Open-source video generation has moved quickly since the first research systems. In 2026, the most useful projects cover text-to-video, image-to-video, editing, audio-video generation, and local workflows, but they differ sharply in quality, speed, hardware requirements, and license terms.
Here are eight open-source video generation model families worth knowing in 2026:
Wan2.2: Alibaba's current downloadable open-weight flagship, released under Apache 2.0. The suite pairs a 27B Mixture-of-Experts design—separate high-noise and low-noise experts for layout and detail—with a dense TI2V-5B model that handles both text-to-video and image-to-video at 720p and 24 fps on a single consumer GPU, thanks to a high-compression VAE. Later Wan-branded hosted releases do not have downloadable model repositories in the official Wan-Video organization, so Wan2.2 remains its newest downloadable video-model release. Official repository
Wan2.1: Still worth knowing as the low-VRAM entry point. The 1.3B text-to-video model needs about 8.19 GB of VRAM for a five-second 480p generation, and the broader suite covers text-to-video, image-to-video, video editing, text-to-image, and video-to-audio workflows. Official repository
HunyuanVideo 1.5: Tencent's November 2025 rebuild dropped from 13B to 8.3B parameters while improving quality, and it now runs on consumer hardware—roughly 14 GB of VRAM minimum with offloading and comfortably on a 24 GB card. A dedicated super-resolution network pushes output to 1080p. Note the custom community license: it does not apply in the European Union, United Kingdom, or South Korea. Official repository
LTX-2: Lightricks released LTX-2's weights, inference package, and training tools in January 2026. It generates synchronized audio and video in one pass, with native 4K output at up to 50 fps. The community license is free for academic use and for entities below $10 million in annual revenue; larger organizations need a commercial license. Earlier LTX-Video models remain available for speed-focused local workflows. Official repository
CogVideoX: The open CogVideo family includes text-to-video and image-to-video checkpoints in several sizes. Smaller variants can run on consumer GPUs with memory-saving settings, making the family useful for experimentation. Licensing differs by checkpoint: CogVideoX-2B is Apache 2.0, while some 5B releases use the custom CogVideoX license. Official repository
Mochi 1: Genmo's 10B-parameter Asymmetric Diffusion Transformer is released with code and weights under Apache 2.0. The reference implementation is hardware-heavy, although community runtimes can reduce memory requirements substantially. Official repository
Kandinsky 5.0: Kandinsky Lab's family spans a 2B Video Lite line with checkpoints for clips up to 10 seconds and a 19B Video Pro line. The official project reports that Video Pro ranked first among open-source text-to-video models on LMArena in December 2025, and the family offers strong English and Russian prompt understanding. The repository is MIT licensed, but always confirm the license on the exact checkpoint. Official repository
Stable Video Diffusion: Stability AI's image-to-video family remains a useful, well-documented baseline for researchers and developers. It is older than the other models in this list, but its Diffusers integration and broad tooling support still make it practical for comparisons and prototypes. Official model card
Four low-friction ways to try these models:
Use an official Hugging Face demo or model Space when one is available.
Install a maintained ComfyUI workflow for the selected checkpoint.
Use the project's official local interface or command-line example.
Test through a hosted inference provider before investing in local GPUs.
FAQ
What is the best open-source AI video generator in 2026?
There is no single best model for every workflow. Wan2.2 is the strongest all-round downloadable open-weight starting point, HunyuanVideo 1.5 offers a strong quality-to-hardware ratio, LTX-2 adds native synchronized audio and 4K generation, and Kandinsky 5.0 Lite and CogVideoX offer accessible model sizes. Test the same prompts across two or three candidates before choosing.
Can I run an open-source video generation model locally?
Yes, and the hardware bar has dropped considerably. Wan2.1's 1.3B and Wan2.2's TI2V-5B models target consumer-grade GPUs, and HunyuanVideo 1.5 can run with 14 GB of VRAM when model offloading is enabled. Larger checkpoints, such as Wan2.2's 27B MoE models or the reference Mochi 1 pipeline, still benefit from high-memory GPUs, quantization, or a hosted service.
How much VRAM do open-source video models need?
Requirements range from roughly 8 GB for optimized small checkpoints to 60 GB or more for large reference implementations. Resolution, frame count, precision, attention implementation, and CPU offloading can change the practical requirement, so always check the specific model card and runtime documentation.
Are open-source AI video generators free for commercial use?
Not automatically. Wan2.2 and Mochi 1 use Apache 2.0, while Kandinsky 5.0's current repository is MIT licensed. CogVideoX licensing varies by checkpoint; LTX-2 requires a paid license for entities with at least $10 million in annual revenue; HunyuanVideo 1.5 excludes the EU, UK, and South Korea; and Stable Video Diffusion uses Stability AI's community terms. Review the exact checkpoint license before using generated video in a product or client project.
What is the difference between text-to-video and image-to-video?
Text-to-video generates a sequence from a written prompt, so the model controls both appearance and motion. Image-to-video starts from a supplied frame and adds motion, which usually gives the creator more control over composition, characters, and visual identity.
