Visit ComfyUI Online for ready-to-use ComfyUI environment
Enhance image details with precision using various models and conditioning inputs for AI-generated images.
The EditDetailerPipeSDXL
node is designed to enhance and refine the details of your AI-generated images by integrating various models and conditioning parameters. This node allows you to customize the detailing process by adding specific models, conditioning inputs, and other parameters to achieve the desired level of detail in your images. It is particularly useful for AI artists looking to fine-tune their outputs with precision, offering a flexible and powerful way to incorporate additional layers of refinement. The node's primary goal is to provide a comprehensive toolset for enhancing image details, making it an essential component for high-quality image generation workflows.
This parameter accepts a DETAILER_PIPE
input, which serves as the primary pipeline for the detailing process. It is a required input that forms the backbone of the detailing operation.
This parameter accepts a STRING
input, allowing for multiline text without dynamic prompts. It is used to introduce variability and randomness in the detailing process, enabling more creative and diverse outputs.
This parameter provides a list of available LoRA (Low-Rank Adaptation) models that can be added to the text. It enhances the detailing process by incorporating specific LoRA models, which can significantly impact the final image quality and style.
This parameter allows you to select a wildcard to add to the text, further enhancing the variability and creativity of the detailing process. It works in conjunction with the wildcard
parameter to introduce additional elements into the detailing pipeline.
An optional MODEL
input that specifies the primary model to be used in the detailing process. This model serves as the foundation for generating detailed images.
An optional CLIP
input that provides a CLIP model for text-to-image conditioning. It helps in aligning the generated details with the textual description provided.
An optional VAE
(Variational Autoencoder) input that aids in the image generation process by providing a latent space representation of the image.
An optional CONDITIONING
input for positive conditioning, which helps in guiding the detailing process towards desired attributes and features.
An optional CONDITIONING
input for negative conditioning, which helps in avoiding unwanted attributes and features in the detailing process.
An optional MODEL
input for a refiner model, which further refines the details of the generated image, enhancing its quality and precision.
An optional CLIP
input for the refiner model, providing additional text-to-image conditioning for the refinement process.
An optional CONDITIONING
input for positive conditioning in the refiner model, guiding the refinement process towards desired attributes.
An optional CONDITIONING
input for negative conditioning in the refiner model, helping to avoid unwanted attributes during refinement.
An optional BBOX_DETECTOR
input that provides bounding box detection capabilities, useful for identifying and focusing on specific regions of the image.
An optional SAM_MODEL
input that provides a SAM (Segment Anything Model) for segmentation tasks, aiding in the detailed segmentation of the image.
An optional SEGM_DETECTOR
input that provides segmentation detection capabilities, useful for identifying and segmenting different parts of the image.
An optional DETAILER_HOOK
input that allows for custom hooks to be added to the detailing process, providing additional flexibility and customization.
The DETAILER_PIPE
output represents the detailed pipeline used in the process, encapsulating all the models and conditioning parameters applied.
The MODEL
output provides the primary model used in the detailing process, reflecting the final model configuration after detailing.
The CLIP
output provides the CLIP model used for text-to-image conditioning, reflecting the final alignment with the textual description.
The VAE
output provides the Variational Autoencoder used in the image generation process, reflecting the final latent space representation.
The CONDITIONING
output for positive conditioning reflects the final positive attributes and features incorporated into the detailed image.
The CONDITIONING
output for negative conditioning reflects the final negative attributes and features avoided in the detailed image.
The MODEL
output for the refiner model reflects the final configuration of the refiner model used in the detailing process.
The CLIP
output for the refiner model reflects the final text-to-image conditioning applied during refinement.
The CONDITIONING
output for positive conditioning in the refiner model reflects the final positive attributes incorporated during refinement.
The CONDITIONING
output for negative conditioning in the refiner model reflects the final negative attributes avoided during refinement.
The BBOX_DETECTOR
output provides the bounding box detection results, useful for identifying specific regions of the image.
The SAM_MODEL
output provides the final SAM model used for segmentation tasks, reflecting the detailed segmentation of the image.
The SEGM_DETECTOR
output provides the final segmentation detection results, useful for identifying and segmenting different parts of the image.
The DETAILER_HOOK
output provides the final custom hooks applied during the detailing process, reflecting any additional customization.
wildcard
and Select to add Wildcard
parameters to introduce creative variability and achieve more diverse outputs.detailer_pipe
input is either missing or not correctly specified.DETAILER_PIPE
input to the node.VAE
input to the node to proceed with the detailing process.© Copyright 2024 RunComfy. All Rights Reserved.