ComfyUI can run at localhost while your video is generated on a remote server. With the official Seedance API nodes, the editor runs locally, while the node submits a generation task and retrieves its result. A working workflow tells you that the connection works; it does not establish offline inference.
There are four different things people call “local”:
- Local interface: the editor or desktop app runs on your computer.
- Local API client: your Python script runs locally and sends generation requests to a hosted model.
- Local workflow: ComfyUI coordinates several steps, which can mix local processing with remote generation.
- Local inference: downloaded model weights are loaded on hardware you control and perform the generation there.
ComfyUI’s Partner Nodes documentation describes connections to external API services. Its ByteDance node implementation lists Seedance 2.0 and Seedance 2.5 model options alongside remote task submission and status endpoints. Installing that node or downloading its workflow JSON gives you an integration. Offline generation would also require the model weights and compatible inference code.
How to check a claimed local setup
- Trace the source. Can you reach the release from the model provider’s official website? A repository name containing “official” is not enough.
- Identify the download. Is it a client, SDK, workflow, generated video, or an actual checkpoint? Look for documented weights and the architecture that loads them.
- Read the relevant license. An open-source license for a plugin may cover only the plugin. Check the terms for the weights and your intended use separately.
- Follow one generation request. Inspect the generation node for uploads, API requests, task polling, and remote output URLs. Map which step sends data out. Reference preparation, prompt enhancement, generation, and upscaling can each have different boundaries.
- Verify offline operation. Once the required files and dependencies are installed, use a non-sensitive sample to check whether the complete workflow finishes without networking. A failure needs diagnosis: it could be a missing asset or a remote dependency.
GPU activity alone is weak evidence. Preprocessing, decoding, or upscaling can use your graphics card even when the video model runs elsewhere. Likewise, saving an MP4 locally tells you where the output ended up, not where it was generated.
Choose by the requirement
If generation must work without internet access, start with a model that has verifiable downloadable weights and an offline inference path. Check every stage of the workflow, including optional helpers.
For batch automation, a cloud API may fit well. Task states, polling, retries, and usage costs then matter more than where the editor window opens.
For confidential references, inspect upload destinations, retention, and access controls before running the workflow. Localhost does not answer those questions.
If browser-based cloud generation fits your task, the Seedance 2.5 page provides an entry point and supported settings to review.
When connecting a video model, which requirement matters most to you: working fully offline, keeping references on your machine, or automating the workflow?
Sources: ComfyUI Partner Nodes · ByteDance API node implementation
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