-
Notifications
You must be signed in to change notification settings - Fork 321
/
config.example.toml
186 lines (151 loc) · 8.15 KB
/
config.example.toml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
[app]
project_version="0.3.9"
# 支持视频理解的大模型提供商
# gemini
# NarratoAPI
# qwen2-vl (待增加)
vision_llm_provider="gemini"
vision_analysis_prompt = "你是资深视频内容分析专家，擅长分析视频画面信息，分析下面视频画面内容，只输出客观的画面描述不要给任何总结或评价"
########## Vision Gemini API Key
vision_gemini_api_key = ""
vision_gemini_model_name = "gemini-1.5-flash"
########## Vision Qwen API Key
vision_qwenvl_api_key = ""
vision_qwenvl_model_name = "qwen-vl-max-latest"
vision_qwenvl_base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
########### Vision NarratoAPI Key
narrato_api_key = "0N0iEjU77aTqPW4d9YHCmTW2mPrfgWjDmaWAz1lTVTM"
narrato_api_url = "https://narratoinsight.scsmtech.cn/api/v1"
narrato_vision_model = "gemini-1.5-flash"
narrato_vision_key = ""
narrato_llm_model = "gpt-4o"
narrato_llm_key = ""
# 用于生成文案的大模型支持的提供商 (Supported providers):
# openai (默认)
# moonshot (月之暗面)
# oneapi
# g4f
# azure
# qwen (通义千问)
# gemini
text_llm_provider="openai"
########## OpenAI API Key
# Get your API key at https://platform.openai.com/api-keys
text_openai_api_key = ""
text_openai_base_url = "https://api.openai.com/v1"
text_openai_model_name = "gpt-4o-mini"
########## Moonshot API Key
# Visit https://platform.moonshot.cn/console/api-keys to get your API key.
text_moonshot_api_key=""
text_moonshot_base_url = "https://api.moonshot.cn/v1"
text_moonshot_model_name = "moonshot-v1-8k"
########## G4F
# Visit https://github.com/xtekky/gpt4free to get more details
# Supported model list: https://github.com/xtekky/gpt4free/blob/main/g4f/models.py
text_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
text_azure_api_key = ""
text_azure_base_url=""
text_azure_model_name="gpt-35-turbo" # replace with your model deployment name
text_azure_api_version = "2024-02-15-preview"
########## Gemini API Key
text_gemini_api_key=""
text_gemini_model_name = "gemini-1.5-flash"
########## 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
text_qwen_api_key = ""
text_qwen_model_name = "qwen-plus-1127"
text_qwen_base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
########## DeepSeek API Key
# Visit https://platform.deepseek.com/api_keys to get your API key
text_deepseek_api_key = ""
text_deepseek_base_url = "https://api.deepseek.com"
text_deepseek_model_name = "deepseek-chat"
# 字幕提供商、可选，支持 whisper 和 faster-whisper-large-v2"whisper"
# 默认为 faster-whisper-large-v2 模型地址：https://huggingface.co/guillaumekln/faster-whisper-large-v2
subtitle_provider = "faster-whisper-large-v2"
subtitle_enabled = true
# ImageMagick
# 安装后，将自动检测到 ImageMagick，Windows 除外！
# 例如，在 Windows 上 "C:\Program Files (x86)\ImageMagick-7.1.1-Q16-HDRI\magick.exe"
# 下载位置 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/
# 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 = ""
# 用于任务的状态管理
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
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="faster-whisper-large-v2"
# 如果要使用 GPU，请设置 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://127.0.0.1:7890"
https = "http://127.0.0.1:7890"
[azure]
# Azure Speech API Key
# Get your API key at https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/SpeechServices
speech_key=""
speech_region=""
[frames]
skip_seconds = 0
# threshold（差异阈值）用于判断两个连续帧之间是否发生了场景切换
# 较小的阈值（如 20）：更敏感，能捕捉到细微的场景变化，但可能会误判，关键帧图片更多
# 较大的阈值（如 40）：更保守，只捕捉明显的场景切换，但可能会漏掉渐变场景，关键帧图片更少
# 默认值 30：在实践中是一个比较平衡的选择
threshold = 30
version = "v2"
# 大模型单次处理的关键帧数量
vision_batch_size = 5