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ComfyUI-Impact-Pack/.gitignore

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+__pycache__
+*.ini
+wildcards/**
+.vscode/
+.idea/

ComfyUI-Impact-Pack/.gitmodules

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+[submodule "subpack"]
+	path = subpack
+	url = https://github.com/ltdrdata/ComfyUI-Impact-Subpack

ComfyUI-Impact-Pack/LICENSE.txt

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+
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+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
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+    <one line to give the program's name and a brief idea of what it does.>
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+    (at your option) any later version.
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
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+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
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+Also add information on how to contact you by electronic and paper mail.
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+notice like this when it starts in an interactive mode:
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+<https://www.gnu.org/licenses/why-not-lgpl.html>.

ComfyUI-Impact-Pack/README.md

@@ -0,0 +1,304 @@
+[![Youtube Badge](https://img.shields.io/badge/Youtube-FF0000?style=for-the-badge&logo=Youtube&logoColor=white&link=https://www.youtube.com/watch?v=AccoxDZIg3Y&list=PL_Ej2RDzjQLGfEeizq4GISeY3FtVyFmGP)](https://www.youtube.com/watch?v=AccoxDZIg3Y&list=PL_Ej2RDzjQLGfEeizq4GISeY3FtVyFmGP)
+
+# ComfyUI-Impact-Pack
+
+**Custom nodes pack for ComfyUI**
+This custom node helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more.
+
+
+## NOTICE
+* Starting from V3.6, requires latest version(Aug 8, 9ccc965) of ComfyUI.
+* **In versions below V3.3.1, there was an issue with the image quality generated after using the UltralyticsDetectorProvider. Please make sure to upgrade to a newer version.**
+* Starting from V3.0, nodes related to `mmdet` are optional nodes that are activated only based on the configuration settings.
+  - Through ComfyUI-Impact-Subpack, you can utilize UltralyticsDetectorProvider to access various detection models.
+* Between versions 2.22 and 2.21, there is partial compatibility loss regarding the Detailer workflow. If you continue to use the existing workflow, errors may occur during execution. An additional output called "enhanced_alpha_list" has been added to Detailer-related nodes.
+* The permission error related to cv2 that occurred during the installation of Impact Pack has been patched in version 2.21.4. However, please note that the latest versions of ComfyUI and ComfyUI-Manager are required.
+* The "PreviewBridge" feature may not function correctly on ComfyUI versions released before July 1, 2023.
+* Attempting to load the "ComfyUI-Impact-Pack" on ComfyUI versions released before June 27, 2023, will result in a failure.
+* With the addition of wildcard support in FaceDetailer, the structure of DETAILER_PIPE-related nodes and Detailer nodes has changed. There may be malfunctions when using the existing workflow.
+
+
+## Custom Nodes
+* SAMLoader - Loads the SAM model.
+* UltralyticsDetectorProvider - Loads the Ultralystics model to provide SEGM_DETECTOR, BBOX_DETECTOR.
+  - Unlike `MMDetDetectorProvider`, for segm models, `BBOX_DETECTOR` is also provided.
+  - The various models available in UltralyticsDetectorProvider can be downloaded through **ComfyUI-Manager**.
+* ONNXDetectorProvider - Loads the ONNX model to provide SEGM_DETECTOR.
+* CLIPSegDetectorProvider - Wrapper for CLIPSeg to provide BBOX_DETECTOR.
+  * You need to install the ComfyUI-CLIPSeg node extension.
+* SEGM Detector (combined) - Detects segmentation and returns a mask from the input image.
+* BBOX Detector (combined) - Detects bounding boxes and returns a mask from the input image.
+* SAMDetector (combined) - Utilizes the SAM technology to extract the segment at the location indicated by the input SEGS on the input image and outputs it as a unified mask.
+* SAMDetector (Segmented) - It is similar to `SAMDetector (combined)`, but it separates and outputs the detected segments. Multiple segments can be found for the same detected area, and currently, a policy is in place to group them arbitrarily in sets of three. This aspect is expected to be improved in the future.
+  * As a result, it outputs the `combined_mask`, which is a unified mask, and `batch_masks`, which are multiple masks grouped together in batch form.
+  * While `batch_masks` may not be completely separated, it provides functionality to perform some level of segmentation.
+* Bitwise(SEGS & SEGS) - Performs a 'bitwise and' operation between two SEGS.
+* Bitwise(SEGS - SEGS) - Subtracts one SEGS from another.
+* Bitwise(SEGS & MASK) - Performs a bitwise AND operation between SEGS and MASK.
+* Bitwise(SEGS & MASKS ForEach) - Performs a bitwise AND operation between SEGS and MASKS.
+  * Please note that this operation is performed with batches of MASKS, not just a single MASK.
+* Bitwise(MASK & MASK) - Performs a 'bitwise and' operation between two masks.
+* Bitwise(MASK - MASK) - Subtracts one mask from another.
+* Bitwise(MASK + MASK) - Combine two masks.
+* SEGM Detector (SEGS) - Detects segmentation and returns SEGS from the input image.
+* BBOX Detector (SEGS) - Detects bounding boxes and returns SEGS from the input image.
+* ONNX Detector (SEGS) - Utilizes the ONNX model to identify the bbox and retrieve the SEGS from the input image.
+* Detailer (SEGS) - Refines the image based on SEGS.
+* DetailerDebug (SEGS) - Refines the image based on SEGS. Additionally, it provides the ability to monitor the cropped image and the refined image of the cropped image.
+  * To prevent regeneration caused by the seed that does not change every time when using 'external_seed', please disable the 'seed random generate' option in the 'Detailer...' node.
+* MASK to SEGS - Generates SEGS based on the mask.
+* ToBinaryMask - Separates the mask generated with alpha values between 0 and 255 into 0 and 255. The non-zero parts are always set to 255.
+* Masks to Mask List - MASKS 
+  * This node converts the MASKS in batch form to a list of individual masks.
+* EmptySEGS - Provides an empty SEGS.
+* MaskPainter - Provides a feature to draw masks.
+* FaceDetailer - Easily detects faces and improves them.
+* FaceDetailer (pipe) - Easily detects faces and improves them (for multipass).
+
+* SEGSDetailer - Performs detailed work on SEGS without pasting it back onto the original image.
+* SEGSPaste - Pastes the results of SEGS onto the original image.
+  * If `ref_image_opt` is present, the images contained within SEGS are ignored. Instead, the image within `ref_image_opt` corresponding to the crop area of SEGS is taken and pasted. The size of the image in `ref_image_opt` should be the same as the original image size. 
+* SEGSPreview - Provides a preview of SEGS.
+   * This option is used to preview the improved image through `SEGSDetailer` before merging it into the original. Prior to going through ```SEGSDetailer```, SEGS only contains mask information without image information. If fallback_image_opt is connected to the original image, SEGS without image information will generate a preview using the original image. However, if SEGS already contains image information, fallback_image_opt will be ignored. 
+* SEGSToImageList - Convert SEGS To Image List
+* SEGSToMaskList - Convert SEGS To Mask List
+* SEGS Filter (label) - This node filters SEGS based on the label of the detected areas. 
+* SEGS Filter (ordered) - This node sorts SEGS based on size and position and retrieves SEGs within a certain range. 
+* SEGS Filter (range) - This node retrieves only SEGs from SEGS that have a size and position within a certain range.
+* SEGSConcat - Concatenate segs1 and segs2. If source shape of segs1 and segs2 are different then segs2 will be ignored. 
+ 
+* Pipe nodes
+   * ToDetailerPipe, FromDetailerPipe - These nodes are used to bundle multiple inputs used in the detailer, such as models and vae, ..., into a single DETAILER_PIPE or extract the elements that are bundled in the DETAILER_PIPE.
+   * ToBasicPipe, FromBasicPipe - These nodes are used to bundle model, clip, vae, positive conditioning, and negative conditioning into a single BASIC_PIPE, or extract each element from the BASIC_PIPE.
+   * EditBasicPipe, EditDetailerPipe - These nodes are used to replace some elements in BASIC_PIPE or DETAILER_PIPE.
+   * FromDetailerPipe_v2, FromBasicPipe_v2 - It has the same functionality as `FromDetailerPipe` and `FromBasicPipe`, but it has an additional output that directly exports the input pipe. It is useful when editing EditBasicPipe and EditDetailerPipe.
+* Latent Scale (on Pixel Space) - This node converts latent to pixel space, upscales it, and then converts it back to latent.
+   * If upscale_model_opt is provided, it uses the model to upscale the pixel and then downscales it using the interpolation method provided in scale_method to the target resolution.
+* PixelKSampleUpscalerProvider - An upscaler is provided that converts latent to pixels using VAEDecode, performs upscaling, converts back to latent using VAEEncode, and then performs k-sampling. This upscaler can be attached to nodes such as 'Iterative Upscale' for use.
+  * Similar to 'Latent Scale (on Pixel Space)', if upscale_model_opt is provided, it performs pixel upscaling using the model.
+* PixelTiledKSampleUpscalerProvider - It is similar to PixelKSampleUpscalerProvider, but it uses ComfyUI_TiledKSampler and Tiled VAE Decoder/Encoder to avoid GPU VRAM issues at high resolutions.
+  * You need to install the [ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension.
+
+* DenoiseScheduleHookProvider - IterativeUpscale provides a hook that gradually changes the denoise to target_denoise as the step progresses.
+* CfgScheduleHookProvider - IterativeUpscale provides a hook that gradually changes the cfg to target_cfg as the step progresses.
+* PixelKSampleHookCombine - This is used to connect two PK_HOOKs. hook1 is executed first and then hook2 is executed.
+  * If you want to simultaneously change cfg and denoise, you can combine the PK_HOOKs of CfgScheduleHookProvider and PixelKSampleHookCombine.
+
+* Iterative Upscale (Latent) - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. 
+This takes latent as input and outputs latent as the result.
+* Iterative Upscale (Image) - The upscaler takes the input upscaler and splits the scale_factor into steps, then iteratively performs upscaling. This takes image as input and outputs image as the result.
+  * Internally, this node uses 'Iterative Upscale (Latent)'.
+
+* TwoSamplersForMask - This node can apply two samplers depending on the mask area. The base_sampler is applied to the area where the mask is 0, while the mask_sampler is applied to the area where the mask is 1.
+  * Note: The latent encoded through VAEEncodeForInpaint cannot be used.
+* KSamplerProvider - This is a wrapper that enables KSampler to be used in TwoSamplersForMask TwoSamplersForMaskUpscalerProvider.
+* TiledKSamplerProvider - ComfyUI_TiledKSampler is a wrapper that provides KSAMPLER.
+  * You need to install the [ComfyUI_TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) node extension.
+  
+* TwoAdvancedSamplersForMask - TwoSamplersForMask is similar to TwoAdvancedSamplersForMask, but they differ in their operation. TwoSamplersForMask performs sampling in the mask area only after all the samples in the base area are finished. On the other hand, TwoAdvancedSamplersForMask performs sampling in both the base area and the mask area sequentially at each step.
+* KSamplerAdvancedProvider - This is a wrapper that enables KSampler to be used in TwoAdvancedSamplersForMask.
+
+* TwoSamplersForMaskUpscalerProvider - This is an Upscaler that extends TwoSamplersForMask to be used in Iterative Upscale.
+  * TwoSamplersForMaskUpscalerProviderPipe - pipe version of TwoSamplersForMaskUpscalerProvider.
+
+* PreviewBridge - This custom node can be used with a bridge when using the MaskEditor feature of Clipspace.
+* ImageSender, ImageReceiver - The images generated in ImageSender are automatically sent to the ImageReceiver with the same link_id.
+* LatentSender, LatentReceiver - The latent generated in LatentSender are automatically sent to the LatentReceiver with the same link_id.
+  * Furthermore, LatentSender is implemented with PreviewLatent, which stores the latent in payload form within the image thumbnail.
+  * Due to the current structure of ComfyUI, it is unable to distinguish between SDXL latent and SD1.5/SD2.1 latent. Therefore, it generates thumbnails by decoding them using the SD1.5 method.
+
+* Switch (image,mask), Switch (latent), Switch (SEGS) - Among multiple inputs, it selects the input designated by the selector and outputs it. The first input must be provided, while the others are optional. However, if the input specified by the selector is not connected, an error may occur.
+* ImpactWildcardProcessor - The text is generated by processing the wildcard in the Text. If the mode is set to "populate", a dynamic prompt is generated with each execution and the input is filled in the second textbox. If the mode is set to "fixed", the content of the second textbox remains unchanged.
+  * When an image is generated with the "fixed" mode, the prompt used for that particular generation is stored in the metadata.
+  * Known Issue: The old version of `presetText.js` script from **pythongosssss's [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts)** is causing a conflict, preventing it from being used together.
+                 If you'd installed that extension before (Jul 23, 2023). Please update to latest version.
+
+* RegionalSampler, CombineRegionalPrompts, RegionalPrompt - experimental feature
+- multiple region version of TwoAdvancedSamplersForMask 
+* 
+* KSampler (pipe), KSampler (advanced/pipe)
+
+* ImpactCompare, ImpactConditionalBranch, ImpactInt, ImpactValueSender, ImpactValueReceiver, ImpactImageInfo, ImpactMinMax, ImpactNeg, ImpactConditionalStopIteration
+- Experimental set of nodes for implementing loop functionality (tutorial to be prepared later / [example workflow](test/loop-test.json)).
+
+* Image batch To Image List - Convert Image batch to Image List
+- You can use images generated in a multi batch to handle them.
+
+
+## MMDet nodes
+* MMDetDetectorProvider - Loads the MMDet model to provide BBOX_DETECTOR and SEGM_DETECTOR.
+* To use the existing MMDetDetectorProvider, you need to enable the MMDet usage configuration.
+
+
+## Feature
+* Interactive SAM Detector (Clipspace) - When you right-click on a node that has 'MASK' and 'IMAGE' outputs, a context menu will open. From this menu, you can either open a dialog to create a SAM Mask using 'Open in SAM Detector', or copy the content (likely mask data) using 'Copy (Clipspace)' and generate a mask using 'Impact SAM Detector' from the clipspace menu, and then paste it using 'Paste (Clipspace)'.
+
+
+## Deprecated
+* The following nodes have been kept only for compatibility with existing workflows, and are no longer supported. Please replace them with new nodes.
+   * MMDetLoader -> MMDetDetectorProvider
+   * SegsMaskCombine -> SEGS to MASK (combined)
+   * BboxDetectorForEach -> BBOX Detector (SEGS)
+   * SegmDetectorForEach -> SEGM Detector (SEGS)
+   * BboxDetectorCombined -> BBOX Detector (combined)
+   * SegmDetectorCombined -> SEGM Detector (combined)
+   * MaskPainter -> PreviewBridge
+* To use the existing deprecated legacy nodes, you need to enable the MMDet usage configuration.
+
+
+## How to activate 'MMDet usage'
+* Upon the initial execution, an `impact-pack.ini` file will be generated in the custom_nodes/ComfyUI-Impact-Pack directory.
+```
+[default]
+dependency_version = 2
+mmdet_skip = True
+```
+* Change `mmdet_skip = True` to `mmdet_skip = False`
+```
+[default]
+dependency_version = 2
+mmdet_skip = False
+```
+* Restart ComfyUI
+
+
+## Installation
+
+1. `cd custom_nodes`
+1. `git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack.git`
+3. `cd ComfyUI-Impact-Pack`
+4. (optional) `git submodule update --init --recursive`
+   * Impact Pack will automatically download subpack during its initial launch.
+5. (optional) `python install.py`
+   * Impact Pack will automatically install its dependencies during its initial launch.
+   * For the portable version, you should execute the command `..\..\..\python_embedded\python.exe install.py` to run the installation script.
+
+
+6. Restart ComfyUI
+
+* NOTE: If an error occurs during the installation process, please refer to [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md) for assistance. 
+* You can use this colab notebook [colab notebook](https://colab.research.google.com/github/ltdrdata/ComfyUI-Impact-Pack/blob/Main/notebook/comfyui_colab_impact_pack.ipynb) to launch it. This notebook automatically downloads the impact pack to the custom_nodes directory, installs the tested dependencies, and runs it.
+
+## Package Dependencies (If you need to manual setup.)
+
+* pip install
+   * openmim
+   * segment-anything
+   * ultralytics
+   * scikit-image
+   * piexif
+   * (optional) pycocotools
+   * (optional) onnxruntime
+   
+* mim install (optional)
+   * mmcv==2.0.0, mmdet==3.0.0, mmengine==0.7.2
+
+* linux packages (ubuntu)
+  * libgl1-mesa-glx
+  * libglib2.0-0
+
+## Other Materials (auto-download on initial startup)
+
+* ComfyUI/models/mmdets/bbox <= https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth
+* ComfyUI/models/mmdets/bbox <= https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py
+* ComfyUI/models/sams <= https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
+
+## Troubleshooting page
+* [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md)
+
+
+## How to use (DDetailer feature)
+
+#### 1. Basic auto face detection and refine exapmle.
+![simple](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple.png)
+* The face that has been damaged due to low resolution is restored with high resolution by generating and synthesizing it, in order to restore the details.
+* The FaceDetailer node is a combination of a Detector node for face detection and a Detailer node for image enhancement. See the [Advanced Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/tutorial/advanced.md) for a more detailed explanation.
+* Pass the MMDetLoader 's bbox model and the detection model loaded by SAMLoader to FaceDetailer . Since it performs the function of KSampler for image enhancement, it overlaps with KSampler's options.
+* The MASK output of FaceDetailer provides a visualization of where the detected and enhanced areas are.
+
+![simple-orig](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple-original.png) ![simple-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/simple-refined.png)
+* You can see that the face in the image on the left has increased detail as in the image on the right.
+
+#### 2. 2Pass refine (restore a severely damaged face)
+![2pass-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-simple.png)
+* Although two FaceDetailers can be attached together for a 2-pass configuration, various common inputs used in KSampler can be passed through DETAILER_PIPE, so FaceDetailerPipe can be used to configure easily.
+* In 1pass, only rough outline recovery is required, so restore with a reasonable resolution and low options. However, if you increase the dilation at this time, not only the face but also the surrounding parts are included in the recovery range, so it is useful when you need to reshape the face other than the facial part.
+
+![2pass-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-original.png) ![2pass-example-middle](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-1pass.png) ![2pass-example-result](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/2pass-2pass.png)
+* In the first stage, the severely damaged face is restored to some extent, and in the second stage, the details are restored
+
+#### 3. Face Bbox(bounding box) + Person silhouette segmentation (prevent distortion of the background.)
+![combination-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination.jpg)
+![combination-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination-original.png) ![combination-example-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/combination-refined.png)
+
+* Facial synthesis that emphasizes details is delicately aligned with the contours of the face, and it can be observed that it does not affect the image outside of the face.
+
+* The BBoxDetectorForEach node is used to detect faces, and the SAMDetectorCombined node is used to find the segment related to the detected face. By using the Segs & Mask node with the two masks obtained in this way, an accurate mask that intersects based on segs can be generated. If this generated mask is input to the DetailerForEach node, only the target area can be created in high resolution from the image and then composited.
+
+#### 4. Iterative Upscale
+![upscale-workflow-example](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-workflow.png)
+ 
+* The IterativeUpscale node is a node that enlarges an image/latent by a scale_factor. In this process, the upscale is carried out progressively by dividing it into steps.
+* IterativeUpscale takes an Upscaler as an input, similar to a plugin, and uses it during each iteration. PixelKSampleUpscalerProvider is an Upscaler that converts the latent representation to pixel space and applies ksampling.
+  * The upscale_model_opt is an optional parameter that determines whether to use the upscale function of the model base if available. Using the upscale function of the model base can significantly reduce the number of iterative steps required. If an x2 upscaler is used, the image/latent is first upscaled by a factor of 2 and then downscaled to the target scale at each step before further processing is done.
+
+* The following image is an image of 304x512 pixels and the same image scaled up to three times its original size using IterativeUpscale.
+
+![combination-example-original](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-original.png) ![combination-example-refined](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/upscale-3x.png)
+
+
+#### 5. Interactive SAM Detector (Clipspace)
+
+* When you right-click on the node that outputs 'MASK' and 'IMAGE', a menu called "Open in SAM Detector" appears, as shown in the following picture. Clicking on the menu opens a dialog in SAM's functionality, allowing you to generate a segment mask.
+![samdetector-menu](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-menu.png)
+
+* By clicking the left mouse button on a coordinate, a positive prompt in blue color is entered, indicating the area that should be included. Clicking the right mouse button on a coordinate enters a negative prompt in red color, indicating the area that should be excluded. Positive prompts represent the areas that should be included, while negative prompts represent the areas that should be excluded.
+* You can remove the points that were added by using the "undo" button. After selecting the points, pressing the "detect" button generates the mask. Additionally, you can adjust the fidelity slider to determine the extent to which the mask belongs to the confidence region.
+
+![samdetector-dialog](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-dialog.jpg)
+
+* If you opened the dialog through "Open in SAM Detector" from the node, you can directly apply the changes by clicking the "Save to node" button. However, if you opened the dialog through the "clipspace" menu, you can save it to clipspace by clicking the "Save" button.
+
+![samdetector-result](https://github.com/ltdrdata/ComfyUI-extension-tutorials/raw/Main/ComfyUI-Impact-Pack/images/SAMDetector-result.jpg)
+
+* When you execute using the reflected mask in the node, you can observe that the image and mask are displayed separately.
+
+
+## Others Tutorials
+* [ComfyUI-extension-tutorials/ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-extension-tutorials/tree/Main/ComfyUI-Impact-Pack) - You can find various tutorials and workflows on this page.
+* [Advanced Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/advanced.md)
+* [SAM Application](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sam.md)
+* [PreviewBridge](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/previewbridge.md)
+* [Mask Pointer](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/maskpointer.md)
+* [ONNX Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ONNX.md)
+* [CLIPSeg Tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/clipseg.md)
+* [Extreme Highresolution Upscale](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/extreme-upscale.md)
+* [TwoSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplers.md)
+* [TwoAdvancedSamplersForMask](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoAdvancedSamplers.md)
+* [Advanced Iterative Upscale: PK_HOOK](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/pk_hook.md)
+* [Advanced Iterative Upscale: TwoSamplersForMask Upscale Provider](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/TwoSamplersUpscale.md)
+* [Interactive SAM + PreviewBridge](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sam_with_preview_bridge.md)
+* [ImageSender/ImageReceiver/LatentSender/LatentReceiver](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/sender_receiver.md)
+* [ImpactWildcardProcessor](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcardProcessor.md)
+
+
+## Credits
+
+ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI.
+
+dustysys/[ddetailer](https://github.com/dustysys/ddetailer) - DDetailer for Stable-diffusion-webUI extension.
+
+Bing-su/[dddetailer](https://github.com/Bing-su/dddetailer) - The anime-face-detector used in ddetailer has been updated to be compatible with mmdet 3.0.0, and we have also applied a patch to the pycocotools dependency for Windows environment in ddetailer.
+
+facebook/[segment-anything](https://github.com/facebookresearch/segment-anything) - Segmentation Anything!
+
+hysts/[anime-face-detector](https://github.com/hysts/anime-face-detector) - Creator of `anime-face_yolov3`, which has impressive performance on a variety of art styles.
+
+open-mmlab/[mmdetection](https://github.com/open-mmlab/mmdetection) - Object detection toolset. `dd-person_mask2former` was trained via transfer learning using their [R-50 Mask2Former instance segmentation model](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former#instance-segmentation) as a base.
+
+biegert/[ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg) - This is a custom node that enables the use of CLIPSeg technology, which can find segments through prompts, in ComfyUI.
+
+BlenderNeok/[ComfyUI-TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) - 
+The tile sampler allows high-resolution sampling even in places with low GPU VRAM.
+
+WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI.

