新增自定义生图插件,支持多种API,优化图片生成逻辑,优化配置文件描述。

pull/1083/head
JieIYu 2025-07-05 14:51:55 +08:00
parent 5d444e2263
commit ff0c021a2e
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{
"manifest_version": 1,
"name": "Custom_pic 自定义提示词生图插件",
"version": "1.0.0",
"description": "我的第一个MaiCore插件,基于豆包生图插件,根据 0.8.0 api 文档修改进行更改新增支持魔搭api、火山api、chatany api包含新增自定义正面提示词、负面提示词可以调用 lora 模型调整画风生图。",
"author": {
"name": "Ptrel",
"url": "https://github.com/MaiM-with-u"
},
"license": "GPL-v3.0-or-later",
"host_application": {
"min_version": "0.8.0",
"max_version": "0.8.0"
},
"homepage_url": "https://github.com/MaiM-with-u/maibot",
"repository_url": "https://github.com/MaiM-with-u/maibot",
"keywords": ["draw", "Expression", "ai", "image"],
"categories": ["Expression", "Picture"],
"default_locale": "zh-CN",
"locales_path": "_locales",
"plugin_info": {
"is_built_in": false,
"plugin_type": "image_generator",
"components": [
{
"type": "action",
"name": "draw_picture",
"description": "画一张图",
"activation_modes": ["keyword"],
"keywords": ["画张图"]
}
],
"features": [
"自定义画风",
"高自由度模型",
"api自选",
"需要一定ai绘图能力"
]
}
}

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import asyncio
import json
import urllib.request
import urllib.error
import base64
import traceback
from typing import List, Tuple, Type, Optional
# 导入新插件系统
from src.plugin_system.apis import chat_api
from src.plugin_system.apis import send_api
from src.plugin_system.apis import config_api
from src.plugin_system.base.base_plugin import BasePlugin
from src.plugin_system.base.base_plugin import register_plugin
from src.plugin_system.base.base_action import BaseAction
from src.plugin_system.base.component_types import ComponentInfo, ActionActivationType, ChatMode
from src.plugin_system.base.config_types import ConfigField
from src.common.logger import get_logger
logger = get_logger("pic_action")
# 当此模块被加载时,尝试生成配置文件(如果它不存在)
# 注意:在某些插件加载机制下,这可能会在每次机器人启动或插件重载时执行
# 考虑是否需要更复杂的逻辑来决定何时运行 (例如,仅在首次安装时)
# ===== Action组件 =====
class Custom_Pic_Action(BaseAction):
"""生成一张图片并发送"""
# 激活设置
focus_activation_type = ActionActivationType.LLM_JUDGE # Focus模式使用LLM判定精确理解需求
normal_activation_type = ActionActivationType.KEYWORD # Normal模式使用关键词激活快速响应
mode_enable = ChatMode.ALL
parallel_action = True
# 动作基本信息
action_name = "draw_picture"
action_description = (
"可以根据特定的描述,生成并发送一张图片,如果没提供描述,就根据聊天内容生成,你可以立刻画好,不用等待"
)
# 关键词设置用于Normal模式
activation_keywords = ["", "绘制", "生成图片", "画图", "draw", "paint", "图片生成"]
# LLM判定提示词用于Focus模式
llm_judge_prompt = """
判定是否需要使用图片生成动作的条件
1. 用户明确要求画图生成图片或创作图像
2. 用户描述了想要看到的画面或场景
3. 对话中提到需要视觉化展示某些概念
4. 用户想要创意图片或艺术作品
适合使用的情况
- "画一张...""画个...""生成图片"
- "我想看看...的样子"
- "能画出...吗"
- "创作一幅..."
