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A simple API server to make ComfyUI easy to scale horizontally. Get outputs directly in the response, or receive them via webhook or s3.

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ComfyUI API - A Stateless and Extendable API for ComfyUI

A simple wrapper that facilitates using ComfyUI as a stateless API, either by receiving images in the response, or by sending completed images to a webhook

Download and Use

Either use a pre-built Docker image, or build your own.

Download the latest version from the release page, and copy it into your existing ComfyUI dockerfile. You can find good base dockerfiles in the docker directory. There are also example dockerfiles for popular models in the SaladCloud Recipes Repo.

If you have your own ComfyUI dockerfile, you can add the comfyui-api server to it like so:

# Change this to the version you want to use
ARG api_version=1.9.0


# Download the comfyui-api binary, and make it executable
ADD https://github.com/SaladTechnologies/comfyui-api/releases/download/${api_version}/comfyui-api .
RUN chmod +x comfyui-api

# Set CMD to launch the comfyui-api binary. The comfyui-api binary will launch ComfyUI as a child process.
CMD ["./comfyui-api"]

The server will be available on port 3000 by default, but this can be customized with the PORT environment variable.

The server hosts swagger docs at /docs, which can be used to interact with the API.

Features

  • Full Power Of ComfyUI: The server supports the full ComfyUI /prompt API, and can be used to execute any ComfyUI workflow.
  • Verified Model/Workflow Support: Stable Diffusion 1.5, Stable Diffusion XL, Stable Diffusion 3.5, Flux, AnimateDiff, LTX Video, Hunyuan Video, CogVideoX, Mochi Video, Cosmos 1.0. My assumption is more model types are supported, but these are the ones I have verified.
  • Stateless API: The server is stateless, and can be scaled horizontally to handle more requests.
  • Swagger Docs: The server hosts swagger docs at /docs, which can be used to interact with the API.
  • "Synchronous" Support: The server will return base64-encoded images directly in the response, if no webhook is provided.
  • Webhook Support: The server can send completed images to a webhook, which can be used to store images, or to send them to a user.
  • S3 Support: The server can be configured to upload images to an S3-compatible object store, and return the S3 URL in the response, or to return 202 immediately and upload the images to S3 in the background.
  • Easily Submit Images: The server can accept images as base64-encoded strings, http(s) urls, and s3 urls. This makes image-to-image workflows much easier to use.
  • Warmup Workflow: The server can be configured to run a warmup workflow on startup, which can be used to load and warm up models, and to ensure the server is ready to accept requests.
  • Return Images In PNG (default), JPEG, or WebP: The server can return images in PNG, JPEG, or WebP format, via a parameter in the API request. Most options supported by sharp are supported.
  • Probes: The server has two probes, /health and /ready, which can be used to check the server's health and readiness to receive traffic.
  • Dynamic Workflow Endpoints: Automatically mount new workflow endpoints by adding conforming .js or .ts files to the /workflows directory in your docker image. See below for more information. A Claude 4 Sonnet prompt is included to assist in automating this process.
  • Bring Your Own Models And Extensions: Use any model or extension you want by adding them to the normal ComfyUI directories /opt/ComfyUI/.
  • Works Great with SaladCloud: The server is designed to work well with SaladCloud, and can be used to host ComfyUI on the SaladCloud platform. It is likely to work well with other platforms as well.
    • Manages Deletion Cost: ONLY ON SALAD. The server will automatically set the instance deletion cost to the queue length, so that busier nodes are less likely to be scaled in while they are processing requests.
  • Single Binary: The server is distributed as a single binary, and can be run with no dependencies.
  • Websocket Events Via Webhook: The server can forward ComfyUI websocket events to a configured webhook, which can be used to monitor the progress of a workflow.
  • Friendly License: The server is distributed under the MIT license, and can be used for any purpose. All of its dependencies are also MIT or Apache 2.0 licensed, except ComfyUI itself, which is GPL-3.0 licensed.

Full ComfyUI Support

ComfyUI API sits in front of ComfyUI, and uses the ComfyUI /prompt API to execute workflows, so any API-formatted prompt can be executed by the server. Before queueing the prompt, the server will download any required inputs, such as images. It also overrides the filename_prefix field in the prompt to ensure that output files are saved with a unique filename. Once the prompt is queued, the server will wait for the prompt to complete, and then return the outputs in the response body, via a webhook, or upload them to S3, depending on the request parameters. Because of this, anything you can run in ComfyUI can be run in the ComfyUI API server, including custom nodes and workflows, and any models ComfyUI supports.

