260 lines
9.9 KiB
Python
260 lines
9.9 KiB
Python
import json
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from zhipuai import ZhipuAI
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import re
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from diffusers import DiffusionPipeline, StableDiffusionPipeline, DPMSolverMultistepScheduler
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from transformers import AutoTokenizer, AutoModel
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import torch
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import argparse
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import flask
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import typing
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import traceback
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ps = argparse.ArgumentParser()
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ps.add_argument("--config", default="config.json", help="Configuration file")
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args = ps.parse_args()
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with open(args.config) as f:
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config_json = json.load(f)
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class GlobalData:
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# OPENAI_ORGID = config_json[""]
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OPENAI_APIKEY = config_json["OpenAI-GPT"]["OpenAI-Key"]
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OPENAI_MODEL = config_json["OpenAI-GPT"]["GPT-Model"]
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OPENAI_MODEL_TEMPERATURE = int(config_json["OpenAI-GPT"]["Temperature"])
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OPENAI_MODEL_MAXTOKENS = min(2048, int(config_json["OpenAI-GPT"]["MaxTokens"]))
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CHATGLM_MODEL = config_json["ChatGLM"]["GPT-Model"]
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context_for_users = {}
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context_for_groups = {}
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GENERATE_PICTURE_ARG_PAT = re.compile("(\(|（)([0-9]+)[ \n\t]+([0-9]+)[ \n\t]+([0-9]+)(\)|）)")
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GENERATE_PICTURE_ARG_PAT2 = re.compile("(\(|（)([0-9]+)[ \n\t]+([0-9]+)[ \n\t]+([0-9]+)[ \n\t]+([0-9]+)(\)|）)")
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GENERATE_PICTURE_NEG_PROMPT_DELIMETER = re.compile("\n+")
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GENERATE_PICTURE_MAX_ITS = 200 # 最大迭代次数
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USE_OPENAIGPT = False
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USE_CHATGLM = False
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if config_json["OpenAI-GPT"]["Enable"]:
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print(f"Use OpenAI GPT Model({GlobalData.OPENAI_MODEL}).")
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USE_OPENAIGPT = True
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elif config_json["ChatGLM"]["Enable"]:
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print(f"Use ChatGLM({GlobalData.CHATGLM_MODEL}) as GPT-Model.")
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chatglm_tokenizer = AutoTokenizer.from_pretrained(GlobalData.CHATGLM_MODEL, trust_remote_code=True)
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chatglm_model = AutoModel.from_pretrained(GlobalData.CHATGLM_MODEL, trust_remote_code=True)
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if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
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chatglm_model = chatglm_model.to('mps')
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elif torch.cuda.is_available():
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chatglm_model = chatglm_model.to('cuda')
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chatglm_model = chatglm_model.eval()
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USE_CHATGLM = True
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app = flask.Flask(__name__)
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# 这个用于放行生成的任何图片，替换掉默认的NSFW检查器，公共场合慎重使用
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def run_safety_nochecker(image, device, dtype):
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print("警告：屏蔽了内容安全性检查，可能会产生有害内容")
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return image, None
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sd_args = {
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"pretrained_model_name_or_path": config_json["Diffusion"]["Diffusion-Model"],
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"torch_dtype": (torch.float16 if config_json["Diffusion"].get("UseFP16", True) else torch.float32)
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}
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sd_pipe = StableDiffusionPipeline.from_pretrained(**sd_args)
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sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
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if config_json["Diffusion"]["NoNSFWChecker"]:
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setattr(sd_pipe, "run_safety_checker", run_safety_nochecker)
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if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
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sd_pipe = sd_pipe.to("mps")
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elif torch.cuda.is_available():
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sd_pipe = sd_pipe.to("cuda")
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GPT_SUCCESS = 0
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GPT_NORESULT = 1
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GPT_ERROR = 2
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def CallOpenAIGPT(prompts: typing.List[str]):
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try:
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res = openai.ChatCompletion.create(
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model=config_json["OpenAI-GPT"]["GPT-Model"],
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messages=prompts
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)
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if len(res["choices"]) > 0:
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return (GPT_SUCCESS, res["choices"][0]["message"]["content"].strip())
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else:
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return (GPT_NORESULT, "")
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except openai.InvalidRequestError as e:
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return (GPT_ERROR, e)
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except Exception as e:
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traceback.print_exception(e)
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return (GPT_ERROR, str(e))
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def CallChatGLM(msg, history: typing.List[str]):
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try:
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resp, hist = chatglm_model.chat(chatglm_tokenizer, msg, history=history)
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if isinstance(resp, tuple):
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resp = resp[0]
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return (GPT_SUCCESS, resp)
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except Exception as e:
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return (GPT_ERROR, str(e))
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def add_context(uid: str, is_user: bool, msg: str):
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if not uid in GlobalData.context_for_users:
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GlobalData.context_for_users[uid] = []
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if USE_OPENAIGPT:
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GlobalData.context_for_users[uid].append({
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"role": "system",
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"content": msg
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}
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)
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elif USE_CHATGLM:
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GlobalData.context_for_users[uid].append(msg)
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def get_context(uid: str):
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if not uid in GlobalData.context_for_users:
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GlobalData.context_for_users[uid] = []
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return GlobalData.context_for_users[uid]
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@app.route("/chat_clear", methods=['POST'])
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def app_chat_clear():
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data = json.loads(flask.globals.request.get_data())
