Best Open Source AI Video Generation Models In 2026

The open source video generation landscape has shifted dramatically. What was once dominated by closed APIs and waitlists now includes several production-capable models you can run locally, fine-tune, and deploy without per-generation fees. Choosing the right one depends on your technical setup, workflow requirements, and what you actually need to generate.

This comparison covers the leading open source AI video generation models active in 2026: LTX-2.3, Wan, HunyuanVideo, CogVideoX, and Mochi 1. Each has distinct architectural choices, licensing terms, and practical trade-offs.

What Makes an Open Source Video Model?

Before comparing models, it helps to be precise about what "open source" means in this context. Models exist on a spectrum:

• Open weights only: The model checkpoint is available for download, but the training code, data, and pipeline implementation may be proprietary or unavailable

• Open weights + inference code: Both the model and the code to run it are available

• Fully open source: Weights, inference code, training code, and optionally training data are publicly available

For practical workflows, the distinction matters most around fine-tuning and deployment. A model with open weights and no training code requires reverse-engineering to fine-tune. A model with full training infrastructure can be adapted to domain-specific data directly.

LTX-2.3

Architecture and Capabilities

LTX-2.3 is a DiT-based audio-video foundation model developed by Lightricks. The architecture uses 14 billion parameters for the video stream and 5 billion for the audio stream, sharing 48 transformer blocks across both modalities. This shared architecture is what enables synchronized audio and video generation in a single diffusion pass — audio and video are generated together, not sequentially.

The model supports eight production pipelines: text-to-video, image-to-video, audio-to-video, video-to-video via IC-LoRA, keyframe interpolation, retake (targeted segment regeneration), LipDub (audio-driven lip sync), and a distilled fast-inference variant. IC-LoRA enables reference-conditioned video generation with pose, depth, and edge control signals without requiring additional training.

Performance

LTX-2.3's distilled pipeline generates video in approximately 4 seconds on an NVIDIA H100 using 8 predefined sigma steps and no guidance computation. The production pipeline (TI2VidTwoStagesPipeline) takes longer but delivers higher fidelity through CFG and STG guidance. Both variants support FP8 quantization for reduced memory footprint.

The model requires a Linux system with CUDA 13+ and NVIDIA GPU. The default configuration needs 80GB+ VRAM. A quantized low-VRAM configuration supports 32GB GPUs. Community members have run the model on consumer GPUs with additional optimizations.

Licensing

LTX-2.3 is released under a dual-license model. Non-commercial use is covered under the Community License. Commercial use requires the Commercial License, available through Lightricks. The licensing distinction affects production deployments and applications that monetize generated content.

Open Source Access

The full codebase, including all eight pipelines, training infrastructure, and model weights, is available on GitHub. The training code (ltx-trainer) supports standard LoRA training and IC-LoRA training for custom control signals. Model weights are on HuggingFace. A hosted API (ltx-2-fast and ltx-2-pro tiers) is available for teams without local GPU infrastructure.

Wan 2.1

Architecture and Capabilities

Wan 2.1 is a video generation model released by Alibaba under an Apache 2.0 license. The architecture uses a causal 3D VAE for temporal compression and supports text-to-video and image-to-video generation. Wan 2.1 comes in two parameter scales: 1.3B and 14B. The 14B model produces higher quality output; the 1.3B model is designed for faster inference on lower-end hardware.

Wan 2.1 supports resolutions up to 1280×720 and clip lengths up to 81 frames. It does not include integrated audio generation — audio is a separate step.

Performance

The 14B model requires significant VRAM (40GB+) for full precision operation. With quantization (INT8 or FP8), it runs on 24GB consumer GPUs. The 1.3B model runs on GPUs with as little as 8GB VRAM. Generation speed varies by configuration but is generally slower than LTX-2.3's distilled pipeline at equivalent quality settings.

Licensing

Apache 2.0 license with no commercial restriction. This is the most permissive licensing among the models in this comparison and makes Wan 2.1 attractive for commercial applications that cannot accommodate dual-license terms.

HunyuanVideo

Architecture and Capabilities

HunyuanVideo is Tencent's open source video generation model, released in late 2024. The architecture is based on a full attention transformer that processes video and text jointly. The model supports text-to-video generation with output up to 1280×720 resolution and 129 frames (approximately 5 seconds at 24 fps).

