Enhancing Stability and Quality in Diffusion-Based Drag Editing
The core message of this work is to introduce GoodDrag, a novel approach that enhances the stability and quality of drag editing using diffusion models. The proposed method addresses the key challenges in existing diffusion-based drag editing techniques through two key contributions: Alternating Drag and Denoising (AlDD) framework and Information-Preserving Motion Supervision.
ByteEdit: A Comprehensive Feedback Learning Framework for Boosting, Complying, and Accelerating Generative Image Editing
ByteEdit introduces a novel feedback learning framework that significantly enhances the performance of generative image editing tasks, including outpainting and inpainting, by improving generation quality, consistency, and inference speed.
ClickDiffusion: An Interactive System for Precise Image Manipulation Using Natural Language and Direct Manipulation
ClickDiffusion is an interactive system that enables users to perform precise image manipulations by seamlessly combining natural language instructions and direct manipulation of visual elements.
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.
Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach
Our method, HairFast, uniquely solves the challenges of hairstyle transfer by using a fast encoder-based approach that achieves high resolution, near real-time performance, and superior reconstruction compared to optimization problem-based methods.
Drag Your Noise: Efficient Point-based Image Editing via Diffusion Semantic Propagation
DragNoise, a novel interactive point-based image editing method, leverages diffusion semantic propagation to enable efficient and stable editing by manipulating the predicted noise in the reverse diffusion process as semantic editors.
Transferring Interpretable Directions from GANs to Enhance Disentangled Image Editing in Diffusion Models
GANTASTIC is a novel framework that transfers interpretable directions from pre-trained GAN models directly into diffusion-based models to enable disentangled and controllable image editing.
LoMOE: Localized Multi-Object Editing via Multi-Diffusion
Introducing a novel framework for zero-shot localized multi-object editing through a multi-diffusion process to overcome challenges in precise image editing.
InstructBrush: Learning Attention-based Instruction Optimization for Image Editing
InstructBrush proposes a method for instruction-based image editing that optimizes editing instructions using attention-based techniques and transformation-oriented initialization, resulting in superior editing performance.
FlexEdit: Flexible and Controllable Object-centric Image Editing Framework
FlexEdit enables flexible and controllable object-centric image editing through optimization and blending mechanisms.
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