In this video, the presenter explores the development of a text-to-image generation gradio application using the diffusion model SSD-1B. This model is 60% faster and 50% smaller than the stable Diffusion Xcel model, making it a promising option for text-to-image generation tasks. The presenter compares the two models and demonstrates the benefits of using SSD-1B. Additionally, they discuss the data used for training the model and the realistic results generated by the Diffusion Xcel and SSD-1B models. The presenter proceeds to provide instructions on how to use the SSD-1B model for text-to-image generation, including the use of stable diffusion Excel pipeline and Image to Text import. Additionally, they demonstrate how to accelerate the process using the Transformer Pipeline and Transformers library. The presenter provides a tutorial on how to use negative prompts to generate cleaner and more polished images with the SSD-1B model, and discusses the benefits of negative prompting. Finally, the presenter demonstrates how to use the SSD-1B model to create a gradio application for text-to-image generation. The gradio application allows users to pass text input and negative prompt as inputs to the generate_image function. The presenter offers tips for creating input prompts that will yield the desired output and encourages viewers to share their findings in the comments.