Using ComfyUI from Python

import json
from urllib import request

def queue_prompt(prompt):
    p = {"prompt": prompt}
    data = json.dumps(p).encode('utf-8')
    req = request.Request("http://127.0.0.1:8188/prompt", data=data)
    request.urlopen(req)

# Load your workflow (this is the API format)
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)

# Modify the prompt parameters
prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
prompt["3"]["inputs"]["seed"] = 5

# Queue the prompt
queue_prompt(prompt)

import websocket  # pip install 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(f"http://{server_address}/prompt", 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(f"http://{server_address}/view?{url_values}") as response:
        return response.read()

def get_history(prompt_id):
    with urllib.request.urlopen(f"http://{server_address}/history/{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 = {}
    
    # Wait for execution to complete
    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:
            continue  # previews are binary data
    
    # Retrieve the generated images
    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

# Connect to WebSocket
ws = websocket.WebSocket()
ws.connect(f"ws://{server_address}/ws?clientId={client_id}")

# Execute your prompt
images = get_images(ws, prompt)

# Close the connection
ws.close()

# Display the 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()