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.
ASUKA framework enhances image inpainting by achieving context-stability and visual-consistency through alignment with a frozen SD model.
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 introduces a dual-branch design for image inpainting, enhancing image quality, masked region preservation, and text alignment.
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.
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.