Magi-1 Now Live on WaveSpeedAI: A New Benchmark in Open-Source Video Generation

WaveSpeedAI,Fri Apr 25 2025

Magi-1, the groundbreaking open-source video generation model by Sand AI, is now available on WaveSpeedAI for real-time inference and API deployment.

This highly evaluated release pushes the frontier of video generation, combining state-of-the-art motion quality, temporal consistency, and visual fidelity—offering a powerful open alternative to proprietary systems.

What is Magi-1?

Magi-1 is a large-scale diffusion-based video generation model built to produce realistic, coherent videos from text prompts, supporting frame lengths up to 4 seconds at high resolution. Developed by Sand AI and released under an open license, it aims to democratize video synthesis with performance on par with or exceeding leading closed-source models.

Its training strategy blends masked video modeling, spatial-temporal consistency learning, and multimodal alignment, making it particularly strong at maintaining identity, structure, and scene logic across time.

Key Features

Diffusion Video Generation

Diffusion Video Generation Built upon denoising diffusion probabilistic models, Magi-1 generates videos by gradually refining a sequence of noise vectors into photorealistic motion. This method allows for exceptional control over motion dynamics and frame coherence.

High-Quality, Temporally Consistent Motion

Unlike typical short-sequence models (e.g. 2s), Magi-1 produces videos up to 64 frames (~4 seconds) while maintaining consistent character identity, background, and action flow.

Strong Visual and Structural Fidelity

The model excels at rendering detailed scenes, capturing fine-grained textures, object interactions, and realistic human body poses.

Multimodal Conditioning

Magi-1 supports text-to-video (T2V) generation with alignment across spatial and temporal dimensions, making prompt-driven video creation more precise and reliable.

Extensive Benchmark Testing

In public evaluations, Magi-1 outperformed all tested open-source models across key metrics like FVD (Fréchet Video Distance), human preference, and identity consistency. See benchmark table below.

Benchmark Comparison (from official tests)

Model	FVD ↓ (16f)	FVD ↓ (64f)	CLIP-S ↑	Human Preference ↑
Magi-1	190.5	274.8	0.321	42.1%
Stable Video Diffusion (SVD)	307.9	489.2	0.313	21.4%
Gen-2 (Runway)	208.4	300.6	0.317	36.5%
Pika-LLaVA	310.3	498.7	0.307	18.6%

Note: Lower FVD is better. Higher CLIP-S and preference scores indicate higher fidelity and user satisfaction.

Use Cases

Whether you’re building generative tools, creative platforms, or experimental media, Magi-1 enables:

Cinematic Video Creation: Generate short clips with compelling motion, scene dynamics, and character consistency.
AI-Enhanced Content: Create trailers, concept visuals, or product animations from simple prompts.
Prototyping in Generative Apps: Integrate Magi-1 into creator tools, games, and AIGC workflows for quick visual ideation.
Research & Benchmarking: A powerful baseline for researchers developing downstream models or comparing open frameworks.

Try Magi-1 on WaveSpeedAI

Magi-1 is now fully integrated into WaveSpeedAI’s inference engine, optimized for responsive video generation via UI or API.

Try Magi-1 on WaveSpeedAI

Magi-1’s release is a major step forward in the open-source video space. It shows that high-fidelity, motion-consistent video generation is no longer locked behind proprietary walls.

WaveSpeedAI is proud to release this milestone on our platform, helping bring next-gen generative video to the global community of creators, researchers, and developers.