DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models (2307.02421v2)

Abstract: Despite the ability of existing large-scale text-to-image (T2I) models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely edit the generated or real images. In this paper, we propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Specifically, we construct classifier guidance based on the strong correspondence of intermediate features in the diffusion model. It can transform the editing signals into gradients via feature correspondence loss to modify the intermediate representation of the diffusion model. Based on this guidance strategy, we also build a multi-scale guidance to consider both semantic and geometric alignment. Moreover, a cross-branch self-attention is added to maintain the consistency between the original image and the editing result. Our method, through an efficient design, achieves various editing modes for the generated or real images, such as object moving, object resizing, object appearance replacement, and content dragging. It is worth noting that all editing and content preservation signals come from the image itself, and the model does not require fine-tuning or additional modules. Our source code will be available at https://github.com/MC-E/DragonDiffusion.

Overview of "DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models"

The paper presents DragonDiffusion, a novel methodology for drag-style image manipulation using diffusion models. This work addresses the limitations of current text-to-image (T2I) diffusion models, specifically their lack of fine-grained image editing capabilities. DragonDiffusion introduces a method for manipulating generated or real images by leveraging feature correspondences within a pre-trained diffusion model. The approach transforms image editing into a gradient guidance problem, facilitated by energy functions focused on semantic and geometric alignment.

Key Contributions

1. Gradient Guidance for Image Editing:
   • DragonDiffusion models image manipulation as adjustments in feature correspondence and converts them into gradient guidance via energy functions.
   • This is done without additional model fine-tuning or new architectural modules, distinguishing it from prior GAN-based techniques.
2. Multi-Scale Feature Utilization:
   • The paper examines the role of features across various layers, developing a multi-scale guidance strategy to accommodate semantic and geometric correspondences.
3. Cross-Attention Mechanisms:
   • A memory bank approach is employed to maintain editing consistency. This approach integrates intermediate features obtained during image inversion, ensuring the edited image aligns closely with the original.

Methodology

DragonDiffusion is built on the StableDiffusion framework, adapting it to facilitate extensive image editing tasks. The method involves several key steps:

• DDIM Inversion: Used to initialize with a good generation prior by transforming the original image into a latent representation.
• Energy Function Construction:
  • Utilizes cosine similarity to measure feature similarities and discrepancies, casting image editing tasks as optimization problems driven by feature correspondence.
  • This mechanism allows for content editing and consistency to be handled directly within the diffusion framework.
• Implementation for Various Applications:
  • The framework supports object moving, resizing, appearance replacing, and inserting content across images, among others.

Experimental Evaluation

Experiments showcase DragonDiffusion's ability to handle diverse editing tasks effectively, demonstrating its robust generalization capabilities inherited from the diffusion models. Comparisons with models like DragGAN and DragDiffusion highlight superior editing quality, content consistency, and efficiency in numerous scenarios, including unaligned and complex image contexts.

Implications and Future Directions

DragonDiffusion provides a promising pathway for implementing direct and nuanced image manipulation using diffusion models. The capacity to handle fine-grained edits without modifying the model's core structure signals potential advancements in intuitive user-guided content creation.

Potential future research directions include further exploration of memory bank optimization, expanding application domains, and integrating more sophisticated attention mechanisms to enhance contextual understanding and editing precision in richer datasets or real-time environments. The work lays significant groundwork for leveraging diffusion models in interactive editing, suggesting that future studies could further examine scalability and integration into existing digital media workflows.