This guide shows how to deploy a ComfyUI server on Beam using Pod. We’ll set up a server to generate images with Flux1 Schnell, but you can easily adapt it to use other models like Stable Diffusion v1.5.
Create a file named app.py with the following code. This script sets up a Beam Pod with ComfyUI, installs dependencies, downloads the Flux1 Schnell model, and launches the server.
Update ORG_NAME, REPO_NAME, WEIGHTS_FILE, and COMMIT with values from the model’s repository. Check the “Files and versions” tab for the weights file and commit hash.
You can also expose ComfyUI workflows as APIs using Beam’s ASGI support. This allows you to programmatically generate images by sending requests with prompts. Below is an example of how to set this up:
Create the API Script
from beam import Image, asgi, Outputimage = ( Image() .add_commands(["apt update && apt install git -y"]) .add_python_packages( [ "fastapi[standard]==0.115.4", "comfy-cli", "huggingface_hub[hf_transfer]==0.26.2", ] ) .add_commands( [ "yes | comfy install --nvidia --version 0.3.10", "comfy node install was-node-suite-comfyui@1.0.2", "mkdir -p /root/comfy/ComfyUI/models/checkpoints/", "huggingface-cli download Comfy-Org/flux1-schnell flux1-schnell-fp8.safetensors --cache-dir /comfy-cache", "ln -s /comfy-cache/models--Comfy-Org--flux1-schnell/snapshots/f2808ab17fe9ff81dcf89ed0301cf644c281be0a/flux1-schnell-fp8.safetensors /root/comfy/ComfyUI/models/checkpoints/flux1-schnell-fp8.safetensors", ] ))def init_models(): import subprocess cmd = "comfy launch --background" subprocess.run(cmd, shell=True, check=True)@asgi( name="comfy", image=image, on_start=init_models, cpu=8, memory="32Gi", gpu="A100-40", timeout=-1,)def handler(): from fastapi import FastAPI, HTTPException import subprocess import json from pathlib import Path import uuid from typing import Dict app = FastAPI() # This is where you specify the path to your workflow file. # Make sure "workflow_api.json" exists in the same directory as this script. WORKFLOW_FILE = Path(__file__).parent / "workflow_api.json" OUTPUT_DIR = Path("/root/comfy/ComfyUI/output") @app.post("/generate") async def generate(item: Dict): if not WORKFLOW_FILE.exists(): raise HTTPException(status_code=500, detail="Workflow file not found.") workflow_data = json.loads(WORKFLOW_FILE.read_text()) workflow_data["6"]["inputs"]["text"] = item["prompt"] request_id = uuid.uuid4().hex workflow_data["9"]["inputs"]["filename_prefix"] = request_id new_workflow_file = Path(f"{request_id}.json") new_workflow_file.write_text(json.dumps(workflow_data, indent=4)) # Run inference cmd = f"comfy run --workflow {new_workflow_file} --wait --timeout 1200 --verbose" subprocess.run(cmd, shell=True, check=True) image_files = list(OUTPUT_DIR.glob("*")) # Find the latest image latest_image = max( (f for f in image_files if f.suffix.lower() in {".png", ".jpg", ".jpeg"}), key=lambda f: f.stat().st_mtime, default=None ) if not latest_image: raise HTTPException(status_code=404, detail="No output image found.") output_file = Output(path=latest_image) output_file.save() public_url = output_file.public_url(expires=-1) print(public_url) return {"output_url": public_url} return app
Prepare a Workflow File
Create a workflow_api.json file in the same directory as app.py. This file should contain your ComfyUI workflow, which you can export from the ComfyUI web interface.
You can also store your workflow_api.json file in your Volume and use it like WORKFLOW_FILE = Path("/your_volume/workflow_api.json")
Deploy the API
beam deploy api.py:handler
Use the API
Send a POST request to the /generate endpoint with a JSON payload containing a prompt:
curl -X POST https://12345.apps.beam.cloud/generate \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer YOUR_BEAM_API' \ -d '{"prompt": "A cat image"}'
The response will include a public URL to the generated image:
