ComfyUI Node: FaceDetailer

FaceDetailer:

The FaceDetailer node is designed to enhance the details of faces in images, making it an essential tool for AI artists who want to improve the quality and realism of facial features in their artwork. This node leverages advanced models and techniques to detect and refine facial regions, ensuring that the faces in your images are rendered with high precision and clarity. It is particularly useful for applications where facial detail is crucial, such as portrait enhancement, character design, and digital art. By focusing on the face, the FaceDetailer helps you achieve a more polished and professional look in your images.

FaceDetailer Input Parameters:

image

The input image that you want to enhance. This parameter accepts a single image and is the primary source for the face detailing process. Ensure that the image is of good quality to achieve the best results.

model

The model used for face enhancement. This parameter determines the specific algorithm or neural network that will be applied to enhance the facial details. Different models may offer varying levels of detail and styles.

clip

A model used for additional processing and refinement of the image. This parameter helps in improving the overall quality and coherence of the enhanced face.

vae

The Variational Autoencoder (VAE) model used in the enhancement process. This model helps in encoding and decoding the image data, contributing to the quality of the final output.

guide_size

The size of the guide used for face enhancement. This parameter controls the scale at which the face is processed, affecting the level of detail and precision.

guide_size_for_bbox

The size of the guide specifically for the bounding box. This parameter helps in accurately detecting and isolating the facial region for enhancement.

max_size

The maximum size of the image to be processed. This parameter ensures that the image is resized appropriately to fit within the processing limits, maintaining quality without overloading the system.

seed

A seed value for random number generation. This parameter ensures reproducibility of the results by controlling the randomness in the enhancement process.

steps

The number of steps for the enhancement process. This parameter determines how many iterations the model will perform to refine the facial details, with more steps generally leading to better results.

cfg

The configuration settings for the enhancement process. This parameter includes various options and settings that control the behavior of the model and the enhancement process.

sampler_name

The name of the sampler used in the enhancement process. This parameter specifies the sampling technique applied during the face detailing, affecting the quality and style of the output.

scheduler

The scheduler used for managing the enhancement process. This parameter helps in organizing and optimizing the steps and resources used during face detailing.

positive

Positive prompts or guidance for the enhancement process. This parameter provides additional information or constraints to guide the model towards desired results.

negative

Negative prompts or constraints for the enhancement process. This parameter helps in avoiding unwanted features or artifacts in the enhanced face.

denoise

The level of denoising applied to the image. This parameter controls the amount of noise reduction, affecting the clarity and smoothness of the final output.

feather

The feathering applied to the edges of the enhanced region. This parameter helps in blending the enhanced face smoothly with the rest of the image.

noise_mask

A mask used for noise reduction. This parameter specifies the areas of the image where noise reduction should be applied, improving the overall quality.

force_inpaint

A flag to force inpainting in the enhancement process. This parameter ensures that missing or corrupted regions in the face are filled in accurately.

bbox_threshold

The threshold for bounding box detection. This parameter controls the sensitivity of the face detection, affecting the accuracy of the region isolated for enhancement.

bbox_dilation

The dilation applied to the bounding box. This parameter helps in expanding the detected facial region to ensure complete coverage during enhancement.

bbox_crop_factor

The crop factor for the bounding box. This parameter controls how much of the surrounding area is included in the enhancement process, affecting the context and blending.

sam_detection_hint

Hints for the SAM (Segment Anything Model) detection. This parameter provides additional information to improve the accuracy of face detection.

sam_dilation

The dilation applied in the SAM detection process. This parameter helps in expanding the detected regions for better coverage and accuracy.

sam_threshold

The threshold for SAM detection. This parameter controls the sensitivity of the SAM model, affecting the accuracy of face detection.

sam_bbox_expansion

The expansion factor for the SAM bounding box. This parameter helps in including more context around the detected face for better enhancement.

sam_mask_hint_threshold

The threshold for SAM mask hints. This parameter controls the sensitivity of the mask hints, affecting the accuracy and quality of the enhancement.

sam_mask_hint_use_negative

A flag to use negative hints in the SAM mask. This parameter helps in avoiding unwanted features or artifacts in the enhanced face.

drop_size

The size of the drop applied during the enhancement process. This parameter controls the scale of certain operations, affecting the level of detail and precision.

bbox_detector

The detector used for bounding box detection. This parameter specifies the algorithm or model used to detect the facial region for enhancement.

segm_detector

The detector used for segmentation. This parameter helps in accurately isolating the facial region for enhancement.

sam_model_opt

Options for the SAM model. This parameter includes various settings and configurations for the SAM model used in face detection and enhancement.

wildcard_opt

Options for wildcard processing. This parameter includes various settings and configurations for handling wildcard inputs during the enhancement process.

detailer_hook

A hook for additional processing during the enhancement. This parameter allows for custom operations or modifications to be applied during the face detailing process.

refiner_ratio

The ratio used for refining the enhancement. This parameter controls the balance between different aspects of the enhancement process, affecting the final quality.

refiner_model

The model used for refining the enhancement. This parameter specifies the algorithm or neural network applied for additional refinement of the facial details.

refiner_clip

A model used for additional refinement. This parameter helps in improving the overall quality and coherence of the enhanced face.

refiner_positive

Positive prompts or guidance for the refiner model. This parameter provides additional information or constraints to guide the refiner model towards desired results.

refiner_negative

Negative prompts or constraints for the refiner model. This parameter helps in avoiding unwanted features or artifacts in the refined face.

cycle

The number of cycles for the enhancement process. This parameter determines how many times the enhancement process is repeated, with more cycles generally leading to better results.

inpaint_model

A flag to use inpainting in the enhancement process. This parameter ensures that missing or corrupted regions in the face are filled in accurately.

noise_mask_feather

The feathering applied to the noise mask. This parameter helps in blending the noise reduction smoothly with the rest of the image.

FaceDetailer Output Parameters:

result_img

The enhanced image with detailed facial features. This output provides the final result of the face detailing process, showcasing improved clarity and precision in the facial region.

result_mask

The mask used during the enhancement process. This output provides the areas of the image that were affected by the face detailing, helping you understand the regions that were enhanced.

result_cropped_enhanced

A list of cropped and enhanced facial regions. This output provides the individual facial regions that were detected and enhanced, allowing for further analysis or processing.

result_cropped_enhanced_alpha

A list of cropped and enhanced facial regions with alpha channels. This output provides the individual facial regions with transparency information, useful for blending and compositing.

result_cnet_images

A list of images used during the enhancement process. This output provides the intermediate images generated during the face detailing, helping you understand the steps and transformations applied.

FaceDetailer Usage Tips:

• Ensure that the input image is of high quality to achieve the best results from the face detailing process.
• Experiment with different models and configurations to find the best settings for your specific use case.
• Use the guide_size and guide_size_for_bbox parameters to control the scale and accuracy of the face detection and enhancement.
• Adjust the steps parameter to balance between processing time and the level of detail in the enhanced face.
• Utilize the positive and negative prompts to guide the enhancement process towards desired results and avoid unwanted features.

FaceDetailer Common Errors and Solutions:

[Impact Pack] WARN: FaceDetailer is not a node designed for video detailing. If you intend to perform video detailing, please use Detailer For AnimateDiff.

• Explanation: This warning indicates that the FaceDetailer node is being used with multiple images, which it is not designed for.
• Solution: Use the Detailer For AnimateDiff node for video detailing or ensure that only a single image is provided as input to the FaceDetailer node.