ComfyUI-Impact-Pack/__init__.py

@@ -0,0 +1,303 @@
+import shutil
+import folder_paths
+import os
+import sys
+
+comfy_path = os.path.dirname(folder_paths.__file__)
+impact_path = os.path.join(os.path.dirname(__file__))
+subpack_path = os.path.join(os.path.dirname(__file__), "subpack")
+modules_path = os.path.join(os.path.dirname(__file__), "modules")
+wildcards_path = os.path.join(os.path.dirname(__file__), "wildcards")
+custom_wildcards_path = os.path.join(os.path.dirname(__file__), "custom_wildcards")
+
+sys.path.append(modules_path)
+sys.path.append(subpack_path)
+
+
+import impact.config
+print(f"### Loading: ComfyUI-Impact-Pack ({impact.config.version})")
+
+
+def do_install():
+    import importlib
+    spec = importlib.util.spec_from_file_location('impact_install', os.path.join(os.path.dirname(__file__), 'install.py'))
+    impact_install = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(impact_install)
+
+
+# ensure dependency
+if impact.config.get_config()['dependency_version'] < impact.config.dependency_version:
+    print(f"## ComfyUI-Impact-Pack: Updating dependencies")
+    do_install()
+
+
+# Core
+# recheck dependencies for colab
+try:
+    import folder_paths
+    import torch
+    import cv2
+    import numpy as np
+    import comfy.samplers
+    import comfy.sd
+    import warnings
+    from PIL import Image, ImageFilter
+    from skimage.measure import label, regionprops
+    from collections import namedtuple
+    import piexif
+
+    if not impact.config.get_config()['mmdet_skip']:
+        import mmcv
+        from mmdet.apis import (inference_detector, init_detector)
+        from mmdet.evaluation import get_classes
+except:
+    import importlib
+    print("### ComfyUI-Impact-Pack: Reinstall dependencies (several dependencies are missing.)")
+    do_install()
+
+import impact.impact_server  # to load server api
+
+def setup_js():
+    # remove garbage
+    old_js_path = os.path.join(comfy_path, "web", "extensions", "core", "impact-pack.js")
+    if os.path.exists(old_js_path):
+        os.remove(old_js_path)
+
+    # setup js
+    js_dest_path = os.path.join(comfy_path, "web", "extensions", "impact-pack")
+    if not os.path.exists(js_dest_path):
+        os.makedirs(js_dest_path)
+
+    js_src_path = os.path.join(impact_path, "js", "impact-pack.js")
+    shutil.copy(js_src_path, js_dest_path)
+
+    js_src_path = os.path.join(impact_path, "js", "impact-sam-editor.js")
+    shutil.copy(js_src_path, js_dest_path)
+
+    js_src_path = os.path.join(impact_path, "js", "comboBoolMigration.js")
+    shutil.copy(js_src_path, js_dest_path)
+    
+setup_js()
+
+from impact.impact_pack import *
+from impact.detectors import *
+from impact.pipe import *
+from impact.logics import *
+
+impact.wildcards.read_wildcard_dict(wildcards_path)
+impact.wildcards.read_wildcard_dict(custom_wildcards_path)
+
+NODE_CLASS_MAPPINGS = {
+    "SAMLoader": SAMLoader,
+    "CLIPSegDetectorProvider": CLIPSegDetectorProvider,
+    "ONNXDetectorProvider": ONNXDetectorProvider,
+
+    "BitwiseAndMaskForEach": BitwiseAndMaskForEach,
+    "SubtractMaskForEach": SubtractMaskForEach,
+
+    "DetailerForEach": DetailerForEach,
+    "DetailerForEachDebug": DetailerForEachTest,
+    "DetailerForEachPipe": DetailerForEachPipe,
+    "DetailerForEachDebugPipe": DetailerForEachTestPipe,
+
+    "SAMDetectorCombined": SAMDetectorCombined,
+    "SAMDetectorSegmented": SAMDetectorSegmented,
+
+    "FaceDetailer": FaceDetailer,
+    "FaceDetailerPipe": FaceDetailerPipe,
+
+    "ToDetailerPipe": ToDetailerPipe,
+    "FromDetailerPipe": FromDetailerPipe,
+    "FromDetailerPipe_v2": FromDetailerPipe_v2,
+    "ToBasicPipe": ToBasicPipe,
+    "FromBasicPipe": FromBasicPipe,
+    "FromBasicPipe_v2": FromBasicPipe_v2,
+    "BasicPipeToDetailerPipe": BasicPipeToDetailerPipe,
+    "DetailerPipeToBasicPipe": DetailerPipeToBasicPipe,
+    "EditBasicPipe": EditBasicPipe,
+    "EditDetailerPipe": EditDetailerPipe,
+
+    "LatentPixelScale": LatentPixelScale,
+    "PixelKSampleUpscalerProvider": PixelKSampleUpscalerProvider,
+    "PixelKSampleUpscalerProviderPipe": PixelKSampleUpscalerProviderPipe,
+    "IterativeLatentUpscale": IterativeLatentUpscale,
+    "IterativeImageUpscale": IterativeImageUpscale,
+    "PixelTiledKSampleUpscalerProvider": PixelTiledKSampleUpscalerProvider,
+    "PixelTiledKSampleUpscalerProviderPipe": PixelTiledKSampleUpscalerProviderPipe,
+    "TwoSamplersForMaskUpscalerProvider": TwoSamplersForMaskUpscalerProvider,
+    "TwoSamplersForMaskUpscalerProviderPipe": TwoSamplersForMaskUpscalerProviderPipe,
+
+    "PixelKSampleHookCombine": PixelKSampleHookCombine,
+    "DenoiseScheduleHookProvider": DenoiseScheduleHookProvider,
+    "CfgScheduleHookProvider": CfgScheduleHookProvider,
+
+    "BitwiseAndMask": BitwiseAndMask,
+    "SubtractMask": SubtractMask,
+    "AddMask": AddMask,
+    "Segs & Mask": SegsBitwiseAndMask,
+    "Segs & Mask ForEach": SegsBitwiseAndMaskForEach,
+    "EmptySegs": EmptySEGS,
+
+    "MaskToSEGS": MaskToSEGS,
+    "ToBinaryMask": ToBinaryMask,
+    "MasksToMaskList": MasksToMaskList,
+
+    "BboxDetectorSEGS": BboxDetectorForEach,
+    "SegmDetectorSEGS": SegmDetectorForEach,
+    "ONNXDetectorSEGS": ONNXDetectorForEach,
+
+    "BboxDetectorCombined_v2": BboxDetectorCombined,
+    "SegmDetectorCombined_v2": SegmDetectorCombined,
+    "SegsToCombinedMask": SegsToCombinedMask,
+
+    "KSamplerProvider": KSamplerProvider,
+    "TwoSamplersForMask": TwoSamplersForMask,
+    "TiledKSamplerProvider": TiledKSamplerProvider,
+
+    "KSamplerAdvancedProvider": KSamplerAdvancedProvider,
+    "TwoAdvancedSamplersForMask": TwoAdvancedSamplersForMask,
+
+    "PreviewBridge": PreviewBridge,
+    "ImageSender": ImageSender,
+    "ImageReceiver": ImageReceiver,
+    "LatentSender": LatentSender,
+    "LatentReceiver": LatentReceiver,
+    "ImageMaskSwitch": ImageMaskSwitch,
+    "LatentSwitch": LatentSwitch,
+    "SEGSSwitch": SEGSSwitch,
+
+    # "SaveConditioning": SaveConditioning,
+    # "LoadConditioning": LoadConditioning,
+
+    "ImpactWildcardProcessor": ImpactWildcardProcessor,
+    "ImpactLogger": ImpactLogger,
+
+    "SEGSDetailer": SEGSDetailer,
+    "SEGSPaste": SEGSPaste,
+    "SEGSPreview": SEGSPreview,
+    "SEGSToImageList": SEGSToImageList,
+    "ImpactSEGSToMaskList": SEGSToMaskList,
+    "ImpactSEGSConcat": SEGSConcat,
+
+    # "SEGPick": SEGPick,
+    # "SEGEdit": SEGEdit,
+
+    "ImpactKSamplerBasicPipe": KSamplerBasicPipe,
+    "ImpactKSamplerAdvancedBasicPipe": KSamplerAdvancedBasicPipe,
+
+    "ReencodeLatent": ReencodeLatent,
+    "ReencodeLatentPipe": ReencodeLatentPipe,
+
+    "ImpactImageBatchToImageList": ImageBatchToImageList,
+
+    "RegionalSampler": RegionalSampler,
+    "CombineRegionalPrompts": CombineRegionalPrompts,
+    "RegionalPrompt": RegionalPrompt,
+
+    "ImpactSEGSLabelFilter": SEGSLabelFilter,
+    "ImpactSEGSRangeFilter": SEGSRangeFilter,
+    "ImpactSEGSOrderedFilter": SEGSOrderedFilter,
+
+    "ImpactCompare": ImpactCompare,
+    "ImpactConditionalBranch": ImpactConditionalBranch,
+    "ImpactInt": ImpactInt,
+    # "ImpactFloat": ImpactFloat,
+    "ImpactValueSender": ImpactValueSender,
+    "ImpactValueReceiver": ImpactValueReceiver,
+    "ImpactImageInfo": ImpactImageInfo,
+    "ImpactMinMax": ImpactMinMax,
+    "ImpactNeg": ImpactNeg,
+    "ImpactConditionalStopIteration": ImpactConditionalStopIteration,
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "BboxDetectorSEGS": "BBOX Detector (SEGS)",
+    "SegmDetectorSEGS": "SEGM Detector (SEGS)",
+    "ONNXDetectorSEGS": "ONNX Detector (SEGS)",
+    "BboxDetectorCombined_v2": "BBOX Detector (combined)",
+    "SegmDetectorCombined_v2": "SEGM Detector (combined)",
+    "SegsToCombinedMask": "SEGS to MASK (combined)",
+    "MaskToSEGS": "MASK to SEGS",
+    "BitwiseAndMaskForEach": "Bitwise(SEGS & SEGS)",
+    "SubtractMaskForEach": "Bitwise(SEGS - SEGS)",
+    "Segs & Mask": "Bitwise(SEGS & MASK)",
+    "Segs & Mask ForEach": "Bitwise(SEGS & MASKS ForEach)",
+    "BitwiseAndMask": "Bitwise(MASK & MASK)",
+    "SubtractMask": "Bitwise(MASK - MASK)",
+    "AddMask": "Bitwise(MASK + MASK)",
+    "DetailerForEach": "Detailer (SEGS)",
+    "DetailerForEachPipe": "Detailer (SEGS/pipe)",
+    "DetailerForEachDebug": "DetailerDebug (SEGS)",
+    "DetailerForEachDebugPipe": "DetailerDebug (SEGS/pipe)",
+    "SAMDetectorCombined": "SAMDetector (combined)",
+    "SAMDetectorSegmented": "SAMDetector (segmented)",
+    "FaceDetailerPipe": "FaceDetailer (pipe)",
+
+    "BasicPipeToDetailerPipe": "BasicPipe -> DetailerPipe",
+    "DetailerPipeToBasicPipe": "DetailerPipe -> BasicPipe",
+    "EditBasicPipe": "Edit BasicPipe",
+    "EditDetailerPipe": "Edit DetailerPipe",
+
+    "LatentPixelScale": "Latent Scale (on Pixel Space)",
+    "IterativeLatentUpscale": "Iterative Upscale (Latent)",
+    "IterativeImageUpscale": "Iterative Upscale (Image)",
+
+    "TwoSamplersForMaskUpscalerProvider": "TwoSamplersForMask Upscaler Provider",
+    "TwoSamplersForMaskUpscalerProviderPipe": "TwoSamplersForMask Upscaler Provider (pipe)",
+
+    "ReencodeLatent": "Reencode Latent",
+    "ReencodeLatentPipe": "Reencode Latent (pipe)",
+
+    "ImpactKSamplerBasicPipe": "KSampler (pipe)",
+    "ImpactKSamplerAdvancedBasicPipe": "KSampler (Advanced/pipe)",
+    "ImpactSEGSLabelFilter": "SEGS Filter (label)",
+    "ImpactSEGSRangeFilter": "SEGS Filter (range)",
+    "ImpactSEGSOrderedFilter": "SEGS Filter (ordered)",
+    "ImpactSEGSConcat": "SEGS Concat",
+
+    "PreviewBridge": "Preview Bridge",
+    "ImageSender": "Image Sender",
+    "ImageReceiver": "Image Receiver",
+    "ImageMaskSwitch": "Switch (images, mask)",
+    "LatentSwitch": "Switch (latent)",
+    "SEGSSwitch": "Switch (SEGS)",
+
+    "MasksToMaskList": "Masks to Mask List",
+    "ImpactImageBatchToImageList": "Image batch to Image List"
+}
+
+if not impact.config.get_config()['mmdet_skip']:
+    from impact.mmdet_nodes import *
+    import impact.legacy_nodes
+    NODE_CLASS_MAPPINGS.update({
+        "MMDetDetectorProvider": MMDetDetectorProvider,
+        "MMDetLoader": impact.legacy_nodes.MMDetLoader,
+        "MaskPainter": impact.legacy_nodes.MaskPainter,
+        "SegsMaskCombine": impact.legacy_nodes.SegsMaskCombine,
+        "BboxDetectorForEach": impact.legacy_nodes.BboxDetectorForEach,
+        "SegmDetectorForEach": impact.legacy_nodes.SegmDetectorForEach,
+        "BboxDetectorCombined": impact.legacy_nodes.BboxDetectorCombined,
+        "SegmDetectorCombined": impact.legacy_nodes.SegmDetectorCombined,
+    })
+
+    NODE_DISPLAY_NAME_MAPPINGS.update({
+        "MaskPainter": "MaskPainter (Deprecated)",
+        "MMDetLoader": "MMDetLoader (Legacy)",
+        "SegsMaskCombine": "SegsMaskCombine (Legacy)",
+        "BboxDetectorForEach": "BboxDetectorForEach (Legacy)",
+        "SegmDetectorForEach": "SegmDetectorForEach (Legacy)",
+        "BboxDetectorCombined": "BboxDetectorCombined (Legacy)",
+        "SegmDetectorCombined": "SegmDetectorCombined (Legacy)",
+    })
+
+try:
+    import impact.subpack_nodes
+
+    NODE_CLASS_MAPPINGS.update(impact.subpack_nodes.NODE_CLASS_MAPPINGS)
+    NODE_DISPLAY_NAME_MAPPINGS.update(impact.subpack_nodes.NODE_DISPLAY_NAME_MAPPINGS)
+
+except:
+    pass
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

