comfyui使用api实现工作流生成图片

苏伦

首先comfyui使用websockt实现请求在script_examples内存在示例py文件那么如何在代码端对comfyui实现api请求步骤：1.在安装comfyui时，有一个python编码文件夹2.在cmd使用文件夹的python执行pipinstallwebsocket-client3.利用编写官方提供的脚本importwebsocket importuuidimportjsonimporturllib.requestimporturllib.parseserver_address="127.0.0.1:8188"#看自己服务器是否是8188这个端口client_id=str(uuid.uuid4())#创建对应的iddefqueue_prompt(prompt):#函数返回对应prompt_id  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)  returnjson.loads(urllib.request.urlopen(req).read())defget_image(filename,subfolder,folder_type):#获取对应图片  data={"filename":filename,"subfolder":subfolder,"type":folder_type}  url_values=urllib.parse.urlencode(data)  withurllib.request.urlopen("http://{}/view?{}".format(server_address,url_values))asresponse:    returnresponse.read()defget_history(prompt_id):  withurllib.request.urlopen("http://{}/history/{}".format(server_address,prompt_id))asresponse:    returnjson.loads(response.read())defget_images(ws,prompt):#返回图篇  prompt_id=queue_prompt(prompt)['prompt_id']  output_images={}  whileTrue:    out=ws.recv()    ifisinstance(out,str):      message=json.loads(out)      ifmessage['type']=='executing':        data=message['data']        ifdata['node']isNoneanddata['prompt_id']==prompt_id:          break#Executionisdone    else:      continue#previewsarebinarydata  history=get_history(prompt_id)[prompt_id]  foroinhistory['outputs']:    fornode_idinhistory['outputs']:      node_output=history['outputs'][node_id]      if'images'innode_output:        images_output=[]        forimageinnode_output['images']:          image_data=get_image(image['filename'],image['subfolder'],image['type'])          images_output.append(image_data)      output_images[node_id]=images_output  returnoutput_images#这个prompt_text可以使用在工作流里面保存api开发，使用open（“地址”，“r”，“编码格式”）来读取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.ckpt"    }  },  "5":{    "class_type":"EmptyLatentImage",    "inputs":{      "batch_size":1,      "height":512,      "width":512    }  },  "6":{    "class_type":"CLIPTextEncode",    "inputs":{      "clip":[        "4",        1      ],      "text":"masterpiecebestqualitygirl"    }  },  "7":{    "class_type":"CLIPTextEncode",    "inputs":{      "clip":[        "4",        1      ],      "text":"badhands"    }  },  "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)prompt["6"]["inputs"]["text"]="masterpiecebestqualityman"#这个是通过json里面的结构进行修改。#settheseedforourKSamplernodeprompt["3"]["inputs"]["seed"]=5ws=websocket.WebSocket()ws.connect("ws://{}/ws?clientId={}".format(server_address,client_id))images=get_images(ws,prompt)#Commentedoutcodetodisplaytheoutputimages:fornode_idinimages:  forimage_datainimages[node_id]:     fromPILimportImage     importio     image=Image.open(io.BytesIO(image_data))     image.show()4.在对应编写的文件里面使用cmd，在使用刚刚下载对应客户端的python.exe执行编写好的文件就可以实现的在代码端调用api对工作流执行