# Text-to-Image Generation

1Prompt1Story is an innovative text-to-image generation technology that creates consistent image sequences from a single prompt without the need for additional training. It leverages the contextual consistency of language models to generate identity-consistent images by concatenating all descriptions into one prompt. The technology supports multi-character generation, spatial control image creation, and real image personalization, demonstrating broad application potential. This model targets creators and developers who require efficient and consistent image generation for storytelling, animation, and more.

Glyph-ByT5 is a custom text encoder aimed at improving the accuracy of visual text rendering in text-to-image generation models. It achieves this by fine-tuning a character-aware ByT5 encoder and utilizing a carefully curated dataset of paired glyph text. Integrating Glyph-ByT5 with SDXL results in the Glyph-SDXL model, enhancing text rendering accuracy in image design generation from below 20% to nearly 90%. This model also enables automatic multi-line layout rendering for paragraph text, maintaining high spelling accuracy for character counts ranging from dozens to hundreds. Furthermore, by fine-tuning on a small set of high-quality real images containing visual text, Glyph-SDXL has significantly improved its scene text rendering capability in open-domain real images. These encouraging results aim to encourage further exploration of designing custom text encoders for various challenging tasks.

LaVi-Bridge is a bridge model designed for text-to-image diffusion models, enabling the connection of various pre-trained language models and generative visual models. It utilizes LoRA and adapters, providing a flexible and plug-and-play approach without modifying the weights of the original language and visual models. Compatible with a variety of language and generative visual models, it accommodates different architectures. Within this framework, we demonstrate that integrating more advanced modules (such as more sophisticated language models or generative visual models) can significantly improve capabilities like text alignment or image quality. The model has been extensively evaluated, confirming its effectiveness.

The study 'Controlling Text-to-Image Diffusion' explores how to effectively guide or control powerful text-to-image generation models for various downstream tasks. The orthogonal finetuning (OFT) method is proposed, which maintains the model's generative ability. OFT preserves the hypershell energy between neurons, preventing the model from collapsing. The authors consider two important fine-tuning tasks: subject-driven generation and controllable generation. Results show that the OFT method outperforms existing methods in terms of generation quality and convergence speed.

AiQuickHelp is an AI assistant that helps improve work efficiency through features like customized prompts, music playback, text-to-image generation, image-to-text generation, and code problem solving. It can provide personalized suggestions and advice based on your needs, play music to relax your mind, generate text summaries and keywords, generate image descriptions and tags, and solve code problems. AiQuickHelp can be applied to various scenarios, helping you work more efficiently.