ComfyUI-Impact-Pack/disable.py

@@ -0,0 +1,38 @@
+import os
+import sys
+import time
+import platform
+import shutil
+import subprocess
+
+comfy_path = '../..'
+
+def rmtree(path):
+    retry_count = 3
+
+    while True:
+        try:
+            retry_count -= 1
+
+            if platform.system() == "Windows":
+                subprocess.check_call(['attrib', '-R', path + '\\*', '/S'])
+
+            shutil.rmtree(path)
+
+            return True
+
+        except Exception as ex:
+            print(f"ex: {ex}")
+            time.sleep(3)
+
+            if retry_count < 0:
+                raise ex
+
+            print(f"Uninstall retry({retry_count})")
+
+js_dest_path = os.path.join(comfy_path, "web", "extensions", "impact-pack")
+
+if os.path.exists(js_dest_path):
+    rmtree(js_dest_path)
+        
+

ComfyUI-Impact-Pack/install.py

@@ -0,0 +1,178 @@
+import os
+import sys
+import subprocess
+
+
+if sys.argv[0] == 'install.py':
+    sys.path.append('.')   # for portable version
+
+
+impact_path = os.path.join(os.path.dirname(__file__), "modules")
+subpack_path = os.path.join(os.path.dirname(__file__), "subpack")
+subpack_repo = ""
+comfy_path = os.path.join(os.path.dirname(__file__), '..', '..')
+
+
+sys.path.append(impact_path)
+sys.path.append(comfy_path)
+
+import platform
+import folder_paths
+from torchvision.datasets.utils import download_url
+import impact.config
+
+
+print("### ComfyUI-Impact-Pack: Check dependencies")
+
+if "python_embeded" in sys.executable or "python_embedded" in sys.executable:
+    pip_install = [sys.executable, '-s', '-m', 'pip', 'install']
+    mim_install = [sys.executable, '-s', '-m', 'mim', 'install']
+else:
+    pip_install = [sys.executable, '-m', 'pip', 'install']
+    mim_install = [sys.executable, '-m', 'mim', 'install']
+
+
+def ensure_subpack():
+    import git
+    repo = git.Repo(os.path.dirname(__file__))
+    origin = repo.remote(name='origin')
+    origin.pull()
+    repo.git.submodule('update', '--init', '--recursive')
+
+
+def remove_olds():
+    global comfy_path
+
+    comfy_path = os.path.dirname(folder_paths.__file__)
+    custom_nodes_path = os.path.join(comfy_path, "custom_nodes")
+    old_ini_path = os.path.join(custom_nodes_path, "impact-pack.ini")
+    old_py_path = os.path.join(custom_nodes_path, "comfyui-impact-pack.py")
+
+    if os.path.exists(impact.config.old_config_path):
+        impact.config.get_config()['mmdet_skip'] = False
+        os.remove(impact.config.old_config_path)
+
+    if os.path.exists(old_ini_path):
+        print(f"Delete legacy file: {old_ini_path}")
+        os.remove(old_ini_path)
+    
+    if os.path.exists(old_py_path):
+        print(f"Delete legacy file: {old_py_path}")
+        os.remove(old_py_path)
+
+
+def ensure_pip_packages_first():
+    subpack_req = os.path.join(subpack_path, "requirements.txt")
+    if os.path.exists(subpack_req):
+        subprocess.run(pip_install + ['-r', 'requirements.txt'], cwd=subpack_path)
+
+    if not impact.config.get_config()['mmdet_skip']:
+        subprocess.run(pip_install + ['openmim'], cwd=subpack_path)
+
+        try:
+            import pycocotools
+        except Exception:
+            if platform.system() not in ["Windows"] or platform.machine() not in ["AMD64", "x86_64"]:
+                print(f"Your system is {platform.system()}; !! You need to install 'libpython3-dev' for this step. !!")
+
+                subprocess.check_call(pip_install + ['pycocotools'])
+            else:
+                pycocotools = {
+                    (3, 8): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp38-cp38-win_amd64.whl",
+                    (3, 9): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp39-cp39-win_amd64.whl",
+                    (3, 10): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp310-cp310-win_amd64.whl",
+                    (3, 11): "https://github.com/Bing-su/dddetailer/releases/download/pycocotools/pycocotools-2.0.6-cp311-cp311-win_amd64.whl",
+                }
+
+                version = sys.version_info[:2]
+                url = pycocotools[version]
+                subprocess.check_call(pip_install + [url])
+
+
+def ensure_pip_packages_last():
+    try:
+        import segment_anything
+        from skimage.measure import label, regionprops
+        import piexif
+    except Exception:
+        my_path = os.path.dirname(__file__)
+        requirements_path = os.path.join(my_path, "requirements.txt")
+        subprocess.check_call(pip_install + ['-r', requirements_path])
+
+    # !! cv2 importing test must be very last !!
+    try:
+        import cv2
+    except Exception:
+        try:
+            subprocess.check_call(pip_install + ['opencv-python'])
+        except:
+            print(f"ComfyUI-Impact-Pack: failed to install 'opencv-python'. Please, install manually.")
+
+    try:
+        import git
+    except Exception:
+        subprocess.check_call(pip_install + ['gitpython'])
+
+
+def ensure_mmdet_package():
+    try:
+        import mmcv
+        import mmdet
+        from mmdet.evaluation import get_classes
+    except Exception:
+        subprocess.check_call(pip_install + ['opendatalab==0.0.9'])
+        subprocess.check_call(pip_install + ['-U', 'openmim'])
+        subprocess.check_call(mim_install + ['mmcv==2.0.0'])
+        subprocess.check_call(mim_install + ['mmdet==3.0.0'])
+        subprocess.check_call(mim_install + ['mmengine==0.7.4'])
+
+
+def install():
+    remove_olds()
+    ensure_pip_packages_first()
+
+    if not impact.config.get_config()['mmdet_skip']:
+        ensure_mmdet_package()
+
+    ensure_pip_packages_last()
+
+    # Download model
+    print("### ComfyUI-Impact-Pack: Check basic models")
+
+    model_path = folder_paths.models_dir
+
+    bbox_path = os.path.join(model_path, "mmdets", "bbox")
+    sam_path = os.path.join(model_path, "sams")
+    onnx_path = os.path.join(model_path, "onnx")
+
+    if not os.path.exists(bbox_path):
+        os.makedirs(bbox_path)
+
+    if not impact.config.get_config()['mmdet_skip']:
+        if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.pth")):
+            download_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth", bbox_path)
+
+        if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.py")):
+            download_url("https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py", bbox_path)
+
+    if not os.path.exists(os.path.join(sam_path, "sam_vit_b_01ec64.pth")):
+        download_url("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", sam_path)
+
+    subpack_install_script = os.path.join(subpack_path, "install.py")
+
+    print(f"### ComfyUI-Impact-Pack: Updating subpack")
+    ensure_subpack()
+
+    if os.path.exists(subpack_install_script):
+        subprocess.run([sys.executable, 'install.py'], cwd=subpack_path)
+        subprocess.run(pip_install + ['-r', 'requirements.txt'], cwd=subpack_path)
+
+    if not os.path.exists(onnx_path):
+        print(f"### ComfyUI-Impact-Pack: onnx model directory created ({onnx_path})")
+        os.mkdir(onnx_path)
+
+    impact.config.write_config()
+
+
+install()
+

ComfyUI-Impact-Pack/js/comboBoolMigration.js

@@ -0,0 +1,31 @@
+import { ComfyApp, app } from "../../scripts/app.js";
+
+let conflict_check = undefined;
+
+app.registerExtension({
+	name: "Comfy.impact.comboBoolMigration",
+
+	nodeCreated(node, app) {
+		for(let i in node.widgets) {
+			let widget = node.widgets[i];
+
+            if(conflict_check == undefined) {
+                conflict_check = !!app.extensions.find((ext) => ext.name === "Comfy.comboBoolMigration");
+            }
+
+            if(conflict_check)
+                return;
+
+			if(widget.type == "toggle") {
+			    let value = widget.value;
+				Object.defineProperty(widget, "value", {
+					set: (value) => {
+							delete widget.value;
+							widget.value = value == true || value == widget.options.on;
+						},
+					get: () => { return value; }
+				});
+			}
+		}
+	}
+});

ComfyUI-Impact-Pack/js/impact-pack.js

@@ -0,0 +1,395 @@
+import { ComfyApp, app } from "../../scripts/app.js";
+import { ComfyDialog, $el } from "../../scripts/ui.js";
+import { api } from "../../scripts/api.js";
+
+// temporary implementation (copying from https://github.com/pythongosssss/ComfyUI-WD14-Tagger)
+// I think this should be included into master!!
+class ImpactProgressBadge {
+	constructor() {
+		if (!window.__progress_badge__) {
+			window.__progress_badge__ = Symbol("__impact_progress_badge__");
+		}
+		this.symbol = window.__progress_badge__;
+	}
+
+	getState(node) {
+		return node[this.symbol] || {};
+	}
+
+	setState(node, state) {
+		node[this.symbol] = state;
+		app.canvas.setDirty(true);
+	}
+
+	addStatusHandler(nodeType) {
+		if (nodeType[this.symbol]?.statusTagHandler) {
+			return;
+		}
+		if (!nodeType[this.symbol]) {
+			nodeType[this.symbol] = {};
+		}
+		nodeType[this.symbol] = {
+			statusTagHandler: true,
+		};
+
+		api.addEventListener("impact/update_status", ({ detail }) => {
+			let { node, progress, text } = detail;
+			const n = app.graph.getNodeById(+(node || app.runningNodeId));
+			if (!n) return;
+			const state = this.getState(n);
+			state.status = Object.assign(state.status || {}, { progress: text ? progress : null, text: text || null });
+			this.setState(n, state);
+		});
+
+		const self = this;
+		const onDrawForeground = nodeType.prototype.onDrawForeground;
+		nodeType.prototype.onDrawForeground = function (ctx) {
+			const r = onDrawForeground?.apply?.(this, arguments);
+			const state = self.getState(this);
+			if (!state?.status?.text) {
+				return r;
+			}
+
+			const { fgColor, bgColor, text, progress, progressColor } = { ...state.status };
+
+			ctx.save();
+			ctx.font = "12px sans-serif";
+			const sz = ctx.measureText(text);
+			ctx.fillStyle = bgColor || "dodgerblue";
+			ctx.beginPath();
+			ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, sz.width + 12, 20, 5);
+			ctx.fill();
+
+			if (progress) {
+				ctx.fillStyle = progressColor || "green";
+				ctx.beginPath();
+				ctx.roundRect(0, -LiteGraph.NODE_TITLE_HEIGHT - 20, (sz.width + 12) * progress, 20, 5);
+				ctx.fill();
+			}
+
+			ctx.fillStyle = fgColor || "#fff";
+			ctx.fillText(text, 6, -LiteGraph.NODE_TITLE_HEIGHT - 6);
+			ctx.restore();
+			return r;
+		};
+	}
+}
+
+const input_tracking = {};
+const input_dirty = {};
+const output_tracking = {};
+
+function progressExecuteHandler(event) {
+	if(event.detail.output.aux){
+		const id = event.detail.node;
+		if(input_tracking.hasOwnProperty(id)) {
+			if(input_tracking.hasOwnProperty(id) && input_tracking[id][0] != event.detail.output.aux[0]) {
+				input_dirty[id] = true;
+			}
+			else{
+
+			}
+		}
+
+		input_tracking[id] = event.detail.output.aux;
+	}
+}
+
+function imgSendHandler(event) {
+	if(event.detail.images.length > 0){
+		let data = event.detail.images[0];
+		let filename = `${data.filename} [${data.type}]`;
+
+		let nodes = app.graph._nodes;
+		for(let i in nodes) {
+			if(nodes[i].type == 'ImageReceiver') {
+				if(nodes[i].widgets[1].value == event.detail.link_id) {
+					if(data.subfolder)
+						nodes[i].widgets[0].value = `${data.subfolder}/${data.filename} [${data.type}]`;
+					else
+						nodes[i].widgets[0].value = `${data.filename} [${data.type}]`;
+
+					let img = new Image();
+					img.src = `/view?filename=${data.filename}&type=${data.type}&subfolder=${data.subfolder}`+app.getPreviewFormatParam();
+					nodes[i].imgs = [img];
+					nodes[i].size[1] = Math.max(200, nodes[i].size[1]);
+				}
+			}
+		}
+	}
+}
+
+
+function latentSendHandler(event) {
+	if(event.detail.images.length > 0){
+		let data = event.detail.images[0];
+		let filename = `${data.filename} [${data.type}]`;
+
+		let nodes = app.graph._nodes;
+		for(let i in nodes) {
+			if(nodes[i].type == 'LatentReceiver') {
+				if(nodes[i].widgets[1].value == event.detail.link_id) {
+					if(data.subfolder)
+						nodes[i].widgets[0].value = `${data.subfolder}/${data.filename} [${data.type}]`;
+					else
+						nodes[i].widgets[0].value = `${data.filename} [${data.type}]`;
+
+					let img = new Image();
+					img.src = `/view?filename=${data.filename}&type=${data.type}&subfolder=${data.subfolder}`+app.getPreviewFormatParam();
+					nodes[i].imgs = [img];
+					nodes[i].size[1] = Math.max(200, nodes[i].size[1]);
+				}
+			}
+		}
+	}
+}
+
+
+function valueSendHandler(event) {
+    let nodes = app.graph._nodes;
+    for(let i in nodes) {
+        if(nodes[i].type == 'ImpactValueReceiver') {
+            if(nodes[i].widgets[2].value == event.detail.link_id) {
+                nodes[i].widgets[1].value = event.detail.value;
+
+                let typ = typeof event.detail.value;
+                if(typ != "number") {
+                    nodes[i].widgets[0].value = typeof event.detail.value;
+                }
+                else if(Number.isInteger(event.detail.value)) {
+                    nodes[i].widgets[0].value = "INT";
+                }
+                else {
+                    nodes[i].widgets[0].value = "FLOAT";
+                }
+            }
+        }
+    }
+}
+
+
+const impactProgressBadge = new ImpactProgressBadge();
+
+api.addEventListener("stop-iteration", () => {
+    document.getElementById("autoQueueCheckbox").checked = false;
+});
+api.addEventListener("value-send", valueSendHandler);
+api.addEventListener("img-send", imgSendHandler);
+api.addEventListener("latent-send", latentSendHandler);
+api.addEventListener("executed", progressExecuteHandler);
+
+app.registerExtension({
+	name: "Comfy.Impack",
+	loadedGraphNode(node, app) {
+		if (node.comfyClass == "PreviewBridge" || node.comfyClass == "MaskPainter") {
+			input_dirty[node.id + ""] = true;
+		}
+	},
+
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name == "IterativeLatentUpscale" || nodeData.name == "IterativeImageUpscale") {
+			impactProgressBadge.addStatusHandler(nodeType);
+		}
+	},
+
+	nodeCreated(node, app) {
+		if(node.comfyClass == "MaskPainter") {
+			node.addWidget("button", "Edit mask", null, () => {
+				ComfyApp.copyToClipspace(node);
+				ComfyApp.clipspace_return_node = node;
+				ComfyApp.open_maskeditor();
+			});
+		}
+
+		switch(node.comfyClass) {
+		    case "ToDetailerPipe":
+		    case "BasicPipeToDetailerPipe":
+		    case "EditDetailerPipe":
+		    case "FaceDetailer":
+		        {
+                    for(let i in node.widgets) {
+                        let widget = node.widgets[i];
+                        if(widget.type === "customtext") {
+                            widget.dynamicPrompts = false;
+                            widget.inputEl.placeholder = "wildcard spec: if kept empty, this option will be ignored";
+                            widget.serializeValue = () => {
+                                return node.widgets[i].value;
+                            };
+                        }
+			        }
+			    }
+		        break;
+		}
+
+		if(node.comfyClass == "ImpactSEGSLabelFilter") {
+			Object.defineProperty(node.widgets[0], "value", {
+				set: (value) => {
+				        const stackTrace = new Error().stack;
+                        if(stackTrace.includes('inner_value_change')) {
+                            if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
+                                node.widgets[1].value += ", "
+
+                            node.widgets[1].value += value;
+                            node.widgets_values[1] = node.widgets[1].value;
+                        }
+
+						this._value = value;
+					},
+				get: () => {
+                        return this._value;
+					 }
+			});
+		}
+
+		if(node.comfyClass == "ImpactWildcardProcessor") {
+			node.widgets[0].inputEl.placeholder = "Wildcard Prompt (User input)";
+			node.widgets[1].inputEl.placeholder = "Populated Prompt (Will be generated automatically)";
+			node.widgets[1].inputEl.disabled = true;
+			node.widgets[0].dynamicPrompts = false;
+			node.widgets[1].dynamicPrompts = false;
+
+            let populate_getter = node.widgets[1].__lookupGetter__('value');
+            let populate_setter = node.widgets[1].__lookupSetter__('value');
+
+			let force_serializeValue = async (n,i) =>
+				{
+					if(!node.widgets[2].value) {
+						return node.widgets[1].value;
+					}
+					else {
+				        let wildcard_text = await node.widgets[0].serializeValue();
+
+						let response = await api.fetchApi(`/impact/wildcards`, {
+																method: 'POST',
+																headers: { 'Content-Type': 'application/json' },
+																body: JSON.stringify({text: wildcard_text})
+															});
+
+						let populated = await response.json();
+
+						n.widgets_values[2] = false;
+						n.widgets_values[1] = populated.text;
+						populate_setter.call(node.widgets[1], populated.text);
+
+						return populated.text;
+					}
+				};
+
+			// mode combo
+			Object.defineProperty(node.widgets[2], "value", {
+				set: (value) => {
+						node._mode_value = value == true || value == "Populate";
+						node.widgets[1].inputEl.disabled = value == true || value == "Populate";
+					},
+				get: () => {
+						if(node._mode_value != undefined)
+							return node._mode_value;
+						else
+							return true;
+					 }
+			});
+
+            // to avoid conflict with presetText.js of pythongosssss
+			Object.defineProperty(node.widgets[1], "value", {
+				set: (value) => {
+				        const stackTrace = new Error().stack;
+                        if(!stackTrace.includes('serializeValue'))
+				            populate_setter.call(node.widgets[1], value);
+					},
+				get: () => {
+				        return populate_getter.call(node.widgets[1]);
+					 }
+			});
+
+            node.widgets[0].serializeValue = (n,i) => {
+                if(node.inputs) {
+	                let link_id = node.inputs.find(x => x.name=="wildcard_text")?.link;
+	                if(link_id != undefined) {
+	                    let link = app.graph.links[link_id];
+	                    let input_widget = app.graph._nodes_by_id[link.origin_id].widgets[link.origin_slot];
+	                    if(input_widget.type == "customtext") {
+	                        return input_widget.value;
+	                    }
+	                }
+	                else {
+	                    return node.widgets[0].value;
+	                }
+                }
+                else {
+                    return node.widgets[0].value;
+                }
+            };
+            node.widgets[1].serializeValue = force_serializeValue;
+		}
+
+		if (node.comfyClass == "PreviewBridge" || node.comfyClass == "MaskPainter") {
+			node.widgets[0].value = '#placeholder';
+
+			Object.defineProperty(node, "images", {
+				set: function(value) {
+					node._images = value;
+				},
+				get: function() {
+					const id = node.id+"";
+					if(node.widgets[0].value != '#placeholder') {
+						var need_invalidate = false;
+
+						if(input_dirty.hasOwnProperty(id) && input_dirty[id]) {
+							node.widgets[0].value = {...input_tracking[id][1]};
+							input_dirty[id] = false;
+							need_invalidate = true
+							this._images = app.nodeOutputs[id].images;
+						}
+
+						let filename = app.nodeOutputs[id]['aux'][1][0]['filename'];
+						let subfolder = app.nodeOutputs[id]['aux'][1][0]['subfolder'];
+						let type = app.nodeOutputs[id]['aux'][1][0]['type'];
+
+						let item =
+							{
+								image_hash: app.nodeOutputs[id]['aux'][0],
+								forward_filename: app.nodeOutputs[id]['aux'][1][0]['filename'],
+								forward_subfolder: app.nodeOutputs[id]['aux'][1][0]['subfolder'],
+								forward_type: app.nodeOutputs[id]['aux'][1][0]['type']
+							};
+
+						app.nodeOutputs[id].images = [{
+								...node._images[0],
+								...item
+							}];
+
+						node.widgets[0].value =
+							{
+								...node._images[0],
+								...item
+							};
+
+						if(need_invalidate) {
+							Promise.all(
+								app.nodeOutputs[id].images.map((src) => {
+									return new Promise((r) => {
+										const img = new Image();
+										img.onload = () => r(img);
+										img.onerror = () => r(null);
+										img.src = "/view?" + new URLSearchParams(src).toString();
+									});
+								})
+							).then((imgs) => {
+								this.imgs = imgs.filter(Boolean);
+								this.setSizeForImage?.();
+								app.graph.setDirtyCanvas(true);
+							});
+
+							app.nodeOutputs[id].images[0] = { ...node.widgets[0].value };
+						}
+
+						return app.nodeOutputs[id].images;
+					}
+					else {
+						return node._images;
+					}
+				}
+			});
+		}
+	}
+});

