiff-VC achieves up to 64.6% bitrate reduction at matched NIQE and improves perceptual metrics like KID and FID by using content-adaptive keyframe selection and budget-aware sparse trajectory selection to condition a diffusion decoder for ultra-low-bitrate video reconstruction.
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NI Sampling accelerates discrete diffusion language models up to 14.3 times by training a neural indicator to select which tokens to sample at each step using a trajectory-preserving objective.
Near-reversible Runge-Kutta diffusion ODE solvers with vector-field smoothing improve stability and edit fidelity for large changes in text-guided image editing compared to exactly reversible alternatives.
citing papers explorer
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Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion
ActDiff-VC achieves up to 64.6% bitrate reduction at matched NIQE and improves perceptual metrics like KID and FID by using content-adaptive keyframe selection and budget-aware sparse trajectory selection to condition a diffusion decoder for ultra-low-bitrate video reconstruction.
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NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
NI Sampling accelerates discrete diffusion language models up to 14.3 times by training a neural indicator to select which tokens to sample at each step using a trajectory-preserving objective.
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Stable and Near-Reversible Diffusion ODE Solvers for Image Editing
Near-reversible Runge-Kutta diffusion ODE solvers with vector-field smoothing improve stability and edit fidelity for large changes in text-guided image editing compared to exactly reversible alternatives.