Stateless API

The ComfyUI API server is designed to be stateless, meaning that it does not store any state between requests. This allows the server to be scaled horizontally behind a load balancer, and to handle more requests by adding more instances of the server. The server uses a configurable warmup workflow to ensure that ComfyUI is ready to accept requests, and to load any required models. The server also self-hosts swagger docs and an openapi spec at /docs, which can be used to interact with the API.

Request Format

Prompts are submitted to the server via the POST /prompt endpoint, which accepts a JSON body containing the prompt graph, as well as any additional parameters such as the webhook URL, S3 bucket and prefix, and image conversion options. A request may look something like:

{
  "id": "123e4567-e89b-12d3-a456-426614174000",
  "prompt": {
    "1": {
      "inputs": {
        "image": "https://salad-benchmark-assets.download/coco2017/train2017/000000000009.jpg",
        "upload": "image"
      },
      "class_type": "LoadImage"
    }
  },
  "webhook": "https://example.com/webhook",
  "convert_output": {
    "format": "jpeg",
    "options": {
      "quality": 80,
      "progressive": true
    }
  },
}
  • Only the prompt field is required. The other fields are optional, and can be omitted if not needed.
  • Your prompt must be a valid ComfyUI prompt graph, which is a JSON object where each key is a node ID, and the value is an object containing the node's inputs, class type, and optional metadata.
  • Your prompt must include a node that saves an output, such as a SaveImage node.

Image To Image Workflows

The ComfyUI API server supports image-to-image workflows, allowing you to submit an image and receive a modified version of that image in response. This is useful for tasks such as image inpainting, style transfer, and other image manipulation tasks.

To use image-to-image workflows, you can submit an image as a base64-encoded string, http(s) URL, or S3 URL. The server will automatically detect the input type and process the image accordingly.

Here's an example of doing this in a LoadImage node:

{
  "inputs": {
    "image": "https://salad-benchmark-assets.download/coco2017/train2017/000000000009.jpg",
    "upload": "image"
  },
  "class_type": "LoadImage",
  "_meta": {
    "title": "Load Image"
  }
}

Server-side image processing

The ComfyUI API server uses the sharp library to process images. This allows you to return the images in different, more compact formats, such as JPEG or WebP. This can be accomplished by including the convert_output object in the request body, which can contain the following fields:

{
  "format": "jpeg|webp",
  "options": {}
}

Omitting the convert_output object will default to PNG format, which is lossless and has the best quality, but is also the largest in size.

JPEG options:

  • quality: The quality of the JPEG image, between 1 and 100. Default is 80.
  • progressive: Use progressive (interlace) scanning. Default is false.
  • chromaSubsampling: Set to 4:4:4 to prevent chroma subsampling otherwise defaults to 4:2:0 chroma subsampling.
  • optimizeCoding: Optimize the Huffman coding tables. Default is true.
  • mozjpeg: use mozjpeg defaults, equivalent to { trellisQuantisation: true, overshootDeringing: true, optimizeScans: true, quantisationTable: 3 }
  • trellisQuantisation: Use trellis quantization. Default is false.
  • overshootDeringing: Use overshoot deringing. Default is false.
  • optimizeScans: Optimize the scan order. Default is false.
  • quantisationTable: Set the quantization table to use, 1 - 8. Default is 0.

WebP options:

  • quality: The quality of the WebP image, between 1 and 100. Default is 80.
  • alphaQuality: The quality of the alpha channel, between 0 and 100. Default is 100.
  • lossless: Use lossless compression. Default is false.
  • nearLossless: Use near-lossless compression. Default is false.
  • smartSubsample: Use smart subsampling. Default is false.
  • preset: named preset for preprocessing/filtering, one of default, picture, photo, drawing, icon, or text. Default is default.
  • effort: CPU effort level, between 0 (fastest) and 6 (slowest). Default is 4.

Probes

The server has two probes, /health and /ready.

  • The /health probe will return a 200 status code once the warmup workflow has completed. It will stay healthy as long as the server is running, even if ComfyUI crashes.
  • The /ready probe will also return a 200 status code once the warmup workflow has completed. It will return a 503 status code if ComfyUI is not running, such as in the case it has crashed, but is being automatically restarted. If you have set MAX_QUEUE_DEPTH to a non-zero value, it will return a 503 status code if ComfyUI's queue has reached the maximum depth.

