ComfyUI Impact Pack

Load the SAM (Segment Anything) model. This can be used in places that utilize SAM detection functionality, such as SAMDetector or SimpleDetector. The SAM detection functionality in Impact Pack must use the SAM_MODEL loaded through this node.

Apply a hook that allows you to use a custom sampler in the Detailer nodes. When using `DetailerHookCombine`, the sampler from the first hook is applied.

It enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.

It enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.

It enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.

This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size. This node is a specialized detailer node for enhancing video details, such as in AnimateDiff. It can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames.

It enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.

It enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.

This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size. This node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node.

This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size. This node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node. As a specialized detailer node for improving video details, such as in AnimateDiff, this node can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames.

This node provides a function to paste the enhanced SEGS, improved through the SEGS detailer, back onto the original image.

When behavior is Stop and mode is active, the input value is passed directly to the output. When behavior is Mute/Bypass and mode is active, the node connected to the output is changed to active state. When behavior is Stop and mode is Stop/Mute/Bypass, the workflow execution of the current node is halted. When behavior is Mute/Bypass and mode is Stop/Mute/Bypass, the node connected to the output is changed to Mute/Bypass state.

Removes only the overlapping areas between the masks included in base_segs and the mask regions of mask_segs. SEGS with no overlapping mask areas are filtered out.

Retains only the overlapping areas between the masks included in base_segs and the mask regions of mask_segs. SEGS with no overlapping mask areas are filtered out.

The 'ImpactWildcardEncode' node processes text prompts written in wildcard syntax and outputs them as conditioning. It also supports LoRA syntax, with the applied LoRA reflected in the model's output. TIP1: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'fixed'. TIP2: If the 'Inspire Pack' is installed, LBW(LoRA Block Weight) syntax can also be applied.

The 'ImpactWildcardProcessor' processes text prompts written in wildcard syntax and outputs the processed text prompt. TIP: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'fixed'.

This node enhances details by automatically detecting specific objects in the input image using detection models (bbox, segm, sam) and regenerating the image by enlarging the detected area based on the guide size. Although this node is specialized to simplify the commonly used facial detail enhancement workflow, it can also be used for various automatic inpainting purposes depending on the detection model.

This node enhances details by automatically detecting specific objects in the input image using detection models (bbox, segm, sam) and regenerating the image by enlarging the detected area based on the guide size. Although this node is specialized to simplify the commonly used facial detail enhancement workflow, it can also be used for various automatic inpainting purposes depending on the detection model.

Provides a detection function using CLIPSeg, which generates masks based on text prompts. To use this node, the CLIPSeg custom node must be installed.

When passing the list output through this node, it collects and organizes the data before forwarding it, which ensures that the previous stage's sub-workflow has been completed.

SEGS contains multiple SEGs. SEGS Merge integrates several SEGs into a single merged SEG. The label is changed to `merged` and the confidence becomes the minimum confidence. The applied controlnet and cropped_image are removed.

Selects the Nth item from a list. If the index is out of range, it returns the last item in the list.

If the SEGS have not passed through the detailer, they contain only detection area information without an image. This node sets a default image for the SEGS.