---
license: apache-2.0
language:
- en
pipeline_tag: image-to-video
---
-----
# MAGI-1: Autoregressive Video Generation at Scale
This repository contains the code for the MAGI-1 model, pre-trained weights and inference code. You can find more information on our [technical report](https://static.magi.world/static/files/MAGI_1.pdf) or directly create magic with MAGI-1 [here](http://sand.ai) . 🚀✨
## 🔥🔥🔥 Latest News
- Apr 21, 2025: MAGI-1 is here 🎉. We've released the model weights and inference code — check it out!
## 1. About
We present MAGI-1, a world model that generates videos by ***autoregressively*** predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames. Trained to denoise per-chunk noise that increases monotonically over time, MAGI-1 enables causal temporal modeling and naturally supports streaming generation. It achieves strong performance on image-to-video (I2V) tasks conditioned on text instructions, providing high temporal consistency and scalability, which are made possible by several algorithmic innovations and a dedicated infrastructure stack. MAGI-1 further supports controllable generation via chunk-wise prompting, enabling smooth scene transitions, long-horizon synthesis, and fine-grained text-driven control. We believe MAGI-1 offers a promising direction for unifying high-fidelity video generation with flexible instruction control and real-time deployment.
## 2. Model Summary
### Transformer-based VAE
- Variational autoencoder (VAE) with transformer-based architecture, 8x spatial and 4x temporal compression.
- Fastest average decoding time and highly competitive reconstruction quality
### Auto-Regressive Denoising Algorithm
MAGI-1 is an autoregressive denoising video generation model generating videos chunk-by-chunk instead of as a whole. Each chunk (24 frames) is denoised holistically, and the generation of the next chunk begins as soon as the current one reaches a certain level of denoising. This pipeline design enables concurrent processing of up to four chunks for efficient video generation.
### Diffusion Model Architecture
MAGI-1 is built upon the Diffusion Transformer, incorporating several key innovations to enhance training efficiency and stability at scale. These advancements include Block-Causal Attention, Parallel Attention Block, QK-Norm and GQA, Sandwich Normalization in FFN, SwiGLU, and Softcap Modulation. For more details, please refer to the [technical report.](https://static.magi.world/static/files/MAGI_1.pdf)
### Distillation Algorithm
We adopt a shortcut distillation approach that trains a single velocity-based model to support variable inference budgets. By enforcing a self-consistency constraint—equating one large step with two smaller steps—the model learns to approximate flow-matching trajectories across multiple step sizes. During training, step sizes are cyclically sampled from {64, 32, 16, 8}, and classifier-free guidance distillation is incorporated to preserve conditional alignment. This enables efficient inference with minimal loss in fidelity.
## 3. Model Zoo
We provide the pre-trained weights for MAGI-1, including the 24B and 4.5B models, as well as the corresponding distill and distill+quant models. The model weight links are shown in the table.
| Model | Link | Recommend Machine |
| ----------------------------- | ------------------------------------------------------------ | ------------------------------- |
| T5 | [T5](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/t5) | - |
| MAGI-1-VAE | [MAGI-1-VAE](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/vae) | - |
| MAGI-1-24B | [MAGI-1-24B](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/magi/24B_base) | H100/H800 \* 8 |
| MAGI-1-24B-distill | [MAGI-1-24B-distill](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/magi/24B_distill) | H100/H800 \* 8 |
| MAGI-1-24B-distill+fp8_quant | [MAGI-1-24B-distill+quant](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/magi/24B_distill_quant) | H100/H800 \* 4 or RTX 4090 \* 8 |
| MAGI-1-4.5B | MAGI-1-4.5B | RTX 4090 \* 1 |
## 4. Evaluation
### In-house Human Evaluation
MAGI-1 achieves state-of-the-art performance among open-source models (surpassing Wan-2.1 and significantly outperforming Hailuo and HunyuanVideo), particularly excelling in instruction following and motion quality, positioning it as a strong potential competitor to closed-source commercial models such as Kling.