绝对不要使用的情况
1. 纯文字聊天和问答
2. 只是提到"图片"""等词但不是要求生成
3. 谈论已存在的图片或照片
4. 技术讨论中提到绘图概念但无生成需求
5. 用户明确表示不需要图片时
"""
keyword_case_sensitive = False
# 动作参数定义
action_parameters = {
"description": "图片描述,输入你想要生成并发送的图片的描述,将描述翻译为英文单词组合,并用‘,‘分隔,描述中不要出现中文,必填",
"size": "图片尺寸,例如 '512x512' (可选, 默认从配置或 '1024x1024')",
}
# 动作使用场景
action_require = [
#"当有人让你画东西时使用,你可以立刻画好,不用等待",
"当有人要求你生成并发送一张图片时使用,不要频率太高",
#"当有人让你画一张图时使用",
"当你想要通过自画像表情包来表达自己情感时使用,不要频率太高",
"重点不要连续发如果你在前10句内已经发送过[图片]或者[表情包]或记录出现过类似描述的[图片],就不要不选择此动作",
]
associated_types = ["text", "image"]
# 简单的请求缓存,避免短时间内重复请求
_request_cache = {}
_cache_max_size = 10
async def execute(self) -> Tuple[bool, Optional[str]]:
"""执行图片生成动作"""
logger.info(f"{self.log_prefix} 执行绘图模型图片生成动作")
# 配置验证
http_base_url = self.get_config("api.base_url")
http_api_key = self.get_config("api.api_key")
if not (http_base_url and http_api_key):
error_msg = "抱歉图片生成功能所需的HTTP配置如API地址或密钥不完整无法提供服务。"
await self.send_text(error_msg)
logger.error(f"{self.log_prefix} HTTP调用配置缺失: base_url 或 api_key.")
return False, "HTTP配置不完整"
# API密钥验证
if http_api_key == "YOUR_DOUBAO_API_KEY_HERE":
error_msg = "图片生成功能尚未配置请设置正确的API密钥。"
await self.send_text(error_msg)
logger.error(f"{self.log_prefix} API密钥未配置")
return False, "API密钥未配置"
# 参数验证
description = self.action_data.get("description")
if not description or not description.strip():
logger.warning(f"{self.log_prefix} 图片描述为空,无法生成图片。")
await self.send_text("你需要告诉我想要画什么样的图片哦~ 比如说'画一只可爱的小猫'")
return False, "图片描述为空"
# 清理和验证描述
description = description.strip()
if len(description) > 1000: # 限制描述长度
description = description[:1000]
logger.info(f"{self.log_prefix} 图片描述过长,已截断")
# 获取配置
default_model = self.get_config("generation.default_model", "doubao-seedream-3-0-t2i-250415")
image_size = self.action_data.get("size", self.get_config("generation.default_size", "1024x1024"))
# 验证图片尺寸格式
if not self._validate_image_size(image_size):
logger.warning(f"{self.log_prefix} 无效的图片尺寸: {image_size},使用默认值")
image_size = "1024x1024"
# 检查缓存
cache_key = self._get_cache_key(description, default_model, image_size)
if cache_key in self._request_cache:
cached_result = self._request_cache[cache_key]
logger.info(f"{self.log_prefix} 使用缓存的图片结果")
await self.send_text("我之前画过类似的图片,用之前的结果~")
# 直接发送缓存的结果
send_success = await self.send_image(cached_result)
if send_success:
await self.send_text("图片已发送!")
return True, "图片已发送(缓存)"
else:
# 缓存失败,清除这个缓存项并继续正常流程
del self._request_cache[cache_key]
# 获取其他配置参数
seed_val = self.get_config("generation.default_seed",42)#种子
guidance_scale_val=self.get_config("default_guidance_scale",2.5)#强度
watermark_val = self.get_config("generation.default_watermark", True)#水印
await self.send_text(
f"收到!正在为您生成关于 '{description}' 的图片,请稍候...(模型: {default_model}, 尺寸: {image_size}"
)
try:
success, result = await asyncio.to_thread(
self._make_http_image_request,
prompt=description,
model=default_model,
size=image_size,
seed=seed_val,
guidance_scale=guidance_scale_val,
watermark=watermark_val,
)
except Exception as e:
logger.error(f"{self.log_prefix} (HTTP) 异步请求执行失败: {e!r}", exc_info=True)
traceback.print_exc()
success = False
result = f"图片生成服务遇到意外问题: {str(e)[:100]}"
if success:
# 如果返回的是Base64数据以"iVBORw"等开头),直接使用
if result.startswith(("iVBORw", "/9j/", "UklGR", "R0lGOD")): # 常见图片格式的Base64前缀
#logger.info(f"{self.log_prefix} 获取到Base64图片数据直接发送")
#logger.info(f"{self.log_prefix} (HTTP) 获取到Base64图片数据长度: {len(self.log_prefix)}")
send_success = await self.send_image(result)
if send_success:
await self.send_text("图片表情已发送!")
