arXiv Computer Vision
LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter
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arXiv Computer Vision
Oct 16
FADRA: Frequency-Aware Diffusion with Residual Adaptation for Video Face Restoration
FADRA is a frequency‑aware diffusion framework designed for video face restoration (VFR). It combines a pre‑trained text‑to‑video diffusion model with lightweight LoRA adapters and a Low‑Quality Pixel‑Alignment Feature Fusion module to adapt the generative prior to VFR. The method introduces a Repeated Residual Adaptation Head that refines residuals step‑wise using low‑quality latent cues, and employs a Frequency‑Aware Loss to supervise multiple spectral bands, improving facial detail recovery and temporal consistency.
Hugging Face Trending Papers
Jul 7
FADRA: Frequency-Aware Diffusion with Residual Adaptation for Video Face Restoration
Video face restoration (VFR) aims to recover high-quality and temporally consistent facial details from severely degraded video sequences; however, existing methods still struggle to balance spatial fidelity and temporal coherence under complex degradations. To address this, we propose FADRA, a frequency-aware diffusion framework with iterative residual adaptation specifically tailored for robust VFR.
arXiv Computer Vision
Oct 16
HonestFace: Towards Honest Face Restoration with One-Step Diffusion Model
HonestFace is a one‑step diffusion model designed for honest face restoration, preserving identity and realistic textures. It introduces an identity embedder that captures key identity features from low‑quality inputs and reference faces, and a masked face alignment method to enhance fine‑grained details. The authors also release a new real‑world dataset, MultiRefCeleb‑Test, and demonstrate that HonestFace outperforms state‑of‑the‑art methods in both visual quality and quantitative metrics.
arXiv Computer Vision
Oct 16
Spectrum Matching: a Unified Perspective for Superior Diffusability in Latent Diffusion
The paper investigates how variational autoencoders (VAEs) can be made more learnable in latent diffusion by analyzing diffusability through spectral properties. It introduces the Spectrum Matching Hypothesis, which posits that latents with better diffusability should have a flattened power‑law power spectral density (Encoding Spectrum Matching) and preserve frequency‑to‑frequency semantic correspondence via the decoder (Decoding Spectrum Matching). The authors implement these ideas by matching PSDs between images and latents and applying shared spectral masking, demonstrating improved diffusion generation on CelebA and ImageNet and extending the approach to representation alignment with a DoG‑based method.
arXiv Computer Vision
4d ago
Efficient One-Step Diffusion Restoration Model with Compact Token Compression and Linear Attention
The paper introduces SANA‑SR, an efficient one‑step diffusion restoration framework for real‑world image super‑resolution. It uses a deep compression autoencoder to reduce latent tokens by a 32× ratio, preserving essential structures and textures, and applies a linear‑attention DiT with LoRA fine‑tuning for linear‑complexity token mixing. Experiments show competitive or superior performance on benchmark datasets, and the pruned model runs in 0.019 s with 407.95 G MACs and 344 M parameters, indicating strong potential for mobile deployment.
Hugging Face Trending Papers
Aug 17
PixRestore: Unified Image Restoration via Pixel Diffusion Transformer
Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors.