Run Flux on ComfyUI interactively and as an API

ComfyUI is an open-source diffusion model platform with a graph/nodes interface that allows you to design and execute advanced image generation pipelines.

In this example, we show you how to

run the Flux diffusion model on ComfyUI interactively to develop workflows
serve a Flux ComfyUI workflow as an API

Quickstart

This example runs workflow_api.json in this directory, which is an adapation of this simple FLUX.1-schnell workflow with an Image Resize custom node added at the end.

For the prompt "Surreal dreamscape with floating islands, upside-down waterfalls, and impossible geometric structures, all bathed in a soft, ethereal light" we got this output:

example comfyui image

To serve the workflow in this example as an API:

Download the Flux models to a Modal Volume:

modal run 06_gpu_and_ml/comfyui/comfyapp.py::download_models

Stand up the ComfyUI server in development mode:

modal serve 06_gpu_and_ml/comfyui/comfyapp.py

In another terminal, run inference:

python 06_gpu_and_ml/comfyui/comfyclient.py --dev --modal-workspace $(modal profile current) --prompt "neon green sign that says Modal"

The first inference will take ~1m since the container needs to launch the ComfyUI server and load Flux into memory. Successive inferences on a warm container should take a few seconds.

Setup

import json
import subprocess
import uuid
from pathlib import Path
from typing import Dict

import modal

Building up the environment

We start from a base image and specify all of our dependencies. We’ll call out the interesting ones as they come up below.

Note that these dependencies are not installed locally. They are only installed in the remote environment where our app runs. This happens the first time. On subsequent runs, the cached image will be reused.

image = (  # build up a Modal Image to run ComfyUI, step by step
    modal.Image.debian_slim(  # start from basic Linux with Python
        python_version="3.11"
    )
    .apt_install("git")  # install git to clone ComfyUI
    .pip_install("fastapi[standard]==0.115.4")  # install web dependencies
    .pip_install("comfy-cli==1.2.7")  # install comfy-cli
    .run_commands(  # use comfy-cli to install the ComfyUI repo and its dependencies
        "comfy --skip-prompt install --nvidia"
    )
    .add_local_file(
        Path(__file__).parent / "workflow_api.json",
        "/root/workflow_api.json",
        copy=True,
    )
)

Downloading custom nodes

We’ll use comfy-cli to download custom nodes, in this case the popular WAS Node Suite pack.

image = (
    image.run_commands(  # download a custom node
        "comfy node install was-node-suite-comfyui"
    )
    # Add .run_commands(...) calls for any other custom nodes you want to download
)

See this post for more on how to install custom nodes on Modal.

Downloading models

You can also use comfy-cli to download models, but for this example we’ll download the Flux models directly from Hugging Face into a Modal Volume. Then on container start, we’ll mount our models Volume into the ComfyUI models directory. This allows us to avoid re-downloading the models every time you rebuild your image.

image = (
    # install huggingface_hub with hf_transfer support to speed up downloads
    image.pip_install("huggingface_hub[hf_transfer]==0.26.2")
    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
    .run_commands(  # needs to be empty for Volume mount to work
        "rm -rf /root/comfy/ComfyUI/models"
    )
)

We create the app and specify the image we built above.

app = modal.App(name="example-comfyui", image=image)

First we need to run a function to download the Flux models to a Modal Volume.

vol = modal.Volume.from_name("comfyui-models", create_if_missing=True)


@app.function(
    volumes={"/root/models": vol},
)
def hf_download(repo_id: str, filename: str, model_type: str):
    from huggingface_hub import hf_hub_download

    hf_hub_download(
        repo_id=repo_id,
        filename=filename,
        local_dir=f"/root/models/{model_type}",
    )

We can kick off the model downloads in parallel using starmap.

@app.local_entrypoint()
def download_models():
    models_to_download = [
        # format is (huggingface repo_id, the model filename, comfyui models subdirectory we want to save the model in)
        (
            "black-forest-labs/FLUX.1-schnell",
            "ae.safetensors",
            "vae",
        ),
        (
            "black-forest-labs/FLUX.1-schnell",
            "flux1-schnell.safetensors",
            "unet",
        ),
        (
            "comfyanonymous/flux_text_encoders",
            "t5xxl_fp8_e4m3fn.safetensors",
            "clip",
        ),
        ("comfyanonymous/flux_text_encoders", "clip_l.safetensors", "clip"),
    ]
    list(hf_download.starmap(models_to_download))

To run the download step, run modal run 06_gpu_and_ml/comfyui/comfyapp.py::download_models. By leveraging hf_transfer, Modal starmap for parallelism, and Volumes, image build time drops from ~10 minutes to ~25 seconds.

