MoneyPrinterTurbo根据关键词自动生成视频

文章目录

简介
Conda
- Conda简介
- - 定义：
  - 常用命令
- Conda下载安装
- Conda使用
Pexels
- 生成Api_Key
MoneyPrinterTurbo
- MoneyPrinterTurbo使用
创建MoneyPrinterTurbo使用环境
- 创建虚拟环境
- 激活
安装依赖
修改配置文件
启动并测试
- 再次启动

简介

Conda

网址：https://docs.conda.io/en/latest

Conda简介

Conda 是一个开源的软件包管理系统和环境管理系统，用于安装多个版本的软件包及其依赖关系，并在它们之间轻松切换。

定义：

Conda 是为 Python 程序创建的，适用于 Linux，OS X 和Windows，也可以打包和分发其他软件 [1]。

最流行的 Python 环境管理工具

常用命令

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conda list
列出当前 conda 环境所链接的软件包 [2]
conda create
# 创建一个 conda 环境，名称为 tf [2]
conda create -n 环境名 -c 镜像源

Conda下载安装

点击上面的链接，登录Conda官网，选择适合的版本进行下载

下载成功后，选择合适的安装位置，尽量不要安装在C盘，且最好不要有中文

然后一路next便可安装成功，安装成功后，本地电脑会出现下面两个项目

Conda使用

点击Anaconda PowerShell Prompt，可以输入下面命令查看版本：

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conda --version

Conda是给MoneyPrinterTurbo安装虚拟环境的工具，需要提前安装

Pexels

网址：https://www.pexels.com/zh-cn/api/key/

生成Api_Key

登录网址注册后，可以生成Api_Key

MoneyPrinterTurbo

MoneyPrinterTurbo-Portable-Windows-1.2.6.7z

百度网盘: https://pan.baidu.com/s/1IOsTm2LZaHLlDtHo7KIjlw?pwd=7anu 提取码: 7anu

MoneyPrinterTurbo使用

下载完成后，解压即可使用

创建MoneyPrinterTurbo使用环境

创建虚拟环境

在解压目录下的MoneyPrinterTurbo目录下

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conda create -n MoneyPrinterTurbo python=3.11

激活

在上一步完成的基础上，继续执行激活命令

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conda activate MoneyPrinterTurbo

安装依赖

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pip install -r requirements.txt

修改配置文件

将config.example.toml复制成config.toml文件

修改配置如下：

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[app]
video_source = "pexels" # "pexels" or "pixabay"

# 是否隐藏配置面板
hide_config = false

# Pexels API Key
# Register at https://www.pexels.com/api/ to get your API key.
# You can use multiple keys to avoid rate limits.
# For example: pexels_api_keys = ["123adsf4567adf89","abd1321cd13efgfdfhi"]
# 特别注意格式，Key 用英文双引号括起来，多个Key用逗号隔开
pexels_api_keys = ["VdkQFCH83gmWA8MnbOsVEfcwxNqwgVyU9AK6AyglrIQ42tRztqPHBwr4"]

# Pixabay API Key
# Register at https://pixabay.com/api/docs/ to get your API key.
# You can use multiple keys to avoid rate limits.
# For example: pixabay_api_keys = ["123adsf4567adf89","abd1321cd13efgfdfhi"]
# 特别注意格式，Key 用英文双引号括起来，多个Key用逗号隔开
pixabay_api_keys = []

# 支持的提供商 (Supported providers):
#   openai
#   moonshot    (月之暗面)
#   azure
#   qwen        (通义千问)
#   deepseek
#   gemini
#   ollama
#   g4f
#   oneapi
#   cloudflare
#   ernie       (文心一言)
llm_provider = "qwen"

########## Ollama Settings
# No need to set it unless you want to use your own proxy
#ollama_base_url = ""
# Check your available models at https://ollama.com/library
#ollama_model_name = ""

