AI-Based Text-to-Video Generation Using Diffusion and Deep Learning Techniques
Recent advances in Generative Artificial Intelligence have significantly improved automatic multimedia content creation. This paper presents an AI-based Text-to-Video Generation framework that converts natural language prompts into short animated video sequences using diffusion and deep learning techniques. The proposed framework employs the Stable Diffusion v1.5 model for generating high-quality image frames and AnimateDiff for introducing smooth temporal motion while preserving scene consistency. The DDIM scheduler is utilized to accelerate the denoising process and improve inference efficiency without compromising output quality. The generated frames are sequentially processed and assembled into MP4 videos using FFmpeg. The proposed system is implemented using Python, PyTorch, Hugging Face Diffusers, and Google Colab, providing a lightweight and costeffective platform for video generation. Experimental evaluation demonstrates that the proposed framework generates visually realistic and semantically meaningful videos with improved motion continuity and reduced computational complexity compared to conventional frame-by-frame generation approaches. The proposed framework has potential applications in digital media, education, entertainment, advertising, animation, and content creation