Here's a markdown guide to configuring the application based on the provided config.ts file:

API Configuration Guide

This guide provides an overview of how to configure the application using environment variables.

Environment Variables

The following table lists the available environment variables and their default values. For historical reasons, the default values mostly assume this will run on top of an ai-dock image, but we currently provide our own more minimal image here in this repo.

If you are using the s3 storage functionality, make sure to set all of the appropriate environment variables for your S3 bucket, such as AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_REGION. The server will automatically use these to upload images to S3.

Variable Default Value Description
ALWAYS_RESTART_COMFYUI "false" If set to "true", the ComfyUI process will be automatically restarted if it exits. Otherwise, the API server will exit when ComfyUI exits.
BASE "ai-dock" There are different ways to load the comfyui environment for determining config values that vary with the base image. Currently only "ai-dock" has preset values. Set to empty string to not use this.
CMD "init.sh" Command to launch ComfyUI
COMFY_HOME "/opt/ComfyUI" ComfyUI home directory
COMFYUI_PORT_HOST "8188" ComfyUI port number
DIRECT_ADDRESS "127.0.0.1" Direct address for ComfyUI
HOST "::" Wrapper host address
INPUT_DIR "/opt/ComfyUI/input" Directory for input files
LOG_LEVEL "info" Log level for the application. One of "trace", "debug", "info", "warn", "error", "fatal".
MARKDOWN_SCHEMA_DESCRIPTIONS "true" If set to "true", the server will use the descriptions in the zod schemas to generate markdown tables in the swagger docs.
MAX_BODY_SIZE_MB "100" Maximum body size in MB
MAX_BODY_SIZE_MB "100" Maximum request body size in MB
MAX_QUEUE_DEPTH "0" Maximum number of queued requests before the readiness probe will return 503. 0 indicates no limit.
MODEL_DIR "/opt/ComfyUI/models" Directory for model files
OUTPUT_DIR "/opt/ComfyUI/output" Directory for output files
PORT "3000" Wrapper port number
PROMPT_WEBHOOK_RETRIES "3" Number of times to retry sending a webhook for a prompt
STARTUP_CHECK_INTERVAL_S "1" Interval in seconds between startup checks
STARTUP_CHECK_MAX_TRIES "10" Maximum number of startup check attempts
SYSTEM_META_* (not set) Any environment variable starting with SYSTEM_META_ will be sent to the system webhook as metadata. i.e. SYSTEM_META_batch=abc will add {"batch": "abc"} to the .metadata field on system webhooks.
SYSTEM_WEBHOOK_EVENTS (not set) Comma separated list of events to send to the webhook. Only selected events will be sent. If not set, no events will be sent. See System Events. You may also use the special value all to subscribe to all event types.
SYSTEM_WEBHOOK_URL (not set) Optionally receive via webhook the events that ComfyUI emits on websocket. This includes progress events.
WARMUP_PROMPT_FILE (not set) Path to warmup prompt file (optional)
WORKFLOW_DIR "/workflows" Directory for workflow files

Configuration Details

  1. ComfyUI Settings:

    • The application uses the CMD environment variable to specify the command for launching ComfyUI.
    • ComfyUI is accessed at http://${DIRECT_ADDRESS}:${COMFYUI_PORT_HOST}.

  2. Wrapper Settings:

    • The wrapper API listens on HOST:PORT.
    • It can be accessed at http://localhost:${PORT}.
    • Use an IPv6 address for HOST when deploying on Salad. This is the default behavior.

  3. Startup Checks:

    • The application performs startup checks at intervals specified by STARTUP_CHECK_INTERVAL_S.
    • It will attempt up to STARTUP_CHECK_MAX_TRIES before giving up.

  4. Directories:

    • The application uses the COMFY_HOME environment variable to locate the ComfyUI installation.
    • Output files are stored in OUTPUT_DIR.
    • Input files are read from INPUT_DIR.
    • Model files are located in MODEL_DIR.
    • Workflow files are stored in WORKFLOW_DIR. See below for more information.

  5. Warmup Prompt:

    • If WARMUP_PROMPT_FILE is set, the application will load and parse a warmup prompt from this file.
    • The checkpoint used in this prompt can be used as the default for workflow models.

