ComfyUI Node: Predict Tag 🍌

Predict Tag 🍌| Predict Tag 🍌:

The PredictTag node is designed to automatically generate descriptive tags for images using a pre-trained tagging model. This node leverages advanced machine learning techniques to analyze the content of an image and predict relevant tags based on predefined categories. The primary benefit of using this node is its ability to streamline the process of image annotation, making it easier for AI artists to organize and categorize their visual content. By providing accurate and contextually relevant tags, the PredictTag node enhances the efficiency of managing large image datasets and improves the overall workflow for creative projects.

Predict Tag 🍌| Predict Tag 🍌 Input Parameters:

tagger

The tagger parameter specifies the pre-trained tagging model to be used for predicting tags. This model is responsible for analyzing the image and generating the relevant tags. The available options for this parameter are models from the SmilingWolf repository, such as wd-vit-tagger-v3, wd-swinv2-tagger-v3, and wd-convnext-tagger-v3. The choice of model can impact the accuracy and type of tags generated, so selecting the appropriate model based on your specific needs is crucial.

labels

The labels parameter is a DataFrame containing the possible tags and their associated probabilities. This DataFrame is used to map the predicted probabilities to actual tag names and categories. It is essential for interpreting the model's output and generating the final list of tags.

image

The image parameter is the input image that you want to tag. This image is preprocessed and fed into the tagging model to generate the relevant tags. The quality and content of the image can significantly influence the accuracy and relevance of the predicted tags.

rating

The rating parameter is a boolean flag that indicates whether to include a rating tag in the output. If set to True, the node will append a rating tag based on the highest probability category. This can be useful for categorizing images based on their content rating.

character_thereshold

The character_thereshold parameter sets the probability threshold for including character tags in the output. Tags with probabilities above this threshold and belonging to the character category will be included. This helps in filtering out less relevant character tags and ensures that only the most probable ones are selected.

general_thereshold

The general_thereshold parameter sets the probability threshold for including general tags in the output. Similar to the character_thereshold, this parameter filters out less relevant general tags by only including those with probabilities above the specified threshold.

Predict Tag 🍌| Predict Tag 🍌 Output Parameters:

prompts

The prompts output parameter is a list of strings, where each string is a comma-separated list of predicted tags for the input image. These tags are generated based on the probabilities and thresholds set in the input parameters. The prompts provide a concise and organized way to view the predicted tags, making it easier to understand and utilize the tagging results.

features

The features output parameter is a dictionary containing various features extracted from the image during the tagging process. This includes the preprocessed image, the feature map generated by the tagging model, and a mapping of tags to their corresponding IDs. These features can be useful for further analysis or for visualizing the tagging process.

Predict Tag 🍌| Predict Tag 🍌 Usage Tips:

• Ensure that the input image is of high quality and relevant to the tags you expect to generate, as this will improve the accuracy of the predicted tags.
• Experiment with different tagging models available in the tagger parameter to find the one that best suits your needs and provides the most accurate tags for your images.
• Adjust the character_thereshold and general_thereshold parameters to fine-tune the selection of tags based on their probabilities, ensuring that only the most relevant tags are included in the output.

Predict Tag 🍌| Predict Tag 🍌 Common Errors and Solutions:

"Model not loaded"

• Explanation: This error occurs when the specified tagging model is not properly loaded.
• Solution: Ensure that the model name provided in the tagger parameter is correct and that the model is available in the specified repository. Check your internet connection if the model needs to be downloaded.

"Invalid image format"

• Explanation: This error occurs when the input image is not in a supported format.
• Solution: Verify that the input image is in a valid format (e.g., JPEG, PNG) and that it is correctly preprocessed before being fed into the node.

"Threshold value out of range"

• Explanation: This error occurs when the character_thereshold or general_thereshold values are set outside the acceptable range.
• Solution: Ensure that the threshold values are within the range of 0 to 1, as these represent probability values. Adjust the thresholds to appropriate levels to filter tags effectively.