Training-Free Image Editing with Visual Context Integration and Concept Alignment
In image editing, it is essential to incorporate a context image to convey the user's precise requirements, such as subject appearance or image style. Existing training-based visual context-aware editing methods incur data collection effort and training cost. On the other hand, the training-free alternatives are typically established on diffusion inversion, which struggles with consistency and flexibility. In this work, we propose VicoEdit, a training-free and inversion-free method to inject the visual context into the pretrained text-prompted editing model. More specifically, VicoEdit directly transforms the source image into the target one based on the visual context, thereby eliminating the need for inversion that can lead to deviated trajectories. Moreover, we design a posterior sampling approach guided by concept alignment to enhance the editing consistency. Empirical results demonstrate that our training-free method achieves even better editing performance than the state-of-the-art training-based models.
Code (0)
등록된 구현이 없습니다.
Tasks
Image EditingSimilar Papers 제목 키워드 기반
Model the Edit, Not the Image: Visual Autoregressive Editing from a Source-Centric Perspective
Next-scale visual autoregressive models (VARs) have emerged as a powerful generative paradigm, producing high-quality images through efficient coarse-to-fine prediction. However, their potential for text-guided image edi…
Image EditingFreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction
Introducing user-specified visual concepts in image editing is highly practical as these concepts convey the user's intent more precisely than text-based descriptions. We propose FreeEdit, a novel approach for achieving …
FlexID: Training-Free Flexible Identity Injection via Intent-Aware Modulation for Text-to-Image Generation
Personalized text-to-image generation aims to seamlessly integrate specific identities into textual descriptions. However, existing training-free methods often rely on rigid visual feature injection, creating a conflict …
Text-to-Image GenerationUniVideo: Unified Understanding, Generation, and Editing for Videos
Unified multimodal models have shown promising results in multimodal content generation and editing but remain largely limited to the image domain. In this work, we present UniVideo, a versatile framework that extends un…
Video GenerationText GenerationStyle TransferImage EditingMaking Implicit Preservation Intent Explicit in Conversational Image Editing
Conversational image editing requires preserving not only visible content, but also content that temporarily disappears across turns. When newly added or modified content occludes a previously visible region, that region…
Image Editing