To run ComfyUI interactively, simply wrap the comfy launch command in a Modal Function and serve it as a web server.

@app.function(
    allow_concurrent_inputs=10,
    concurrency_limit=1,
    container_idle_timeout=30,
    timeout=1800,
    gpu="A10G",
    volumes={"/root/comfy/ComfyUI/models": vol},
)
@modal.web_server(8000, startup_timeout=60)
def ui():
    subprocess.Popen("comfy launch -- --listen 0.0.0.0 --port 8000", shell=True)

Remember to close your UI tab when you are done developing to avoid accidental charges to your account. This will close the connection with the container serving ComfyUI, which will spin down based on your container_idle_timeout setting.

To run an existing workflow as an API, we use Modal’s class syntax to run our customized ComfyUI environment and workflow on Modal.

Here’s the basic breakdown of how we do it:

We stand up a “headless” ComfyUI server in the background when the app starts.
We define an infer method that takes in a workflow path and runs the workflow on the ComfyUI server.
We stand up an api with web_endpoint, so that we can run our workflows as a service.

For more on how to run web services on Modal, check out this guide.

@app.cls(
    allow_concurrent_inputs=10,
    container_idle_timeout=300,
    gpu="A10G",
    volumes={"/root/comfy/ComfyUI/models": vol},
)
class ComfyUI:
    @modal.enter()
    def launch_comfy_background(self):
        cmd = "comfy launch --background"
        subprocess.run(cmd, shell=True, check=True)

    @modal.method()
    def infer(self, workflow_path: str = "/root/workflow_api.json"):
        # runs the comfy run --workflow command as a subprocess
        cmd = f"comfy run --workflow {workflow_path} --wait --timeout 1200"
        subprocess.run(cmd, shell=True, check=True)

        # completed workflows write output images to this directory
        output_dir = "/root/comfy/ComfyUI/output"
        # looks up the name of the output image file based on the workflow
        workflow = json.loads(Path(workflow_path).read_text())
        file_prefix = [
            node.get("inputs")
            for node in workflow.values()
            if node.get("class_type") == "SaveImage"
        ][0]["filename_prefix"]

        # returns the image as bytes
        for f in Path(output_dir).iterdir():
            if f.name.startswith(file_prefix):
                return f.read_bytes()

    @modal.web_endpoint(method="POST")
    def api(self, item: Dict):
        from fastapi import Response

        workflow_data = json.loads(
            (Path(__file__).parent / "workflow_api.json").read_text()
        )

        # insert the prompt
        workflow_data["6"]["inputs"]["text"] = item["prompt"]

        # give the output image a unique id per client request
        client_id = uuid.uuid4().hex
        workflow_data["9"]["inputs"]["filename_prefix"] = client_id

        # save this updated workflow to a new file
        new_workflow_file = f"{client_id}.json"
        json.dump(workflow_data, Path(new_workflow_file).open("w"))

        # run inference on the currently running container
        img_bytes = self.infer.local(new_workflow_file)

        return Response(img_bytes, media_type="image/jpeg")

The workflow for developing workflows

When you run this script with modal deploy 06_gpu_and_ml/comfyui/comfyapp.py, you’ll see a link that includes ui. Head there to interactively develop your ComfyUI workflow. All of your models and custom nodes specified in the image build step will be loaded in.

To serve the workflow after you’ve developed it, first export it as “API Format” JSON:

Click the gear icon in the top-right corner of the menu
Select “Enable Dev mode Options”
Go back to the menu and select “Save (API Format)”

Save the exported JSON to the workflow_api.json file in this directory.

Then, redeploy the app with this new workflow by running modal deploy 06_gpu_and_ml/comfyui/comfyapp.py again.

Further optimizations

To decrease inference latency, you can process multiple inputs in parallel by setting allow_concurrent_inputs=1, which will run each input on its own container. See our Scaling ComfyUI blog post for more details.
If you’re noticing long startup times for the ComfyUI server (e.g. >30s), this is likely due to too many custom nodes being loaded in. Consider breaking out your deployments into one App per unique combination of models and custom nodes.
For those who prefer to run a ComfyUI workflow directly as a Python script, see this blog post.