官方给的示例：

https://github.com/comfyanonymous/ComfyUI/blob/master/script_examples/websockets_api_example.pyhttps://github.com/comfyanonymous/ComfyUI/blob/master/script_examples/websockets_api_example.pyhttps://github.com/comfyanonymous/ComfyUI/blob/master/script_examples/websockets_api_example.py

一、环境准备

1.1 comfyui环境安装参考

【本地运行AI绘画】ComfyUI的安装与使用（一）（windows+1660ti 6G显存）

1.2  调用可能需要安装的python包 

对应  import websocket

pip install websocket-client 
pip install json 

1.3 comfyui打开开发者模式，可以直接下载任意工作流 api文件

1.3.1 在英文最新界面打开

1.3.2  中文老界面打开

1.4 加载默认工作流，下载api，看看

保存结果是基本json 与工作流相似 不要弄混了

二、 代码

2.1  指定任意模型进行生图（找到名字和在api中的位置）

局部代码

def text_to_image(prompt_text,model_name="SD1.X/dreamshaper_8.safetensors",seed=6,steps=20,width=512,height=256,batch_size=2,local_save_dir='./output',api_file='api_demo.json'):prompt = read_json(api_file)prompt["3"]["inputs"]["seed"] = seed  # 换一个数字不一样的图prompt["3"]["inputs"]["steps"] =steps   # 换一个数字不一样的图prompt["4"]["inputs"]["ckpt_name"] = model_nameprompt["5"]["inputs"]["width"] = widthprompt["5"]["inputs"]["height"] = heightprompt["5"]["inputs"]["batch_size"]=batch_sizeprompt["6"]["inputs"]["text"] = prompt_text  

2.1.1  运行结果

2.1.2 参数解析

在comfyui的界面里确认有的模型名免得传错

打开保存的  api_demo.json,找到相应的代码改写

2.1.3 完整代码

#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 endpointimport websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) #  pip install websocket-client
import uuid
import json
import urllib.request
import urllib.parse
import osserver_address = "127.0.0.1:8188"
client_id = str(uuid.uuid4())def queue_prompt(prompt):p = {"prompt": prompt, "client_id": client_id}data = json.dumps(p).encode('utf-8')req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)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("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 = 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 doneelse:# 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 globalcontinue #previews are binary datahistory = 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_outputreturn output_imagesdef read_json(api_file="controlnet_api.json"):# print('api_file',api_file)with open (api_file,"r",encoding="utf-8") as file_json:prompt_text=json.load(file_json)return prompt_textdef text_to_image(prompt_text,model_name="SD1.X/dreamshaper_8.safetensors",seed=6,steps=20,width=512,height=256,batch_size=2,local_save_dir='./output',api_file='api_demo.json'):prompt = read_json(api_file)prompt["3"]["inputs"]["seed"] = seed  # 换一个数字不一样的图prompt["3"]["inputs"]["steps"] =steps   # 换一个数字不一样的图prompt["4"]["inputs"]["ckpt_name"] = model_nameprompt["5"]["inputs"]["width"] = widthprompt["5"]["inputs"]["height"] = heightprompt["5"]["inputs"]["batch_size"]=batch_sizeprompt["6"]["inputs"]["text"] = prompt_text  ws = websocket.WebSocket()ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))images = get_images(ws, prompt)os.makedirs(local_save_dir, exist_ok=True)for node_id in images:for i,image_data in enumerate(images[node_id]):from PIL import Imageimport ioimage = Image.open(io.BytesIO(image_data))# image.show()save_path=f"{local_save_dir}/{prompt_text[:20]}_{i}.png"image.save(save_path)print(f"Saved image to {save_path}")ws.close()if __name__ == "__main__":text_to_image(prompt_text="a boy",api_file=r'D:\deploy2\api_demo.json')