GlyphDiffusion: Text Generation as Image Generation

• URL: http://arxiv.org/abs/2304.12519v2
• Date: Mon, 8 May 2023 07:44:48 GMT
• Title: GlyphDiffusion: Text Generation as Image Generation
• Authors: Junyi Li, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
• Abstract summary: We propose GlyphDiffusion, a novel diffusion approach for text generation via text-guided image generation. Our key idea is to render the target text as a glyph image containing visual language content. Our model also makes significant improvements compared to the recent diffusion model.
• Score: 100.98428068214736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have become a new generative paradigm for text generation. Considering the discrete categorical nature of text, in this paper, we propose GlyphDiffusion, a novel diffusion approach for text generation via text-guided image generation. Our key idea is to render the target text as a glyph image containing visual language content. In this way, conditional text generation can be cast as a glyph image generation task, and it is then natural to apply continuous diffusion models to discrete texts. Specially, we utilize a cascaded architecture (ie a base and a super-resolution diffusion model) to generate high-fidelity glyph images, conditioned on the input text. Furthermore, we design a text grounding module to transform and refine the visual language content from generated glyph images into the final texts. In experiments over four conditional text generation tasks and two classes of metrics (ie quality and diversity), GlyphDiffusion can achieve comparable or even better results than several baselines, including pretrained language models. Our model also makes significant improvements compared to the recent diffusion model.

Related papers

• Visual Text Generation in the Wild [67.37458807253064]
  We propose a visual text generator (termed SceneVTG) which can produce high-quality text images in the wild. The proposed SceneVTG significantly outperforms traditional rendering-based methods and recent diffusion-based methods in terms of fidelity and reasonability. The generated images provide superior utility for tasks involving text detection and text recognition.
  arXiv  Detail & Related papers  (2024-07-19T09:08:20Z)
• ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models [52.23899502520261]
  We introduce a new framework named ARTIST to focus on the learning of text structures. We finetune a visual diffusion model, enabling it to assimilate textual structure information from the pretrained textual model. Empirical results on the MARIO-Eval benchmark underscore the effectiveness of the proposed method, showing an improvement of up to 15% in various metrics.
  arXiv  Detail & Related papers  (2024-06-17T19:31:24Z)
• CustomText: Customized Textual Image Generation using Diffusion Models [13.239661107392324]
  Textual image generation spans diverse fields like advertising, education, product packaging, social media, information visualization, and branding. Despite recent strides in language-guided image synthesis using diffusion models, current models excel in image generation but struggle with accurate text rendering and offer limited control over font attributes. In this paper, we aim to enhance the synthesis of high-quality images with precise text customization, thereby contributing to the advancement of image generation models.
  arXiv  Detail & Related papers  (2024-05-21T06:43:03Z)
• UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models [25.219960711604728]
  This paper proposes a novel approach for text image generation, utilizing a pre-trained diffusion model. Our approach involves the design and training of a light-weight character-level text encoder, which replaces the original CLIP encoder. By employing an inference stage refinement process, we achieve a notably high sequence accuracy when synthesizing text in arbitrarily given images.
  arXiv  Detail & Related papers  (2023-12-08T07:47:46Z)
• Paragraph-to-Image Generation with Information-Enriched Diffusion Model [67.9265336953134]
  ParaDiffusion is an information-enriched diffusion model for paragraph-to-image generation task. It delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation. The code and dataset will be released to foster community research on long-text alignment.
  arXiv  Detail & Related papers  (2023-11-24T05:17:01Z)
• GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation [18.396131717250793]
  We introduce GlyphDraw, a general learning framework aiming to endow image generation models with the capacity to generate images coherently embedded with text for any specific language. Our method not only produces accurate language characters as in prompts, but also seamlessly blends the generated text into the background.
  arXiv  Detail & Related papers  (2023-03-31T08:06:33Z)
• Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation [63.061871048769596]
  We present a novel Unified Multi-Modal Latent Diffusion (UMM-Diffusion) which takes joint texts and images containing specified subjects as input sequences. To be more specific, both input texts and images are encoded into one unified multi-modal latent space. Our method is able to generate high-quality images with complex semantics from both aspects of input texts and images.
  arXiv  Detail & Related papers  (2023-03-16T13:50:20Z)
• eDiffi: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers [87.52504764677226]
  Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. We train an ensemble of text-to-image diffusion models specialized for different stages synthesis. Our ensemble of diffusion models, called eDiffi, results in improved text alignment while maintaining the same inference cost.
  arXiv  Detail & Related papers  (2022-11-02T17:43:04Z)

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.