ComfyUI-Impact-Pack/js/impact-sam-editor.js

@@ -0,0 +1,636 @@
+import { app } from "../../scripts/app.js";
+import { ComfyDialog, $el } from "../../scripts/ui.js";
+import { ComfyApp } from "../../scripts/app.js";
+import { ClipspaceDialog } from "../../extensions/core/clipspace.js";
+
+function addMenuHandler(nodeType, cb) {
+	const getOpts = nodeType.prototype.getExtraMenuOptions;
+	nodeType.prototype.getExtraMenuOptions = function () {
+		const r = getOpts.apply(this, arguments);
+		cb.apply(this, arguments);
+		return r;
+	};
+}
+
+// Helper function to convert a data URL to a Blob object
+function dataURLToBlob(dataURL) {
+	const parts = dataURL.split(';base64,');
+	const contentType = parts[0].split(':')[1];
+	const byteString = atob(parts[1]);
+	const arrayBuffer = new ArrayBuffer(byteString.length);
+	const uint8Array = new Uint8Array(arrayBuffer);
+	for (let i = 0; i < byteString.length; i++) {
+		uint8Array[i] = byteString.charCodeAt(i);
+	}
+	return new Blob([arrayBuffer], { type: contentType });
+}
+
+function loadedImageToBlob(image) {
+	const canvas = document.createElement('canvas');
+
+	canvas.width = image.width;
+	canvas.height = image.height;
+
+	const ctx = canvas.getContext('2d');
+
+	ctx.drawImage(image, 0, 0);
+
+	const dataURL = canvas.toDataURL('image/png', 1);
+	const blob = dataURLToBlob(dataURL);
+
+	return blob;
+}
+
+async function uploadMask(filepath, formData) {
+	await fetch('/upload/mask', {
+		method: 'POST',
+		body: formData
+	}).then(response => {}).catch(error => {
+		console.error('Error:', error);
+	});
+
+	ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']] = new Image();
+	ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src = `view?filename=${filepath.filename}&type=${filepath.type}`;
+
+	if(ComfyApp.clipspace.images)
+		ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']] = filepath;
+
+	ClipspaceDialog.invalidatePreview();
+}
+
+class ImpactSamEditorDialog extends ComfyDialog {
+	static instance = null;
+
+	static getInstance() {
+		if(!ImpactSamEditorDialog.instance) {
+			ImpactSamEditorDialog.instance = new ImpactSamEditorDialog();
+		}
+
+		return ImpactSamEditorDialog.instance;
+	}
+
+	constructor() {
+		super();
+		this.element = $el("div.comfy-modal", { parent: document.body }, 
+			[ $el("div.comfy-modal-content", 
+				[...this.createButtons()]),
+			]);
+	}
+
+	createButtons() {
+		return [];
+	}
+
+	createButton(name, callback) {
+		var button = document.createElement("button");
+		button.innerText = name;
+		button.addEventListener("click", callback);
+		return button;
+	}
+
+	createLeftButton(name, callback) {
+		var button = this.createButton(name, callback);
+		button.style.cssFloat = "left";
+		button.style.marginRight = "4px";
+		return button;
+	}
+
+	createRightButton(name, callback) {
+		var button = this.createButton(name, callback);
+		button.style.cssFloat = "right";
+		button.style.marginLeft = "4px";
+		return button;
+	}
+
+	createLeftSlider(self, name, callback) {
+		const divElement = document.createElement('div');
+		divElement.id = "sam-confidence-slider";
+		divElement.style.cssFloat = "left";
+		divElement.style.fontFamily = "sans-serif";
+		divElement.style.marginRight = "4px";
+		divElement.style.color = "var(--input-text)";
+		divElement.style.backgroundColor = "var(--comfy-input-bg)";
+		divElement.style.borderRadius = "8px";
+		divElement.style.borderColor = "var(--border-color)";
+		divElement.style.borderStyle = "solid";
+		divElement.style.fontSize = "15px";
+		divElement.style.height = "21px";
+		divElement.style.padding = "1px 6px";
+		divElement.style.display = "flex";
+		divElement.style.position = "relative";
+		divElement.style.top = "2px";
+		self.confidence_slider_input = document.createElement('input');
+		self.confidence_slider_input.setAttribute('type', 'range');
+		self.confidence_slider_input.setAttribute('min', '0');
+		self.confidence_slider_input.setAttribute('max', '100');
+		self.confidence_slider_input.setAttribute('value', '70');
+		const labelElement = document.createElement("label");
+		labelElement.textContent = name;
+
+		divElement.appendChild(labelElement);
+		divElement.appendChild(self.confidence_slider_input);
+
+		self.confidence_slider_input.addEventListener("change", callback);
+
+		return divElement;
+	}
+
+	async detect_and_invalidate_mask_canvas(self) {
+		const mask_img = await self.detect(self);
+
+		const canvas = self.maskCtx.canvas;
+		const ctx = self.maskCtx;
+
+		ctx.clearRect(0, 0, canvas.width, canvas.height);
+
+		await new Promise((resolve, reject) => {
+						self.mask_image = new Image();
+						self.mask_image.onload = function() {
+							ctx.drawImage(self.mask_image, 0, 0, canvas.width, canvas.height);
+							resolve();
+						};
+						self.mask_image.onerror = reject;
+						self.mask_image.src = mask_img.src;
+				});
+	}
+
+	setlayout(imgCanvas, maskCanvas, pointsCanvas) {
+		const self = this;
+
+		// If it is specified as relative, using it only as a hidden placeholder for padding is recommended
+		// to prevent anomalies where it exceeds a certain size and goes outside of the window.
+		var placeholder = document.createElement("div");
+		placeholder.style.position = "relative";
+		placeholder.style.height = "50px";
+
+		var bottom_panel = document.createElement("div");
+		bottom_panel.style.position = "absolute";
+		bottom_panel.style.bottom = "0px";
+		bottom_panel.style.left = "20px";
+		bottom_panel.style.right = "20px";
+		bottom_panel.style.height = "50px";
+
+		var brush = document.createElement("div");
+		brush.id = "sam-brush";
+		brush.style.backgroundColor = "blue";
+		brush.style.outline = "2px solid pink";
+		brush.style.borderRadius = "50%";
+		brush.style.MozBorderRadius = "50%";
+		brush.style.WebkitBorderRadius = "50%";
+		brush.style.position = "absolute";
+		brush.style.zIndex = 100;
+		brush.style.pointerEvents = "none";
+		this.brush = brush;
+		this.element.appendChild(imgCanvas);
+		this.element.appendChild(maskCanvas);
+		this.element.appendChild(pointsCanvas);
+		this.element.appendChild(placeholder); // must below z-index than bottom_panel to avoid covering button
+		this.element.appendChild(bottom_panel);
+		document.body.appendChild(brush);
+		this.brush_size = 5;
+
+		var confidence_slider = this.createLeftSlider(self, "Confidence", (event) => {
+			self.confidence = event.target.value;
+		});
+
+		var clearButton = this.createLeftButton("Clear", () => {
+				self.maskCtx.clearRect(0, 0, self.maskCanvas.width, self.maskCanvas.height);
+				self.pointsCtx.clearRect(0, 0, self.pointsCanvas.width, self.pointsCanvas.height);
+
+				self.prompt_points = [];
+
+				self.invalidatePointsCanvas(self);
+			});
+
+		var detectButton = this.createLeftButton("Detect", () => self.detect_and_invalidate_mask_canvas(self));
+		
+		var cancelButton = this.createRightButton("Cancel", () => {
+				document.removeEventListener("mouseup", ImpactSamEditorDialog.handleMouseUp);
+				document.removeEventListener("keydown", ImpactSamEditorDialog.handleKeyDown);
+				self.close();
+			});
+
+		self.saveButton = this.createRightButton("Save", () => {
+				document.removeEventListener("mouseup", ImpactSamEditorDialog.handleMouseUp);
+				document.removeEventListener("keydown", ImpactSamEditorDialog.handleKeyDown);
+				self.save(self);
+			});
+
+		var undoButton = this.createLeftButton("Undo", () => {
+				if(self.prompt_points.length > 0) {
+					self.prompt_points.pop();
+					self.pointsCtx.clearRect(0, 0, self.pointsCanvas.width, self.pointsCanvas.height);
+					self.invalidatePointsCanvas(self);
+				}
+			});
+
+		bottom_panel.appendChild(clearButton);
+		bottom_panel.appendChild(detectButton);
+		bottom_panel.appendChild(self.saveButton);
+		bottom_panel.appendChild(cancelButton);
+		bottom_panel.appendChild(confidence_slider);
+		bottom_panel.appendChild(undoButton);
+
+		imgCanvas.style.position = "relative";
+		imgCanvas.style.top = "200";
+		imgCanvas.style.left = "0";
+
+		maskCanvas.style.position = "absolute";
+		maskCanvas.style.opacity = 0.5;
+		pointsCanvas.style.position = "absolute";
+	}
+
+	show() {
+		this.mask_image = null;
+		self.prompt_points = [];
+
+		this.message_box = $el("p", ["Please wait a moment while the SAM model and the image are being loaded."]);
+		this.element.appendChild(this.message_box);
+
+		if(self.imgCtx) {
+			self.imgCtx.clearRect(0, 0, self.imageCanvas.width, self.imageCanvas.height);
+		}
+
+		const target_image_path = ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src;
+		this.load_sam(target_image_path);
+
+		if(!this.is_layout_created) {
+			// layout
+			const imgCanvas = document.createElement('canvas');
+			const maskCanvas = document.createElement('canvas');
+			const pointsCanvas = document.createElement('canvas');
+
+			imgCanvas.id = "imageCanvas";
+			maskCanvas.id = "maskCanvas";
+			pointsCanvas.id = "pointsCanvas";
+
+			this.setlayout(imgCanvas, maskCanvas, pointsCanvas);
+
+			// prepare content
+			this.imgCanvas = imgCanvas;
+			this.maskCanvas = maskCanvas;
+			this.pointsCanvas = pointsCanvas;
+			this.maskCtx = maskCanvas.getContext('2d');
+			this.pointsCtx = pointsCanvas.getContext('2d');
+
+			this.is_layout_created = true;
+
+			// replacement of onClose hook since close is not real close
+			const self = this;
+			const observer = new MutationObserver(function(mutations) {
+			mutations.forEach(function(mutation) {
+					if (mutation.type === 'attributes' && mutation.attributeName === 'style') {
+						if(self.last_display_style && self.last_display_style != 'none' && self.element.style.display == 'none') {
+							ComfyApp.onClipspaceEditorClosed();
+						}
+
+						self.last_display_style = self.element.style.display;
+					}
+				});
+			});
+
+			const config = { attributes: true };
+			observer.observe(this.element, config);
+		}
+
+		this.setImages(target_image_path, this.imgCanvas, this.pointsCanvas);
+
+		if(ComfyApp.clipspace_return_node) {
+			this.saveButton.innerText = "Save to node";
+		}
+		else {
+			this.saveButton.innerText = "Save";
+		}
+		this.saveButton.disabled = true;
+
+		this.element.style.display = "block";
+		this.element.style.zIndex = 8888; // NOTE: alert dialog must be high priority.
+	}
+
+	updateBrushPreview(self, event) {
+		event.preventDefault();
+
+		const centerX = event.pageX;
+		const centerY = event.pageY;
+
+		const brush = self.brush;
+
+		brush.style.width = self.brush_size * 2 + "px";
+		brush.style.height = self.brush_size * 2 + "px";
+		brush.style.left = (centerX - self.brush_size) + "px";
+		brush.style.top = (centerY - self.brush_size) + "px";
+	}
+
+	setImages(target_image_path, imgCanvas, pointsCanvas) {
+		const imgCtx = imgCanvas.getContext('2d');
+		const maskCtx = this.maskCtx;
+		const maskCanvas = this.maskCanvas;
+
+		const self = this;
+
+		// image load
+		const orig_image = new Image();
+		window.addEventListener("resize", () => {
+			// repositioning
+			imgCanvas.width = window.innerWidth - 250;
+			imgCanvas.height = window.innerHeight - 200;
+
+			// redraw image
+			let drawWidth = orig_image.width;
+			let drawHeight = orig_image.height;
+
+			if (orig_image.width > imgCanvas.width) {
+				drawWidth = imgCanvas.width;
+				drawHeight = (drawWidth / orig_image.width) * orig_image.height;
+			}
+
+			if (drawHeight > imgCanvas.height) {
+				drawHeight = imgCanvas.height;
+				drawWidth = (drawHeight / orig_image.height) * orig_image.width;
+			}
+
+			imgCtx.drawImage(orig_image, 0, 0, drawWidth, drawHeight);
+
+			// update mask
+			pointsCanvas.width = drawWidth;
+			pointsCanvas.height = drawHeight;
+			pointsCanvas.style.top = imgCanvas.offsetTop + "px";
+			pointsCanvas.style.left = imgCanvas.offsetLeft + "px";
+
+			maskCanvas.width = drawWidth;
+			maskCanvas.height = drawHeight;
+			maskCanvas.style.top = imgCanvas.offsetTop + "px";
+			maskCanvas.style.left = imgCanvas.offsetLeft + "px";
+
+			self.invalidateMaskCanvas(self);
+			self.invalidatePointsCanvas(self);
+		});
+
+		// original image load
+		orig_image.onload = () => self.onLoaded(self);
+		const rgb_url = new URL(target_image_path);
+		rgb_url.searchParams.delete('channel');
+		rgb_url.searchParams.set('channel', 'rgb');
+		orig_image.src = rgb_url;
+		self.image = orig_image;
+	}
+
+	onLoaded(self) {
+		if(self.message_box) {
+			self.element.removeChild(self.message_box);
+			self.message_box = null;
+		}
+
+		window.dispatchEvent(new Event('resize'));
+
+		self.setEventHandler(pointsCanvas);
+		self.saveButton.disabled = false;
+	}
+
+	setEventHandler(targetCanvas) {
+		targetCanvas.addEventListener("contextmenu", (event) => {
+			event.preventDefault();
+		});
+
+		const self = this;
+		targetCanvas.addEventListener('pointermove', (event) => this.updateBrushPreview(self,event));
+		targetCanvas.addEventListener('pointerdown', (event) => this.handlePointerDown(self,event));
+		targetCanvas.addEventListener('pointerover', (event) => { this.brush.style.display = "block"; });
+		targetCanvas.addEventListener('pointerleave', (event) => { this.brush.style.display = "none"; });
+		document.addEventListener('keydown', ImpactSamEditorDialog.handleKeyDown);
+	}
+
+	static handleKeyDown(event) {
+		const self = ImpactSamEditorDialog.instance;
+		if (event.key === '=') { // positive
+			brush.style.backgroundColor = "blue";
+			brush.style.outline = "2px solid pink";
+			self.is_positive_mode = true;
+		} else if (event.key === '-') { // negative
+			brush.style.backgroundColor = "red";
+			brush.style.outline = "2px solid skyblue";
+			self.is_positive_mode = false;
+		}
+	}
+
+	is_positive_mode = true;
+	prompt_points = [];
+	confidence = 70;
+
+	invalidatePointsCanvas(self) {
+		const ctx = self.pointsCtx;
+
+		for (const i in self.prompt_points) {
+			const [is_positive, x, y] = self.prompt_points[i];
+
+			const scaledX = x * ctx.canvas.width / self.image.width;
+			const scaledY = y * ctx.canvas.height / self.image.height;
+
+			if(is_positive)
+				ctx.fillStyle = "blue";
+			else
+				ctx.fillStyle = "red";
+			ctx.beginPath();
+			ctx.arc(scaledX, scaledY, 3, 0, 3 * Math.PI);
+			ctx.fill();
+		}
+	}줘
+
+	invalidateMaskCanvas(self) {
+		if(self.mask_image) {
+			self.maskCtx.clearRect(0, 0, self.maskCanvas.width, self.maskCanvas.height);
+			self.maskCtx.drawImage(self.mask_image, 0, 0, self.maskCanvas.width, self.maskCanvas.height);
+		}
+	}
+
+	async load_sam(url) {
+		const parsedUrl = new URL(url);
+		const searchParams = new URLSearchParams(parsedUrl.search);
+
+		const filename = searchParams.get("filename") || "";
+		const fileType = searchParams.get("type") || "";
+		const subfolder = searchParams.get("subfolder") || "";
+
+		const data = {
+				sam_model_name: "sam_vit_b_01ec64.pth",
+				filename: filename,
+				type: fileType,
+				subfolder: subfolder
+			};
+
+		fetch('/sam/prepare', {
+			method: 'POST',
+			headers: { 'Content-Type': 'application/json' },
+			body: JSON.stringify(data)
+		});
+	}
+
+	async detect(self) {
+		const positive_points = [];
+		const negative_points = [];
+
+		for(const i in self.prompt_points) {
+			const [is_positive, x, y] = self.prompt_points[i];
+			const point = [x,y];
+			if(is_positive)
+				positive_points.push(point);
+			else
+				negative_points.push(point);
+		}
+
+		const data = {
+			positive_points: positive_points,
+			negative_points: negative_points,
+			threshold: self.confidence/100
+		};
+		
+		const response = await fetch('/sam/detect', {
+			method: 'POST',
+			headers: { 'Content-Type': 'image/png' },
+			body: JSON.stringify(data)
+		});
+		
+		const blob = await response.blob();
+		const url = URL.createObjectURL(blob);
+
+		return new Promise((resolve, reject) => {
+			const image = new Image();
+			image.onload = () => resolve(image);
+			image.onerror = reject;
+			image.src = url;
+		});
+	}
+
+	handlePointerDown(self, event) {
+		if ([0, 2, 5].includes(event.button)) {
+			event.preventDefault();
+			const x = event.offsetX || event.targetTouches[0].clientX - maskRect.left;
+			const y = event.offsetY || event.targetTouches[0].clientY - maskRect.top;
+
+			const originalX = x * self.image.width / self.pointsCanvas.width;
+			const originalY = y * self.image.height / self.pointsCanvas.height;
+
+			var point = null;
+			if (event.button == 0) {
+				// positive
+				point = [true, originalX, originalY];
+			} else {
+				// negative
+				point = [false, originalX, originalY];
+			}
+
+			self.prompt_points.push(point);
+
+			self.invalidatePointsCanvas(self);
+		}
+	}
+
+	async save(self) {
+		if(!self.mask_image) {
+			this.close();
+			return;
+		}
+
+		const save_canvas = document.createElement('canvas');
+
+		const save_ctx = save_canvas.getContext('2d', {willReadFrequently:true});
+		save_canvas.width = self.mask_image.width;
+		save_canvas.height = self.mask_image.height;
+
+		save_ctx.drawImage(self.mask_image, 0, 0, save_canvas.width, save_canvas.height);
+
+		const save_data = save_ctx.getImageData(0, 0, save_canvas.width, save_canvas.height);
+
+		// refine mask image
+		for (let i = 0; i < save_data.data.length; i += 4) {
+			if(save_data.data[i]) {
+				save_data.data[i+3] = 0;
+			}
+			else {
+				save_data.data[i+3] = 255;
+			}
+
+			save_data.data[i] = 0;
+			save_data.data[i+1] = 0;
+			save_data.data[i+2] = 0;
+		}
+
+		save_ctx.globalCompositeOperation = 'source-over';
+		save_ctx.putImageData(save_data, 0, 0);
+
+		const formData = new FormData();
+		const filename = "clipspace-mask-" + performance.now() + ".png";
+
+		const item =
+			{
+				"filename": filename,
+				"subfolder": "",
+				"type": "temp",
+			};
+
+		if(ComfyApp.clipspace.images)
+			ComfyApp.clipspace.images[0] = item;
+
+		if(ComfyApp.clipspace.widgets) {
+			const index = ComfyApp.clipspace.widgets.findIndex(obj => obj.name === 'image');
+
+			if(index >= 0)
+				ComfyApp.clipspace.widgets[index].value = `${filename} [temp]`;
+		}
+
+		const dataURL = save_canvas.toDataURL();
+		const blob = dataURLToBlob(dataURL);
+
+		let original_url = new URL(this.image.src);
+
+		const original_ref = { filename: original_url.searchParams.get('filename') };
+
+		let original_subfolder = original_url.searchParams.get("subfolder");
+		if(original_subfolder)
+			original_ref.subfolder = original_subfolder;
+
+		let original_type = original_url.searchParams.get("type");
+		if(original_type)
+			original_ref.type = original_type;
+
+		formData.append('image', blob, filename);
+		formData.append('original_ref', JSON.stringify(original_ref));
+		formData.append('type', "temp");
+
+		await uploadMask(item, formData);
+		ComfyApp.onClipspaceEditorSave();
+		this.close();
+	}
+}
+
+app.registerExtension({
+	name: "Comfy.Impact.SAMEditor",
+	init(app) {
+		const callback =
+			function () {
+				let dlg = ImpactSamEditorDialog.getInstance();
+				dlg.show();
+			};
+
+		const context_predicate = () => ComfyApp.clipspace && ComfyApp.clipspace.imgs && ComfyApp.clipspace.imgs.length > 0
+		ClipspaceDialog.registerButton("Impact SAM Detector", context_predicate, callback);
+	},
+
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.output.includes("MASK") && nodeData.output.includes("IMAGE")) {
+			addMenuHandler(nodeType, function (_, options) {
+				options.unshift({
+					content: "Open in SAM Detector",
+					callback: () => {
+						ComfyApp.copyToClipspace(this);
+						ComfyApp.clipspace_return_node = this;
+
+						let dlg = ImpactSamEditorDialog.getInstance();
+						dlg.show();
+					},
+				});
+			});
+		}
+	}
+});
+