### Physical Evaluation
Thanks to the natural advantages of autoregressive architecture, Magi achieves far superior precision in predicting physical behavior through video continuation—significantly outperforming all existing models.
| Model | Phys. IQ Score ↑ | Spatial IoU ↑ | Spatio Temporal ↑ | Weighted Spatial IoU ↑ | MSE ↓ |
|----------------|------------------|---------------|-------------------|-------------------------|--------|
| **V2V Models** | | | | | |
| **Magi (V2V)** | **56.02** | **0.367** | **0.270** | **0.304** | **0.005** |
| VideoPoet (V2V)| 29.50 | 0.204 | 0.164 | 0.137 | 0.010 |
| **I2V Models** | | | | | |
| **Magi (I2V)** | **30.23** | **0.203** | **0.151** | **0.154** | **0.012** |
| Kling1.6 (I2V) | 23.64 | 0.197 | 0.086 | 0.144 | 0.025 |
| VideoPoet (I2V)| 20.30 | 0.141 | 0.126 | 0.087 | 0.012 |
| Gen 3 (I2V) | 22.80 | 0.201 | 0.115 | 0.116 | 0.015 |
| Wan2.1 (I2V) | 20.89 | 0.153 | 0.100 | 0.112 | 0.023 |
| Sora (I2V) | 10.00 | 0.138 | 0.047 | 0.063 | 0.030 |
| **GroundTruth**| **100.0** | **0.678** | **0.535** | **0.577** | **0.002** |
## 5. How to run
### Environment Preparation
We provide two ways to run MAGI-1, with the Docker environment being the recommended option.
**Run with Docker Environment (Recommend)**
```bash
docker pull sandai/magi:latest
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
```
**Run with Source Code**
```bash
# Create a new environment
conda create -n magi python==3.10.12
# Install pytorch
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
# Install other dependencies
pip install -r requirements.txt
# Install ffmpeg
conda install -c conda-forge ffmpeg=4.4
# Install MagiAttention, for more information, please refer to https://github.com/SandAI-org/MagiAttention#
git clone git@github.com:SandAI-org/MagiAttention.git
cd MagiAttention
git submodule update --init --recursive
pip install --no-build-isolation .
```
### Inference Command
To run the `MagiPipeline`, you can control the input and output by modifying the parameters in the `example/24B/run.sh` or `example/4.5B/run.sh` script. Below is an explanation of the key parameters:
#### Parameter Descriptions
- `--config_file`: Specifies the path to the configuration file, which contains model configuration parameters, e.g., `example/24B/24B_config.json`.
- `--mode`: Specifies the mode of operation. Available options are:
- `t2v`: Text to Video
- `i2v`: Image to Video
- `v2v`: Video to Video
- `--prompt`: The text prompt used for video generation, e.g., `"Good Boy"`.
- `--image_path`: Path to the image file, used only in `i2v` mode.
- `--prefix_video_path`: Path to the prefix video file, used only in `v2v` mode.
- `--output_path`: Path where the generated video file will be saved.
#### Bash Script
```bash
#!/bin/bash
# Run 24B MAGI-1 model
bash example/24B/run.sh
# Run 4.5B MAGI-1 model
bash example/4.5B/run.sh
```
#### Customizing Parameters
You can modify the parameters in `run.sh` as needed. For example:
- To use the Image to Video mode (`i2v`), set `--mode` to `i2v` and provide `--image_path`:
```bash
--mode i2v \
--image_path example/assets/image.jpeg \
```
- To use the Video to Video mode (`v2v`), set `--mode` to `v2v` and provide `--prefix_video_path`:
```bash
--mode v2v \
--prefix_video_path example/assets/prefix_video.mp4 \
```
By adjusting these parameters, you can flexibly control the input and output to meet different requirements.
### Some Useful Configs (for config.json)
| Config | Help |
| -------------- | ------------------------------------------------------------ |
| seed | Random seed used for video generation |
| video_size_h | Height of the video |
| video_size_w | Width of the video |
| num_frames | Controls the duration of generated video |
| fps | Frames per second, 4 video frames correspond to 1 latent_frame |
| cfg_number | Base model uses cfg_number==2, distill and quant model uses cfg_number=1 |
| load | Directory containing a model checkpoint. |
| t5_pretrained | Path to load pretrained T5 model |
| vae_pretrained | Path to load pretrained VAE model |
## 6. License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
## 7. Citation
If you find our code or model useful 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},
}
```
## 8. Contact
If you have any questions, please feel free to raise an issue or contact us at [support@sand.ai](support@sand.ai) .