@stable-canvas/comfyui-client

Javascript api Client for ComfyUI that supports both NodeJS and Browser environments.

This client provides comprehensive support for all available RESTful and WebSocket APIs, with built-in TypeScript typings for enhanced development experience. Additionally, it introduces a programmable workflow interface, making it easy to create and manage workflows in a human-readable format.

documentations:

• Rest API
• WebSocket API
• Workflow API

examples:

• nodejs
  • client api
  • workflow factory api
• Web

Features

• Environment Compatibility: Seamlessly functions in both NodeJS and Browser environments.
• Comprehensive API Support: Provides full support for all available RESTful and WebSocket APIs.
• TypeScript Typings: Comes with built-in TypeScript support for type safety and better development experience.
• Programmable Workflows: Introduces a human-readable and highly customizable workflow interface inspired by this issue and this library.
• Ease of Use: Both implementation and usage are designed to be intuitive and user-friendly.

By incorporating these features, @stable-canvas/comfyui-client provides a robust and versatile solution for integrating ComfyUI capabilities into your projects effortlessly.

Installation

Use npm or yarn to install the @stable-canvas/comfyui-client package.

pnpm add @stable-canvas/comfyui-client

Client Usage

First, import the ComfyUIApiClient class from the package.

import { ComfyUIApiClient } from "@stable-canvas/comfyui-client";

Client instance, in Browser

const client = new ComfyUIApiClient({
    api_host: "127.0.0.1:8188",
})
// connect ws client
client.connect();

Client instance, in NodeJs

import WebSocket from "ws";
import fetch from "node-fetch";

const client = new ComfyUIApiClient({
    //...
    WebSocket,
    fetch,
});
// connect ws client
client.connect();

Advanced functions

In addition to the standard API interfaces provided by comfyui, this library also wraps them to provide advanced calls

enqueue

const result = await client.enqueue(
  { /* workflow prompt */ },
  {
    progress: ({max,value}) => console.log(`progress: ${value}/${max}`);
  }
);

It's very simple; it includes the entire prompt interface life cycle and waits for and collectively returns the result at the end of the request

In some cases you might not want to use ws, then you can use enqueue_polling, this function will perform similar behavior to enqueue, but uses rest http to poll the task status

Get XXX

Sometimes you may need to check some configurations of ComfyUI, such as whether a deployment service contains the needed model or lora, then these interfaces will be useful

getSamplers getSchedulers getSDModels getCNetModels getUpscaleModels getHyperNetworks getLoRAs getVAEs

InvokedWorkflow

If you need to manage the life cycle of your request, then this class can be very convenient

instance

// You can instantiate manually
const invoked = new InvokedWorkflow({ /* workflow */ }, client);
// or use the workflow api to instantiate
const invoked = your_workflow.instance();

running

// job enqueue
invoked.enqueue();
// job result promise
const job_promise = invoked.wait();
// if you want interrupt it
invoked.interrupt();
// query job status
invoked.query();

Workflow Usage

Programmable/Human-readable pattern

Inspired by this issue and this library, this library provides a programmable workflow interface.

It has the following use cases:

• Interactive GUI Integration: Offers support for seamless integration with ComfyUI's new GUI, enhancing user interaction possibilities.
• LLMs for Workflow Generation: Leverages the ability of large language models to understand Javascript for creating workflows.
• Cross-Project Workflow Reuse: Enables the sharing and repurposing of workflow components across different projects using ComfyUI.
• Custom Node Creation: Assists in developing and integrating custom nodes into existing workflows for expanded functionality.
• Workflow Visualization: Facilitates a clearer understanding of workflows by translating them into a visual format suitable for ComfyUI's GUI.
• Model Research and Development: Provides a framework for leveraging ComfyUI nodes in machine learning research without execution capabilities.
• Script-Driven Workflow Templates: Enables the generation of templated workflows through scripting for consistent and efficient project setups.
• Web UI-independent Workflow Deployment: Enables the creation and deployment of workflows without reliance on a web-based user interface.

