T2V Studio: A Diffusion-Based Framework for Text-to-Video Generation

The design and implementation of T2V Studio, a diffusion-based framework for text-to-video generation, are presented in this paper. The proposed system combines motion-aware temporal conditioning with image-based diffusion models to produce short animated videos from natural language prompts. AnimateDiff motion adapters are incorporated to preserve temporal consistency across video frames, and Stable Diffusion 1.5 is used for frame-wise image synthesis. The framework’s ability to produce coherent short video clips under constrained computational resources is demonstrated through its implementation and evaluation on Google Colab using an NVIDIA T4 GPU. The findings demonstrate that the system can generate visually consistent animations from text descriptions without the need for manual video editing or model training, which makes it appropriate for applications involving rapid content creation and prototyping.