ComfyUI-Impact-Pack/modules/impact/additional_dependencies.py

@@ -0,0 +1,12 @@
+import sys
+import subprocess
+
+
+def ensure_onnx_package():
+    try:
+        import onnxruntime
+    except Exception:
+        if "python_embeded" in sys.executable or "python_embedded" in sys.executable:
+            subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'onnxruntime'])
+        else:
+            subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', 'onnxruntime'])

ComfyUI-Impact-Pack/modules/impact/config.py

@@ -0,0 +1,51 @@
+import configparser
+import os
+
+
+version = "V3.12.1"
+
+dependency_version = 9
+
+my_path = os.path.dirname(__file__)
+old_config_path = os.path.join(my_path, "impact-pack.ini")
+config_path = os.path.join(my_path, "..", "..", "impact-pack.ini")
+latent_letter_path = os.path.join(my_path, "..", "..", "latent.png")
+
+MAX_RESOLUTION = 8192
+
+
+def write_config():
+    config = configparser.ConfigParser()
+    config['default'] = {
+        'dependency_version': dependency_version,
+        'mmdet_skip': get_config()['mmdet_skip'],
+    }
+    with open(config_path, 'w') as configfile:
+        config.write(configfile)
+
+
+def read_config():
+    try:
+        config = configparser.ConfigParser()
+        config.read(config_path)
+        default_conf = config['default']
+
+        return {
+                    'dependency_version': int(default_conf['dependency_version']),
+                    'mmdet_skip': default_conf['mmdet_skip'].lower() == 'true'
+               }
+
+    except Exception:
+        return {'dependency_version': 0, 'mmdet_skip': True}
+
+
+cached_config = None
+
+
+def get_config():
+    global cached_config
+
+    if cached_config is None:
+        cached_config = read_config()
+
+    return cached_config