  6. Models:

    • The application scans the MODEL_DIR for subdirectories and creates configurations for each model type found.
    • Each model type will have its directory path, list of available models, and a Zod enum for validation.
    • The model names are exposed via the GET /models endpoint, and via the config object throughout the application.

  7. ComfyUI Description:

    • The application retrieves available samplers and schedulers from ComfyUI itself.
    • This information is used to create Zod enums for validation.

Additional Notes

  • The application uses Zod for runtime type checking and validation of configuration values.
  • The configuration includes setup for both the wrapper application and ComfyUI itself.

Remember to set these environment variables according to your specific deployment needs before running the application.

Using Synchronously

The default behavior of the API is to return an array of base64-encoded outputs in the response body. All that is needed to do this is to omit the .webhook and .s3 field in the request body.

Using with Webhooks

ComfyUI API sends two types of webhooks: System Events, which are emitted by ComfyUI itself, and Workflow Events, which are emitted by the API server. See System Events for more information on System Events.

If a user includes the .webhook field in a request to /prompt or any of the workflow endpoints, the server will send any completed outputs to the webhook URL provided in the request. It will also send a webhook if the request fails.

For successful requests, every output from the workflow will be sent as individual webhook requests. That means if your request generates 4 images, you will receive 4 webhook requests, each with a single image.

output.complete

The webhook event name for a completed output is output.complete. The webhook will have the following schema:

{
  "event": "output.complete",
  "image": "base64-encoded-image",
  "id": "request-id",
  "filename": "output-filename.png",
  "prompt": {}
}

prompt.failed

The webhook event name for a failed request is prompt.failed. The webhook will have the following schema:

{
  "event": "prompt.failed",
  "error": "error-message",
  "id": "request-id",
  "prompt": {}
}

Using with S3

You must provide the necessary AWS environment variables for the API to be able to upload images to S3. These include AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_REGION. The API will use these to upload images to the specified S3 bucket and prefix in the request body.

To use S3 to store the outputs of your workflows, you can set the .s3 field in the request body to an object with the following schema:

{
  "bucket": "your-s3-bucket-name",
  "prefix": "prefix-for-outputs-from-this-request",
  "async": false
}

The bucket field is the name of the S3 bucket to upload the outputs to, and the prefix field is an optional prefix to add to the output filenames. The async field is a boolean that determines whether the API should return a 202 response immediately, or wait for the uploads to complete before returning a response.

If async is set to true, the API will return a 202 response immediately, and the outputs will be uploaded to S3 in the background. You will need to poll S3 or configure bucket events to be notified when the uploads are complete.

If async is set to false, the API will wait for the uploads to complete before returning a response. The response will include the S3 URLs of the uploaded outputs in the .images field, which will be an array of strings.

System Events

ComfyUI emits a number of events over websocket during the course of a workflow. These can be configured to be sent to a webhook using the SYSTEM_WEBHOOK_URL and SYSTEM_WEBHOOK_EVENTS environment variables. Additionally, any environment variable starting with SYSTEM_META_ will be sent as metadata with the event.

All webhooks have the same format, which is as follows:

{
  "event": "event_name",
  "data": {},
  "metadata": {}
}

When running on SaladCloud, .metadata will always include salad_container_group_id and salad_machine_id.

The following events are available:

  • "status"
  • "progress"
  • "executing"
  • "execution_start"
  • "execution_cached"
  • "executed"
  • "execution_success"
  • "execution_interrupted"
  • "execution_error"

The SYSTEM_WEBHOOK_EVENTS environment variable should be a comma-separated list of the events you want to send to the webhook. If not set, no events will be sent.

The event name received in the webhook will be comfy.${event_name}, i.e. comfy.progress.

Example:

export SYSTEM_WEBHOOK_EVENTS="progress,execution_start,execution_success,execution_error"

This will cause the API to send the progress, execution_start, execution_success, and execution_error events to the webhook.

The SYSTEM_META_* environment variables can be used to add metadata to the webhook events. For example:

export SYSTEM_META_batch=abc
export SYSTEM_META_purpose=testing

Will add {"batch": "abc", "purpose": "testing"} to the .metadata field on system webhooks.

