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#This is an example that uses the websockets api to know when a prompt execution is done

#Once the prompt execution is done it downloads the images using the /history endpoint

import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)

import uuid

import json

import urllib.request

import urllib.parse

server_address = "127.0.0.1:8188"

client_id = str(uuid.uuid4())

def queue_prompt(prompt, prompt_id):

p = {"prompt": prompt, "client_id": client_id, "prompt_id": prompt_id}

data = json.dumps(p).encode('utf-8')

req = urllib.request.Request("http://{}/prompt".format(server_address), data=data)

urllib.request.urlopen(req).read()

def get_image(filename, subfolder, folder_type):

data = {"filename": filename, "subfolder": subfolder, "type": folder_type}

url_values = urllib.parse.urlencode(data)

with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:

return response.read()

def get_history(prompt_id):

with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:

return json.loads(response.read())

def get_images(ws, prompt):

prompt_id = str(uuid.uuid4())

queue_prompt(prompt, prompt_id)

output_images = {}

while True:

out = ws.recv()

if isinstance(out, str):

message = json.loads(out)

if message['type'] == 'executing':

data = message['data']

if data['node'] is None and data['prompt_id'] == prompt_id:

break #Execution is done

else:

# If you want to be able to decode the binary stream for latent previews, here is how you can do it:

# bytesIO = BytesIO(out[8:])

# preview_image = Image.open(bytesIO) # This is your preview in PIL image format, store it in a global

continue #previews are binary data

history = get_history(prompt_id)[prompt_id]

for node_id in history['outputs']:

node_output = history['outputs'][node_id]

images_output = []

if 'images' in node_output:

for image in node_output['images']:

image_data = get_image(image['filename'], image['subfolder'], image['type'])

images_output.append(image_data)

output_images[node_id] = images_output

return output_images

prompt_text = """

{

"3": {

"class_type": "KSampler",

"inputs": {

"cfg": 8,

"denoise": 1,

"latent_image": [

"5",

0

],

"model": [

"4",

0

],

"negative": [

"7",

0

],

"positive": [

"6",

0

],

"sampler_name": "euler",

"scheduler": "normal",

"seed": 8566257,

"steps": 20

}

},

"4": {

"class_type": "CheckpointLoaderSimple",

"inputs": {

"ckpt_name": "v1-5-pruned-emaonly.safetensors"

}

},

"5": {

"class_type": "EmptyLatentImage",

"inputs": {

"batch_size": 1,

"height": 512,

"width": 512

}

},

"6": {

"class_type": "CLIPTextEncode",

"inputs": {

"clip": [

"4",

1

],

"text": "masterpiece best quality girl"

}

},

"7": {

"class_type": "CLIPTextEncode",

"inputs": {

"clip": [

"4",

1

],

"text": "bad hands"

}

},

"8": {

"class_type": "VAEDecode",

"inputs": {

"samples": [

"3",

0

],

"vae": [

"4",

2

]

}

},

"9": {

"class_type": "SaveImage",

"inputs": {

"filename_prefix": "ComfyUI",

"images": [

"8",

0

]

}

}

}

"""

prompt = json.loads(prompt_text)

#set the text prompt for our positive CLIPTextEncode

prompt["6"]["inputs"]["text"] = "masterpiece best quality man"

#set the seed for our KSampler node

prompt["3"]["inputs"]["seed"] = 5

ws = websocket.WebSocket()

ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))

images = get_images(ws, prompt)

ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts

#Commented out code to display the output images:

# for node_id in images:

# for image_data in images[node_id]:

# from PIL import Image

# import io

# image = Image.open(io.BytesIO(image_data))

# image.show()