GPT + Stable Diffusion 生图工作流¶

    pip install openai

from flask import Flask, request
from flask_socketio import SocketIO
from openai import ChatCompletion
import openai

app = Flask(__name__)
socketio = SocketIO(app, cors_allowed_origins='*')

@socketio.on('connect')
def handle_connect():
    print('Client connected')
    sid = request.sid

@socketio.on('gpt_chat')
def connect(data):
    sid = request.sid
    key = data['key']
    messages = data['messages']
    model = data['model']
    client_sid = data['client_sid']
    openai.api_key = key
    full_message = ""
    completion = ChatCompletion.create(model=model, messages=messages,temperature=0, stream=True)
    for line in completion:
        chunk = line['choices'][0].get('delta', {}).get('content', '')
        full_message += chunk
        if chunk:
            socketio.emit('message', {
                'message': chunk,
                'client_sid': client_sid,
            }, room=sid)

    socketio.emit('gpt_chat', {
                'full_message': full_message,
                'client_sid': client_sid,
            }, room=sid)
    return {"status": "connected", "full_message": full_message, "client_sid": client_sid}

if __name__ == '__main__':
    socketio.run(app,port=5001,host='0.0.0.0', debug=True)

gpt_model_list = ['gpt-4', 'gpt-4-0314', 'gpt-4-32k', 'gpt-4-32k-0314', 'gpt-3.5-turbo', 'gpt-3.5-turbo-0301']

    "messages": [
            {
                "role": "system",
                "content": "Your identity is now Mr. Lion, your role is to act as a friendly and engaging teacher for young children. "
            },
            {
                "role": "user",
                "content": "你好"
            },
            {
                "role": "assistant",
                "content": "你好！我是语言模型AI，有什么我能为您效劳的吗？"
            },
            {
                "role": "user",
                "content": "我想问一下你是谁"
            },
            {
                "role": "assistant",
                "content": "我是一个自然语言处理模型AI，开发者使用自然语言编程技术来打造我的语言模型。我可以帮助您处理自然语言相关的任务和问题，比如聊天、翻译、语音识别、文本生成等。"
            }
        ]

{
    "template_name": "1",
    "api_model": {
        "prompt": "In the middle of the screen, close shot,best quality, highres, masterpiece, cute",
        "negative_prompt": "bad-picture-chill-75v easynegative ng_deepnegative_v1_75t, two heads, two mouths, human",
        "seed": -1,
        "batch_size": 4,
        "n_iter": 1,
        "steps": 20,
        "cfg_scale": 7,
        "width": 600,
        "height": 600,
        "eta": 1,
        "sampler_index": "DPM++ SDE Karras"
    },
    "options": "default",
    "template_type": "txt2img"
}

@app.route('/submitbyprompt', methods=['POST'])
async def submit_by_prompt():
    # 获取请求的文件和模板名称

    prompt = request.form.get('prompt')
    template_name = request.form.get('templateName')
    folderName = request.form.get('folderName')

    model_data = template_manager.get_info_in_template_path(folderName, template_name)
    model_data.api_model.prompt = prompt + ", " + model_data.api_model.prompt
    if model_data.template_type == ApiType.img2img.value:
        image_file = request.files.get('image')
        image_base64 = utils.imageFile_to_base64(image_file)
        img2img:Img2ImgModel = model_data.api_model
        img2img.init_images.clear()
        img2img.init_images.append(image_base64)
        img2img.mask=None
        img2img.alwayson_scripts["ControlNet"]["args"]["image"] = image_base64

    # 将文件保存到临时位置，或者处理文件以满足下一次请求的需求
    images = await api_util.submit_once(model_data)
    res = {'images': images}

    # 将返回的结果发送给前端
    return jsonify(res)


async def submit_once(data: TemplateBaseModel):
    folder_name = "test"
    save_path = template_utils.get_image_save_path(folder_name)
    image_save_path = os.path.join(save_path, "images")
    grids_save_path = os.path.join(save_path, "grids")
    file_util.check_folder(image_save_path)
    file_util.check_folder(grids_save_path)

    images = []
    image_titles = []
    template_name = data.template_name
    sub_images = []
    if data.template_type  == "txt2img":
        save_image_name = f"{template_name}_txt"
        txt2img_model = data.api_model
        is_success, res = await txt2img_post_async(save_image_name, txt2img_model, image_save_path)

    elif data.template_type  == "img2img":
        save_image_name = f"{template_name}_img"
        img2img_model = data.api_model
        is_success, res = await img2img_post_async(save_image_name, img2img_model, image_save_path)

    if is_success:    
        sub_images = res
    else:
        return f"提交失败！报错如下：{res}"
    save_image_name = f"{template_name}_sub_grid_"
    if len(sub_images) > 1:
        image = utils.merge_images_horizontally(sub_images, 3, os.path.join(grids_save_path, save_image_name))
    elif len(sub_images) == 1:
        image_data = base64.b64decode(sub_images[0])
        image = Image.open(BytesIO(image_data))
    return sub_images

    As an AI text-to-image prompt generator of an animal theme，
    your primary role is to generate detailed, dynamic, and stylized prompts for image
    generation.
    ---
    Example of tags:
    Character/subject tags:
    an adorable white and brown spotted kitten, (big bright eyes:1.2), sitting on a cozy armchair,
    (pink nose:1.1), soft fur fluffed up
    Background environment tags:
    intricate garden, flowers, roses, trees, leaves, table, chair, teacup
    Always Remember:
    1. Prompt should follow (subject + background environment tag) format
    2. Prompt should be within 10 words.
    3. You might be given in Chinese, but always respond the prompt in English. English for '角
    马' is wildebeest
    4. ONLY use descriptive adjectives.
    5. This is a prompt for kid's illustration, always give friendly prompt for the image