arXiv AI
InverFill: One-Step Inversion for Enhanced Few-Step Diffusion Inpainting
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arXiv AI
Jul 31
Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives
arXiv:2607. 26735v1 Announce Type: cross Abstract: Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without extensive prompt engineering.
arXiv Computer Vision
Sep 1
Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation
arXiv:2608.29997v1 Announce Type: new Abstract: We propose Discrete Diffusion Bridges (DDB), a novel framework designed to resolve the fundamental spatiotemporal misalignment of standard discrete dif...
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 Machine Learning
Jun 10
Cost-Aware Routing for Efficient Text-To-Image Generation
arXiv:2506. 14753v3 Announce Type: replace-cross Abstract: Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process.
arXiv Machine Learning
Aug 31
There and Back Again: Bidirectional Diffusion Bridges for Multimodality Translation
arXiv:2608.27885v1 Announce Type: new Abstract: Multimodality translation (e.g., text-to-image) is a core generative AI task. However, existing approaches (1) follow generative paths that do not dire...
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.