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DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing

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arxiv 2402.02583 v1 pith:23MAMVGR submitted 2024-02-04 cs.CV cs.LG

Chong Mou , Xintao Wang , Jiechong Song , Ying Shan , Jian Zhang This is my paper · ORCID
classification cs.CVcs.LG
keywords editingimagecontentfine-grainedflexibilityaccuracydiffeditordifferential
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Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities, translating these abilities to fine-grained image editing remains challenging. In this paper, we propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing: (1) in complex scenarios, editing results often lack editing accuracy and exhibit unexpected artifacts; (2) lack of flexibility to harmonize editing operations, e.g., imagine new content. In our solution, we introduce image prompts in fine-grained image editing, cooperating with the text prompt to better describe the editing content. To increase the flexibility while maintaining content consistency, we locally combine stochastic differential equation (SDE) into the ordinary differential equation (ODE) sampling. In addition, we incorporate regional score-based gradient guidance and a time travel strategy into the diffusion sampling, further improving the editing quality. Extensive experiments demonstrate that our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks, including editing within a single image (e.g., object moving, resizing, and content dragging) and across images (e.g., appearance replacing and object pasting). Our source code is released at https://github.com/MC-E/DragonDiffusion.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors

    cs.CV 2025-01 conditional novelty 7.0 of 10

    Interactive image editing can be cast as image-to-video generation: initializing from Stable Video Diffusion plus a new matching attention mechanism yields high-quality sketch, drag, and coarse-edit results with far l...

  2. Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing via Bidirectional Warping

    cs.CV 2025-09 conditional novelty 6.0 of 10

    Drag-based editing becomes pixel-space bidirectional warping plus inpainting, giving real-time previews and 0.3s final edits at 512x512.

  3. PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and Generation

    cs.CV 2024-12 conditional novelty 6.0 of 10

    An inversion-free, training-free diffusion editing method that anchors output latents to a pixel-manipulated copy of the image achieves consistent object repositioning, resizing, and pasting in 16 steps.

  4. GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object Generation

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A point-cloud-structured latent space with cascaded flow matching enables high-quality text- and image-conditioned 3D object generation and interactive editing.

  5. PEMF-VTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A mask-free video virtual try-on model that uses sparse point correspondences between garment and frames, plus frame-to-frame tracking, to improve garment transfer and temporal coherence.

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