The following are the schemas for the event data that will be sent to the webhook. This will populate the .data field on the webhook.

status

{
  "type": "status",
  "data": {
    "status": {
      "exec_info": {
        "queue_remaining": 3
      }
    }
  },
  "sid": "abc123"
}

progress

{
  "type": "progress",
  "data": {
    "value": 45,
    "max": 100,
    "prompt_id": "123e4567-e89b-12d3-a456-426614174000",
    "node": "42"
  },
  "sid": "xyz789"
}

executing

{
  "type": "executing",
  "data": {
    "node": "42",
    "display_node": "42",
    "prompt_id": "123e4567-e89b-12d3-a456-426614174000"
  },
  "sid": "xyz789"
}

execution_start

{
  "type": "execution_start",
  "data": {
    "prompt_id": "123e4567-e89b-12d3-a456-426614174000",
    "timestamp": 1705505423000
  },
  "sid": "xyz789"
}

execution_cached

{
  "type": "execution_cached",
  "data": {
    "nodes": ["42", "7", "13"],
    "prompt_id": "123e4567-e89b-12d3-a456-426614174000",
    "timestamp": 1705505423000
  },
  "sid": "xyz789"
}

executed

{
  "type": "executed",
  "data": {
    "node": "42",
    "display_node": "42",
    "output": {},
    "prompt_id": "123e4567-e89b-12d3-a456-426614174000"
  },
  "sid": "xyz789"
}

execution_success

{
  "type": "execution_success",
  "data": {
    "prompt_id": "123e4567-e89b-12d3-a456-426614174000",
    "timestamp": 1705505423000
  },
  "sid": "xyz789"
}

execution_interrupted

{
  "type": "execution_interrupted",
  "data": {
    "prompt_id": "123e4567-e89b-12d3-a456-426614174000",
    "node_id": "42",
    "node_type": "KSampler",
    "executed": []
  },
  "sid": "xyz789"
}

execution_error

{
  "type": "execution_error",
  "data": {
    "prompt_id": "123e4567-e89b-12d3-a456-426614174000",
    "node_id": "42",
    "node_type": "KSampler",
    "executed": [],
    "exception_message": "CUDA out of memory. Tried to allocate 2.20 GiB",
    "exception_type": "RuntimeError",
    "traceback": "Traceback (most recent call last):\n  File \"nodes.py\", line 245, in sample\n    samples = sampler.sample(model, noise, steps)",
    "current_inputs": {
      "seed": 42,
      "steps": 20,
      "cfg": 7.5,
      "sampler_name": "euler"
    },
    "current_outputs": []
  },
  "sid": "xyz789"
}

Generating New Workflow Endpoints

Since the ComfyUI prompt format is a little obtuse, it's common to wrap the workflow endpoints with a more user-friendly interface.

This can be done by adding conforming .js or .ts files to the /workflows directory in your dockerfile. You can see some examples in ./workflows. Typescript files will be automatically transpiled to javascript files, so you can use either.

Endpoints are loaded at runtime via eval in the context of src/workflows, so you can use any Node.js or TypeScript features you want, including importing other files such as the API config object. By loading extra endpoints this way, no rebuild is required to add new endpoints, and you can continue using the pre-built binary. You can see many examples of this in the Salad Recipes repo, where this API powers all of the ComfyUI recipes.

Here is an example text-to-image workflow file.

import { z } from "zod";
import config from "../config";

const ComfyNodeSchema = z.object({
  inputs: z.any(),
  class_type: z.string(),
  _meta: z.any().optional(),
});

type ComfyNode = z.infer<typeof ComfyNodeSchema>;
type ComfyPrompt = Record<string, ComfyNode>;

interface Workflow {
  RequestSchema: z.ZodObject<any, any>;
  generateWorkflow: (input: any) => Promise<ComfyPrompt> | ComfyPrompt;
  description?: string;
  summary?: string;
}

// This defaults the checkpoint to whatever was used in the warmup workflow
let checkpoint: any = config.models.checkpoints.enum.optional();
if (config.warmupCkpt) {
  checkpoint = checkpoint.default(config.warmupCkpt);
}