########## OpenAI API Key
# Get your API key at https://platform.openai.com/api-keys
#openai_api_key = ""
# No need to set it unless you want to use your own proxy
openai_base_url = ""
# Check your available models at https://platform.openai.com/account/limits
#openai_model_name = "gpt-4o-mini"

########## Moonshot API Key
# Visit https://platform.moonshot.cn/console/api-keys to get your API key.
moonshot_api_key = "sk-7szdfZ03frtN3IBz70O0U5oBFsWLDQWOJmh6ZNB1zMk2RNoP"
moonshot_base_url = "https://api.moonshot.cn/v1"
moonshot_model_name = "moonshot-v1-8k"

########## OneAPI API Key
# Visit https://github.com/songquanpeng/one-api to get your API key
#oneapi_api_key = ""
#oneapi_base_url = ""
#oneapi_model_name = ""

########## G4F
# Visit https://github.com/xtekky/gpt4free to get more details
# Supported model list: https://github.com/xtekky/gpt4free/blob/main/g4f/models.py
g4f_model_name = "gpt-3.5-turbo"

########## Azure API Key
# Visit https://learn.microsoft.com/zh-cn/azure/ai-services/openai/ to get more details
# API documentation: https://learn.microsoft.com/zh-cn/azure/ai-services/openai/reference
#azure_api_key = ""
#azure_base_url = ""
#azure_model_name = "gpt-35-turbo"        # replace with your model deployment name
#azure_api_version = "2024-02-15-preview"

########## Gemini API Key
#gemini_api_key = ""
#gemini_model_name = "gemini-1.0-pro"

########## Qwen API Key
# Visit https://dashscope.console.aliyun.com/apiKey to get your API key
# Visit below links to get more details
# https://tongyi.aliyun.com/qianwen/
# https://help.aliyun.com/zh/dashscope/developer-reference/model-introduction
qwen_api_key = "sk-c860168de7184769abd0b068a4486ad9"
qwen_model_name = "qwen-max"


########## DeepSeek API Key
# Visit https://platform.deepseek.com/api_keys to get your API key
deepseek_api_key = "sk-c03753147d7c48d486a33ff24d480123"
deepseek_base_url = "https://api.deepseek.com"
deepseek_model_name = "deepseek-chat"

# Subtitle Provider, "edge" or "whisper"
# If empty, the subtitle will not be generated
subtitle_provider = "edge"

#
# ImageMagick
#
# Once you have installed it, ImageMagick will be automatically detected, except on Windows!
# On Windows, for example "C:\Program Files (x86)\ImageMagick-7.1.1-Q16-HDRI\magick.exe"
# Download from https://imagemagick.org/archive/binaries/ImageMagick-7.1.1-29-Q16-x64-static.exe

imagemagick_path = "D:\\devSoftware\\AI_create_vedio\\ImageMagickInstall\\ImageMagick-7.1.1-Q16-HDRI\\magick.exe"


#
# FFMPEG
#
# 通常情况下，ffmpeg 会被自动下载，并且会被自动检测到。
# 但是如果你的环境有问题，无法自动下载，可能会遇到如下错误：
#   RuntimeError: No ffmpeg exe could be found.
#   Install ffmpeg on your system, or set the IMAGEIO_FFMPEG_EXE environment variable.
# 此时你可以手动下载 ffmpeg 并设置 ffmpeg_path，下载地址：https://www.gyan.dev/ffmpeg/builds/

# Under normal circumstances, ffmpeg is downloaded automatically and detected automatically.
# However, if there is an issue with your environment that prevents automatic downloading, you might encounter the following error:
#   RuntimeError: No ffmpeg exe could be found.
#   Install ffmpeg on your system, or set the IMAGEIO_FFMPEG_EXE environment variable.
# In such cases, you can manually download ffmpeg and set the ffmpeg_path, download link: https://www.gyan.dev/ffmpeg/builds/

# ffmpeg_path = "C:\\Users\\harry\\Downloads\\ffmpeg.exe"
#########################################################################################

