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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 or directly create magic with MAGI-1 here . 🚀✨

🔥🔥🔥 Latest News

• Jun 17, 2026: The weights of MAGI-1.1 24B before distillation, as well as after distillation and quantization, have finally been open-sourced!
• May 30, 2025: Support for ComfyUI is added 🎉 — the custom nodes for MAGI-1 are now available. Try them out in your workflows!
• May 26, 2025: MAGI-1 4.5B distill and distill+quant models has been released 🎉 — we’ve updated the model weights - check it out!
• May 14, 2025: Added Dify DSL for prompt enhancement 🎉 — import it into Dify to boost prompt quality!
• Apr 30, 2025: MAGI-1 4.5B model has been released 🎉. We've updated the model weights — check it out!
• 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.

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.

4. Evaluation

In-house Human Evaluation

MAGI-1 achieves state-of-the-art performance among open-source models like Wan-2.1 and HunyuanVideo and closed-source model like Hailuo (i2v-01), 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 on the Physics-IQ benchmark through video continuation—significantly outperforming all existing models.