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ComfyUI Workflow Compatibility Check

Paste your workflow JSON — we'll tell you what custom nodes it needs, how much VRAM it requires, and what model files to download.

Free. No signup required.

Export from ComfyUI: Menu → Save (API Format)

Direct answer

What should I do before deploying a custom ComfyUI workflow?

Run the workflow through the free ModelPilot compatibility checker. It reads the ComfyUI JSON, identifies custom node packages, detects model files, estimates VRAM needs, and shows whether the workflow is ready for a cloud GPU deployment.

Key facts

  • iNo signup is required for the compatibility check.
  • iThe checker supports frontend and API-format ComfyUI workflow JSON.
  • iThe report is most useful before choosing a GPU or trying to deploy on RunPod.

Next step

  1. 1Paste the workflow JSON and review missing nodes, model files, and GPU requirements.
  2. 2If the report is clean, deploy through a supported ModelPilot template or continue with a managed workflow deployment.

Not a fit when

  • Do not deploy private or licensed model assets until the user has reviewed model and LoRA usage rights.
  • Use a production-ready template page instead when the user does not have a custom workflow JSON.

What This Tool Checks

Custom Node Resolution

Identifies every custom node in your workflow and resolves it to a GitHub repository. Uses the ComfyUI Registry API, Manager database, and GitHub search to find the correct package for each node type.

GPU Requirements

Estimates VRAM requirements based on the models and node types in your workflow. Recommends the right GPU: L4 (24GB) for image generation, A6000 (48GB) for video and upscaling, A100 (80GB) for 14B+ parameter models.

Model Detection

Extracts all model file references — checkpoints, UNETs, VAEs, LoRAs, ControlNets, and CLIP models. Shows you exactly what needs to be downloaded before the workflow can run.

Model Dependencies

Lists every model file your workflow needs — checkpoints, LoRAs, VAEs, ControlNets, CLIP. Know exactly what to download before running.

How It Works

  1. 1.Export your workflow from ComfyUI using Menu → Save (API Format). This creates a JSON file with all node types and connections.
  2. 2.Paste the JSON above or drag-and-drop the .json file. The workflow JSON is sent securely for server-side analysis and is not stored. No account is needed.
  3. 3.Get a compatibility report: which custom nodes are resolved, which models are needed, what GPU you need, and how much it costs per hour. If everything resolves, your workflow is deploy-ready.

Common ComfyUI Workflow Issues

Missing custom nodes (red boxes)

When you load a workflow and see red “missing node” boxes, it means the required custom node packages aren't installed. This tool identifies which packages you need and where to find them on GitHub.

CUDA out of memory (OOM) errors

Workflows with large models (Wan 2.2 14B, HunyuanVideo, SUPIR upscaling) need more VRAM than consumer GPUs provide. This tool estimates VRAM requirements so you can choose the right GPU before deploying.

Deploying to cloud GPUs

Running ComfyUI workflows on cloud GPUs (RunPod, vast.ai) requires knowing which Docker image, custom nodes, and models to install. This tool generates a complete dependency report that makes cloud deployment straightforward.

Supported Workflow Types

Recognizes many standard ComfyUI workflows in frontend or API format, including:

  • Flux Dev / Schnell image generation
  • SDXL and SD 1.5 workflows
  • Wan 2.2 text-to-video and image-to-video
  • HunyuanVideo and LTX 2.3 video
  • SUPIR and SeedVR2 upscaling
  • ControlNet, IP-Adapter, InstantID
  • Qwen Image Edit and generation
  • Custom LoRA and multi-model pipelines

Compatibility depends on exact model files, custom-node versions, resolution, and batch size. A successful check is not an execution guarantee.