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Facilitates text translation and encoding using CLIP model for AI artists working with multilingual inputs.
The ArgosTranslateCLIPTextEncodeNode is designed to facilitate the translation of text from one language to another and subsequently encode the translated text using a CLIP (Contrastive Language-Image Pre-Training) model. This node is particularly useful for AI artists who work with multilingual text inputs and need to process these texts into a format that can be used for further AI-driven tasks, such as generating images from text descriptions. By leveraging the Argos Translate library, this node ensures accurate and efficient translation, while the integration with CLIP allows for the encoding of the translated text into a form that can be used for conditioning in various AI models. This combination of translation and encoding streamlines the workflow for artists, enabling them to work seamlessly with text data in different languages.
This parameter specifies the source language of the text that needs to be translated. It accepts a list of language options, with the default set to "russian". The choice of source language impacts the translation process, as it determines the initial language context for the text. Selecting the correct source language ensures accurate translation results.
This parameter defines the target language into which the text will be translated. It accepts a list of supported target languages, with the default set to "english". The target language determines the final language of the translated text, which is crucial for ensuring that the output is in the desired language for further processing or use.
This parameter is the input text that you want to translate and encode. It is a string input that supports multiline text and includes a placeholder "Input text" to guide you. The text provided here will be translated from the source language to the target language and then encoded using the CLIP model.
This parameter represents the CLIP model that will be used to tokenize and encode the translated text. The CLIP model is essential for converting the text into a format that can be used for conditioning in AI models. The quality and type of the CLIP model can affect the encoding results.
This output parameter provides the conditioning data generated by the CLIP model after encoding the translated text. The conditioning data includes the encoded tokens and a pooled output, which can be used for various AI tasks that require text conditioning.
This output parameter returns the translated text as a string. This allows you to see the result of the translation process and use the translated text for other purposes or further processing.
from_translate
and to_translate
parameters are set correctly to match the source and target languages of your text. This will ensure accurate translation results.text
parameter to input the text you want to translate and encode. Make sure the text is clear and free of errors to avoid translation issues.clip
parameter to ensure high-quality encoding of the translated text. Different models may yield different results, so choose one that fits your needs.from_translate
and to_translate
parameters are set to valid language options. Ensure that the necessary translation packages are installed and available.clip
parameter is set to a valid and compatible CLIP model. Ensure that the model is properly loaded and accessible.text
parameter is left empty or contains only whitespace.text
parameter. Ensure that the text is not empty and contains meaningful content for translation and encoding.© Copyright 2024 RunComfy. All Rights Reserved.