@kwj-team/comfyui-client
Advanced tools
api Client for ComfyUI that supports both NodeJS and Browser environments. It provides full support for all RESTful / WebSocket APIs.
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:
examples:
By incorporating these features, @stable-canvas/comfyui-client provides a robust and versatile solution for integrating ComfyUI capabilities into your projects effortlessly.
Use npm or yarn to install the @stable-canvas/comfyui-client package.
pnpm add @stable-canvas/comfyui-client
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();
In addition to the standard API interfaces provided by comfyui, this library also wraps them to provide advanced calls
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 toenqueue, but uses rest http to poll the task status
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
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();
Inspired by this issue and this library, this library provides a programmable workflow interface.
It has the following use cases:
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],
});
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: {...} }
const wf1 = createWorkflow();
const result = await wf1.invoke(client);
npm install @stable-canvas/comfyui-client-cli
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);
});
Contributions are welcome! Please feel free to submit a pull request.
MIT
FAQs
api Client for ComfyUI that supports both NodeJS and Browser environments. It provides full support for all RESTful / WebSocket APIs.
We found that @kwj-team/comfyui-client demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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