#text-to-video

Discover the method for creating text-to-video models using GANs in Python. This guide covers key processes such as data coding, pre-processing, and GAN implementation for efficient video generation, suitable for those with limited computing resources.

The VGen project is an open-source video synthesis platform from the Tongyi Lab at Alibaba, featuring state-of-the-art models for generating videos. It facilitates the creation of high-quality videos from text and images, with the capability to integrate feedback from users. The repository includes various models like I2VGen-xl for image-to-video conversion and VideoComposer for videos with controlled motion. VGen offers comprehensive tools for visualization, training, and performance evaluation in video generation. Known for its flexibility and high performance across video tasks, recent updates include the release of InstructVideo and the ModelScopeT2V V1.5 model, which enhances video synthesis through improved customization and scalability.

This zero-shot video editing framework uses pre-trained diffusion models for text-based modifications, preserving video structure and motion by using intermediate attention maps. It enhances consistency with spatial-temporal attention, offering style and attribute changes in videos. The method allows for shape-aware adjustments, as demonstrated through empirical evaluations.

Explore a tool that converts text into videos by merging images, audio, and subtitles. Utilizing stable-diffusion for visuals and edge-tts for audio, this solution creates multimedia content via opencv and ffmpeg, supporting MP4 format. With OpenAI and huggingface models for enhanced imagery, the tool is ready for Docker and macOS development environments.

The project involves tuning text-to-image diffusion models like Stable Diffusion and DreamBooth for streamlined text-to-video generation. Using a distinct video-text pair as input, it adjusts the models for tailored video creation. Methods like DDIM inversion improve output stability, and the setup allows for various downloadable, style-specific models. Users may train custom models or directly employ pretrained models via platforms such as Hugging Face and Google Colab. This technique supports fast video rendering on advanced GPUs, offering a flexible solution for AI-driven video editing.

Delve into innovative methods that transform text into infinite video outputs without training requirements, utilizing minimal VRAM for greater accessibility. The project supports VideoCrafter2 for single GPU use and Open-Sora Plan for distributed inference, catering to varied creative applications with reduced computational load. Uncover advancements in video generation demonstrated at NeurIPS 2024.