GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation

• URL: http://arxiv.org/abs/2303.17870v2
• Date: Tue, 23 May 2023 04:07:00 GMT
• Title: GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
• Authors: Jian Ma, Mingjun Zhao, Chen Chen, Ruichen Wang, Di Niu, Haonan Lu, Xiaodong Lin
• Abstract summary: 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.
• Score: 18.396131717250793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions.Although the synthesis performance is fascinating, one significant limitation of current image generation models is their insufficient ability to generate text coherently within images, particularly for complex glyph structures like Chinese characters. To address this problem, 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.We first sophisticatedly design the image-text dataset's construction strategy, then build our model specifically on a diffusion-based image generator and carefully modify the network structure to allow the model to learn drawing language characters with the help of glyph and position information.Furthermore, we maintain the model's open-domain image synthesis capability by preventing catastrophic forgetting by using parameter-efficient fine-tuning techniques.Extensive qualitative and quantitative experiments demonstrate that our method not only produces accurate language characters as in prompts, but also seamlessly blends the generated text into the background.Please refer to our \href{https://1073521013.github.io/glyph-draw.github.io/}{project page}. \end{abstract}

Related papers

• Conditional Text-to-Image Generation with Reference Guidance [81.99538302576302]
  This paper explores using additional conditions of an image that provides visual guidance of the particular subjects for diffusion models to generate. We develop several small-scale expert plugins that efficiently endow a Stable Diffusion model with the capability to take different references. Our expert plugins demonstrate superior results than the existing methods on all tasks, each containing only 28.55M trainable parameters.
  arXiv  Detail & Related papers  (2024-11-22T21:38:51Z)
• Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Models [20.19571676239579]
  We introduce a novel diffusion-based framework to enhance the alignment of generated images with their corresponding descriptions. Our framework is built upon a comprehensive analysis of inconsistency phenomena, categorizing them based on their manifestation in the image. We then integrate a state-of-the-art controllable image generation model with a visual text generation module to generate an image that is consistent with the original prompt.
  arXiv  Detail & Related papers  (2024-06-24T06:12:16Z)
• 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)
• AutoStory: Generating Diverse Storytelling Images with Minimal Human Effort [55.83007338095763]
  We propose an automated story visualization system that can effectively generate diverse, high-quality, and consistent sets of story images. We utilize the comprehension and planning capabilities of large language models for layout planning, and then leverage large-scale text-to-image models to generate sophisticated story images.
  arXiv  Detail & Related papers  (2023-11-19T06:07:37Z)
• LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation [121.45667242282721]
  We propose a coarse-to-fine paradigm to achieve layout planning and image generation. Our proposed method outperforms the state-of-the-art models in terms of photorealistic layout and image generation.
  arXiv  Detail & Related papers  (2023-08-09T17:45:04Z)
• GlyphDiffusion: Text Generation as Image Generation [100.98428068214736]
  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.
  arXiv  Detail & Related papers  (2023-04-25T02:14:44Z)
• 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)
• Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation [10.39028769374367]
  We present a new framework that takes text-to-image synthesis to the realm of image-to-image translation. Our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text.
  arXiv  Detail & Related papers  (2022-11-22T20:39:18Z)

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.