arXiv AI
Aug 21
Scale-Separated Conditioning for Style-Encoder-Free Diffusion Stylization
arXiv:2608. 19719v1 Announce Type: cross Abstract: Reference-based diffusion stylization requires separating target geometry from transferable appearance.
Scale-Separated Conditioning for Style-Encoder-Free Diffusion Stylization
Reference-based diffusion stylization requires separating target geometry from transferable appearance. Existing tuning-based methods often rely on aligned content-style-target triplets or auxiliary visual encoders, which increases data cost and can transfer unintended scene structure from the style reference.
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arXiv Computer Vision
Sep 21
SafeStyle: Calibrated Style Residual Injection for Controllable Style-Leakage Trade-off in Diffusion Stylization
SafeStyle is a training‑free framework that injects calibrated style residuals into frozen diffusion models for reference‑guided stylization. It estimates style‑supported and content‑associated subspaces from small calibration sets, then transports purified style evidence across adaptive spatial granularity while limiting its influence with a residual‑norm budget. Experiments on texture‑ and geometry‑dominant styles show high DINO style similarity (0.432–0.474) with minimal semantic leakage (0.8%).
arXiv AI
Sep 10
MAST: Mask-Guided Attention Control for Training-Free Regional-Multi Style Transfer
MAST (Mask‑Guided Attention Control for Training‑Free Regional‑Multi Style Transfer) is a framework that enables diffusion models to apply multiple reference styles to user‑specified regions of a content image without any training or optimization. It introduces logit‑level attention mass allocation, sharpness‑aware temperature scaling, and discrepancy‑aware detail injection to address mass allocation, selectivity, and detail loss problems in regional‑multi style transfer. Experiments with two to five styles show that MAST outperforms baselines in ArtFID, FID, and R‑FID, achieving high regional style fidelity, content preservation, and scalability.
arXiv Computer Vision
Sep 30
CLeaR: A Unified Framework for Resolving the Leakage-Degradation Dilemma in Style Transfer
arXiv:2609.38136v1 Announce Type: new Abstract: Style transfer aims to render target content in the style of a reference image, but existing methods often suffer from content leakage, where objects,...
arXiv AI
Sep 3
ContextAnyone: Context-Aware Diffusion for Character-Consistent Text-to-Video Generation
ContextAnyone is a context‑aware diffusion framework that treats a reference image as an explicitly preserved appearance anchor rather than a simple conditioning signal. By jointly reconstructing the reference image and generating the target video within a shared diffusion transformer, it provides direct supervision for maintaining identity and fine‑grained appearance throughout denoising. The method introduces asymmetric information flow and Gap‑RoPE positional representations to keep the reference stable while allowing selective access by video tokens, and demonstrates improved identity and appearance consistency on an OpenVid‑HD benchmark.
arXiv Computer Vision
Oct 7
Local Content-Style Control for Diffusion-based Image Stylization
arXiv:2610.08704v1 Announce Type: cross Abstract: Image stylization with latent-diffusion models entangles two independently refined axes: what a region depicts and how it is depicted. Such pipelines...
arXiv AI
6d ago
LociArt: A Unified Paradigm for Free-Style Local Video Stylization
LociArt introduces a unified framework for free‑style local video stylization, allowing precise specification of where to stylize and which style to apply while preserving non‑target content. The core method, GRNStyle, builds on Generative Refinement Networks and uses a polar style‑aware mask to embed style identities as equal‑norm directions within selected regions. An automated pipeline generates paired supervision, leading to the LociArt‑100K dataset and benchmark, which evaluate regional style quality, non‑target preservation, and temporal consistency, and experiments show GRNStyle outperforms existing methods in these aspects.
arXiv Machine Learning
Sep 15
Abstract-LoRA: Unlocking Single-Image Style Transfer through Targeted U-Net Block Training
Abstract‑LoRA introduces a lightweight LoRA training approach that targets specific U‑Net blocks in diffusion models to improve single‑image style transfer. By refining block selection, adding more blocks, and using clustering‑based style abstraction, it better disentangles and balances style and content compared to prior methods like B‑LoRA. Experiments show that the method produces more harmonious artistic images while quantitatively preserving both style and content.
Hugging Face Trending Papers
Aug 12
Through Van Gogh's Eyes: Global Style Transfer with Diffusion Mod
Artistic image synthesis aims to recreate the expressive visual identity of a target artist, yet existing methods often fail to capture an artist's global style. Conventional style transfer methods transfer the style of one or a few reference artworks to a content image in a One-to-One manner, making them effective for artwork-level stylization but limited in representing the broader stylistic distribution of an artist.
arXiv Computer Vision
Oct 1
BTC3D: Blended Tile Conditioning for Detail-Enhancing Image-to-3D Generation
arXiv:2609.39709v1 Announce Type: new Abstract: Recent diffusion-based pipelines have achieved promising progress in image-to-3D synthesis. However, generating high-fidelity details remains challengi...
Hugging Face Trending Papers
Sep 30
BTC3D: Blended Tile Conditioning for Detail-Enhancing Image-to-3D Generation
Recent diffusion-based pipelines have achieved promising progress in image-to-3D synthesis. However, generating high-fidelity details remains challenging, especially when the input image contains rich...
Hugging Face Trending Papers
Jun 11
DuET: Dual Expert Trajectories for Diffusion Image Editing
Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent source-image conditioning can limit how fully an edit is executed and how natural the result appears, especially when the target scene diverges substantially from the input.