ColorEdit: Training-free Image-Guided Color editing with diffusion model

• URL: http://arxiv.org/abs/2411.10232v1
• Date: Fri, 15 Nov 2024 14:45:58 GMT
• Title: ColorEdit: Training-free Image-Guided Color editing with diffusion model
• Authors: Xingxi Yin, Zhi Li, Jingfeng Zhang, Chenglin Li, Yin Zhang,
• Abstract summary: Text-to-image (T2I) diffusion models have been adopted for image editing tasks, demonstrating remarkable efficacy. However, due to attention leakage and collision between the cross-attention map of the object and the new color attribute from the text prompt, text-guided image editing methods may fail to change the color of an object. We propose a straightforward, yet stable, and effective image-guided method to modify the color of an object without requiring any additional fine-tuning or training.
• Score: 23.519884152019642
• License:
• Abstract: Text-to-image (T2I) diffusion models, with their impressive generative capabilities, have been adopted for image editing tasks, demonstrating remarkable efficacy. However, due to attention leakage and collision between the cross-attention map of the object and the new color attribute from the text prompt, text-guided image editing methods may fail to change the color of an object, resulting in a misalignment between the resulting image and the text prompt. In this paper, we conduct an in-depth analysis on the process of text-guided image synthesizing and what semantic information different cross-attention blocks have learned. We observe that the visual representation of an object is determined in the up-block of the diffusion model in the early stage of the denoising process, and color adjustment can be achieved through value matrices alignment in the cross-attention layer. Based on our findings, we propose a straightforward, yet stable, and effective image-guided method to modify the color of an object without requiring any additional fine-tuning or training. Lastly, we present a benchmark dataset called COLORBENCH, the first benchmark to evaluate the performance of color change methods. Extensive experiments validate the effectiveness of our method in object-level color editing and surpass the performance of popular text-guided image editing approaches in both synthesized and real images.

Related papers

• DiffUHaul: A Training-Free Method for Object Dragging in Images [78.93531472479202]
  We propose a training-free method, dubbed DiffUHaul, for the object dragging task. We first apply attention masking in each denoising step to make the generation more disentangled across different objects. In the early denoising steps, we interpolate the attention features between source and target images to smoothly fuse new layouts with the original appearance.
  arXiv  Detail & Related papers  (2024-06-03T17:59:53Z)
• Tuning-Free Image Customization with Image and Text Guidance [65.9504243633169]
  We introduce a tuning-free framework for simultaneous text-image-guided image customization. Our approach preserves the semantic features of the reference image subject while allowing modification of detailed attributes based on text descriptions. Our approach outperforms previous methods in both human and quantitative evaluations.
  arXiv  Detail & Related papers  (2024-03-19T11:48:35Z)
• Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing [47.71851180196975]
  tuning-free Text-guided Image Editing (TIE) is of greater importance for application developers. We conduct an in-depth probing analysis and demonstrate that cross-attention maps in Stable Diffusion often contain object attribution information. In contrast, self-attention maps play a crucial role in preserving the geometric and shape details of the source image.
  arXiv  Detail & Related papers  (2024-03-06T03:32:56Z)
• Diffusing Colors: Image Colorization with Text Guided Diffusion [11.727899027933466]
  We present a novel image colorization framework that utilizes image diffusion techniques with granular text prompts. Our method provides a balance between automation and control, outperforming existing techniques in terms of visual quality and semantic coherence. Our approach holds potential particularly for color enhancement and historical image colorization.
  arXiv  Detail & Related papers  (2023-12-07T08:59:20Z)
• DiffColor: Toward High Fidelity Text-Guided Image Colorization with Diffusion Models [12.897939032560537]
  We propose a new method called DiffColor to recover vivid colors conditioned on a prompt text. We first fine-tune a pre-trained text-to-image model to generate colorized images using a CLIP-based contrastive loss. Then we try to obtain an optimized text embedding aligning the colorized image and the text prompt, and a fine-tuned diffusion model enabling high-quality image reconstruction. Our method can produce vivid and diverse colors with a few iterations, and keep the structure and background intact while having colors well-aligned with the target language guidance.
  arXiv  Detail & Related papers  (2023-08-03T09:38:35Z)
• Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model [22.975965453227477]
  We introduce a new framework called textitPaste, Inpaint and Harmonize via Denoising (PhD) In our experiments, we apply PhD to both subject-driven image editing tasks and explore text-driven scene generation given a reference subject.
  arXiv  Detail & Related papers  (2023-06-13T07:43:10Z)
• Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models [6.34777393532937]
  We propose an accurate and quick inversion technique, Prompt Tuning Inversion, for text-driven image editing. Our proposed editing method consists of a reconstruction stage and an editing stage. Experiments on ImageNet demonstrate the superior editing performance of our method compared to the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-05-08T03:34:33Z)
• Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting [53.708523312636096]
  We present Imagen Editor, a cascaded diffusion model built, by fine-tuning on text-guided image inpainting. edits are faithful to the text prompts, which is accomplished by using object detectors to propose inpainting masks during training. To improve qualitative and quantitative evaluation, we introduce EditBench, a systematic benchmark for text-guided image inpainting.
  arXiv  Detail & Related papers  (2022-12-13T21:25:11Z)
• 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)
• Instance-aware Image Colorization [51.12040118366072]
  In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images. We use a similar network to extract the full-image features and apply a fusion module to predict the final colors.
  arXiv  Detail & Related papers  (2020-05-21T17:59:23Z)

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.