⚡ ComfyUI Fast API

Transform your ComfyUI into a powerful API, exposing all your saved workflows as ready-to-use HTTP endpoints.

WIP Warning heavy development and not fully battle-tested, this package may contain bugs, please do not use in production for now.

Key features :

• ✨ Plug and play - Automatically serve your ComfyUI workflows into /api/workflows/* HTTP endpoints
• 🏷️ Annotations - Expose your inputs and outputs by [tagging] your node names.
• ⚡ Fast - No added overload, powerful node caching.

Planned :

• 📖 OpenAPI Documentation - Automated OpenAPI documentation of all available workflows.
• 🔀 Load Balancer - Connect each ComfyUI instance to a Load Balancer, features :
  • Workflow syncing between all instances.
  • Heartbeat and speed priority check for best request routing
  • Maybe a small UI for statistics about instances, runs ?
  • I am working on this on a separate project, stay tuned

Installation

Install by cloning this project into your custom_nodes folder.

cd custom_nodes
git clone https://github.com/IfnotFr/ComfyUI-Fast-API

Quick Start

1. Annotate editable inputs, for example rename the KSampler by KSampler [my-sampler]

2. Annotate your output, for example Preview Image into Preview Image [my-output]

3. Click on Workflow > Save API Endpoint and type your endpoint name.

4. You can now run the workflow from the API by doing a POST http://localhost:8188/api/workflows/ENDPOINT_NAME with a json payload like :

   {
     "my-sampler": {
       "seed": 1234
     }
   }
   

5. Handle the API response by your client, in our example we have annotated one out [my-output] :

   {
     "my-output": [
       "V2VsY29tZSB0byA8Yj5iYXNlNjQuZ3VydTwvYj4h..."
     ]
   }
   

Node Annotations

Free Annotations

• [my-node]: Annotate editable nodes or output nodes.
• [my-sampler:seed,steps,cfg]: Limit exposed inputs of annoted nodes.

Internal Annotations

• [!bypass]: Bypass this node when running from API (but keep it in ComfyUI).
  • Usefull if you want to remove debug nodes from the workflow when running from API.
• [!cache]: Globally register this node to be included on each API call.
  • It allows you to keep the node in memory, denying ComfyUI to unload it. See How to cache models.

API Call Payloads

Each annotated node exposing inputs can be changed by a payload where <node-name>.<input-name> = <value>.

Simple primitive values :

{
  "my-sampler": {
    "seed": 1234,
    "steps": 20,
    "cfg": 7
  },
  "my-positive-prompt": {
    "text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
  }
}

Uploading images (from base64, from url) :

{
  "my-image-base64": {
    "image": {
      "type": "file",
      "url": "https://foo.bar/image.png",
      "name": "optional_name.png"
    }
  },
  "my-image-url": {
    "image": {
      "type": "file",
      "content": "V2VsY29tZSB0byA8Yj5iYXNlNjQuZ3VydTwvYj4h ...",
      "name": "required_name.jpg",
    }
  }
}

You can also bypass node by passing the false value instead of an object, it will bypass it like the [!bypass] annotation :

{
  "my-node-to-bypass": false
}

How to cache models

Imagine you have two workflows a.json and b.json, each loading a different model (two different Load Checkpoint nodes loading dreamshaper.safetensors and juggernaut.safetensors).

Running a.json will :

• Load dreamshaper.safetensors into VRAM
• Execute the rest ...

Then, running b.json will :

• Unload dreamshaper.safetensors from VRAM
• Load juggernaut.safetensors into VRAM
• Execute the rest ...

Finally, running a.json again will :

• Unload juggernaut.safetensors from VRAM
• Load dreamshaper.safetensors into VRAM
• Execute the rest ...

By putting [!cache] annotation on both Load Checkpoint workflows you will instruct ComfyUI to force them to stay in memory reducing loading times (but increasing VRAM usage).

Now, running a.json will :

• Load dreamshaper.safetensors into VRAM
• Load juggernaut.safetensors into VRAM
• Execute the rest ...

Then, running b.json will :

• Execute the rest ...

Then, running a.json will :

• Execute the rest ...

Note : Caching is not limited to Load Checkpoint. Each node keeping stuff in memory like models will benefit from caching. For example : Load ControlNet Model, SAM2ModelLoader, Load Upscale Model, etc ...

TODO

• [] Find a way to hook the save event, for replacing the "Save API Endpoint" step for updating workflows
• [] Default configuration should be loaded from environment (paths, endpoint ...)
• [] Editable configuration (from the ComfyUI config interface ?)
• [] Test edge cases like image batches, complex workflows ...
• [] Output as download url instead of base64 option, for bigger files

Why This ?

ComfyUI ecosystem is actually working to solve the deployment and scalability approach when it comes to run ComfyUI Workflows, but ...

Working with JSON workflows has limitations

• Complicated json file versionning, and it is a pain to export each time you do a modification.
• I can be a challenge to edit workflows on the fly by your app (specially for bypassing nodes etc ...)

Pusing workflows into clouds (ComfyDeploy, RunComfy, Replicate, RunPod etc ...) can have insane speed issues, hard pricing, and features limitations

• Simple workflows of 5s can take 15s, 30s and up to minutes due to cold start and the provider queue system overload.
• In managed cloud it can be faster without cold start, but you are limited to available models, custom nodes, etc ...

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Made with ❤️ by Ifnot.