LocInv: Localization-aware Inversion for Text-Guided Image Editing
- URL: http://arxiv.org/abs/2405.01496v1
- Date: Thu, 2 May 2024 17:27:04 GMT
- Title: LocInv: Localization-aware Inversion for Text-Guided Image Editing
- Authors: Chuanming Tang, Kai Wang, Fei Yang, Joost van de Weijer,
- Abstract summary: Text-guided image editing research aims to empower users to manipulate generated images by altering the text prompts.
Existing image editing techniques are prone to editing over unintentional regions that are beyond the intended target area.
We propose localization-aware Inversion (LocInv), which exploits segmentation maps or bounding boxes as extra localization priors to refine the cross-attention maps.
- Score: 17.611103794346857
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large-scale Text-to-Image (T2I) diffusion models demonstrate significant generation capabilities based on textual prompts. Based on the T2I diffusion models, text-guided image editing research aims to empower users to manipulate generated images by altering the text prompts. However, existing image editing techniques are prone to editing over unintentional regions that are beyond the intended target area, primarily due to inaccuracies in cross-attention maps. To address this problem, we propose Localization-aware Inversion (LocInv), which exploits segmentation maps or bounding boxes as extra localization priors to refine the cross-attention maps in the denoising phases of the diffusion process. Through the dynamic updating of tokens corresponding to noun words in the textual input, we are compelling the cross-attention maps to closely align with the correct noun and adjective words in the text prompt. Based on this technique, we achieve fine-grained image editing over particular objects while preventing undesired changes to other regions. Our method LocInv, based on the publicly available Stable Diffusion, is extensively evaluated on a subset of the COCO dataset, and consistently obtains superior results both quantitatively and qualitatively.The code will be released at https://github.com/wangkai930418/DPL
Related papers
- Enhancing Text-to-Image Editing via Hybrid Mask-Informed Fusion [61.42732844499658]
This paper systematically improves the text-guided image editing techniques based on diffusion models.
We incorporate human annotation as an external knowledge to confine editing within a Mask-informed'' region.
arXiv Detail & Related papers (2024-05-24T07:53:59Z) - LIME: Localized Image Editing via Attention Regularization in Diffusion Models [69.33072075580483]
This paper introduces LIME for localized image editing in diffusion models.
LIME does not require user-specified regions of interest (RoI) or additional text input, but rather employs features from pre-trained methods and a straightforward clustering method to obtain precise editing mask.
We propose a novel cross-attention regularization technique that penalizes unrelated cross-attention scores in the RoI during the denoising steps, ensuring localized edits.
arXiv Detail & Related papers (2023-12-14T18:59:59Z) - Dynamic Prompt Learning: Addressing Cross-Attention Leakage for
Text-Based Image Editing [23.00202969969574]
We propose Dynamic Prompt Learning (DPL) to force cross-attention maps to focus on correct noun words in the text prompt.
We show improved prompt editing results for Word-Swap, Prompt Refinement, and Attention Re-weighting, especially for complex multi-object scenes.
arXiv Detail & Related papers (2023-09-27T13:55:57Z) - StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing [115.49488548588305]
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.
They suffer from two problems: Unsatisfying results for selected regions and unexpected changes in non-selected regions.
arXiv Detail & Related papers (2023-03-28T00:16:45Z) - Zero-shot Image-to-Image Translation [57.46189236379433]
We propose pix2pix-zero, an image-to-image translation method that can preserve the original image without manual prompting.
We propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process.
Our method does not need additional training for these edits and can directly use the existing text-to-image diffusion model.
arXiv Detail & Related papers (2023-02-06T18:59:51Z) - Exploring Stroke-Level Modifications for Scene Text Editing [86.33216648792964]
Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text.
Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously.
We propose a novel network by MOdifying Scene Text image at strokE Level (MOSTEL)
arXiv Detail & Related papers (2022-12-05T02:10:59Z) - DiffEdit: Diffusion-based semantic image editing with mask guidance [64.555930158319]
DiffEdit is a method to take advantage of text-conditioned diffusion models for the task of semantic image editing.
Our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited.
arXiv Detail & Related papers (2022-10-20T17:16:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.