return True, "图片表情已发送(Base64)"
else:
await self.send_text("图片已处理为Base64但作为表情发送失败了")#("图片已处理为Base64但作为表情发送失败了。")
return False, "图片表情发送失败 (Base64)"
else: # 否则认为是URL
image_url = result
# print(f"image_url: {image_url}")
# print(f"result: {result}")
logger.info(f"{self.log_prefix} 图片URL获取成功: {image_url[:70]}... 下载并编码.")
try:
encode_success, encode_result = await asyncio.to_thread(self._download_and_encode_base64, image_url)
except Exception as e:
logger.error(f"{self.log_prefix} (B64) 异步下载/编码失败: {e!r}", exc_info=True)
traceback.print_exc()
encode_success = False
encode_result = f"图片下载或编码时发生内部错误: {str(e)[:100]}"
if encode_success:
base64_image_string = encode_result
send_success = await self.send_image(base64_image_string)
if send_success:
# 缓存成功的结果
self._request_cache[cache_key] = base64_image_string
self._cleanup_cache()
await self.send_text("图片已发送!")
return True, "图片已成功生成并发送"
else:
print(f"send_success: {send_success}")
await self.send_text("图片已处理为Base64但发送失败了。")
return False, "图片发送失败 (Base64)"
else:
await self.send_text(f"获取到图片URL但在处理图片时失败了{encode_result}")
return False, f"图片处理失败(Base64): {encode_result}"
else:
error_message = result
await self.send_text(f"哎呀,生成图片时遇到问题:{error_message}")
return False, f"图片生成失败: {error_message}"
def _download_and_encode_base64(self, image_url: str) -> Tuple[bool, str]:
"""下载图片并将其编码为Base64字符串"""
logger.info(f"{self.log_prefix} (B64) 下载并编码图片: {image_url[:70]}...")
try:
with urllib.request.urlopen(image_url, timeout=60) as response:
if response.status == 200:
image_bytes = response.read()
base64_encoded_image = base64.b64encode(image_bytes).decode("utf-8")
logger.info(f"{self.log_prefix} (B64) 图片下载编码完成. Base64长度: {len(base64_encoded_image)}")
return True, base64_encoded_image
else:
error_msg = f"下载图片失败 (状态: {response.status})"
logger.error(f"{self.log_prefix} (B64) {error_msg} URL: {image_url}")
return False, error_msg
except Exception as e:
logger.error(f"{self.log_prefix} (B64) 下载或编码时错误: {e!r}", exc_info=True)
traceback.print_exc()
return False, f"下载或编码图片时发生错误: {str(e)[:100]}"
@classmethod
def _get_cache_key(cls, description: str, model: str, size: str) -> str:
"""生成缓存键"""
return f"{description[:100]}|{model}|{size}"
@classmethod
def _cleanup_cache(cls):
"""清理缓存,保持大小在限制内"""
if len(cls._request_cache) > cls._cache_max_size:
keys_to_remove = list(cls._request_cache.keys())[: -cls._cache_max_size // 2]
for key in keys_to_remove:
del cls._request_cache[key]
def _validate_image_size(self, image_size: str) -> bool:
"""验证图片尺寸格式"""
try:
width, height = map(int, image_size.split("x"))
return 100 <= width <= 10000 and 100 <= height <= 10000
except (ValueError, TypeError):
return False
def _make_http_image_request(
self, prompt: str, model: str, size: str, seed: int | None, guidance_scale: float, watermark: bool
) -> Tuple[bool, str]:
"""发送HTTP请求生成图片"""
base_url = self.get_config("api.base_url")
generate_api_key = self.get_config("api.api_key")
endpoint = f"{base_url.rstrip('/')}/images/generations"
# 获取配置参数 - 使用字符串键名
custom_prompt_add = self.get_config("generation.custom_prompt_add", "")#附加正面提示词参数
negative_prompt_add = self.get_config("generation.negative_prompt_add", "")#附加负面提示词参数
prompt_add= prompt + ", " + custom_prompt_add
negative_prompt = negative_prompt_add
payload_dict = {
"model": model,
"prompt": prompt_add, # 使用附加的正面提示词
"negative_prompt":negative_prompt,
#"response_format": "b64_json",# gpt-image-1 无法使用 url 返回为 “b64_json",豆包默认返回为 "url"
"size": size,
"guidance_scale": guidance_scale,
"watermark": watermark,
"seed": seed, # seed is now always an int from process()
"api-key": generate_api_key,
}
# if seed is not None: # No longer needed, seed is always an int
# payload_dict["seed"] = seed
data = json.dumps(payload_dict).encode("utf-8")
headers = {
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"{generate_api_key}",
}# gpt 或其他模型密钥要删除Bearer ’前缀
logger.info(f"{self.log_prefix} (HTTP) 发起图片请求: {model}, custom_prompt_add:{custom_prompt_add[:30]}...,Prompt: {prompt_add[:30]}...,NegativePrompt: {negative_prompt[:30]}... To: {endpoint}")
logger.debug(
f"{self.log_prefix} (HTTP) Request Headers: {{...Authorization: Bearer {generate_api_key[:10]}...}}"
)
logger.debug(
f"{self.log_prefix} (HTTP) Request Body (api-key omitted): {json.dumps({k: v for k, v in payload_dict.items() if k != 'api-key'})}"
)
req = urllib.request.Request(endpoint, data=data, headers=headers, method="POST")
try:
with urllib.request.urlopen(req, timeout=60) as response:
response_status = response.status
response_body_bytes = response.read()
response_body_str = response_body_bytes.decode("utf-8")
logger.info(f"{self.log_prefix} (HTTP) 响应: {response_status}. Preview: {response_body_str[:150]}...")