ComfyUI-Impact-Pack/modules/impact/core.py

@@ -0,0 +1,1330 @@
+import os
+from segment_anything import SamPredictor
+import torch.nn.functional as F
+
+from impact.utils import *
+from collections import namedtuple
+import numpy as np
+from skimage.measure import label, regionprops
+
+import nodes
+import comfy_extras.nodes_upscale_model as model_upscale
+from server import PromptServer
+import comfy
+import impact.wildcards as wildcards
+import math
+
+SEG = namedtuple("SEG", ['cropped_image', 'cropped_mask', 'confidence', 'crop_region', 'bbox', 'label'],
+                 defaults=[None])
+
+
+def erosion_mask(mask, grow_mask_by):
+    w = mask.shape[1]
+    h = mask.shape[0]
+
+    mask = mask.clone()
+
+    mask2 = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(w, h),
+                                            mode="bilinear")
+    if grow_mask_by == 0:
+        mask_erosion = mask2
+    else:
+        kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
+        padding = math.ceil((grow_mask_by - 1) / 2)
+
+        mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask2.round(), kernel_tensor, padding=padding), 0, 1)
+
+    return mask_erosion[:, :, :w, :h].round()
+
+
+class REGIONAL_PROMPT:
+    def __init__(self, mask, sampler):
+        self.mask = mask
+        self.sampler = sampler
+        self.mask_erosion = None
+        self.erosion_factor = None
+
+    def get_mask_erosion(self, factor):
+        if self.mask_erosion is None or self.erosion_factor != factor:
+            self.mask_erosion = erosion_mask(self.mask, factor)
+
+        return self.mask_erosion
+
+
+class NO_BBOX_DETECTOR:
+    pass
+
+
+class NO_SEGM_DETECTOR:
+    pass
+
+
+def create_segmasks(results):
+    bboxs = results[1]
+    segms = results[2]
+    confidence = results[3]
+
+    results = []
+    for i in range(len(segms)):
+        item = (bboxs[i], segms[i].astype(np.float32), confidence[i])
+        results.append(item)
+    return results
+
+
+def gen_detection_hints_from_mask_area(x, y, mask, threshold, use_negative):
+    points = []
+    plabs = []
+
+    # minimum sampling step >= 3
+    y_step = max(3, int(mask.shape[0]/20))
+    x_step = max(3, int(mask.shape[1]/20))
+    
+    for i in range(0, len(mask), y_step):
+        for j in range(0, len(mask[i]), x_step):
+            if mask[i][j] > threshold:
+                points.append((x+j, y+i))
+                plabs.append(1)
+            elif use_negative and mask[i][j] == 0:
+                points.append((x+j, y+i))
+                plabs.append(0)
+    
+    return points, plabs
+
+
+def gen_negative_hints(w, h, x1, y1, x2, y2):
+    npoints = []
+    nplabs = []
+
+    # minimum sampling step >= 3
+    y_step = max(3, int(w/20))
+    x_step = max(3, int(h/20))
+    
+    for i in range(10, h-10, y_step):
+        for j in range(10, w-10, x_step):
+            if not (x1-10 <= j and j <= x2+10 and y1-10 <= i and i <= y2+10):
+                npoints.append((j, i))
+                nplabs.append(0)
+
+    return npoints, nplabs
+
+
+def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max_size, bbox, seed, steps, cfg, sampler_name,
+                   scheduler, positive, negative, denoise, noise_mask, force_inpaint, wildcard_opt=None):
+    if wildcard_opt is not None and wildcard_opt != "":
+        model, positive = wildcards.process_with_loras(wildcard_opt, model, clip)
+
+    h = image.shape[1]
+    w = image.shape[2]
+
+    bbox_h = bbox[3] - bbox[1]
+    bbox_w = bbox[2] - bbox[0]
+
+    # Skip processing if the detected bbox is already larger than the guide_size
+    if not force_inpaint and bbox_h >= guide_size and bbox_w >= guide_size:
+        print(f"Detailer: segment skip (enough big)")
+        return None
+
+    if guide_size_for_bbox: # == "bbox"
+        # Scale up based on the smaller dimension between width and height.
+        upscale = guide_size / min(bbox_w, bbox_h)
+    else:
+        # for cropped_size
+        upscale = guide_size / min(w, h)
+
+    new_w = int(w * upscale)
+    new_h = int(h * upscale)
+
+    # safeguard
+    if new_w > max_size or new_h > max_size:
+        upscale *= max_size / max(new_w, new_h)
+        new_w = int(w * upscale)
+        new_h = int(h * upscale)
+
+    if not force_inpaint:
+        if upscale <= 1.0:
+            print(f"Detailer: segment skip [determined upscale factor={upscale}]")
+            return None
+
+        if new_w == 0 or new_h == 0:
+            print(f"Detailer: segment skip [zero size={new_w, new_h}]")
+            return None
+    else:
+        if upscale <= 1.0 or new_w == 0 or new_h == 0:
+            print(f"Detailer: force inpaint")
+            upscale = 1.0
+            new_w = w
+            new_h = h
+
+    print(f"Detailer: segment upscale for ({bbox_w, bbox_h}) | crop region {w, h} x {upscale} -> {new_w, new_h}")
+
+    # upscale
+    upscaled_image = scale_tensor(new_w, new_h, torch.from_numpy(image))
+
+    # ksampler
+    latent_image = to_latent_image(upscaled_image, vae)
+
+    if noise_mask is not None:
+        # upscale the mask tensor by a factor of 2 using bilinear interpolation
+        noise_mask = torch.from_numpy(noise_mask)
+        upscaled_mask = torch.nn.functional.interpolate(noise_mask.unsqueeze(0).unsqueeze(0), size=(new_h, new_w),
+                                                        mode='bilinear', align_corners=False)
+
+        # remove the extra dimensions added by unsqueeze
+        upscaled_mask = upscaled_mask.squeeze().squeeze()
+        latent_image['noise_mask'] = upscaled_mask
+
+    refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)[0]
+
+    # non-latent downscale - latent downscale cause bad quality
+    refined_image = vae.decode(refined_latent['samples'])
+
+    # downscale
+    refined_image = scale_tensor_and_to_pil(w, h, refined_image)
+
+    # don't convert to latent - latent break image
+    # preserving pil is much better
+    return refined_image
+
+
+def composite_to(dest_latent, crop_region, src_latent):
+    x1 = crop_region[0]
+    y1 = crop_region[1]
+
+    # composite to original latent
+    lc = nodes.LatentComposite()
+
+    # 현재 mask 를 고려한 composite 가 없음... 이거 처리 필요.
+
+    orig_image = lc.composite(dest_latent, src_latent, x1, y1)
+
+    return orig_image[0]
+
+
+def sam_predict(predictor, points, plabs, bbox, threshold):
+    point_coords = None if not points else np.array(points)
+    point_labels = None if not plabs else np.array(plabs)
+
+    box = np.array([bbox]) if bbox is not None else None
+
+    cur_masks, scores, _ = predictor.predict(point_coords=point_coords, point_labels=point_labels, box=box)
+
+    total_masks = []
+
+    selected = False
+    max_score = 0
+    for idx in range(len(scores)):
+        if scores[idx] > max_score:
+            max_score = scores[idx]
+            max_mask = cur_masks[idx]
+
+        if scores[idx] >= threshold:
+            selected = True
+            total_masks.append(cur_masks[idx])
+        else:
+            pass
+
+    if not selected:
+        total_masks.append(max_mask)
+
+    return total_masks
+
+
+def make_sam_mask(sam_model, segs, image, detection_hint, dilation,
+              threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
+
+    if sam_model.is_auto_mode:
+        device = comfy.model_management.get_torch_device()
+        sam_model.to(device=device)
+
+    try:
+        predictor = SamPredictor(sam_model)
+        image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+        predictor.set_image(image, "RGB")
+
+        total_masks = []
+
+        use_small_negative = mask_hint_use_negative == "Small"
+
+        # seg_shape = segs[0]
+        segs = segs[1]
+        if detection_hint == "mask-points":
+            points = []
+            plabs = []
+
+            for i in range(len(segs)):
+                bbox = segs[i].bbox
+                center = center_of_bbox(segs[i].bbox)
+                points.append(center)
+
+                # small point is background, big point is foreground
+                if use_small_negative and bbox[2] - bbox[0] < 10:
+                    plabs.append(0)
+                else:
+                    plabs.append(1)
+
+            detected_masks = sam_predict(predictor, points, plabs, None, threshold)
+            total_masks += detected_masks
+
+        else:
+            for i in range(len(segs)):
+                bbox = segs[i].bbox
+                center = center_of_bbox(bbox)
+
+                x1 = max(bbox[0] - bbox_expansion, 0)
+                y1 = max(bbox[1] - bbox_expansion, 0)
+                x2 = min(bbox[2] + bbox_expansion, image.shape[1])
+                y2 = min(bbox[3] + bbox_expansion, image.shape[0])
+
+                dilated_bbox = [x1, y1, x2, y2]
+
+                points = []
+                plabs = []
+                if detection_hint == "center-1":
+                    points.append(center)
+                    plabs = [1]  # 1 = foreground point, 0 = background point
+
+                elif detection_hint == "horizontal-2":
+                    gap = (x2 - x1) / 3
+                    points.append((x1 + gap, center[1]))
+                    points.append((x1 + gap * 2, center[1]))
+                    plabs = [1, 1]
+
+                elif detection_hint == "vertical-2":
+                    gap = (y2 - y1) / 3
+                    points.append((center[0], y1 + gap))
+                    points.append((center[0], y1 + gap * 2))
+                    plabs = [1, 1]
+
+                elif detection_hint == "rect-4":
+                    x_gap = (x2 - x1) / 3
+                    y_gap = (y2 - y1) / 3
+                    points.append((x1 + x_gap, center[1]))
+                    points.append((x1 + x_gap * 2, center[1]))
+                    points.append((center[0], y1 + y_gap))
+                    points.append((center[0], y1 + y_gap * 2))
+                    plabs = [1, 1, 1, 1]
+
+                elif detection_hint == "diamond-4":
+                    x_gap = (x2 - x1) / 3
+                    y_gap = (y2 - y1) / 3
+                    points.append((x1 + x_gap, y1 + y_gap))
+                    points.append((x1 + x_gap * 2, y1 + y_gap))
+                    points.append((x1 + x_gap, y1 + y_gap * 2))
+                    points.append((x1 + x_gap * 2, y1 + y_gap * 2))
+                    plabs = [1, 1, 1, 1]
+
+                elif detection_hint == "mask-point-bbox":
+                    center = center_of_bbox(segs[i].bbox)
+                    points.append(center)
+                    plabs = [1]
+
+                elif detection_hint == "mask-area":
+                    points, plabs = gen_detection_hints_from_mask_area(segs[i].crop_region[0], segs[i].crop_region[1],
+                                                                       segs[i].cropped_mask,
+                                                                       mask_hint_threshold, use_small_negative)
+
+                if mask_hint_use_negative == "Outter":
+                    npoints, nplabs = gen_negative_hints(image.shape[0], image.shape[1],
+                                                         segs[i].crop_region[0], segs[i].crop_region[1],
+                                                         segs[i].crop_region[2], segs[i].crop_region[3])
+
+                    points += npoints
+                    plabs += nplabs
+
+                detected_masks = sam_predict(predictor, points, plabs, dilated_bbox, threshold)
+                total_masks += detected_masks
+
+        # merge every collected masks
+        mask = combine_masks2(total_masks)
+
+    finally:
+        if sam_model.is_auto_mode:
+            print(f"semd to {device}")
+            sam_model.to(device="cpu")
+
+    if mask is not None:
+        mask = mask.float()
+        mask = dilate_mask(mask.cpu().numpy(), dilation)
+        mask = torch.from_numpy(mask)
+    else:
+        mask = torch.zeros((8, 8), dtype=torch.float32, device="cpu")  # empty mask
+
+    return mask
+
+def generate_detection_hints(image,seg,center,detection_hint,dilated_bbox,mask_hint_threshold, use_small_negative, mask_hint_use_negative):
+
+    [x1, y1, x2, y2] = dilated_bbox
+
+    points = []
+    plabs = []
+    if detection_hint == "center-1":
+        points.append(center)
+        plabs = [1]  # 1 = foreground point, 0 = background point
+
+    elif detection_hint == "horizontal-2":
+        gap = (x2 - x1) / 3
+        points.append((x1 + gap, center[1]))
+        points.append((x1 + gap * 2, center[1]))
+        plabs = [1, 1]
+
+    elif detection_hint == "vertical-2":
+        gap = (y2 - y1) / 3
+        points.append((center[0], y1 + gap))
+        points.append((center[0], y1 + gap * 2))
+        plabs = [1, 1]
+
+    elif detection_hint == "rect-4":
+        x_gap = (x2 - x1) / 3
+        y_gap = (y2 - y1) / 3
+        points.append((x1 + x_gap, center[1]))
+        points.append((x1 + x_gap * 2, center[1]))
+        points.append((center[0], y1 + y_gap))
+        points.append((center[0], y1 + y_gap * 2))
+        plabs = [1, 1, 1, 1]
+
+    elif detection_hint == "diamond-4":
+        x_gap = (x2 - x1) / 3
+        y_gap = (y2 - y1) / 3
+        points.append((x1 + x_gap, y1 + y_gap))
+        points.append((x1 + x_gap * 2, y1 + y_gap))
+        points.append((x1 + x_gap, y1 + y_gap * 2))
+        points.append((x1 + x_gap * 2, y1 + y_gap * 2))
+        plabs = [1, 1, 1, 1]
+
+    elif detection_hint == "mask-point-bbox":
+        center = center_of_bbox(seg.bbox)
+        points.append(center)
+        plabs = [1]
+
+    elif detection_hint == "mask-area":
+        points, plabs = gen_detection_hints_from_mask_area(seg.crop_region[0], seg.crop_region[1],
+                                                            seg.cropped_mask,
+                                                            mask_hint_threshold, use_small_negative)
+
+    if mask_hint_use_negative == "Outter":
+        npoints, nplabs = gen_negative_hints(image.shape[0], image.shape[1],
+                                                seg.crop_region[0], seg.crop_region[1],
+                                                seg.crop_region[2], seg.crop_region[3])
+
+        points += npoints
+        plabs += nplabs
+
+    return points, plabs
+
+
+def convert_and_stack_masks(masks):
+    if len(masks) == 0:
+        return None
+
+    mask_tensors = []
+    for mask in masks:
+        mask_array = np.array(mask, dtype=np.uint8)
+        mask_tensor = torch.from_numpy(mask_array).bool()
+        mask_tensors.append(mask_tensor)
+
+    stacked_masks = torch.stack(mask_tensors, dim=0)
+    stacked_masks = stacked_masks.unsqueeze(1)
+
+    return stacked_masks
+
+
+def merge_and_stack_masks(stacked_masks, group_size):
+    if stacked_masks is None:
+        return None
+
+    num_masks = stacked_masks.size(0)
+    merged_masks = []
+
+    for i in range(0, num_masks, group_size):
+        subset_masks = stacked_masks[i:i+group_size]
+        merged_mask = torch.any(subset_masks, dim=0)
+        merged_masks.append(merged_mask)
+
+    if len(merged_masks) > 0:
+        merged_masks = torch.stack(merged_masks, dim=0)
+
+    return merged_masks
+
+
+# Used Python's slicing feature. stacked_masks[2::3] means starting from index 2, selecting every third tensor with a step size of 3.
+# This allows for quickly obtaining the last tensor of every three tensors in stacked_masks.
+def every_three_pick_last(stacked_masks):
+    selected_masks = stacked_masks[2::3]
+    return selected_masks
+
+
+def make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
+                            threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
+
+    if sam_model.is_auto_mode:
+        device = comfy.model_management.get_torch_device()
+        sam_model.to(device=device)
+
+    try:
+        predictor = SamPredictor(sam_model)
+        image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+        predictor.set_image(image, "RGB")
+
+        total_masks = []
+
+        use_small_negative = mask_hint_use_negative == "Small"
+
+        # seg_shape = segs[0]
+        segs = segs[1]
+        if detection_hint == "mask-points":
+            points = []
+            plabs = []
+
+            for i in range(len(segs)):
+                bbox = segs[i].bbox
+                center = center_of_bbox(bbox)
+                points.append(center)
+
+                # small point is background, big point is foreground
+                if use_small_negative and bbox[2] - bbox[0] < 10:
+                    plabs.append(0)
+                else:
+                    plabs.append(1)
+
+            detected_masks = sam_predict(predictor, points, plabs, None, threshold)
+            total_masks += detected_masks
+
+        else:
+            for i in range(len(segs)):
+                bbox = segs[i].bbox
+                center = center_of_bbox(bbox)
+                x1 = max(bbox[0] - bbox_expansion, 0)
+                y1 = max(bbox[1] - bbox_expansion, 0)
+                x2 = min(bbox[2] + bbox_expansion, image.shape[1])
+                y2 = min(bbox[3] + bbox_expansion, image.shape[0])
+
+                dilated_bbox = [x1, y1, x2, y2]
+
+                points, plabs = generate_detection_hints(image, segs[i],center, detection_hint, dilated_bbox, mask_hint_threshold, use_small_negative, mask_hint_use_negative)
+
+                detected_masks = sam_predict(predictor, points, plabs, dilated_bbox, threshold)
+
+                total_masks += detected_masks
+
+        # merge every collected masks
+        mask = combine_masks2(total_masks)
+
+    finally:
+        if sam_model.is_auto_mode:
+            print(f"semd to {device}")
+            sam_model.to(device="cpu")
+
+    if mask is not None:
+        mask = mask.float()
+        mask = dilate_mask(mask.cpu().numpy(), dilation)
+        mask = torch.from_numpy(mask)
+    else:
+        mask = torch.zeros((8, 8), dtype=torch.float32, device="cpu")  # empty mask
+
+    stacked_masks = convert_and_stack_masks(total_masks)
+
+    return (mask, merge_and_stack_masks(stacked_masks, group_size=3))
+    # return every_three_pick_last(stacked_masks)
+
+
+def segs_bitwise_and_mask(segs, mask):
+    if mask is None:
+        print("[SegsBitwiseAndMask] Cannot operate: MASK is empty.")
+        return ([], )
+
+    items = []
+
+    mask = (mask.cpu().numpy() * 255).astype(np.uint8)
+
+    for seg in segs[1]:
+        cropped_mask = (seg.cropped_mask * 255).astype(np.uint8)
+        crop_region = seg.crop_region
+
+        cropped_mask2 = mask[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]]
+
+        new_mask = np.bitwise_and(cropped_mask.astype(np.uint8), cropped_mask2)
+        new_mask = new_mask.astype(np.float32) / 255.0
+
+        item = SEG(seg.cropped_image, new_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label)
+        items.append(item)
+
+    return segs[0], items
+
+
+def apply_mask_to_each_seg(segs, masks):
+    if masks is None:
+        print("[SegsBitwiseAndMask] Cannot operate: MASK is empty.")
+        return (segs[0], [], )
+
+    items = []
+
+    masks = masks.squeeze(1)
+
+    for seg, mask in zip(segs[1], masks):
+        cropped_mask = (seg.cropped_mask * 255).astype(np.uint8)
+        crop_region = seg.crop_region
+
+        cropped_mask2 = (mask.cpu().numpy() * 255).astype(np.uint8)
+        cropped_mask2 = cropped_mask2[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]]
+
+        new_mask = np.bitwise_and(cropped_mask.astype(np.uint8), cropped_mask2)
+        new_mask = new_mask.astype(np.float32) / 255.0
+
+        item = SEG(seg.cropped_image, new_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label)
+        items.append(item)
+
+    return segs[0], items
+
+
+class ONNXDetector:
+    onnx_model = None
+
+    def __init__(self, onnx_model):
+        self.onnx_model = onnx_model
+
+    def detect(self, image, threshold, dilation, crop_factor, drop_size=1):
+        drop_size = max(drop_size, 1)
+        try:
+            import impact.onnx as onnx
+
+            h = image.shape[1]
+            w = image.shape[2]
+
+            labels, scores, boxes = onnx.onnx_inference(image, self.onnx_model)
+
+            # collect feasible item
+            result = []
+
+            for i in range(len(labels)):
+                if scores[i] > threshold:
+                    item_bbox = boxes[i]
+                    x1, y1, x2, y2 = item_bbox
+
+                    if x2 - x1 > drop_size and y2 - y1 > drop_size:  # minimum dimension must be (2,2) to avoid squeeze issue
+                        crop_region = make_crop_region(w, h, item_bbox, crop_factor)
+                        crop_x1, crop_y1, crop_x2, crop_y2, = crop_region
+
+                        # prepare cropped mask
+                        cropped_mask = np.zeros((crop_y2-crop_y1,crop_x2-crop_x1))
+                        inner_mask = np.ones((y2-y1, x2-x1))
+                        cropped_mask[y1-crop_y1:y2-crop_y1, x1-crop_x1:x2-crop_x1] = inner_mask
+
+                        # make items
+                        item = SEG(None, cropped_mask, scores[i], crop_region, item_bbox)
+                        result.append(item)
+
+            shape = h, w
+            return shape, result
+        except Exception as e:
+            print(f"ONNXDetector: unable to execute.\n{e}")
+            pass
+
+    def detect_combined(self, image, threshold, dilation):
+        return segs_to_combined_mask(self.detect(image, threshold, dilation, 1))
+
+    def setAux(self, x):
+        pass
+
+
+def mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1):
+    drop_size = max(drop_size, 1)
+    if mask is None:
+        print("[mask_to_segs] Cannot operate: MASK is empty.")
+        return ([],)
+
+    if isinstance(mask, np.ndarray):
+        pass  # `mask` is already a NumPy array
+    else:
+        try:
+            mask = mask.numpy()
+        except AttributeError:
+            print("[mask_to_segs] Cannot operate: MASK is not a NumPy array or Tensor.")
+            return ([],)
+
+    if mask is None:
+        print("[mask_to_segs] Cannot operate: MASK is empty.")
+        return ([],)
+
+    result = []
+
+    if len(mask.shape) == 2:
+        mask = np.expand_dims(mask, axis=0)
+    else:
+        mask = np.squeeze(mask, axis=1)
+
+    for i in range(mask.shape[0]):
+        mask_i = mask[i]
+
+        if combined:
+            indices = np.nonzero(mask_i)
+            if len(indices[0]) > 0 and len(indices[1]) > 0:
+                bbox = (
+                    np.min(indices[1]),
+                    np.min(indices[0]),
+                    np.max(indices[1]),
+                    np.max(indices[0]),
+                )
+                crop_region = make_crop_region(
+                    mask_i.shape[1], mask_i.shape[0], bbox, crop_factor
+                )
+                x1, y1, x2, y2 = crop_region
+                if x2 - x1 > 0 and y2 - y1 > 0:
+                    cropped_mask = mask_i[y1:y2, x1:x2]
+
+                    if cropped_mask is not None:
+                        item = SEG(None, cropped_mask, 1.0, crop_region, bbox, "A")
+                        result.append(item)
+
+        else:
+            mask_i_uint8 = (mask_i * 255.0).astype(np.uint8)
+            contours, _ = cv2.findContours(mask_i_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            for contour in contours:
+                separated_mask = np.zeros_like(mask_i_uint8)
+                cv2.drawContours(separated_mask, [contour], 0, 255, -1)
+                separated_mask = np.array(separated_mask/255.0).astype(np.float32)
+
+                x, y, w, h = cv2.boundingRect(contour)
+                bbox = x, y, x+w, y+h
+                crop_region = make_crop_region(
+                    mask_i.shape[1], mask_i.shape[0], bbox, crop_factor
+                )
+
+                if w > drop_size and h > drop_size:
+                    cropped_mask = np.array(
+                        separated_mask[
+                            crop_region[1]: crop_region[3],
+                            crop_region[0]: crop_region[2],
+                        ]
+                    )
+
+                    if bbox_fill:
+                        cropped_mask.fill(1.0)
+
+                    if cropped_mask is not None:
+                        item = SEG(None, cropped_mask, 1.0, crop_region, bbox, "A")
+                        result.append(item)
+
+    if not result:
+        print(f"[mask_to_segs] Empty mask.")
+
+    print(f"# of Detected SEGS: {len(result)}")
+    # for r in result:
+    #     print(f"\tbbox={r.bbox}, crop={r.crop_region}, label={r.label}")
+
+    # shape: (b,h,w) -> (h,w)
+    return (mask.shape[1], mask.shape[2]), result
+
+
+def segs_to_combined_mask(segs):
+    shape = segs[0]
+    h = shape[0]
+    w = shape[1]
+
+    mask = np.zeros((h, w), dtype=np.uint8)
+
+    for seg in segs[1]:
+        cropped_mask = seg.cropped_mask
+        crop_region = seg.crop_region
+        mask[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]] |= (cropped_mask * 255).astype(np.uint8)
+
+    return torch.from_numpy(mask.astype(np.float32) / 255.0)
+
+
+def segs_to_masklist(segs):
+    shape = segs[0]
+    h = shape[0]
+    w = shape[1]
+
+    masks = []
+    for seg in segs[1]:
+        mask = np.zeros((h, w), dtype=np.uint8)
+        cropped_mask = seg.cropped_mask
+        crop_region = seg.crop_region
+        mask[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]] |= (cropped_mask * 255).astype(np.uint8)
+        mask = torch.from_numpy(mask.astype(np.float32) / 255.0)
+        masks.append(mask)
+
+    return masks
+
+
+def vae_decode(vae, samples, use_tile, hook):
+    if use_tile:
+        pixels = nodes.VAEDecodeTiled().decode(vae, samples)[0]
+    else:
+        pixels = nodes.VAEDecode().decode(vae, samples)[0]
+
+    if hook is not None:
+        hook.post_decode(pixels)
+
+    return pixels
+
+
+def vae_encode(vae, pixels, use_tile, hook):
+    if use_tile:
+        samples = nodes.VAEEncodeTiled().encode(vae, pixels)[0]
+    else:
+        samples = nodes.VAEEncode().encode(vae, pixels)[0]
+
+    if hook is not None:
+        hook.post_encode(samples)
+
+    return samples
+
+
+class KSamplerWrapper:
+    params = None
+
+    def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise):
+        self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
+
+    def sample(self, latent_image, hook=None):
+        model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
+
+        if hook is not None:
+            model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
+                hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
+
+        return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)[0]
+
+
+class KSamplerAdvancedWrapper:
+    params = None
+
+    def __init__(self, model, cfg, sampler_name, scheduler, positive, negative):
+        self.params = model, cfg, sampler_name, scheduler, positive, negative
+
+    def sample_advanced(self, add_noise, seed, steps, latent_image, start_at_step, end_at_step, return_with_leftover_noise, hook=None):
+        model, cfg, sampler_name, scheduler, positive, negative = self.params
+
+        if hook is not None:
+            model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent = \
+                hook.pre_ksample_advanced(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
+                                          positive, negative, latent_image, start_at_step, end_at_step,
+                                          return_with_leftover_noise)
+
+        return nodes.KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
+                                               positive, negative, latent_image, start_at_step, end_at_step,
+                                               return_with_leftover_noise)[0]
+
+
+class PixelKSampleHook:
+    cur_step = 0
+    total_step = 0
+
+    def __init__(self):
+        pass
+        
+    def set_steps(self, info):
+        self.cur_step, self.total_step = info
+
+    def post_decode(self, pixels):
+        return pixels
+
+    def post_upscale(self, pixels):
+        return pixels
+
+    def post_encode(self, samples):
+        return samples
+
+    def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
+         return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
+
+
+class PixelKSampleHookCombine(PixelKSampleHook):
+    hook1 = None
+    hook2 = None
+    
+    def __init__(self, hook1, hook2):
+        super().__init__()
+        self.hook1 = hook1
+        self.hook2 = hook2
+
+    def set_steps(self, info):
+        self.hook1.set_steps(info)
+        self.hook2.set_steps(info)
+
+    def post_decode(self, pixels):
+        return self.hook2.post_decode(self.hook1.post_decode(pixels))
+
+    def post_upscale(self, pixels):
+        return self.hook2.post_upscale(self.hook1.post_upscale(pixels))
+
+    def post_encode(self, samples):
+        return self.hook2.post_encode(self.hook1.post_encode(samples))
+
+    def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent,
+                    denoise):
+        model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
+            self.hook1.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise)
+
+        return self.hook2.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise)
+
+
+class SimpleCfgScheduleHook(PixelKSampleHook):
+    target_cfg = 0
+
+    def __init__(self, target_cfg):
+        super().__init__()
+        self.target_cfg = target_cfg
+    
+    def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
+        progress = self.cur_step/self.total_step
+        gap = self.target_cfg - cfg
+        current_cfg = cfg + gap*progress
+        return model, seed, steps, current_cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
+
+
+class SimpleDenoiseScheduleHook(PixelKSampleHook):
+    target_denoise = 0
+
+    def __init__(self, target_denoise):
+        super().__init__()
+        self.target_denoise = target_denoise
+
+    def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
+        progress = self.cur_step / self.total_step
+        gap = self.target_denoise - denoise
+        current_denoise = denoise + gap * progress
+        return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, current_denoise
+
+
+def latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, use_tile=False, save_temp_prefix=None, hook=None):
+    pixels = vae_decode(vae, samples, use_tile, hook)
+
+    if save_temp_prefix is not None:
+        nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
+
+    pixels = nodes.ImageScale().upscale(pixels, scale_method, int(w), int(h), False)[0]
+
+    if hook is not None:
+        pixels = hook.post_upscale(pixels)
+
+    return vae_encode(vae, pixels, use_tile, hook)
+
+
+def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, save_temp_prefix=None, hook=None):
+    pixels = vae_decode(vae, samples, use_tile, hook)
+
+    if save_temp_prefix is not None:
+        nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
+
+    w = pixels.shape[2] * scale_factor
+    h = pixels.shape[1] * scale_factor
+    pixels = nodes.ImageScale().upscale(pixels, scale_method, int(w), int(h), False)[0]
+
+    if hook is not None:
+        pixels = hook.post_upscale(pixels)
+
+    return vae_encode(vae, pixels, use_tile, hook)
+
+
+def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, save_temp_prefix=None, hook=None):
+    pixels = vae_decode(vae, samples, use_tile, hook)
+
+    if save_temp_prefix is not None:
+        nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
+
+    w = pixels.shape[2]
+
+    # upscale by model upscaler
+    current_w = w
+    while current_w < new_w:
+        pixels = model_upscale.ImageUpscaleWithModel().upscale(upscale_model, pixels)[0]
+        current_w = pixels.shape[2]
+
+    # downscale to target scale
+    pixels = nodes.ImageScale().upscale(pixels, scale_method, int(new_w), int(new_h), False)[0]
+
+    if hook is not None:
+        pixels = hook.post_upscale(pixels)
+
+    return vae_encode(vae, pixels, use_tile, hook)
+
+
+def latent_upscale_on_pixel_space_with_model(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False, save_temp_prefix=None, hook=None):
+    pixels = vae_decode(vae, samples, use_tile, hook)
+
+    if save_temp_prefix is not None:
+        nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
+
+    w = pixels.shape[2]
+    h = pixels.shape[1]
+
+    new_w = w * scale_factor
+    new_h = h * scale_factor
+
+    # upscale by model upscaler
+    current_w = w
+    while current_w < new_w:
+        pixels = model_upscale.ImageUpscaleWithModel().upscale(upscale_model, pixels)[0]
+        current_w = pixels.shape[2]
+
+    # downscale to target scale
+    pixels = nodes.ImageScale().upscale(pixels, scale_method, int(new_w), int(new_h), False)[0]
+
+    if hook is not None:
+        pixels = hook.post_upscale(pixels)
+
+    return vae_encode(vae, pixels, use_tile, hook)
+
+
+class TwoSamplersForMaskUpscaler:
+    params = None
+    upscale_model = None
+    hook_base = None
+    hook_mask = None
+    hook_full = None
+    use_tiled_vae = False
+    is_tiled = False
+
+    def __init__(self, scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae,
+                 full_sampler_opt=None, upscale_model_opt=None, hook_base_opt=None, hook_mask_opt=None, hook_full_opt=None):
+        mask = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
+
+        self.params = scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae
+        self.upscale_model = upscale_model_opt
+        self.full_sampler = full_sampler_opt
+        self.hook_base = hook_base_opt
+        self.hook_mask = hook_mask_opt
+        self.hook_full = hook_full_opt
+        self.use_tiled_vae = use_tiled_vae
+
+    def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None):
+        scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae = self.params
+
+        self.prepare_hook(step_info)
+
+        # upscale latent
+        if self.upscale_model is None:
+            upscaled_latent = latent_upscale_on_pixel_space(samples, scale_method, upscale_factor, vae,
+                                                            use_tile=self.use_tiled_vae,
+                                                            save_temp_prefix=save_temp_prefix, hook=self.hook_base)
+        else:
+            upscaled_latent = latent_upscale_on_pixel_space_with_model(samples, scale_method, self.upscale_model, upscale_factor, vae,
+                                                                       use_tile=self.use_tiled_vae,
+                                                                       save_temp_prefix=save_temp_prefix, hook=self.hook_mask)
+
+        return self.do_samples(step_info, base_sampler, mask_sampler, sample_schedule, mask, upscaled_latent)
+
+    def prepare_hook(self, step_info):
+        if self.hook_base is not None:
+            self.hook_base.set_steps(step_info)
+        if self.hook_mask is not None:
+            self.hook_mask.set_steps(step_info)
+        if self.hook_full is not None:
+            self.hook_full.set_steps(step_info)
+
+    def upscale_shape(self, step_info, samples, w, h, save_temp_prefix=None):
+        scale_method, sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae = self.params
+
+        self.prepare_hook(step_info)
+
+        # upscale latent
+        if self.upscale_model is None:
+            upscaled_latent = latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae,
+                                                                  use_tile=self.use_tiled_vae,
+                                                                  save_temp_prefix=save_temp_prefix, hook=self.hook_base)
+        else:
+            upscaled_latent = latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, self.upscale_model, w, h, vae,
+                                                                             use_tile=self.use_tiled_vae,
+                                                                             save_temp_prefix=save_temp_prefix, hook=self.hook_mask)
+
+        return self.do_samples(step_info, base_sampler, mask_sampler, sample_schedule, mask, upscaled_latent)
+
+    def is_full_sample_time(self, step_info, sample_schedule):
+        cur_step, total_step = step_info
+
+        # make start from 1 instead of zero
+        cur_step += 1
+        total_step += 1
+
+        if sample_schedule == "none":
+            return False
+
+        elif sample_schedule == "interleave1":
+            return cur_step % 2 == 0
+
+        elif sample_schedule == "interleave2":
+            return cur_step % 3 == 0
+
+        elif sample_schedule == "interleave3":
+            return cur_step % 4 == 0
+
+        elif sample_schedule == "last1":
+            return cur_step == total_step
+
+        elif sample_schedule == "last2":
+            return cur_step >= total_step-1
+
+        elif sample_schedule == "interleave1+last1":
+            return cur_step % 2 == 0 or cur_step >= total_step-1
+
+        elif sample_schedule == "interleave2+last1":
+            return cur_step % 2 == 0 or cur_step >= total_step-1
+
+        elif sample_schedule == "interleave3+last1":
+            return cur_step % 2 == 0 or cur_step >= total_step-1
+
+    def do_samples(self, step_info, base_sampler, mask_sampler, sample_schedule, mask, upscaled_latent):
+        if self.is_full_sample_time(step_info, sample_schedule):
+            print(f"step_info={step_info} / full time")
+
+            upscaled_latent = base_sampler.sample(upscaled_latent, self.hook_base)
+            sampler = self.full_sampler if self.full_sampler is not None else base_sampler
+            return sampler.sample(upscaled_latent, self.hook_full)
+
+        else:
+            print(f"step_info={step_info} / non-full time")
+            # upscale mask
+            upscaled_mask = F.interpolate(mask, size=(upscaled_latent['samples'].shape[2], upscaled_latent['samples'].shape[3]),
+                                          mode='bilinear', align_corners=True)
+            upscaled_mask = upscaled_mask[:, :, :upscaled_latent['samples'].shape[2], :upscaled_latent['samples'].shape[3]]
+
+            # base sampler
+            upscaled_inv_mask = torch.where(upscaled_mask != 1.0, torch.tensor(1.0), torch.tensor(0.0))
+            upscaled_latent['noise_mask'] = upscaled_inv_mask
+            upscaled_latent = base_sampler.sample(upscaled_latent, self.hook_base)
+
+            # mask sampler
+            upscaled_latent['noise_mask'] = upscaled_mask
+            upscaled_latent = mask_sampler.sample(upscaled_latent, self.hook_mask)
+
+            # remove mask
+            del upscaled_latent['noise_mask']
+            return upscaled_latent
+
+
+class PixelKSampleUpscaler:
+    params = None
+    upscale_model = None
+    hook = None
+    use_tiled_vae = False
+    is_tiled = False
+
+    def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
+                 use_tiled_vae, upscale_model_opt=None, hook_opt=None):
+        self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
+        self.upscale_model = upscale_model_opt
+        self.hook = hook_opt
+        self.use_tiled_vae = use_tiled_vae
+
+    def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None):
+        scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
+
+        if self.hook is not None:
+            self.hook.set_steps(step_info)
+
+        if self.upscale_model is None:
+            upscaled_latent = latent_upscale_on_pixel_space(samples, scale_method, upscale_factor, vae,
+                                                            use_tile=self.use_tiled_vae,
+                                                            save_temp_prefix=save_temp_prefix, hook=self.hook)
+        else:
+            upscaled_latent = latent_upscale_on_pixel_space_with_model(samples, scale_method, self.upscale_model, upscale_factor, vae,
+                                                                       use_tile=self.use_tiled_vae,
+                                                                       save_temp_prefix=save_temp_prefix, hook=self.hook)
+
+        if self.hook is not None:
+            model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
+                self.hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise)
+
+        refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler,
+                                                 positive, negative, upscaled_latent, denoise)[0]
+        return refined_latent
+
+    def upscale_shape(self, step_info, samples, w, h, save_temp_prefix=None):
+        scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
+
+        if self.hook is not None:
+            self.hook.set_steps(step_info)
+
+        if self.upscale_model is None:
+            upscaled_latent = latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae,
+                                                                  use_tile=self.use_tiled_vae,
+                                                                  save_temp_prefix=save_temp_prefix, hook=self.hook)
+        else:
+            upscaled_latent = latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, self.upscale_model, w, h, vae,
+                                                                             use_tile=self.use_tiled_vae,
+                                                                             save_temp_prefix=save_temp_prefix, hook=self.hook)
+
+        if self.hook is not None:
+            model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
+                self.hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise)
+
+        refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler,
+                                                 positive, negative, upscaled_latent, denoise)[0]
+        return refined_latent
+
+
+# REQUIREMENTS: BlenderNeko/ComfyUI_TiledKSampler
+try:
+    class TiledKSamplerWrapper:
+        params = None
+
+        def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
+                     tile_width, tile_height, tiling_strategy):
+            self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, tile_width, tile_height, tiling_strategy
+
+        def sample(self, latent_image, hook=None):
+            from custom_nodes.ComfyUI_TiledKSampler.nodes import TiledKSampler
+
+            model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, tile_width, tile_height, tiling_strategy = self.params
+
+            if hook is not None:
+                model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
+                    hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
+
+            return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler,
+                                          positive, negative, latent_image, denoise)[0]
+
+    class PixelTiledKSampleUpscaler:
+        params = None
+        upscale_model = None
+        tile_params = None
+        hook = None
+        is_tiled = True
+
+        def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, 
+                     tile_width, tile_height, tiling_strategy,
+                     upscale_model_opt=None, hook_opt=None):
+            self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
+            self.tile_params = tile_width, tile_height, tiling_strategy
+            self.upscale_model = upscale_model_opt
+            self.hook = hook_opt
+
+        def tiled_ksample(self, latent):
+            from custom_nodes.ComfyUI_TiledKSampler.nodes import TiledKSampler
+
+            scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
+            tile_width, tile_height, tiling_strategy = self.tile_params
+
+            return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler,
+                                          positive, negative, latent, denoise)[0]
+
+        def upscale(self, step_info, samples, upscale_factor, save_temp_prefix=None):
+            scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
+
+            if self.hook is not None:
+                self.hook.set_steps(step_info)
+
+            if self.upscale_model is None:
+                upscaled_latent = latent_upscale_on_pixel_space(samples, scale_method, upscale_factor, vae,
+                                                                use_tile=True, save_temp_prefix=save_temp_prefix, hook=self.hook)
+            else:
+                upscaled_latent = latent_upscale_on_pixel_space_with_model(samples, scale_method, self.upscale_model, upscale_factor, vae,
+                                                                           use_tile=True, save_temp_prefix=save_temp_prefix, hook=self.hook)
+
+            refined_latent = self.tiled_ksample(upscaled_latent)
+
+            return refined_latent
+
+        def upscale_shape(self, step_info, samples, w, h, save_temp_prefix=None):
+            scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
+
+            if self.hook is not None:
+                self.hook.set_steps(step_info)
+
+            if self.upscale_model is None:
+                upscaled_latent = latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae,
+                                                                      use_tile=True, save_temp_prefix=save_temp_prefix, hook=self.hook)
+            else:
+                upscaled_latent = latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, self.upscale_model, w, h, vae,
+                                                                                 use_tile=True, save_temp_prefix=save_temp_prefix, hook=self.hook)
+
+            refined_latent = self.tiled_ksample(upscaled_latent)
+
+            return refined_latent
+except:
+    pass
+
+
+# REQUIREMENTS: biegert/ComfyUI-CLIPSeg
+try:
+    class BBoxDetectorBasedOnCLIPSeg:
+        prompt = None
+        blur = None
+        threshold = None
+        dilation_factor = None
+        aux = None
+
+        def __init__(self, prompt, blur, threshold, dilation_factor):
+            self.prompt = prompt
+            self.blur = blur
+            self.threshold = threshold
+            self.dilation_factor = dilation_factor
+
+        def detect(self, image, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size=1):
+            mask = self.detect_combined(image, bbox_threshold, bbox_dilation)
+            segs = mask_to_segs(mask, False, bbox_crop_factor, True, drop_size)
+            return segs
+
+        def detect_combined(self, image, bbox_threshold, bbox_dilation):
+            from custom_nodes.clipseg import CLIPSeg
+
+            if self.threshold is None:
+                threshold = bbox_threshold
+            else:
+                threshold = self.threshold
+
+            if self.dilation_factor is None:
+                dilation_factor = bbox_dilation
+            else:
+                dilation_factor = self.dilation_factor
+
+            prompt = self.aux if self.prompt == '' and self.aux is not None else self.prompt
+
+            mask, _, _ = CLIPSeg().segment_image(image, prompt, self.blur, threshold, dilation_factor)
+            mask = to_binary_mask(mask)
+            return mask
+
+        def setAux(self, x):
+            self.aux = x
+except:
+    pass
+
+
+def update_node_status(node, text, progress=None):
+    if PromptServer.instance.client_id is None:
+        return
+
+    PromptServer.instance.send_sync("impact/update_status", {
+        "node": node,
+        "progress": progress,
+        "text": text
+    }, PromptServer.instance.client_id)
+
+
+from comfy.cli_args import args, LatentPreviewMethod
+import folder_paths
+from latent_preview import TAESD, TAESDPreviewerImpl, Latent2RGBPreviewer
+
+try:
+    import comfy.latent_formats as latent_formats
+
+    def get_previewer(device, latent_format=latent_formats.SD15(), force=False):
+        previewer = None
+        method = args.preview_method
+        if method != LatentPreviewMethod.NoPreviews or force:
+            # TODO previewer methods
+            taesd_decoder_path = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
+
+            if method == LatentPreviewMethod.Auto:
+                method = LatentPreviewMethod.Latent2RGB
+                if taesd_decoder_path:
+                    method = LatentPreviewMethod.TAESD
+
+            if method == LatentPreviewMethod.TAESD:
+                if taesd_decoder_path:
+                    taesd = TAESD(None, taesd_decoder_path).to(device)
+                    previewer = TAESDPreviewerImpl(taesd)
+                else:
+                    print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
+
+            if previewer is None:
+                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)
+        return previewer
+
+except:
+    print(f"#########################################################################")
+    print(f"[ERROR] ComfyUI-Impact-Pack: Please update ComfyUI to the latest version.")
+    print(f"#########################################################################")
+

