arXiv Computer Vision
P-PatchDiff: Progressive Patch Diffusion Models for Low-light Image Enhancement
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P-PatchDiff: Progressive Patch Diffusion Models for Low-light Image Enhancement
Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detailed images. Patch diffusion models further offer a...
arXiv Computer Vision
Oct 12
ForcingBooster: Adaptive Patch-wise Denoising for Fast Autoregressive Video Diffusion
ForcingBooster is a framework that adaptively allocates denoising steps to individual video patches in autoregressive diffusion models, allowing easy patches to stop early while difficult patches receive more refinement. This dynamic scheduling reduces the total number of denoising steps by about 50% and improves generation efficiency with negligible quality loss. The method requires only a lightweight routing module and works with most existing few‑step autoregressive diffusion models.
arXiv Machine Learning
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Efficient and Training-Free Single-Image Diffusion Models
arXiv:2606. 04299v1 Announce Type: cross Abstract: We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image.
arXiv AI
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ScaleResfusion: Residual Rectified Flow based on Residual Vector Field
arXiv:2607. 25275v1 Announce Type: cross Abstract: Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations.
arXiv Computer Vision
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DARD: Zero-Shot Degradation-Aware Retinex-Guided Diffusion for Low-Light Image Enhancement
arXiv:2608.29243v1 Announce Type: new Abstract: Existing diffusion-based enhancement methods provide strong generative capability for low-light image enhancement (LLIE), yet they either rely on paire...
arXiv Computer Vision
Sep 22
PixelDiT2: Representation-Grounded Pixel Diffusion Transformers
arXiv:2609.24919v1 Announce Type: new Abstract: Recent advances in pixel-space diffusion models have narrowed the image quality gap with latent-space diffusion, but still converge more slowly and lag...