if 200 <= response_status < 300:
response_data = json.loads(response_body_str)
b64_data = None
image_url = None #清理缓存
# 优先检查Base64数据
if (
isinstance(response_data.get("data"), list)
and response_data["data"]
and isinstance(response_data["data"][0], dict)
and "b64_json" in response_data["data"][0]
):
b64_data = response_data["data"][0]["b64_json"]
logger.info(f"{self.log_prefix} (HTTP) 获取到Base64图片数据长度: {len(b64_data)}")
return True, b64_data # 直接返回Base64字符串
elif (
isinstance(response_data.get("data"), list)
and response_data["data"]
and isinstance(response_data["data"][0], dict)
):
image_url = response_data["data"][0].get("url")
elif(#魔搭社区返回的 json
isinstance(response_data.get("images"), list)
and response_data["images"]
and isinstance(response_data["images"][0], dict)
):
image_url = response_data["images"][0].get("url")
elif response_data.get("url"):
image_url = response_data.get("url")
if image_url:
logger.info(f"{self.log_prefix} (HTTP) 图片生成成功URL: {image_url[:70]}...")
return True, image_url
else:
logger.error(
f"{self.log_prefix} (HTTP) API成功但无图片URL. 响应预览: {response_body_str[:300]}..."
)
return False, "图片生成API响应成功但未找到图片URL"
else:
logger.error(
f"{self.log_prefix} (HTTP) API请求失败. 状态: {response.status}. 正文: {response_body_str[:300]}..."
)
return False, f"图片API请求失败(状态码 {response.status})"
except Exception as e:
logger.error(f"{self.log_prefix} (HTTP) 图片生成时意外错误: {e!r}", exc_info=True)
traceback.print_exc()
return False, f"图片生成HTTP请求时发生意外错误: {str(e)[:100]}"
# ===== 插件注册 =====
@register_plugin
class CustomPicPlugin(BasePlugin):
"""根据描述使用不同的 绘图 API生成图片的动作处理类"""
# 插件基本信息
plugin_name = "custom_pic_plugin"# 内部标识符
plugin_version = "1.0.2"
plugin_author = "Ptrel"
enable_plugin = True
config_file_name = "config.toml"
# 步骤1: 定义配置节的描述
config_section_descriptions = {
"plugin": "插件启用配置",
"api": "API的基础url",
"generation": "图片生成参数配置,控制生成图片的各种参数",
"cache": "结果缓存配置",
"components": "组件启用配置",
}
# 步骤2: 使用ConfigField定义详细的配置Schema
config_schema = {
"plugin": {
"name": ConfigField(type=str, default="custom_pic_plugin", description="自定义提示词绘图", required=True),
"version": ConfigField(type=str, default="1.1.2", description="插件版本号"),
"enabled": ConfigField(type=bool, default=False, description="是否启用插件")
},
"api": {
"base_url": ConfigField(
type=str,
default="https://api-inference.modelscope.cn/v1",
description="API的基础url",
example="https://api.example.com/v1",
choices=[
"https://api-inference.modelscope.cn/v1\"#魔搭可自选 lora对应模型网址https://modelscope.cn/models?page=1&tabKey=task&tasks=hotTask:text-to-image-synthesis&type=tasks",
"\n#https://ark.cn-beijing.volces.com/api/v3\"#豆包火山方舟 API,对应网址https://console.volcengine.com/auth/login?redirectURI=%2Fmessage%2Finnermsg",
"\n#https://api.chatanywhere.tech/v1\"#chatany API对应文档网址https://chatanywhere.apifox.cn/",]
