Diffusing Colors: Image Colorization with Text Guided Diffusion

• URL: http://arxiv.org/abs/2312.04145v1
• Date: Thu, 7 Dec 2023 08:59:20 GMT
• Title: Diffusing Colors: Image Colorization with Text Guided Diffusion
• Authors: Nir Zabari, Aharon Azulay, Alexey Gorkor, Tavi Halperin, Ohad Fried
• Abstract summary: 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.
• Score: 11.727899027933466
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The colorization of grayscale images is a complex and subjective task with significant challenges. Despite recent progress in employing large-scale datasets with deep neural networks, difficulties with controllability and visual quality persist. To tackle these issues, we present a novel image colorization framework that utilizes image diffusion techniques with granular text prompts. This integration not only produces colorization outputs that are semantically appropriate but also greatly improves the level of control users have over the colorization process. Our method provides a balance between automation and control, outperforming existing techniques in terms of visual quality and semantic coherence. We leverage a pretrained generative Diffusion Model, and show that we can finetune it for the colorization task without losing its generative power or attention to text prompts. Moreover, we present a novel CLIP-based ranking model that evaluates color vividness, enabling automatic selection of the most suitable level of vividness based on the specific scene semantics. Our approach holds potential particularly for color enhancement and historical image colorization.

Related papers

• L-C4: Language-Based Video Colorization for Creative and Consistent Color [59.069498113050436]
  We present Language-based video colorization for Creative and Consistent Colors (L-C4) Our model is built upon a pre-trained cross-modality generative model. We propose temporally deformable attention to prevent flickering or color shifts, and cross-clip fusion to maintain long-term color consistency.
  arXiv  Detail & Related papers  (2024-10-07T12:16:21Z)
• Transforming Color: A Novel Image Colorization Method [8.041659727964305]
  This paper introduces a novel method for image colorization that utilizes a color transformer and generative adversarial networks (GANs) The proposed method integrates a transformer architecture to capture global information and a GAN framework to improve visual quality. Experimental results show that the proposed network significantly outperforms other state-of-the-art colorization techniques.
  arXiv  Detail & Related papers  (2024-10-07T07:23:42Z)
• Automatic Controllable Colorization via Imagination [55.489416987587305]
  We propose a framework for automatic colorization that allows for iterative editing and modifications. By understanding the content within a grayscale image, we utilize a pre-trained image generation model to generate multiple images that contain the same content. These images serve as references for coloring, mimicking the process of human experts.
  arXiv  Detail & Related papers  (2024-04-08T16:46:07Z)
• Control Color: Multimodal Diffusion-based Interactive Image Colorization [81.68817300796644]
  Control Color (Ctrl Color) is a multi-modal colorization method that leverages the pre-trained Stable Diffusion (SD) model. We present an effective way to encode user strokes to enable precise local color manipulation. We also introduce a novel module based on self-attention and a content-guided deformable autoencoder to address the long-standing issues of color overflow and inaccurate coloring.
  arXiv  Detail & Related papers  (2024-02-16T17:51:13Z)
• Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics [54.980359694044566]
  This paper tries to utilize corresponding audio, which naturally contains extra semantic information about the same scene. Experiments demonstrate that audio guidance can effectively improve the performance of automatic colorization.
  arXiv  Detail & Related papers  (2024-01-24T07:22:05Z)
• 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)
• Improved Diffusion-based Image Colorization via Piggybacked Models [19.807766482434563]
  We introduce a colorization model piggybacking on the existing powerful T2I diffusion model. A diffusion guider is designed to incorporate the pre-trained weights of the latent diffusion model. A lightness-aware VQVAE will then generate the colorized result with pixel-perfect alignment to the given grayscale image.
  arXiv  Detail & Related papers  (2023-04-21T16:23:24Z)
• PalGAN: Image Colorization with Palette Generative Adversarial Networks [51.59276436217957]
  We propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. PalGAN outperforms state-of-the-arts in quantitative evaluation and visual comparison, delivering notable diverse, contrastive, and edge-preserving appearances.
  arXiv  Detail & Related papers  (2022-10-20T12:28:31Z)
• TIC: Text-Guided Image Colorization [24.317541784957285]
  We propose a novel deep network that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color information of the objects present in the scene, the text encoding helps to improve the overall quality of the predicted colors. We have evaluated our proposed model using different metrics and found that it outperforms the state-of-the-art colorization algorithms both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-08-04T18:40:20Z)
• Attention-based Stylisation for Exemplar Image Colourisation [3.491870689686827]
  This work reformulates the existing methodology introducing a novel end-to-end colourisation network. The proposed architecture integrates attention modules at different resolutions that learn how to perform the style transfer task. Experimental validations demonstrate efficiency of the proposed methodology which generates high quality and visual appealing colourisation.
  arXiv  Detail & Related papers  (2021-05-04T18:56:26Z)
• Image Colorization: A Survey and Dataset [94.59768013860668]
  This article presents a comprehensive survey of state-of-the-art deep learning-based image colorization techniques. It categorizes the existing colorization techniques into seven classes and discusses important factors governing their performance. We perform an extensive experimental evaluation of existing image colorization methods using both existing datasets and our proposed one.
  arXiv  Detail & Related papers  (2020-08-25T01:22:52Z)

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.