BlipGlyphDiff: Glyph-Based Text to Image Generation With Different Styles and Background

The creation of text-to-image generative models that use diffusion-based methods to generate logical and aesthetically pleasing textual content has attracted increasing attention in recent times. To address this goal, a new and effective technique called BlipGlyphDiff is presented in this study. In contrast to previous methods that rely on character-sensitive text encoders like ByT5 and necessitate retraining and fine-tuning of text-to-image models, our approach makes use of extra glyph conditional information to improve the performance of the readily available Stable-Diffusion model for producing accurate visual text. Users can adjust the generated text's content, layout, and size to suit their needs by integrating glyph instructions. This research project, titled “BlipGlyphDiff: Glyph-Based Text to Image Generation with Different Styles and Background” utilizes a pre-trained benchmark dataset called LAION-Glyph[1]. Our experimental assessments highlight the efficacy of our approach by showing that our model outperforms contemporary methods incorporated in DALL·E 2, Midjourney, and Ideogram in terms of Fréchet Inception Distance score, similarity score in text embeddings, and Optical Character Recognition accuracy.