:  In the rapidly evolving digital landscape, video content has become a dominant medium for communication, education, entertainment, and marketing due to its high engagement and effectiveness. However, traditional video production is a complex, time-consuming, and resource-intensive process that requires significant technical expertise. To address these challenges, this project presents the design and development of an AI-powered one-minute text-to-video generation web application that automates the creation of short, high-quality videos from user-provided textual inputs. The proposed system integrates advanced Artificial Intelligence techniques, including Natural Language Processing (NLP), Text-to-Speech (TTS), and diffusion-based generative models, to convert textual descriptions into dynamic video content. The input text is processed into semantic representations and structured into meaningful scenes, ensuring logical flow and coherence. For each scene, relevant visual content is generated using AI-based image and video synthesis, while voice narration is produced through TTS systems. Additional multimedia elements such as subtitles, transitions, and background music are incorporated to enhance the quality and effectiveness of the generated video. The system is built upon a pretrained video diffusion pipeline that iteratively refines latent representations to produce temporally consistent video frames. These frames are then encoded into standard video formats using multimedia processing techniques. The application is developed with a user-friendly web interface and a robust backend powered by modern deep learning frameworks.

To ensure efficient performance, optimization techniques such as mixed-precision computation, memory-efficient processing, and GPU acceleration are employed. These enhancements enable faster inference and improved scalability, making the system suitable for deployment in GPU-enabled environments. The proposed solution significantly reduces manual effort, production time, and cost, thereby making video creation accessible to students, educators, content creators, and businesses. This project demonstrates the practical application of generative artificial intelligence in multimedia content creation and highlights the transition from static image synthesis to automated video generation. Despite its advantages, challenges such as computational complexity ,  hardware