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DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
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DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
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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.
Forward citations
Cited by 7 Pith papers
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From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing
A planner-orchestrator system learns long-horizon image editing by maximizing outcome-based rewards from a vision-language judge and refining plans from successful trajectories.
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AttriStory: Fine-grained Attribute Realization for Visual Storytelling with Diffusion Models
AttriStory adds a benchmark and AttriLoss-based latent optimization to improve faithful rendering of fine-grained attributes such as clothing color and texture in diffusion-model visual storytelling.
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StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generation
StructDiff adds adaptive receptive fields and 3D positional encoding to a single-scale diffusion model to preserve structure and enable spatial control in single-image generation.
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Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing via Bidirectional Warping
Drag-based editing becomes pixel-space bidirectional warping plus inpainting, giving real-time previews and 0.3s final edits at 512x512.
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DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation
DissolveStereo injects coarse dissolved depth maps into video diffusion latents via noisy restart and iterative refinement to produce temporally coherent stereo videos zero-shot.
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CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation
Coupling a global latent code with a 3D feature volume lets off-the-shelf 3D generators perform local semantic edits — copy, delete, resize, mix, and drag — across object categories while preserving unedited regions.
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{\Phi}-Noise: Training-Free Temporal Video Conditioning via Phase-Based Noise Manipulation
Training-free motion conditioning for latent video diffusion by direct injection of low-frequency phase from a reference video into the diffusion noise.
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