HunyuanVideo does not support image conditioning or audio generation in its base release. Community extensions (I2V adapters) have been developed outside Tencent's official codebase.

Performance

HunyuanVideo requires significant GPU resources. The base model needs 60GB+ VRAM for full precision. Quantized versions (INT8) run on 24GB consumer GPUs with some quality trade-off. Generation speed at 720p/129 frames is measured in minutes on most consumer hardware, which makes iteration slower than distilled models like LTX-2.3.

Licensing

Tencent HunyuanVideo Community License permits commercial use up to 100 million monthly active users. Beyond that threshold, a separate commercial license is required. This makes it effectively open for most applications but introduces restrictions at scale.

CogVideoX

Architecture and Capabilities

CogVideoX is developed by Zhipu AI and is available in 2B and 5B parameter variants. The model uses an expert adaptive LayerNorm architecture and a 3D causal VAE with spatial (8x8) and temporal (4 frames) compression. It supports text-to-video and image-to-video generation. A separate CogVideoX-Fun variant extends the base model with additional capabilities including resolution flexibility and LoRA support.

CogVideoX does not include integrated audio generation. Output quality is generally competitive for prompt adherence and temporal consistency at its parameter scale.

Performance

The 5B model runs on 24GB consumer GPUs with standard precision. The 2B model runs on 16GB GPUs. Generation time at 720p/49 frames is approximately 2-3 minutes on a 4090 without optimizations. FP8 quantization reduces VRAM usage further.

Licensing

CogVideoX 2B uses the Apache 2.0 license. CogVideoX 5B uses Tsinghua University's model license, which permits commercial use with restrictions on prohibited applications. The licensing terms require review for production deployments.

Mochi 1

Architecture and Capabilities

Mochi 1 is developed by Genmo and released under an Apache 2.0 license. The architecture uses an Asymmetric Diffusion Transformer (AsymmDiT) that processes video tokens asymmetrically with different attention configurations for the conditioning and generation streams. Mochi 1 targets high motion quality and realistic motion dynamics.

Mochi 1 supports text-to-video generation at 480p resolution. It does not support image conditioning, audio generation, or the range of pipeline variants available in LTX-2.3. The model is positioned as a research release focused on motion quality rather than a production pipeline.

Performance

Mochi 1 requires 80GB+ VRAM for full precision operation. With linear quadrant attention (linear_quadrant), it runs on 24GB GPUs. Generation speed is competitive at its resolution target.

Licensing

Apache 2.0 license with no commercial restriction.

Comparison Summary

Choosing the Right Model for Your Use Case

Production Pipelines Requiring Audio

If your use case requires synchronized audio-video generation — commercial production, content creation at scale, or any workflow where audio is a deliverable rather than a post-production addition — LTX-2.3 is the only model in this comparison that supports it natively. The shared transformer architecture generates audio and video together, avoiding the sync drift that occurs when audio is added as a separate step.

Commercial Workflows with Simple Licensing Requirements

If Apache 2.0 licensing is a requirement for your production or legal context, Wan 2.1 and Mochi 1 are the options. Wan 2.1 provides the stronger feature set of the two (image conditioning, higher resolution, larger parameter scale). CogVideoX 2B also uses Apache 2.0 for its smaller variant.

Consumer GPU Deployment

For deployment on consumer GPUs with 24GB VRAM, Wan 2.1, HunyuanVideo (quantized), CogVideoX, and Mochi 1 all run without 80GB enterprise hardware. LTX-2.3 supports a 32GB quantized configuration. For sub-24GB consumer GPUs, the Wan 2.1 1.3B model is the most accessible option.

Maximum Pipeline Flexibility

LTX-2.3 provides the widest set of production pipelines: T2V, I2V, A2V, V2V via IC-LoRA, retake, LipDub, and keyframe interpolation, plus a distilled variant for fast inference. If your workflow requires multiple generation modes within a single model ecosystem, LTX-2.3 covers more use cases without switching models.

Getting Started

All five models are available through their respective GitHub repositories and HuggingFace pages. For LTX-2.3, the official repository includes setup instructions, pipeline documentation, and hardware requirements for each configuration. For teams without local GPU access, the LTX-2.3 hosted API provides access to the same model without infrastructure overhead.