Minimal case

Here is a minimal example demonstrating how to create and execute a simple workflow using this library.

const workflow = new ComfyUIWorkflow();
const cls = workflow.classes;
const [model, clip, vae] = cls.CheckpointLoaderSimple({
  ckpt_name: "lofi_v5.baked.fp16.safetensors",
});
const enc = (text: string) => cls.CLIPTextEncode({ text, clip })[0];
const [samples] = cls.KSampler({
  seed: Math.floor(Math.random() * 2 ** 32),
  steps: 35,
  cfg: 4,
  sampler_name: "dpmpp_2m_sde_gpu",
  scheduler: "karras",
  denoise: 1,
  model,
  positive: enc("best quality, 1girl"),
  negative: enc(
    "worst quality, bad anatomy, embedding:NG_DeepNegative_V1_75T"
  ),
  latent_image: cls.EmptyLatentImage({
    width: 512,
    height: 512,
    batch_size: 1,
  })[0],
});
cls.SaveImage({
  filename_prefix: "from-sc-comfy-ui-client",
  images: cls.VAEDecode({ samples, vae })[0],
});

Programable case

Both implementation and usage are extremely simple and human-readable. Below is a simple example of creating a workflow:

const createWorkflow = () => {
  const workflow = new ComfyUIWorkflow();
  const {
    KSampler,
    CheckpointLoaderSimple,
    EmptyLatentImage,
    CLIPTextEncode,
    VAEDecode,
    SaveImage,
    NODE1,
  } = workflow.classes;

  const seed = Math.floor(Math.random() * 2 ** 32);
  const pos = "best quality, 1girl";
  const neg = "worst quality, bad anatomy, embedding:NG_DeepNegative_V1_75T";
  const model1_name = "lofi_v5.baked.fp16.safetensors";
  const model2_name = "case-h-beta.baked.fp16.safetensors";
  const sampler_settings = {
    seed,
    steps: 35,
    cfg: 4,
    sampler_name: "dpmpp_2m_sde_gpu",
    scheduler: "karras",
    denoise: 1,
  };

  const [model1, clip1, vae1] = CheckpointLoaderSimple({
    ckpt_name: model1_name,
  });
  const [model2, clip2, vae2] = CheckpointLoaderSimple({
    ckpt_name: model2_name,
  });

  const dress_case = [
    "white yoga",
    "black office",
    "pink sportswear",
    "cosplay",
  ];

  const generate_pipeline = (model, clip, vae, pos, neg) => {
    const [latent_image] = EmptyLatentImage({
      width: 640,
      height: 960,
      batch_size: 1,
    });
    const [positive] = CLIPTextEncode({ text: pos, clip });
    const [negative] = CLIPTextEncode({ text: neg, clip });
    const [samples] = KSampler({
      ...sampler_settings,
      model,
      positive,
      negative,
      latent_image,
    });
    const [image] = VAEDecode({ samples, vae });
    return image;
  };

  for (const cloth of dress_case) {
    const input_pos = `${pos}, ${cloth} dress`;
    const image = generate_pipeline(model1, clip1, vae1, input_pos, neg);
    SaveImage({
      images: image,
      filename_prefix: `${cloth}-lofi-v5`,
    });

    const input_pos2 = `${pos}, ${cloth} dress`;
    const image2 = generate_pipeline(model2, clip2, vae2, input_pos2, neg);
    SaveImage({
      images: image2,
      filename_prefix: `${cloth}-case-h-beta`,
    });
  }

  return workflow;
};

const wf1 = createWorkflow();
// { prompt: {...}, workflow: {...} }

type support

• builtin node types

• builtin node params

• any other node

Invoke workflow

const wf1 = createWorkflow();
const result = await wf1.invoke(client);

CLI

install

npm install @stable-canvas/comfyui-client-cli

Workflow to code

This tool converts the input workflow into executable code that uses this library.

