/
config.example.toml
194 lines (154 loc) · 8.21 KB
/
config.example.toml
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[app]
video_source = "pexels" # "pexels" or "pixabay"
# 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 = []
# 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 = []
# 如果你没有 OPENAI API Key，可以使用 g4f 代替，或者使用国内的 Moonshot API
# If you don't have an OPENAI API Key, you can use g4f instead
# 支持的提供商 (Supported providers):
# openai
# moonshot (月之暗面)
# oneapi
# g4f
# azure
# qwen (通义千问)
# gemini
llm_provider="openai"
########## 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-4-turbo"
########## Moonshot API Key
# Visit https://platform.moonshot.cn/console/api-keys to get your API key.
moonshot_api_key=""
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 = ""
qwen_model_name = "qwen-max"
########## DeepSeek API Key
# Visit https://platform.deepseek.com/api_keys to get your API key
deepseek_api_key = ""
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 = "C:\\Program Files (x86)\\ImageMagick-7.1.1-Q16\\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
# webui界面是否显示配置项
# webui hide baisc config panel
hide_config = false
[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=""