Stream-DiffVSR enables practical low-latency video super-resolution by combining a four-step distilled denoiser, auto-regressive temporal guidance, and a temporal processor in a strictly causal pipeline.
Diffvsr: Enhancing real-world video super-resolution with diffusion models for advanced visual quality and temporal consistency
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LiteVSR performs video super-resolution on a completely frozen Diffusion Transformer via a lightweight State-Aware Adapter that uses dual-stream extraction and time-dependent cross-attention, reaching competitive quality with 11.25% trainable parameters after 12 GPU-hours.
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.
TIGER is a tri-prior fusion method for face video restoration using identity, geometry, and generative priors with progressive training to achieve SOTA identity fidelity and temporal stability on a new large-scale dataset.
citing papers explorer
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Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion
Stream-DiffVSR enables practical low-latency video super-resolution by combining a four-step distilled denoiser, auto-regressive temporal guidance, and a temporal processor in a strictly causal pipeline.
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LiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution
LiteVSR performs video super-resolution on a completely frozen Diffusion Transformer via a lightweight State-Aware Adapter that uses dual-stream extraction and time-dependent cross-attention, reaching competitive quality with 11.25% trainable parameters after 12 GPU-hours.
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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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TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration
TIGER is a tri-prior fusion method for face video restoration using identity, geometry, and generative priors with progressive training to achieve SOTA identity fidelity and temporal stability on a new large-scale dataset.