Usage: nodejs-comfy-ui-client-code-gen [options]

Use this tool to generate the corresponding calling code using workflow

Options:
  -V, --version              output the version number
  -t, --template [template]  Specify the template for generating code, builtin tpl: [esm,cjs,web,none] (default: "esm")     
  -o, --out [output]         Specify the output file for the generated code
  -i, --in <input>           Specify the input file, support .json file
  -h, --help                 display help for command

example

cuc-w2c -i workflow.json -o out.js -t esm

{
  "prompt": {
    "1": {
      "class_type": "CheckpointLoaderSimple",
      "inputs": {
        "ckpt_name": "lofi_v5.baked.fp16.safetensors"
      }
    },
    "2": {
      "class_type": "CLIPTextEncode",
      "inputs": {
        "text": "best quality, 1girl",
        "clip": [
          "1",
          1
        ]
      }
    },
    "3": {
      "class_type": "CLIPTextEncode",
      "inputs": {
        "text": "worst quality, bad anatomy, embedding:NG_DeepNegative_V1_75T",
        "clip": [
          "1",
          1
        ]
      }
    },
    "4": {
      "class_type": "EmptyLatentImage",
      "inputs": {
        "width": 512,
        "height": 512,
        "batch_size": 1
      }
    },
    "5": {
      "class_type": "KSampler",
      "inputs": {
        "seed": 2765233096,
        "steps": 35,
        "cfg": 4,
        "sampler_name": "dpmpp_2m_sde_gpu",
        "scheduler": "karras",
        "denoise": 1,
        "model": [
          "1",
          0
        ],
        "positive": [
          "2",
          0
        ],
        "negative": [
          "3",
          0
        ],
        "latent_image": [
          "4",
          0
        ]
      }
    },
    "6": {
      "class_type": "VAEDecode",
      "inputs": {
        "samples": [
          "5",
          0
        ],
        "vae": [
          "1",
          2
        ]
      }
    },
    "7": {
      "class_type": "SaveImage",
      "inputs": {
        "filename_prefix": "from-sc-comfy-ui-client",
        "images": [
          "6",
          0
        ]
      }
    }
  }
}

import {
  ComfyUIApiClient,
  ComfyUIWorkflow,
} from "@stable-canvas/comfyui-client";

async function main(envs = {}) {
  const env = (k) => envs[k];

  const client = new ComfyUIApiClient({
    api_host: env("COMFYUI_CLIENT_API_HOST"),
    api_host: env("COMFYUI_CLIENT_API_BASE"),
    clientId: env("COMFYUI_CLIENT_CLIENT_ID"),
  });

  const createWorkflow = () => {
    const workflow = new ComfyUIWorkflow();
    const cls = workflow.classes;

    const [MODEL_1, CLIP_1, VAE_1] = cls.CheckpointLoaderSimple({
      ckpt_name: "lofi_v5.baked.fp16.safetensors",
    });
    const [CONDITIONING_1] = cls.CLIPTextEncode({
      text: "best quality, 1girl",
      clip: CLIP_1,
    });
    const [CONDITIONING_2] = cls.CLIPTextEncode({
      text: "worst quality, bad anatomy, embedding:NG_DeepNegative_V1_75T",
      clip: CLIP_1,
    });
    const [LATENT_1] = cls.EmptyLatentImage({
      width: 512,
      height: 512,
      batch_size: 1,
    });
    const [LATENT_2] = cls.KSampler({
      seed: 2765233096,
      steps: 35,
      cfg: 4,
      sampler_name: "dpmpp_2m_sde_gpu",
      scheduler: "karras",
      denoise: 1,
      model: MODEL_1,
      positive: CONDITIONING_1,
      negative: CONDITIONING_2,
      latent_image: LATENT_1,
    });
    const [IMAGE_1] = cls.VAEDecode({
      samples: LATENT_2,
      vae: VAE_1,
    });
    const [] = cls.SaveImage({
      filename_prefix: "from-sc-comfy-ui-client",
      images: IMAGE_1,
    });

    return workflow;
  };

  const workflow = createWorkflow();

  console.time("enqueue workflow");
  try {
    return await workflow.invoke(client);
  } catch (error) {
    throw error;
  } finally {
    console.timeEnd("enqueue workflow");
  }
}

main("process" in globalThis ? globalThis.process.env : globalThis)
  .then(() => {
    console.log("DONE");
  })
  .catch((err) => {
    console.error("ERR", err);
  });

TODOs

• [x] workflow to code: Transpiler workflow to code
• [ ] code to workflow: Output a json file that can be imported into the web front end
• [ ] Output type hints

Contributing

Contributions are welcome! Please feel free to submit a pull request.

License

MIT