BrushNet: A Novel Image Inpainting Model with Dual-Branch Diffusion
BrushNet introduces a dual-branch design for image inpainting, enhancing coherence and quality. The model's architecture allows for pixel-level masked image feature insertion, leading to superior performance across various metrics.
Towards Context-Stable and Visual-Consistent Image Inpainting: ASUKA Framework
ASUKA framework enhances image inpainting by achieving context-stability and visual-consistency through alignment with a frozen SD model.
BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion
BrushNetは、画像修復に革新的なアプローチを導入し、優れた性能を提供します。
BrushNet: A Novel Image Inpainting Model with Dual-Branch Design
BrushNet introduces a dual-branch design for image inpainting, enhancing image quality, masked region preservation, and text alignment.
Image Inpainting via Dual Generation of Texture and Structure
The core message of this paper is that image inpainting can be effectively achieved by jointly modeling structure-constrained texture synthesis and texture-guided structure reconstruction in a two-stream network architecture, which allows the two subtasks to better leverage each other for more plausible generation.
A Task-Decoupled Image Inpainting Framework for Building Class-Specific Object Removers
Decoupling object removal and restoration tasks in image inpainting networks leads to superior class-specific object removal, as demonstrated by a novel framework that leverages separate models for each task and a data curation method.
© 2024 by Linnk AI