esto esta bien ? 
# Este es un ejemplo que utiliza la API de websockets para saber cuándo se ha completado la ejecución de un prompt.
# Una vez que la ejecución del prompt ha finalizado, descarga las imágenes usando el endpoint /history.
import websocket
import uuid
import json
import urllib.request
import urllib.parse
import requests

server_address = "127.0.0.1:8188"
client_id = str(uuid.uuid4())

def queue_prompt(workflow):
    p = {"workflow": workflow, "client_id": client_id}
    data = json.dumps(p).encode('utf-8')
    print(f"Enviando prompt: {p}")  # Salida para depuración
    headers = {'Content-Type': 'application/json'}
    req = urllib.request.Request(f"http://{server_address}/prompt", data=data, headers=headers)
    return json.loads(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, workflow):
    prompt_id = queue_prompt(workflow)['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  # La ejecución ha finalizado
        else:
            continue  # Las previsualizaciones son datos binarios

    history = get_history(prompt_id)[prompt_id]
    for node_id in history['outputs']:
        node_output = history['outputs'][node_id]
        if 'images' in node_output:
            images_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

def upload_file(file, subfolder="", overwrite=False):
    try:
        # Envolver el archivo en formdata para que incluya el nombre del archivo
        body = {"image": file}
        data = {}

        if overwrite:
            data["overwrite"] = "true"

        if subfolder:
            data["subfolder"] = subfolder

        resp = requests.post(f"http://{server_address}/upload/image", files=body, data=data)

        if resp.status_code == 200:
            data = resp.json()
            # Agregar el archivo a la lista desplegable y actualizar el valor del widget
            path = data["name"]
            if "subfolder" in data and data["subfolder"]:
                path = f"{data['subfolder']}/{path}"
            return path
        else:
            print(f"{resp.status_code} - {resp.reason}")
    except Exception as error:
        print(error)
        return None

# Semilla aleatoria
seed = 7777777777777777

# Subir una imagen
with open("example.png", "rb") as f:
    comfyui_path_image = upload_file(f, "", True)

# Cargar el workflow desde un archivo
with open("c:\\Users\\saka\\workflow.json", "r", encoding="utf-8") as f:
    workflow_data = f.read()

workflow = json.loads(workflow_data)

# Agregar un nodo LoadImage con el ID "11"
workflow["11"] = {
    "inputs": {
        "image": comfyui_path_image
    },
    "class_type": "LoadImage",
    "_meta": {
        "title": "Load Image"
    }
}

# Ajustar las conexiones en tu workflow para incluir el nuevo nodo
# Por ejemplo, si deseas usar la imagen cargada en el nodo KSampler (ID "3")
workflow["3"]["inputs"]["latent_image"] = ["11", 0]

# Establecer el texto del prompt para nuestros nodos CLIPTextEncode positivos y negativos
workflow["6"]["inputs"]["text"] = "masterpiece, best quality, a wide angle shot from the front of a girl posing on a bench in a beautiful meadow, :o face, short and rose hair, perfect legs, perfect arms, perfect eyes, perfect body, perfect feet, blue day sky, shorts, beautiful eyes, sharp focus, full body shot"
workflow["7"]["inputs"]["text"] = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, deformed, nsfw, deformed legs"

# Establecer la semilla para nuestro nodo KSampler
workflow["3"]["inputs"]["seed"] = seed

# Establecer el modelo
workflow["4"]["inputs"]["ckpt_name"] = "E:\\pinokio\\api\\comfy.git\\app\\models\\checkpoints\\flux1-schnell-fp8.safetensors"

# Conexión websocket
ws = websocket.create_connection(f"ws://{server_address}/ws?clientId={client_id}")
images = get_images(ws, workflow)

# Guardar las imágenes de salida
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.save(f"{node_id}-{seed}.png")


es para conectar con la api de comfyui