Top 5 Open-Source Video Generation Models in 2026
AI video generation has moved rapidly from experimental research into practical creative and development workflows. Open-source and open-weight models are particularly interesting because developers can inspect model implementations, download available weights, run models locally, and build customized applications around them. However, “open source” can mean different things across projects, so users should always check the specific model's licence and usage conditions.
Below are five notable open video-generation model families to explore in 2026.
1. Wan 2.1
Wan 2.1, developed by Alibaba's Wan team, is a versatile video-generation family supporting several generation and editing tasks. Its capabilities include text-to-video, image-to-video, video editing, text-to-image and video-to-audio workflows.
One of its notable features is the availability of a 1.3B-parameter model, which has substantially lower hardware requirements than the larger 14B version. The project's documentation states that the T2V-1.3B model can operate with around 8.19 GB of VRAM under its specified setup.
Key features
- Text-to-video generation
- Image-to-video generation
- Video editing capabilities
- Multiple model sizes
- Community support for tools such as ComfyUI
- Apache 2.0 licensing for the Wan 2.1 models, according to its repository
Best suited for: Developers and creators looking for a flexible model family with options for different hardware levels.
2. HunyuanVideo
HunyuanVideo is Tencent's open video-generation project and is built around a large-scale video foundation model. The original model contains more than 13 billion parameters and uses a combination of video-focused architecture, a 3D VAE and language-based conditioning.
The project provides inference code, model checkpoints and integrations with technologies such as Diffusers and ComfyUI. It also has an image-to-video model within the broader HunyuanVideo ecosystem.
The major consideration is hardware. Tencent's documentation lists peak GPU memory requirements of approximately 45 GB for a 544×960 configuration and 60 GB for a 720×1280 configuration for the original model.
Key features
- High-quality text-to-video generation
- Image-to-video development
- Large-scale video foundation architecture
- Multi-GPU inference support
- Diffusers and ComfyUI ecosystem
Best suited for: Researchers and developers with powerful GPUs who want to experiment with a large video-generation architecture.
3. CogVideoX
CogVideoX is an open video-generation model family associated with THUDM and the wider Hugging Face ecosystem. It has become popular among developers because it can be integrated into Python-based workflows and Diffusers-based projects.
The model family includes different parameter sizes, making it possible to experiment with configurations suited to different computational environments.
CogVideoX is particularly interesting for programmers who want to integrate video generation into their own applications rather than relying exclusively on a graphical interface.
Key features
- Text-to-video generation
- Image-to-video capabilities
- Python-based workflows
- Hugging Face Diffusers integration
- Open model ecosystem
Best suited for: Python developers, AI researchers and people experimenting with custom video-generation applications.
4. LTX-Video
LTX-Video, developed by Lightricks, focuses strongly on efficient video generation. The model is based on a diffusion-transformer architecture and has been designed to make video generation comparatively fast on capable hardware.
The LTX ecosystem has also continued to evolve, with newer LTX model generations adding capabilities beyond the original LTX-Video project. Current open-weight comparisons include the newer LTX family alongside models such as Wan and HunyuanVideo.
Key features
- Text-to-video generation
- Image-to-video workflows
- Fast generation on suitable hardware
- ComfyUI support
- Developer-oriented workflows
Best suited for: Creators and developers who place a high priority on generation speed and iterative experimentation.
5. Mochi 1
Mochi 1, created by Genmo, is another important open video-generation model. It uses an Asymmetric Diffusion Transformer architecture and has been released as an open model under the Apache 2.0 licence, according to curated open-model references.
Mochi 1 is designed primarily for text-to-video generation and has become part of the wider ecosystem of models that researchers can download, experiment with and integrate into different video-generation pipelines.
Key features
- Text-to-video generation
- Diffusion Transformer architecture
- Open model weights
- Apache 2.0 licensing
- Integration with community AI-video workflows
Best suited for: AI enthusiasts and researchers interested in experimenting with open video-generation architectures.
Comparison at a Glance
| Model | Main Strength | Generation Focus | Hardware Consideration |
|---|---|---|---|
| Wan 2.1 | Versatility | T2V, I2V, editing | Multiple model sizes |
| HunyuanVideo | Large-scale generation | T2V, I2V | Very demanding for original model |
| CogVideoX | Developer ecosystem | T2V, I2V | Depends on model variant |
| LTX-Video | Speed and workflow flexibility | T2V, I2V | Designed for efficient generation |
| Mochi 1 | Open research | T2V | Requires capable hardware |
T2V = text-to-video; I2V = image-to-video.
Why Open-Source Video Models Matter
Open video models are changing how developers approach generative media. Instead of sending every prompt to a proprietary cloud service, developers can potentially run compatible models locally or deploy them on their own infrastructure.
This provides several advantages:
1. More experimentation
Researchers can investigate model architecture, inference techniques and different generation workflows.
2. Greater customization
Developers can connect models to applications, automation systems and creative pipelines.
3. Local generation
Where hardware and licences permit, users can generate content locally rather than relying entirely on an external service.
4. Growing community ecosystems
Projects such as ComfyUI, Diffusers and other open-source tools make it easier to experiment with multiple video models in a common workflow.
What Should You Consider Before Choosing a Model?
There isn't one model that is suitable for every project. Consider:
Hardware: Large video models can require substantial GPU memory. HunyuanVideo's original configuration, for example, can require tens of gigabytes of VRAM.
Generation type: Decide whether you need text-to-video, image-to-video, video editing or another workflow.
Speed: If you need to test many prompts, an efficient model may be more practical than a much larger model.
Licence: Carefully read the licence for the exact checkpoint you intend to use, particularly for commercial projects. Open weights do not automatically mean unrestricted commercial usage.
Software compatibility: Check whether your preferred workflow supports the model through tools such as Diffusers or ComfyUI.
Conclusion
Open-source and open-weight video generation is becoming one of the most active areas of generative AI. Wan 2.1, HunyuanVideo, CogVideoX, LTX-Video and Mochi 1 represent five important model families for developers and researchers exploring AI-generated video.
The biggest difference between them is not simply visual quality. Hardware requirements, generation modes, speed, software support, customization options and licensing can all affect which model fits a particular project. As new releases continue to appear, checking the official repository and licence for the specific model version is essential.