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GlobalData.context_for_users[data["user_id"]] = []
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print(f"Cleared context for {data['user_id']}")
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return ""
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@app.route("/chat", methods=['POST'])
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def app_chat():
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data = json.loads(flask.globals.request.get_data())
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# print(data)
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uid = data["user_id"]
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if not data["text"][-1] in ['?', '？', '.', '。', ',', '，', '!', '！']:
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data["text"] += "。"
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# 使用ZhipuAI库调用模型生成回复
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client = ZhipuAI(api_key="73bdeed728677bc80efc6956478a2315.VerNWJMCwN9L5gTi") # 请填写您自己的APIKey
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response = client.chat.completions.create(
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model="glm-4", # 请填写您要调用的模型名称
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messages=[
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{"role": "user", "content": data["text"]},
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],
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)
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# 获取模型的回复
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resp = response.choices[0].message.content
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if resp:
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return json.dumps({"user_id": data["user_id"], "text": resp, "error": False, "error_msg": ""})
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else:
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return json.dumps({"user_id": data["user_id"], "text": "", "error": True, "error_msg": "模型未返回回复"})
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@app.route("/draw", methods=['POST'])
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def app_draw():
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data = json.loads(flask.globals.request.get_data())
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prompt = data["prompt"]
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i = 0
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for i in range(len(prompt)):
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if prompt[i] == ':' or prompt[i] == '：':
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break
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if i == len(prompt):
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return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
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"error_msg": "格式不对，正确的格式是：生成图片：Prompt 或者 生成图片(宽 高 迭代次数 [图片最大数量(缺省1)])：Prompt"})
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match_args = re.match(GlobalData.GENERATE_PICTURE_ARG_PAT2, prompt[:i])
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if not match_args is None:
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W = int(match_args.group(2))
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H = int(match_args.group(3))
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ITS = int(match_args.group(4))
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NUM_PIC = int(match_args.group(5))
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else:
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match_args = re.match(GlobalData.GENERATE_PICTURE_ARG_PAT, prompt[:i])
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if not match_args is None:
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W = int(match_args.group(2))
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H = int(match_args.group(3))
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ITS = int(match_args.group(4))
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NUM_PIC = 1
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else:
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if len(prompt[:i].strip()) != 0:
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return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
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"error_msg": "格式不对，正确的格式是：生成图片：Prompt 或者 生成图片(宽 高 迭代次数 [图片最大数量(缺省1)])：Prompt"})
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else:
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W = 768
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H = 768
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ITS = config_json.get('DefaultDiffutionIterations', 20)
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NUM_PIC = 1
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if W > 2500 or H > 2500:
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return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
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"error_msg": "你要求的图片太大了，我不干了～"})
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if ITS > GlobalData.GENERATE_PICTURE_MAX_ITS:
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return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
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"error_msg": f"迭代次数太多了，不要超过{GlobalData.GENERATE_PICTURE_MAX_ITS}次"})
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prompt = prompt[(i + 1):].strip()
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prompts = re.split(GlobalData.GENERATE_PICTURE_NEG_PROMPT_DELIMETER, prompt)
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prompt = prompts[0]
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neg_prompt = None
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if len(prompts) > 1:
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neg_prompt = prompts[1]
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print(f"Generating {NUM_PIC} picture(s) with prompt = {prompt} , negative prompt = {neg_prompt}")
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try:
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if NUM_PIC > 1 and torch.backends.mps.is_available(): # Apple silicon上的bug：https://github.com/huggingface/diffusers/issues/363
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return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
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"error_msg": "单prompt生成多张图像在Apple silicon上无法实现，相关讨论参考https://github.com/huggingface/diffusers/issues/363"})
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images = sd_pipe(prompt=prompt, negative_prompt=neg_prompt, width=W, height=H, num_inference_steps=ITS,
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num_images_per_prompt=NUM_PIC).images[:NUM_PIC]
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if len(images) == 0:
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return json.dumps(
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{"user_name": data["user_name"], "filenames": [], "error": True, "error_msg": "没有产生任何图像"})
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filenames = []
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for i, img in enumerate(images):
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img.save(f"latest-{i}.png")
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filenames.append(f"latest-{i}.png")
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return json.dumps({"user_name": data["user_name"], "filenames": filenames, "error": False, "error_msg": ""})
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except Exception as e:
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return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True, "error_msg": str(e)})
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@app.route("/info", methods=['POST', 'GET'])
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def app_info():
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return "\n".join(
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[f"GPT模型：{config_json['OpenAI-GPT']['GPT-Model'] if USE_OPENAIGPT else config_json['ChatGLM']['GPT-Model']}",
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f"Diffusion模型：{config_json['Diffusion']['Diffusion-Model']}",
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"默认图片规格：768x768 RGB三通道", "Diffusion默认迭代轮数：20",
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f"使用半精度浮点数 : {'是' if config_json['Diffusion'].get('UseFP16', True) else '否'}",
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f"屏蔽NSFW检查：{'是' if config_json['Diffusion']['NoNSFWChecker'] else '否'}",
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"清空上下文指令：重置上下文",
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"生成图片指令：生成图片(宽 高 迭代次数)：正面提示 换行写负面提示，其中(宽 高 迭代次数)和换行写的负面提示都是可以省略的"])
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if __name__ == "__main__":
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if USE_OPENAIGPT:
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openai.api_key = GlobalData.OPENAI_APIKEY
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app.run(host="0.0.0.0", port=11111)
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