wtf is magi-1?

MAGI-1 is an AI model built by Sand AI that generates videos from images and text instructions. Given a starting image and a written description of what should happen, the model produces a video that follows those directions. It is designed to maintain visual consistency across the whole clip, so objects and scenes look stable as time progresses rather than flickering or drifting. The technical approach works by breaking a video into short fixed-length segments called chunks, then predicting each chunk one at a time in sequence. This allows the model to handle long videos and supports streaming output, meaning frames can appear as the generation is still in progress rather than requiring the full video to complete before showing anything. Users can also supply different text instructions for different chunks, which makes it possible to describe scene transitions or changing actions over time using plain language. The repository provides pre-trained model weights, inference code, and instructions for running the model locally. Several weight variants are available, including a smaller distilled version for faster output and a quantized version that requires less GPU memory. A ComfyUI integration is also included for users who prefer a node-based visual workflow. The weights are hosted on Hugging Face and can be downloaded separately. Running the model requires a machine with capable NVIDIA GPUs. The README specifies minimum GPU memory requirements and provides Docker-based setup instructions to handle the software dependencies. An API option via the Sand AI website is available for those who do not have the hardware to run it locally. MAGI-1 is released under the Apache 2.0 license. The project is accompanied by a technical report that describes the model architecture, training approach, and benchmarks in detail for readers with a research background.