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Facilitates seamless language translation in AI art projects using Google and Baidu services for multilingual content.
The Translate node is designed to facilitate seamless language translation within your AI art projects. It leverages the power of both Google and Baidu translation services to convert text from one language to another, making it easier for you to work with multilingual content. This node is particularly useful for artists who need to translate text prompts, descriptions, or any other textual content into different languages to reach a broader audience or to better understand content in a foreign language. By integrating this node, you can automate the translation process, ensuring accuracy and saving time.
This parameter specifies the source language of the text you want to translate. You can choose from a list of supported languages, including "中文" (Chinese), "英语" (English), "日语" (Japanese), and many others. If you are unsure of the source language, you can set this parameter to "自动" (Auto), and the node will automatically detect the language. The default value is "自动".
This parameter defines the target language into which the text will be translated. Similar to from_lang
, you can select from a variety of supported languages. The default value is "英语" (English).
This is the actual text string that you want to translate. It can be a single line or multiline text, depending on your needs. The default value is an empty string.
This parameter allows you to choose the translation service provider. You can select either "Google" or "百度" (Baidu). The default value is "百度".
This optional parameter is used for additional processing of the translated text. If provided, the node will tokenize and encode the translated text using the CLIP model, which can be useful for further AI-based text analysis or generation tasks.
This output parameter returns the translated text as a string. It is the primary result of the translation process and can be used directly in your projects.
This output parameter provides additional data in the form of conditioning, which includes encoded tokens and other relevant information. This is particularly useful if you are using the translated text in conjunction with AI models that require such conditioning data.
from_lang
) if you know it, rather than relying on automatic detection.clip
parameter if you need to perform further AI-based processing on the translated text, such as generating new content or analyzing the text's semantics.from_lang
or to_lang
.baidu_lang_list
or google_lang_list
). Double-check the spelling and case of the language names.© Copyright 2024 RunComfy. All Rights Reserved.