ComfyUI Node: Primere Image Segments

Primere Image Segments:

PrimereImageSegments is a powerful node designed to enhance and segment images, providing AI artists with the ability to process and manipulate images with precision. This node is particularly useful for tasks that require detailed image segmentation and enhancement, such as object detection, image cropping, and guided image enhancement. By leveraging advanced models and techniques, PrimereImageSegments can identify and isolate specific regions within an image, apply enhancements, and generate masks that can be used for further processing. The node is designed to handle various image segmentation scenarios, making it a versatile tool for AI-driven image editing and manipulation.

Primere Image Segments Input Parameters:

use_segments

This parameter determines whether the segmentation functionality is enabled. When set to True, the node will perform image segmentation based on the provided settings. If set to False, the node will bypass segmentation and return the original image. This parameter is crucial for controlling the node's behavior and ensuring that segmentation is only performed when needed. The default value is True.

bbox_segm_model_name

Specifies the name of the bounding box segmentation model to be used. This model is responsible for detecting and segmenting objects within the image. The choice of model can significantly impact the accuracy and quality of the segmentation results. Common options include models trained on specific datasets or tailored for particular types of objects.

sam_model_name

Defines the name of the SAM (Segment Anything Model) to be used for segmentation. This model helps in identifying and segmenting various parts of the image based on the provided prompts and settings. The choice of SAM model can affect the granularity and precision of the segmentation.

sam_device_mode

Indicates the device mode for running the SAM model, such as cpu or gpu. This parameter allows you to choose the appropriate hardware for running the model, balancing performance and resource availability.

image

The input image to be processed by the node. This parameter accepts a single image tensor and is the primary data source for segmentation and enhancement operations.

threshold

A threshold value used for segmentation. This parameter helps in filtering out less significant segments by setting a minimum confidence level for detected segments. The value typically ranges from 0 to 1, with a default value that balances precision and recall.

dilation

Specifies the dilation factor for the segmentation mask. Dilation helps in expanding the boundaries of the detected segments, which can be useful for ensuring that the entire object is included in the segment. The default value is usually set to a small integer.

crop_factor

Determines the factor by which the image is cropped around the detected segments. This parameter helps in focusing on the relevant parts of the image and discarding unnecessary background. The value is typically a positive integer, with a default value that ensures a balanced crop.

drop_size

Sets the minimum size of segments to be considered. Segments smaller than this size will be ignored, helping to filter out noise and irrelevant details. The value is usually specified in pixels.

segment_prompt_data

Contains the prompt data used for guiding the segmentation process. This data includes information such as positive and negative prompts, token normalization settings, and weight interpretation. The prompt data helps in fine-tuning the segmentation results based on specific requirements.

trigger_high_off

An optional parameter that sets a high threshold for triggering certain actions during segmentation. This parameter can be used to control the sensitivity of the segmentation process.

trigger_low_off

An optional parameter that sets a low threshold for triggering certain actions during segmentation. This parameter can be used to control the sensitivity of the segmentation process.

search_yolov8s

Specifies the object category to be searched using the YOLOv8s model. This parameter helps in focusing the segmentation on specific types of objects, such as person or car.

search_deepfashion2_yolov8s

Defines the object category to be searched using the DeepFashion2 YOLOv8s model. This parameter is useful for segmenting fashion-related objects, such as short_sleeved_shirt.

search_facial_features_yolo8x

Indicates the facial feature to be searched using the YOLO8x model. This parameter helps in segmenting specific facial features, such as eye or nose.

model_version

Specifies the version of the model to be used for segmentation. This parameter allows you to choose between different versions of the model, each with its own characteristics and performance.

square_shape

Sets the shape of the square used for segmentation. This parameter helps in defining the size and aspect ratio of the segments, ensuring that they are appropriately sized for further processing.

dino_search_prompt

An optional prompt used for guiding the DINO model during segmentation. This prompt helps in fine-tuning the segmentation results based on specific requirements.

dino_replace_prompt

An optional prompt used for replacing certain segments with new content. This prompt helps in customizing the segmentation results by introducing new elements.

Primere Image Segments Output Parameters:

result_img

The enhanced image resulting from the segmentation and enhancement process. This output provides the final processed image, which can be used for further editing or analysis.

result_cropped_enhanced

A list of cropped and enhanced segments from the original image. These segments are isolated parts of the image that have been enhanced based on the provided settings.

segment_settings

A dictionary containing various settings and parameters used during the segmentation process. This output provides detailed information about the segmentation configuration, which can be useful for debugging and fine-tuning.

image_size

The size of the input image, represented as a tuple of width and height. This output provides information about the dimensions of the original image.

Primere Image Segments Usage Tips:

• Ensure that the use_segments parameter is set to True if you want to perform segmentation; otherwise, the node will return the original image.
• Choose the appropriate segmentation model (bbox_segm_model_name and sam_model_name) based on the type of objects you want to detect and segment.
• Adjust the threshold and dilation parameters to fine-tune the segmentation results, balancing precision and recall.
• Use the crop_factor parameter to focus on relevant parts of the image and discard unnecessary background.
• Provide meaningful prompts in segment_prompt_data to guide the segmentation process and achieve the desired results.

Primere Image Segments Common Errors and Solutions:

[Primere] ERROR: does not allow image batches.

• Explanation: This error occurs when the input contains multiple images instead of a single image.
• Solution: Ensure that the input image tensor contains only one image. If you have multiple images, process them one at a time.

Segment model not found.

• Explanation: This error occurs when the specified segmentation model is not available or incorrectly named.
• Solution: Verify that the bbox_segm_model_name and sam_model_name parameters are correctly specified and that the models are available in the system.

Invalid threshold value.

• Explanation: This error occurs when the threshold parameter is set to a value outside the acceptable range (0 to 1).
• Solution: Ensure that the threshold parameter is set to a value between 0 and 1.

Image size mismatch.

• Explanation: This error occurs when there is a discrepancy between the input image size and the expected size.
• Solution: Verify that the input image dimensions match the expected size specified in the segment_settings.

Missing segment prompt data.

• Explanation: This error occurs when the segment_prompt_data parameter is not provided or incomplete.
• Solution: Ensure that the segment_prompt_data parameter contains all necessary information, including positive and negative prompts, token normalization settings, and weight interpretation.