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StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing

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arxiv 2303.15649 v3 pith:CXUOA7NI submitted 2023-03-28 cs.CV

Senmao Li , Joost van de Weijer , Taihang Hu , Fahad Shahbaz Khan , Qibin Hou , Yaxing Wang , Jian Yang , Ming-Ming Cheng This is my paper
classification cs.CV
keywords editingimagestylediffusiontheyattentionchangesimagesinput
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A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.They either finetune the model, or invert the image in the latent space of the pretrained model. However, they suffer from two problems: (1) Unsatisfying results for selected regions and unexpected changes in non-selected regions.(2) They require careful text prompt editing where the prompt should include all visual objects in the input image.To address this, we propose two improvements: (1) Only optimizing the input of the value linear network in the cross-attention layers is sufficiently powerful to reconstruct a real image. (2) We propose attention regularization to preserve the object-like attention maps after reconstruction and editing, enabling us to obtain accurate style editing without invoking significant structural changes. We further improve the editing technique that is used for the unconditional branch of classifier-free guidance as used by P2P. Extensive experimental prompt-editing results on a variety of images demonstrate qualitatively and quantitatively that our method has superior editing capabilities compared to existing and concurrent works. See our accompanying code in Stylediffusion: \url{https://github.com/sen-mao/StyleDiffusion}.

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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. Translation of Text Embedding via Delta Vector to Suppress Strongly Entangled Content in Text-to-Image Diffusion Models

    cs.CV 2025-08 conditional novelty 5.0 of 10

    Subtracting the text embedding of an unwanted concept from a target word's embedding, with cross-attention keys and values steered in opposite directions, suppresses strongly entangled content in Stable Diffusion and ...

  2. FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields

    cs.GR 2025-07 conditional novelty 5.0 of 10

    FlowDrag combines 3D mesh deformation with diffusion-based drag editing, using the resulting 2D vector flow to steer the denoising process, and adds a ground-truth benchmark built from video frames.

  3. Instruction-based Image Editing: A Survey on Data, Models, Evaluation, and Applications

    cs.CV 2026-07 conditional novelty 4.0 of 10

    A survey of instruction-based image editing plus a new 21-task benchmark, CDD-IIE, on which ten open models are scored by human experts.

  4. Stable Score Distillation

    cs.CV 2025-07 conditional novelty 4.0 of 10

    SSD is a diffusion score-distillation loss for text-guided 2D and 3D editing that combines a CFG cross-prompt term, a null-text cross-trajectory regularizer, and a prompt-enhancement term to stabilize edits.

  5. DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing

    cs.CV 2025-06 reject novelty 4.0 of 10

    DCI combines reference-guided noise correction with fixed-point latent refinement and reports state-of-the-art reconstruction and editing metrics on PIE-Bench.

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