const RequestSchema = z.object({
  prompt: z.string().describe("The positive prompt for image generation"),
  negative_prompt: z
    .string()
    .optional()
    .default("text, watermark")
    .describe("The negative prompt for image generation"),
  width: z
    .number()
    .int()
    .min(256)
    .max(2048)
    .optional()
    .default(512)
    .describe("Width of the generated image"),
  height: z
    .number()
    .int()
    .min(256)
    .max(2048)
    .optional()
    .default(512)
    .describe("Height of the generated image"),
  seed: z
    .number()
    .int()
    .optional()
    .default(() => Math.floor(Math.random() * 100000000000))
    .describe("Seed for random number generation"),
  steps: z
    .number()
    .int()
    .min(1)
    .max(100)
    .optional()
    .default(20)
    .describe("Number of sampling steps"),
  cfg_scale: z
    .number()
    .min(0)
    .max(20)
    .optional()
    .default(8)
    .describe("Classifier-free guidance scale"),
  sampler_name: config.samplers
    .optional()
    .default("euler")
    .describe("Name of the sampler to use"),
  scheduler: config.schedulers
    .optional()
    .default("normal")
    .describe("Type of scheduler to use"),
  denoise: z
    .number()
    .min(0)
    .max(1)
    .optional()
    .default(1)
    .describe("Denoising strength"),
  checkpoint,
});

type InputType = z.infer<typeof RequestSchema>;

function generateWorkflow(input: InputType): ComfyPrompt {
  return {
    "3": {
      inputs: {
        seed: input.seed,
        steps: input.steps,
        cfg: input.cfg_scale,
        sampler_name: input.sampler_name,
        scheduler: input.scheduler,
        denoise: input.denoise,
        model: ["4", 0],
        positive: ["6", 0],
        negative: ["7", 0],
        latent_image: ["5", 0],
      },
      class_type: "KSampler",
      _meta: {
        title: "KSampler",
      },
    },
    "4": {
      inputs: {
        ckpt_name: input.checkpoint,
      },
      class_type: "CheckpointLoaderSimple",
      _meta: {
        title: "Load Checkpoint",
      },
    },
    "5": {
      inputs: {
        width: input.width,
        height: input.height,
        batch_size: 1,
      },
      class_type: "EmptyLatentImage",
      _meta: {
        title: "Empty Latent Image",
      },
    },
    "6": {
      inputs: {
        text: input.prompt,
        clip: ["4", 1],
      },
      class_type: "CLIPTextEncode",
      _meta: {
        title: "CLIP Text Encode (Prompt)",
      },
    },
    "7": {
      inputs: {
        text: input.negative_prompt,
        clip: ["4", 1],
      },
      class_type: "CLIPTextEncode",
      _meta: {
        title: "CLIP Text Encode (Prompt)",
      },
    },
    "8": {
      inputs: {
        samples: ["3", 0],
        vae: ["4", 2],
      },
      class_type: "VAEDecode",
      _meta: {
        title: "VAE Decode",
      },
    },
    "9": {
      inputs: {
        filename_prefix: "ComfyUI",
        images: ["8", 0],
      },
      class_type: "SaveImage",
      _meta: {
        title: "Save Image",
      },
    },
  };
}

const workflow: Workflow = {
  RequestSchema,
  generateWorkflow,
  summary: "Text to Image",
  description: "Generate an image from a text prompt",
};

export default workflow;

Note your file MUST export a Workflow object, which contains a RequestSchema and a generateWorkflow function. The RequestSchema is a zod schema that describes the input to the workflow, and the generateWorkflow function takes the input and returns a ComfyUI API-format prompt.

The workflow endpoints will follow whatever directory structure you provide. For example, a directory structure like this:

/workflows
└── sdxl
    ├── img2img.ts
    ├── txt2img-with-refiner.ts
    └── txt2img.ts

Would yield the following endpoints:

  • POST /workflows/sdxl/img2img
  • POST /workflows/sdxl/txt2img-with-refiner
  • POST /workflows/sdxl/txt2img

These endpoints will be present in the swagger docs, and can be used to interact with the API. If you provide descriptions in your zod schemas, these will be used to create a table of inputs in the swagger docs.

Automating with Claude 4 Sonnet

Note: This requires having an account with Anthropic, and your anthropic API key in the environment variable ANTHROPIC_API_KEY.

Creating these endpoints can be done mostly automatically by Claude 4 Sonnet, given the JSON prompt graph. A system prompt to do this is included in this repository, as is a script that uses this prompt to create endpoints. It requires jq and curl to be installed.

./generate-workflow <inputFile> <outputFile>

Where <inputFile> is the JSON prompt graph, and <outputFile> is the output file to write the generated workflow to.

As with all AI-generated code, it is strongly recommended to review the generated code before using it in production.

Prebuilt Docker Images

You can find ready-to-go docker images under Packages in this repository.

The images are tagged with the comfyui-api version they are built with, and the comfyui version they are built for, along with their pytorch version and CUDA version. There are versions for both CUDA runtime and CUDA devel, so you can choose the one that best fits your needs.