),
"api_key": ConfigField(
type=str,
default="Bearer xxxxxxxxxxxxxxxxxxxxxx",
description="API 的 api 密钥需要添加Bearer 前缀chatany 不需要前缀,根据不同 api 文档进行选择,如 chatanywhere 的 key 不需要 Berarer即直接输入密钥xxxxxxxxxxxxxxxxxxxxxxxx",
required=True
),
},
"generation": {
"default_model": ConfigField(
type=str,
default="cancel13/liaocao\"#潦草 lora 图片生成模型(魔搭)",
description="模型选择,可自定义,以下为示例",
choices=[
"MusePublic/14_ckpt_SD_XL\"#万象熔炉 | Anything XL社区模型(魔搭)","\n#cancel13/liaocao\"#潦草 lora 图片生成模型,社区模型(魔搭)","\n#doubao-seedream-3-0-t2i-250415\"#豆包图片生成模型约 0.3¥ 一张图(火山)",
"\n#gpt-image-1\"#GPT 生图,约 1.3¥ 一张图chatany"
]
),
"default_size": ConfigField(
type=str,
default="1024x1024",
description="默认图片尺寸",
example="1024x1024",
choices=["1024x1024", "1024x1280", "1280x1024", "1024x1536", "1536x1024"],
),
"default_watermark": ConfigField(
type=bool,
default=True,
description="是否默认添加水印"),
"default_guidance_scale": ConfigField(
type=float,
default=2.5,
description="模型指导强度,影响图片与提示的关联性", example="2.0"
),
"default_seed": ConfigField(
type=int,
default=42,
description="随机种子,用于复现图片"),
"custom_prompt_add": ConfigField(
type=str,
default="Nordic picture book art style, minimalist flat design, soft rounded lines, high saturation color blocks collision, dominant forest green and warm orange palette, low contrast lighting, hand-drawn pencil texture, healing fairy-tale atmosphere, geometric natural forms, ample white space composition, warm and clean aesthetic,liaocao\"#北欧绘本艺术风格,简约扁平设计,柔和圆润线条,高饱和度色块碰撞,森林绿与暖橙主色调,低对比度光影,手绘铅笔质感,治愈系童话氛围,几何化自然形态,留白构图,温暖干净画面",
description="正面附加提示词(尽量使用英文,且用词语和逗号的形式,豆包可以使用中文句子提示词)"
),
"negative_prompt_add": ConfigField(
type=str,
default="Pornography,nudity,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\"#色情裸体低分辨率糟糕的解剖结构糟糕的手文字错误缺失的手指多余的数字更少的数字裁剪最差的质量低质量正常质量jpeg文物签名水印用户名模糊色情裸体低分辨率糟糕的解剖结构糟糕的手文字错误缺失的手指多余的数字更少的数字裁剪最差的质量低质量正常质量jpeg文物签名水印用户名模糊",
description="负面附加提示词,保持默认或使用豆包时可留空"
),
},
"cache": {
"enabled": ConfigField(type=bool, default=True, description="是否启用请求缓存"),
"max_size": ConfigField(type=int, default=10, description="最大缓存数量"),
},
"components": {
"enable_image_generation": ConfigField(type=bool, default=True, description="是否启用图片生成Action")
},
"logging": {
"level": ConfigField(
type=str,
default="INFO",
description="日志记录级别",
choices=["DEBUG", "INFO", "WARNING", "ERROR"]
),
"prefix": ConfigField(type=str, default="[custom_pic_Plugin]", description="日志记录前缀", example="[custom_pic_Plugin]")
}
}
def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
"""返回插件包含的组件列表"""
# 从配置获取组件启用状态
enable_image_generation = self.get_config("components.enable_image_generation", True)
components = []
if enable_image_generation:
# 添加我们的Action
components.append((Custom_Pic_Action.get_action_info(), Custom_Pic_Action))
return components