GlyphControl: Glyph Conditional Control for Visual Text Generation
This addresses the need for customizable and accurate visual text generation in AI applications, representing an incremental improvement over existing methods.
The paper tackles the problem of generating coherent visual text in images by proposing GlyphControl, which uses glyph conditional information to enhance Stable-Diffusion without retraining, resulting in improved OCR accuracy, CLIP score, and FID compared to DeepFloyd IF.
Recently, there has been an increasing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this paper, we propose a novel and efficient approach called GlyphControl to address this task. Unlike existing methods that rely on character-aware text encoders like ByT5 and require retraining of text-to-image models, our approach leverages additional glyph conditional information to enhance the performance of the off-the-shelf Stable-Diffusion model in generating accurate visual text. By incorporating glyph instructions, users can customize the content, location, and size of the generated text according to their specific requirements. To facilitate further research in visual text generation, we construct a training benchmark dataset called LAION-Glyph. We evaluate the effectiveness of our approach by measuring OCR-based metrics, CLIP score, and FID of the generated visual text. Our empirical evaluations demonstrate that GlyphControl outperforms the recent DeepFloyd IF approach in terms of OCR accuracy, CLIP score, and FID, highlighting the efficacy of our method.