The tag pattern is ghcr.io/saladtechnologies/comfyui-api:comfy<comfy-version>-api<api-version>-torch<pytorch-version>-cuda<cuda-version>-<runtime|devel> where:

  • <comfy-version> is the version of ComfyUI used
  • <api-version> is the version of the comfyui-api server
  • <pytorch-version> is the version of PyTorch used
  • <cuda-version> is the version of CUDA used
  • <runtime|devel> is whether the image is built with the CUDA runtime or the CUDA devel image. The devel image is much larger, but includes the full CUDA toolkit, which is required for some custom nodes.

If the tag doesn't have api<api-version>, it does not include the api, and is just the ComfyUI base image.

Included in the API images are the following utilities:

  • git
  • curl
  • wget
  • unzip
  • ComfyUI
  • comfy cli

All of SaladCloud's image and video generation recipes are built on top of these images, so you can use them as a base for your own workflows. For examples of using this with custom models and nodes, check out the Salad Recipes repository on GitHub.

Considerations for Running on SaladCloud

  • SaladCloud's Container Gateway has a 100s timeout. It is possible to construct very long running ComfyUI workflows, such as for video generation, that would exceed this timeout. In this scenario, you will need to either use a webhook to receive the results, or integrate with SaladCloud's Job Queues to handle long-running workflows.
  • SaladCloud's maximum container image size is 35GB(compressed). The base comfyui-api image is around 3.25GB(compressed), so any models and extensions must fit in the remaining space.

Contributing

Contributions are welcome! ComfyUI is a powerful tool with MANY options, and it's likely that not all of them are currently supported by the comfyui-api server. Please open an issue with as much information as possible about the problem you're facing or the feature you need. If you have encountered a bug, please include the steps to reproduce it, and any relevant logs or error messages. If you are able, adding a failing test is the best way to ensure your issue is resolved quickly. Let's make productionizing ComfyUI as easy as possible!

Testing

Required Models

Automated tests for this project require model files to be present in the ./test/docker-image/models directory. The following models are required:

They should be in the correct comfyui directory structure, like so:

./test/docker-image/models
├── animatediff_models
│   └── AnimateLCM_sd15_t2v.ckpt
├── checkpoints
│   ├── dreamshaper_8.safetensors
│   ├── flux1-schnell-fp8.safetensors
│   ├── ltx-video-2b-v0.9.1.safetensors
|   ├── mochi_preview_fp8_scaled.safetensors
│   ├── sd3.5_medium.safetensors
│   ├── sd_xl_base_1.0.safetensors
│   └── sd_xl_refiner_1.0.safetensors
├── clip
│   ├── clip_g.safetensors
│   ├── clip_l.safetensors
│   ├── t5xxl_fp16.safetensors
│   └── t5xxl_fp8_e4m3fn.safetensors
├── CogVideo
│   └── CogVideo2B/
├── controlnet
│   ├── openpose-sd1.5-1.1.safetensors
├── diffusion_models
│   ├── hunyuan_video_t2v_720p_bf16.safetensors
|   └── Cosmos-1_0-Diffusion-7B-Text2World.safetensors
├── loras
│   ├── jump_V2.safetensors
├── text_encoders
│   ├── clip_l.safetensors
│   ├── llava_llama3_fp8_scaled.safetensors
|   └── oldt5_xxl_fp8_e4m3fn_scaled.safetensors
├── vae
│   ├── hunyuan_video_vae_bf16.safetensors
│   ├── vae-ft-mse-840000-ema-pruned.ckpt
│   └── cosmos_cv8x8x8_1.0.safetensors

Running Tests

In one terminal, start the test server:

docker compose up --build

--build is only needed the first time, or if you make changes to the server code

In another terminal, run the tests:

npm test

This will take quite a long time, and requires a minimum of 24gb of RAM. I did these tests on my RTX 3080ti Laptop Edition w/ 16gb VRAM, and 24gb WSL RAM. It takes about 30 minutes to run all the tests.

Architecture

The server is built with Fastify, a fast and low overhead web framework for Node.js. It sits in front of ComfyUI, and provides a RESTful API for interacting with ComfyUI.

Architecture Diagram

About

A simple API server to make ComfyUI easy to scale horizontally. Get outputs directly in the response, or receive them via webhook or s3.

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Releases 23

1.9.2 - Support S3 output in workflow endpoints Latest
Jul 23, 2025
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