A downloadable video generation family with text, image and continuation workflows, Python inference tools and fine-tuning resources. The 2B model provides an Apache 2.0 starting route.
CogVideoX is a video model family with downloadable weights, inference examples and development resources. It is useful for experimenting with text-conditioned video and, with the appropriate checkpoint, image-conditioned generation or continuation. The project makes it possible to inspect a workflow and retain its parameters rather than relying only on a hosted creation interface.
The important first decision is the exact model. The 2B release, 5B variants and later 1.5 variants do not have identical capabilities, hardware needs or licensing. This page identifies the 2B model as the clear Apache 2.0 starting route and describes the rest as separate choices that need their own model-card review.
Learn a video pipeline
Explore short creative shots
Prototype image animation
Research adaptation
Official product image. Click to inspect the details.
Choose a model and read its card
Use a documented environment
Generate a controlled sample
Compare, edit and document
Example prompt or task: A small sailboat crossing a calm lake at sunrise, gentle ripples, distant mountains, slow tracking camera, one continuous shot with no sudden scene changes.
This is a suggested starting prompt and review method, not a claim that the model guarantees exact motion or faithful physical simulation.
The 5B license requires commercial registration and authorization; do not apply the 2B license to the entire family.
The repository code and CogVideoX-2B model use Apache 2.0. This gives a continuing downloadable self-hosted route, while GPU rental, storage and electricity remain your responsibility.
The linked 5B agreement is different: academic use is free, while commercial users must register and obtain basic authorization. It sets a monthly commercial-visit limit of one million under that basic route, with further permission required above it. Check the exact agreement for each later model; hosted QingYing and API access have separate commercial terms.
The 2B model has an Apache 2.0 self-hosted route. Other weights must be checked separately.
No. The linked 5B agreement has commercial registration and scale conditions.
No. Choose the appropriate image-to-video variant.
Check the exact checkpoint and optimization settings; no universal guarantee applies.
The project provides guidance, but data, compute and model terms still matter.
No unlimited availability is promised here.
No. Enhancement and interpolation can be separate steps.
Explore platforms, inputs and outputs, licensing, and access requirements.
The official examples include memory-saving configurations. Published memory figures depend on those settings and on the hardware tested; disabling optimizations can greatly change memory use. A community quantized model may introduce additional compatibility and quality tradeoffs.
The repository’s historical demo interface shows optional enhancement and interpolation steps. These are downstream operations, not proof that the base model natively produces the displayed final resolution or frame rate. Preserve that distinction when describing your own outputs.
Local inference can keep source assets within your environment. Optional prompt-rewriting APIs, hosted demos and cloud storage change that data path, so check them before using private footage.
Use authorized training and reference material. A downloadable weight file does not grant rights to third-party characters, people or footage, and generated clips still need ordinary editorial review before publication.
Reviewed October 3, 2026. Product facts come from the official sources below. Suggested projects, prompts and review methods are FindGoodAI editorial guidance, not measured performance results.
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