ComfyUI-Impact-Pack/modules/impact/detectors.py

@@ -0,0 +1,144 @@
+import impact.core as core
+from impact.config import MAX_RESOLUTION
+
+
+class SAMDetectorCombined:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "sam_model": ("SAM_MODEL", ),
+                        "segs": ("SEGS", ),
+                        "image": ("IMAGE", ),
+                        "detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area",
+                                            "mask-points", "mask-point-bbox", "none"],),
+                        "dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
+                        "threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
+                        "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "mask_hint_use_negative": (["False", "Small", "Outter"], )
+                      }
+                }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detector"
+
+    def doit(self, sam_model, segs, image, detection_hint, dilation,
+             threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
+        return (core.make_sam_mask(sam_model, segs, image, detection_hint, dilation,
+                                   threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative), )
+
+
+class SAMDetectorSegmented:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "sam_model": ("SAM_MODEL", ),
+                        "segs": ("SEGS", ),
+                        "image": ("IMAGE", ),
+                        "detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area",
+                                            "mask-points", "mask-point-bbox", "none"],),
+                        "dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
+                        "threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
+                        "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "mask_hint_use_negative": (["False", "Small", "Outter"], )
+                      }
+                }
+
+    RETURN_TYPES = ("MASK", "MASKS")
+    RETURN_NAMES = ("combined_mask", "batch_masks")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detector"
+
+    def doit(self, sam_model, segs, image, detection_hint, dilation,
+             threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
+        combined_mask, batch_masks = core.make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
+                                                                  threshold, bbox_expansion, mask_hint_threshold,
+                                                                  mask_hint_use_negative)
+        return (combined_mask, batch_masks, )
+
+
+class BboxDetectorForEach:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "bbox_detector": ("BBOX_DETECTOR", ),
+                        "image": ("IMAGE", ),
+                        "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "dilation": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
+                        "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
+                        "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
+                      }
+                }
+
+    RETURN_TYPES = ("SEGS", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detector"
+
+    def doit(self, bbox_detector, image, threshold, dilation, crop_factor, drop_size):
+        segs = bbox_detector.detect(image, threshold, dilation, crop_factor, drop_size)
+        return (segs, )
+
+
+class SegmDetectorForEach:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "segm_detector": ("SEGM_DETECTOR", ),
+                        "image": ("IMAGE", ),
+                        "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "dilation": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
+                        "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
+                        "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
+                      }
+                }
+
+    RETURN_TYPES = ("SEGS", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detector"
+
+    def doit(self, segm_detector, image, threshold, dilation, crop_factor, drop_size):
+        segs = segm_detector.detect(image, threshold, dilation, crop_factor, drop_size)
+        return (segs, )
+
+
+class SegmDetectorCombined:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "segm_detector": ("SEGM_DETECTOR", ),
+                        "image": ("IMAGE", ),
+                        "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
+                      }
+                }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detector"
+
+    def doit(self, segm_detector, image, threshold, dilation):
+        mask = segm_detector.detect_combined(image, threshold, dilation)
+        return (mask,)
+
+
+class BboxDetectorCombined(SegmDetectorCombined):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "bbox_detector": ("BBOX_DETECTOR", ),
+                        "image": ("IMAGE", ),
+                        "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "dilation": ("INT", {"default": 4, "min": 0, "max": 255, "step": 1}),
+                      }
+                }
+
+    def doit(self, bbox_detector, image, threshold, dilation):
+        mask = bbox_detector.detect_combined(image, threshold, dilation)
+        return (mask,)