# 当视频生成成功后，API服务提供的视频下载接入点，默认为当前服务的地址和监听端口
# 比如 http://127.0.0.1:8080/tasks/6357f542-a4e1-46a1-b4c9-bf3bd0df5285/final-1.mp4
# 如果你需要使用域名对外提供服务（一般会用nginx做代理），则可以设置为你的域名
# 比如 https://xxxx.com/tasks/6357f542-a4e1-46a1-b4c9-bf3bd0df5285/final-1.mp4
# endpoint="https://xxxx.com"

# When the video is successfully generated, the API service provides a download endpoint for the video, defaulting to the service's current address and listening port.
# For example, http://127.0.0.1:8080/tasks/6357f542-a4e1-46a1-b4c9-bf3bd0df5285/final-1.mp4
# If you need to provide the service externally using a domain name (usually done with nginx as a proxy), you can set it to your domain name.
# For example, https://xxxx.com/tasks/6357f542-a4e1-46a1-b4c9-bf3bd0df5285/final-1.mp4
# endpoint="https://xxxx.com"
endpoint = ""


# Video material storage location
# material_directory = ""                    # Indicates that video materials will be downloaded to the default folder, the default folder is ./storage/cache_videos under the current project
# material_directory = "/user/harry/videos"  # Indicates that video materials will be downloaded to a specified folder
# material_directory = "task"                # Indicates that video materials will be downloaded to the current task's folder, this method does not allow sharing of already downloaded video materials

# 视频素材存放位置
# material_directory = ""                    #表示将视频素材下载到默认的文件夹，默认文件夹为当前项目下的 ./storage/cache_videos
# material_directory = "/user/harry/videos"  #表示将视频素材下载到指定的文件夹中
# material_directory = "task"                #表示将视频素材下载到当前任务的文件夹中，这种方式无法共享已经下载的视频素材

material_directory = ""

# Used for state management of the task
enable_redis = false
redis_host = "localhost"
redis_port = 6379
redis_db = 0
redis_password = ""

# 文生视频时的最大并发任务数
max_concurrent_tasks = 5


[whisper]
# Only effective when subtitle_provider is "whisper"

# Run on GPU with FP16
# model = WhisperModel(model_size, device="cuda", compute_type="float16")

# Run on GPU with INT8
# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")

# Run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")

# recommended model_size: "large-v3"
model_size = "large-v3"
# if you want to use GPU, set device="cuda"
device = "CPU"
compute_type = "int8"


[proxy]
### Use a proxy to access the Pexels API
### Format: "http://<username>:<password>@<proxy>:<port>"
### Example: "http://user:pass@proxy:1234"
### Doc: https://requests.readthedocs.io/en/latest/user/advanced/#proxies

# http = "http://10.10.1.10:3128"
# https = "http://10.10.1.10:1080"

[azure]
# Azure Speech API Key
# Get your API key at https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/SpeechServices
speech_key = ""
speech_region = ""

[siliconflow]
# SiliconFlow API Key
# Get your API key at https://siliconflow.cn
api_key = ""

[ui]
# UI related settings
# 是否隐藏日志信息
# Whether to hide logs in the UI
hide_log = false

必须要配置的参数：

pexels_api_keys：视频来源，必须要配置
llm_provider：大语言模型，必须要进行配置
qwen_api_key：根据上面的大语言模型，进行配置api_key，必须要进行配置

启动并测试

配置完上面的文件后，如果命令行前面带（MoneyPrinterTurbo）使用webui.bat命令便可启动,否则参考再次启动

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webui.bat

再次启动

再次启动项目需要使用conda

进入目录，先激活在使用webui.bat再次启动

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//先进入目录
cd D:\devSoftware\AI_create_vedio\MoneyPrinterTurbo-Portable-Windows-1.2.6\MoneyPrinterTurbo
//激活
conda activate MoneyPrinterTurbo
//启动
webui.bat

启动后页面如下：