ComfyUI Workflows
ModelPilot can inspect ComfyUI workflow JSON, identify recognized models and custom nodes, recommend a GPU, and prepare supported files for a dedicated deployment. Compatibility depends on the exact graph; arbitrary workflows are not guaranteed to run unchanged.
Start with a workflow guide
A workflow guide describes its downloadable JSON, model filenames, directories, GPU guidance, and known limitations. Read its evidence label: catalog membership does not mean every output has a verified example. You may download and run the JSON yourself without deploying it on ModelPilot.
Check your own workflow
- Export the workflow in ComfyUI frontend or API JSON format.
- Open the workflow checker and paste or drop the JSON.
- Review detected built-in nodes, custom nodes, models, unknown items, warnings, and the recommended GPU.
- Correct unknown filenames or node sources before deploying. A downloadable lockfile records what the checker recognized.
- Continue to deployment only after the requirements match the graph you intended to run.
Workflow checker privacy
The web checker sends the workflow JSON securely to ModelPilot for server-side analysis and does not store it. It does not upload the image, video, model-weight, or output files referenced by the graph. Remove secrets from node fields before submitting any workflow.
What the checker can and cannot prove
It can identify
- Known built-in and custom node types
- Recognized model filenames and download mappings
- Likely GPU capacity and dependency warnings
- Unsupported or unknown requirements
It cannot guarantee
- That every custom-node version is compatible
- That gated or removed files can be downloaded
- That the graph fits memory at every resolution or batch size
- That the workflow produces the desired output
Prepare a custom workflow for deployment
- Run the workflow successfully in ComfyUI first.
- Use exact relative model filenames, not paths from one computer.
- Prefer public, versioned custom-node repositories.
- Confirm each model file is publicly downloadable or provide the required access through the deployment settings.
- Start with conservative resolution, frame count, and batch size.
- Treat the recommended GPU as a starting estimate and review every warning before paying to deploy.
If deployment fails
Check the first model-download, custom-node import, or out-of-memory error in the deployment logs. Do not repeatedly redeploy an unchanged workflow. Follow the troubleshooting guide and include the deployment ID if you contact support.