In a world where artificial intelligence is reshaping creative industries, Sand AI has unveiled a game-changing innovation: Magi-1, the world’s first autoregressive video generation model. Released on April 21, 2025, this open-source titan, boasting 24 billion parameters, promises to transform how creators, filmmakers, and developers craft high-quality videos. With unmatched control over timing, motion, and storytelling, Magi-1 is not just a tool—it’s a revolution in generative AI. Here’s everything you need to know about this groundbreaking model and how you can start using it today.
A New Era for Video Creation
Unlike traditional video generation models that process entire clips at once, Magi-1 takes a novel approach. It generates videos chunk by chunk, with each chunk consisting of 24 frames. This autoregressive method—where each new chunk builds on the previous one—enables seamless, infinite video extension. Imagine creating a short clip that effortlessly grows into a full-length cinematic narrative without awkward cuts or stitching. Magi-1 makes this possible, offering creators the ability to craft continuous stories with studio-grade motion and detail.
What sets Magi-1 apart is its precision. It achieves second-level timeline control, allowing users to fine-tune every moment of their video. Whether you’re animating a character walking across a red carpet or simulating complex physical interactions, Magi-1 delivers natural, fluid results. In benchmark tests, it scored an impressive 56.02 on the Physics-IQ benchmark for video continuation, nearly doubling the score of its closest competitor. This means Magi-1 doesn’t just look good—it understands the laws of physics, making movements and interactions in videos feel authentic.
Why Open-Source Matters
Magi-1 is released under the Apache 2.0 license, making it 100% open-source. Sand AI has shared the model’s code, pre-trained weights, and a detailed technical report on platforms like GitHub and Hugging Face. This openness invites developers, researchers, and creators worldwide to experiment, customize, and build upon the model. “Magi-1 is a major breakthrough in video generation, and open-sourcing it invites more innovation,” said Dr. Cao Yue, founder of Sand AI and a recipient of the prestigious Marr Prize.
The open-source nature of Magi-1 democratizes access to cutting-edge technology. From hobbyists with a single RTX 4090 GPU to professionals with high-end hardware, there’s a version of Magi-1 for everyone. The flagship 24-billion-parameter model requires significant computing power (recommended: 8x H100 GPUs), but a lighter 4.5-billion-parameter variant runs on a single RTX 4090, ensuring broader accessibility. Sand AI also offers distilled and quantized versions to balance quality and computational efficiency.
The Tech Behind the Magic
Magi-1 is built on a diffusion transformer architecture, enhanced by a suite of innovations. It uses a transformer-based variational autoencoder (VAE) with 8x spatial and 4x temporal compression, enabling fast decoding and high-quality reconstruction. Key advancements include:
- Block-Causal Attention: Ensures stable generation for long videos by focusing on causal relationships between chunks.
- Parallel Attention Blocks: Allow simultaneous processing of up to four chunks, boosting efficiency.
- Sandwich Normalization and SwiGLU: Enhance training stability at scale.
- Shortcut Distillation: Trains a single velocity-based model to support flexible inference budgets, maintaining fidelity with minimal computational cost.
These features make Magi-1 not only powerful but also practical for real-time applications like streaming generation. The model excels in image-to-video (I2V) tasks, where it transforms static images or text prompts into dynamic videos with high temporal consistency. For example, when tested with a prompt like “Elon Musk walking the red carpet,” Magi-1 produced a smooth, cinematic result that rivaled commercial tools.
Applications and Impact
Magi-1’s versatility makes it a game-changer across industries. Content creators can use it to produce engaging social media videos, while game developers can generate dynamic cutscenes. In film post-production, Magi-1’s infinite video extension and precise control streamline editing and scene transitions. Educators can even leverage it to create immersive visual aids.
The model’s open-source availability has sparked excitement in the AI community. On platforms like Reddit, users praise its quality but note its high computational demands. “The 24B variant requires 8x H100 to run lol,” one user commented, while others eagerly await lighter versions for consumer hardware. Despite these challenges, Magi-1’s performance outshines other open-source models like Wan-2.1 and HunyuanVideo, particularly in instruction-following and motion quality.
However, Magi-1 isn’t without limitations. Some users report that it struggles with non-realistic content, such as animation, and its hardware requirements can be a barrier for casual users. Sand AI has promised lighter versions and improved hardware optimization in the future, hinting at potential applications in real-time generation and virtual reality.
