arXiv Computer Vision
Preserving Subject-Clarity in Image Outpainting with Multiscale Wavelet Supervision
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arXiv AI
1d ago
ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images
ReGain is a training‑free correction that improves subject fidelity in text‑to‑image diffusion models personalized with synthetic images. The authors show that fine‑tuning on synthetic images degrades fidelity due to inflated classifier‑free guidance, especially at high frequencies. ReGain measures this inflation per frequency band and scales it down during sampling, closing 51‑64% of the fidelity gap on Stable Diffusion v1.5 and improving performance on SDXL and SD 3.5 while preserving text alignment.
arXiv Computer Vision
Sep 11
Overpainting: Localized Context-aware Diffusion Image Editing
The paper introduces "overpainting," a localized, context-aware image editing technique that allows users to specify precise or loose editing regions via a trimap. The method adapts a pretrained diffusion model with joint attention and low‑rank adaptation, incorporating attention‑dropout to balance noise, source, and mask inputs. An automated pipeline generates training data by pairing images from language‑based editing models, curating them, and extracting trimaps, enabling the model to perform a wide range of editing tasks.
arXiv Computer Vision
Sep 14
Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision
arXiv:2504.04903v3 Announce Type: replace Abstract: We present Lunima-OmniLV (abbreviated as OmniLV), a universal multimodal multi-task framework for low-level vision that addresses over 100 sub-task...
Hugging Face Trending Papers
Aug 10
Preserve More Details: Mitigating Content Drift in Real-World Image Super-Resolution
Real-world image super-resolution (Real-ISR) aims to reconstruct high-quality (HQ) images from low-quality (LQ) inputs subject to diverse real-world degradations. Recent advances have leveraged the LQ inputs and natural image priors learned by Stable Diffusion models to achieve impressive results.
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
Aug 26
AffineTok: Semantic Affine Consistency for Diffusion-Friendly Visual Tokenizer
arXiv:2608.23864v1 Announce Type: new Abstract: Visual tokenizers increasingly inject semantic supervision into latent spaces to make downstream diffusion easier. Yet how these semantics should be or...