Robust Real Image Editing via Geometric Accumulation Inversion in Guided Diffusion Models
A novel image editing technique that seamlessly integrates text prompts and image prompts to yield diverse and precise editing outcomes, leveraging a geometric accumulation loss to faithfully preserve pixel space geometry and layout.
Localization-aware Inversion for Precise Text-Guided Image Editing
Localization-aware Inversion (LocInv) enhances cross-attention maps in diffusion models to enable fine-grained text-guided image editing while preventing unintended changes.
Text-Driven Image Editing via Learnable Regions
Our method enables mask-free local image editing by learning to generate bounding boxes that align with the provided text descriptions, without requiring user-specified masks or regions.
Contrastive Denoising Score: A Powerful Approach for Preserving Structural Consistency in Text-Guided Latent Diffusion Image Editing
The core message of this paper is that by integrating Contrastive Unpaired Translation (CUT) loss into the Delta Denoising Score (DDS) framework, the proposed Contrastive Denoising Score (CDS) method can effectively balance the preservation of structural details from the source image and the transformation of content to align with the target text prompt.
StyleHumanCLIP: Text-guided Garment Manipulation for StyleGAN-Human
提案されたStyleGANベースのフレームワークは、テキストに基づいた服装の編集を可能にし、人物のアイデンティティを保持しながら画像生成を制御する。
Forgedit: Text-guided Image Editing via Learning and Forgetting
Forgedit introduces a novel text-guided image editing method, addressing overfitting issues and achieving state-of-the-art results on TEdBench.
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