How to Use Magi-1: A Step-by-Step Guide
Ready to dive into Magi-1? Here’s how you can start creating stunning videos, whether you’re using the online platform or running it locally.
Option 1: Try Magi-1 Online
Sand AI’s web interface at magi.sand.ai is the easiest way to experiment. New users get 500 free credits (10 credits per second of video), enough to generate several short clips. Follow these steps:
- Sign Up: Create an account at magi.sand.ai.
- Enter a Prompt: Write a detailed text prompt describing your video, e.g., “A futuristic cityscape at sunset with flying cars.” Specify motion and scene details for best results.
- Set Parameters: Choose video length (1–10 seconds) and adjust settings like motion intensity or scene transitions.
- Generate: Click “Generate” and watch your video come to life in real-time (a 10-second clip takes 1–2 minutes).
- Download: Preview your video and download it in your preferred format.
For video extension, upload an existing clip, and Magi-1 will generate new segments that maintain consistency.
Option 2: Run Magi-1 Locally
For developers or users with powerful hardware, running Magi-1 locally offers more control. The recommended method is using Docker:
- Install Docker: Ensure Docker is installed on your system.
- Pull the Image: Run the following command to download the Magi-1 Docker image:
docker pull sandai/magi:latest - Run the Container: Launch the container with GPU support:
docker run -it --gpus all --privileged --shm-size=32g --name magi --net=host --ipc=host --ulimit memlock=-1 --ulimit stack=6710886 sandai/magi:latest /bin/bash - Configure the Model: Edit the config.json file to set parameters like video size, frame rate, or random seed. For example:
- video_size_h: Video height (e.g., 1440)
- video_size_w: Video width (e.g., 2568)
- num_frames: Video duration
- fps: Frames per second
- Generate Videos: Use the provided inference code to generate videos from text prompts or images. Refer to the GitHub repository for detailed instructions.
Hardware Requirements:
- 24B Model: 8x H100/H800 GPUs
- 4.5B Model: Single RTX 4090
- Distilled/Quantized Models: Reduced requirements for resource-constrained setups
For manual installation, use a Conda environment with Python 3.10.12, PyTorch 2.4.0, and dependencies listed in the requirements.txt file.
Tips for Best Results
- Craft Detailed Prompts: Be specific about motion, lighting, and scene transitions to maximize quality.
- Use Chunk-Wise Prompting: For long videos, treat each chunk like a stop-motion sequence to maintain coherence.
- Experiment with Parameters: Adjust cfg_number (2 for base model, 1 for distilled) to balance quality and speed.
- Check the Technical Report: The technical report offers insights into optimizing performance.
A Word of Caution
While Magi-1’s capabilities are impressive, there are some considerations. The model’s large size makes it resource-intensive, and running the 24B version locally is out of reach for most consumer hardware. Additionally, Sand AI’s online platform has been reported to block politically sensitive images, likely due to compliance with Chinese regulations. This filtering may limit certain use cases, though the model appears less restricted for non-political content compared to some Western counterparts.
The Future of Video AI
Magi-1 is more than a model—it’s a vision for the future of video creation. By combining autoregressive generation with open-source accessibility, Sand AI is paving the way for a new era of AI-driven storytelling. As the company plans to release lighter versions and enhance hardware optimization, Magi-1 could soon power real-time applications in virtual reality, gaming, and beyond.
For creators, Magi-1 is an invitation to dream big. Whether you’re crafting a short social media clip or a sprawling cinematic epic, this model offers the tools to bring your vision to life. “Magi-1’s streaming generation feature has transformed my workflow,” said one user. “The ability to control every second precisely helps me create exactly what I envision.”
Ready to unleash your creativity? Head to magi.sand.ai to start generating videos, or dive into the code on GitHub. With Magi-1, the only limit is your imagination.
If you use Magi-1 in your research, please cite:
bibtex
@misc{magi1, title={MAGI-1: Autoregressive Video Generation at Scale},
author={Sand-AI},
year={2025},
url={https://static.magi.world/static/files/MAGI_1.pdf},
}
```[](https://huggingface.co/sand-ai/MAGI-1/resolve/main/README.md?download=true)