ComfyUI-Impact-Pack/modules/impact/impact_server.py

@@ -0,0 +1,179 @@
+import os
+import threading
+
+from aiohttp import web
+import server
+import folder_paths
+
+import impact.core as core
+import impact.impact_pack as impact_pack
+from segment_anything import SamPredictor, sam_model_registry
+import numpy as np
+import nodes
+from PIL import Image
+import io
+import impact.wildcards as wildcards
+import comfy
+
+@server.PromptServer.instance.routes.post("/upload/temp")
+async def upload_image(request):
+    upload_dir = folder_paths.get_temp_directory()
+
+    if not os.path.exists(upload_dir):
+        os.makedirs(upload_dir)
+    
+    post = await request.post()
+    image = post.get("image")
+
+    if image and image.file:
+        filename = image.filename
+        if not filename:
+            return web.Response(status=400)
+
+        split = os.path.splitext(filename)
+        i = 1
+        while os.path.exists(os.path.join(upload_dir, filename)):
+            filename = f"{split[0]} ({i}){split[1]}"
+            i += 1
+
+        filepath = os.path.join(upload_dir, filename)
+
+        with open(filepath, "wb") as f:
+            f.write(image.file.read())
+        
+        return web.json_response({"name": filename})
+    else:
+        return web.Response(status=400)
+
+
+sam_predictor = None
+default_sam_model_name = os.path.join(impact_pack.model_path, "sams", "sam_vit_b_01ec64.pth")
+
+sam_lock = threading.Condition()
+
+last_prepare_data = None
+
+
+def async_prepare_sam(image_dir, model_name, filename):
+    with sam_lock:
+        global sam_predictor
+
+        if 'vit_h' in model_name:
+            model_kind = 'vit_h'
+        elif 'vit_l' in model_name:
+            model_kind = 'vit_l'
+        else:
+            model_kind = 'vit_b'
+
+        sam_model = sam_model_registry[model_kind](checkpoint=model_name)
+        sam_predictor = SamPredictor(sam_model)
+
+        image_path = os.path.join(image_dir, filename)
+        image = nodes.LoadImage().load_image(image_path)[0]
+        image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+
+        device = comfy.model_management.get_torch_device()
+        sam_predictor.model.to(device=device)
+        sam_predictor.set_image(image, "RGB")
+        sam_predictor.model.cpu()
+
+
+@server.PromptServer.instance.routes.post("/sam/prepare")
+async def sam_prepare(request):
+    global sam_predictor
+    global last_prepare_data
+    data = await request.json()
+
+    with sam_lock:
+        if last_prepare_data is not None and last_prepare_data == data:
+            # already loaded: skip -- prevent redundant loading
+            return web.Response(status=200)
+
+        last_prepare_data = data
+
+        model_name = os.path.join(impact_pack.model_path, "sams", data['sam_model_name'])
+
+        print(f"ComfyUI-Impact-Pack: Loading SAM model '{impact_pack.model_path}'")
+
+        filename, image_dir = folder_paths.annotated_filepath(data["filename"])
+
+        if image_dir is None:
+            typ = data['type'] if data['type'] != '' else 'output'
+            image_dir = folder_paths.get_directory_by_type(typ)
+            if data['subfolder'] is not None and data['subfolder'] != '':
+                image_dir += f"/{data['subfolder']}"
+
+        if image_dir is None:
+            return web.Response(status=400)
+
+        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_name, filename,))
+        thread.start()
+
+        print(f"ComfyUI-Impact-Pack: SAM model loaded. ")
+
+
+@server.PromptServer.instance.routes.post("/sam/release")
+async def release_sam(request):
+    global sam_predictor
+
+    with sam_lock:
+        del sam_predictor
+        sam_predictor = None
+
+    print(f"ComfyUI-Impact-Pack: unloading SAM model")
+
+
+@server.PromptServer.instance.routes.post("/sam/detect")
+async def sam_detect(request):
+    global sam_predictor
+    with sam_lock:
+        if sam_predictor is not None:
+            device = comfy.model_management.get_torch_device()
+            sam_predictor.model.to(device=device)
+            try:
+                data = await request.json()
+
+                positive_points = data['positive_points']
+                negative_points = data['negative_points']
+                threshold = data['threshold']
+
+                points = []
+                plabs = []
+
+                for p in positive_points:
+                    points.append(p)
+                    plabs.append(1)
+
+                for p in negative_points:
+                    points.append(p)
+                    plabs.append(0)
+
+                detected_masks = core.sam_predict(sam_predictor, points, plabs, None, threshold)
+                mask = core.combine_masks2(detected_masks)
+
+                if mask is None:
+                    return web.Response(status=400)
+
+                image = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
+                i = 255. * image.cpu().numpy()
+
+                img = Image.fromarray(np.clip(i[0], 0, 255).astype(np.uint8))
+
+                img_buffer = io.BytesIO()
+                img.save(img_buffer, format='png')
+
+                headers = {'Content-Type': 'image/png'}
+            finally:
+                sam_predictor.model.to(device="cpu")
+
+            return web.Response(body=img_buffer.getvalue(), headers=headers)
+
+        else:
+            return web.Response(status=400)
+
+
+@server.PromptServer.instance.routes.post("/impact/wildcards")
+async def populate_wildcards(request):
+    data = await request.json()
+    populated = wildcards.process(data['text'])
+    return web.json_response({"text": populated})

ComfyUI-Impact-Pack/modules/impact/legacy_nodes.py

@@ -0,0 +1,273 @@
+import folder_paths
+
+import impact.mmdet_nodes as mmdet_nodes
+from impact.utils import *
+from impact.core import SEG
+import impact.core as core
+import nodes
+
+class NO_BBOX_MODEL:
+    pass
+
+
+class NO_SEGM_MODEL:
+    pass
+
+
+class MMDetLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("mmdets_bbox")]
+        segms = ["segm/"+x for x in folder_paths.get_filename_list("mmdets_segm")]
+        return {"required": {"model_name": (bboxs + segms, )}}
+    RETURN_TYPES = ("BBOX_MODEL", "SEGM_MODEL")
+    FUNCTION = "load_mmdet"
+
+    CATEGORY = "ImpactPack/Legacy"
+
+    def load_mmdet(self, model_name):
+        mmdet_path = folder_paths.get_full_path("mmdets", model_name)
+        model = mmdet_nodes.load_mmdet(mmdet_path)
+
+        if model_name.startswith("bbox"):
+            return model, NO_SEGM_MODEL()
+        else:
+            return NO_BBOX_MODEL(), model
+
+
+class BboxDetectorForEach:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "bbox_model": ("BBOX_MODEL", ),
+                        "image": ("IMAGE", ),
+                        "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "dilation": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
+                        "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
+                      }
+                }
+
+    RETURN_TYPES = ("SEGS", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Legacy"
+
+    @staticmethod
+    def detect(bbox_model, image, threshold, dilation, crop_factor, drop_size=1):
+        mmdet_results = mmdet_nodes.inference_bbox(bbox_model, image, threshold)
+        segmasks = core.create_segmasks(mmdet_results)
+
+        if dilation > 0:
+            segmasks = dilate_masks(segmasks, dilation)
+
+        items = []
+        h = image.shape[1]
+        w = image.shape[2]
+        for x in segmasks:
+            item_bbox = x[0]
+            item_mask = x[1]
+
+            y1, x1, y2, x2 = item_bbox
+
+            if x2 - x1 > drop_size and y2 - y1 > drop_size:
+                crop_region = make_crop_region(w, h, item_bbox, crop_factor)
+                cropped_image = crop_image(image, crop_region)
+                cropped_mask = crop_ndarray2(item_mask, crop_region)
+                confidence = x[2]
+                # bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
+
+                item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox)
+                items.append(item)
+
+        shape = h, w
+        return shape, items
+
+    def doit(self, bbox_model, image, threshold, dilation, crop_factor):
+        return (BboxDetectorForEach.detect(bbox_model, image, threshold, dilation, crop_factor), )
+
+
+class SegmDetectorCombined:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "segm_model": ("SEGM_MODEL", ),
+                        "image": ("IMAGE", ),
+                        "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
+                      }
+                }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Legacy"
+
+    def doit(self, segm_model, image, threshold, dilation):
+        mmdet_results = mmdet_nodes.inference_segm(image, segm_model, threshold)
+        segmasks = core.create_segmasks(mmdet_results)
+        if dilation > 0:
+            segmasks = dilate_masks(segmasks, dilation)
+
+        mask = combine_masks(segmasks)
+        return (mask,)
+
+
+class BboxDetectorCombined(SegmDetectorCombined):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "bbox_model": ("BBOX_MODEL", ),
+                        "image": ("IMAGE", ),
+                        "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "dilation": ("INT", {"default": 4, "min": 0, "max": 255, "step": 1}),
+                      }
+                }
+
+    def doit(self, bbox_model, image, threshold, dilation):
+        mmdet_results = mmdet_nodes.inference_bbox(bbox_model, image, threshold)
+        segmasks = core.create_segmasks(mmdet_results)
+        if dilation > 0:
+            segmasks = dilate_masks(segmasks, dilation)
+
+        mask = combine_masks(segmasks)
+        return (mask,)
+
+
+class SegmDetectorForEach:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "segm_model": ("SEGM_MODEL", ),
+                        "image": ("IMAGE", ),
+                        "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "dilation": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
+                        "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
+                      }
+                }
+
+    RETURN_TYPES = ("SEGS", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Legacy"
+
+    def doit(self, segm_model, image, threshold, dilation, crop_factor):
+        mmdet_results = mmdet_nodes.inference_segm(image, segm_model, threshold)
+        segmasks = core.create_segmasks(mmdet_results)
+
+        if dilation > 0:
+            segmasks = dilate_masks(segmasks, dilation)
+
+        items = []
+        h = image.shape[1]
+        w = image.shape[2]
+        for x in segmasks:
+            item_bbox = x[0]
+            item_mask = x[1]
+
+            crop_region = make_crop_region(w, h, item_bbox, crop_factor)
+            cropped_image = crop_image(image, crop_region)
+            cropped_mask = crop_ndarray2(item_mask, crop_region)
+            confidence = x[2]
+
+            item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox)
+            items.append(item)
+
+        shape = h,w
+        return ((shape, items), )
+
+
+class SegsMaskCombine:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "segs": ("SEGS", ),
+                        "image": ("IMAGE", ),
+                      }
+                }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Legacy"
+
+    @staticmethod
+    def combine(segs, image):
+        h = image.shape[1]
+        w = image.shape[2]
+
+        mask = np.zeros((h, w), dtype=np.uint8)
+
+        for seg in segs[1]:
+            cropped_mask = seg.cropped_mask
+            crop_region = seg.crop_region
+            mask[crop_region[1]:crop_region[3], crop_region[0]:crop_region[2]] |= (cropped_mask * 255).astype(np.uint8)
+
+        return torch.from_numpy(mask.astype(np.float32) / 255.0)
+
+    def doit(self, segs, image):
+        return (SegsMaskCombine.combine(segs, image), )
+
+
+class MaskPainter(nodes.PreviewImage):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"images": ("IMAGE",), },
+                "hidden": {
+                    "prompt": "PROMPT",
+                    "extra_pnginfo": "EXTRA_PNGINFO",
+                },
+                "optional": {"mask_image": ("IMAGE_PATH",), },
+                "optional": {"image": (["#placeholder"], )},
+                }
+
+    RETURN_TYPES = ("MASK",)
+
+    FUNCTION = "save_painted_images"
+
+    CATEGORY = "ImpactPack/Legacy"
+
+    def save_painted_images(self, images, filename_prefix="impact-mask",
+                            prompt=None, extra_pnginfo=None, mask_image=None, image=None):
+        if image == "#placeholder" or image['image_hash'] != id(images):
+            # new input image
+            res = self.save_images(images, filename_prefix, prompt, extra_pnginfo)
+
+            item = res['ui']['images'][0]
+
+            if not item['filename'].endswith(']'):
+                filepath = f"{item['filename']} [{item['type']}]"
+            else:
+                filepath = item['filename']
+
+            _, mask = nodes.LoadImage().load_image(filepath)
+
+            res['ui']['aux'] = [id(images), res['ui']['images']]
+            res['result'] = (mask, )
+
+            return res
+
+        else:
+            # new mask
+            if '0' in image:  # fallback
+                image = image['0']
+
+            forward = {'filename': image['forward_filename'],
+                       'subfolder': image['forward_subfolder'],
+                       'type': image['forward_type'], }
+
+            res = {'ui': {'images': [forward]}}
+
+            imgpath = ""
+            if 'subfolder' in image and image['subfolder'] != "":
+                imgpath = image['subfolder'] + "/"
+
+            imgpath += f"{image['filename']}"
+
+            if 'type' in image and image['type'] != "":
+                imgpath += f" [{image['type']}]"
+
+            res['ui']['aux'] = [id(images), [forward]]
+            _, mask = nodes.LoadImage().load_image(imgpath)
+            res['result'] = (mask, )
+
+            return res

ComfyUI-Impact-Pack/modules/impact/logics.py

@@ -0,0 +1,253 @@
+import sys
+from server import PromptServer
+
+# wildcard trick is taken from pythongossss's
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+any = AnyType("*")
+
+
+class ImpactCompare:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "cmp": (['a = b', 'a <> b', 'a > b', 'a < b', 'a >= b', 'a <= b', 'tt', 'ff'],),
+                "a": (any, ),
+                "b": (any, ),
+            },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ("BOOLEAN", )
+
+    def doit(self, cmp, a, b):
+        if cmp == "a = b":
+            return (a == b, )
+        elif cmp == "a <> b":
+            return (a != b, )
+        elif cmp == "a > b":
+            return (a > b, )
+        elif cmp == "a < b":
+            return (a < b, )
+        elif cmp == "a >= b":
+            return (a >= b, )
+        elif cmp == "a <= b":
+            return (a <= b, )
+        elif cmp == 'tt':
+            return (True, )
+        else:
+            return (False, )
+
+
+class ImpactConditionalBranch:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "cond": ("BOOLEAN", {"forceInput": True}),
+                "tt_value": (any,),
+                "ff_value": (any,),
+            },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = (any, )
+
+    def doit(self, cond, tt_value, ff_value):
+        if cond:
+            return (tt_value,)
+        else:
+            return (ff_value,)
+
+
+class ImpactConditionalStopIteration:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": { "cond": ("BOOLEAN", {"forceInput": True}), },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ()
+
+    OUTPUT_NODE = True
+
+    def doit(self, cond):
+        if cond:
+            PromptServer.instance.send_sync("stop-iteration", {})
+        return {}
+
+
+class ImpactNeg:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": { "value": ("BOOLEAN", {"forceInput": True}), },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ("BOOLEAN", )
+
+    def doit(self, value):
+        return (not value, )
+
+
+class ImpactInt:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
+            },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ("INT", )
+
+    def doit(self, value):
+        return (value, )
+
+
+class ImpactFloat:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {"default": 1.0, "min": -3.402823466e+38, "max": 3.402823466e+38}),
+            },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ("FLOAT", )
+
+    def doit(self, value):
+        return (value, )
+
+
+class ImpactValueSender:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "value": (any, ),
+                    "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
+                    },
+                }
+
+    OUTPUT_NODE = True
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ()
+
+    def doit(self, value, link_id=0):
+        PromptServer.instance.send_sync("value-send", {"link_id": link_id, "value": value})
+        return {}
+
+
+class ImpactIntConstSender:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "signal": (any, ),
+                    "value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
+                    "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
+                    },
+                }
+
+    OUTPUT_NODE = True
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ()
+
+    def doit(self, signal, value, link_id=0):
+        PromptServer.instance.send_sync("value-send", {"link_id": link_id, "value": value})
+        return {}
+
+
+class ImpactValueReceiver:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "typ": (["STRING", "INT", "FLOAT", "BOOLEAN"], ),
+                    "value": ("STRING", {"default": ""}),
+                    "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
+                    },
+                }
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = (any, )
+
+    def doit(self, typ, value, link_id=0):
+        if typ == "INT":
+            return (int(value), )
+        elif typ == "FLOAT":
+            return (float(value), )
+        elif typ == "BOOLEAN":
+            return (bool(value), )
+        else:
+            return (value, )
+
+
+class ImpactImageInfo:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "value": ("IMAGE", ),
+                    },
+                }
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic/_for_test"
+
+    RETURN_TYPES = ("INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("batch", "height", "width", "channel")
+
+    def doit(self, value):
+        return (value.shape[0], value.shape[1], value.shape[2], value.shape[3])
+
+
+class ImpactMinMax:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "mode": ("BOOLEAN", {"default": True, "label_on": "max", "label_off": "min"}),
+                    "a": (any,),
+                    "b": (any,),
+                    },
+                }
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic/_for_test"
+
+    RETURN_TYPES = ("INT", )
+
+    def doit(self, mode, a, b):
+        if mode:
+            return (max(a, b), )
+        else:
+            return (min(a, b),)

ComfyUI-Impact-Pack/modules/impact/mmdet_nodes.py

@@ -0,0 +1,213 @@
+import folder_paths
+from impact.core import *
+
+import mmcv
+from mmdet.apis import (inference_detector, init_detector)
+from mmdet.evaluation import get_classes
+
+
+def load_mmdet(model_path):
+    model_config = os.path.splitext(model_path)[0] + ".py"
+    model = init_detector(model_config, model_path, device="cpu")
+    return model
+
+
+def inference_segm_old(model, image, conf_threshold):
+    image = image.numpy()[0] * 255
+    mmdet_results = inference_detector(model, image)
+
+    bbox_results, segm_results = mmdet_results
+    label = "A"
+
+    classes = get_classes("coco")
+    labels = [
+        np.full(bbox.shape[0], i, dtype=np.int32)
+        for i, bbox in enumerate(bbox_results)
+    ]
+    n, m = bbox_results[0].shape
+    if n == 0:
+        return [[], [], []]
+    labels = np.concatenate(labels)
+    bboxes = np.vstack(bbox_results)
+    segms = mmcv.concat_list(segm_results)
+    filter_idxs = np.where(bboxes[:, -1] > conf_threshold)[0]
+    results = [[], [], []]
+    for i in filter_idxs:
+        results[0].append(label + "-" + classes[labels[i]])
+        results[1].append(bboxes[i])
+        results[2].append(segms[i])
+
+    return results
+
+
+def inference_segm(image, modelname, conf_thres, lab="A"):
+    image = image.numpy()[0] * 255
+    mmdet_results = inference_detector(modelname, image).pred_instances
+    bboxes = mmdet_results.bboxes.numpy()
+    segms = mmdet_results.masks.numpy()
+    scores = mmdet_results.scores.numpy()
+
+    classes = get_classes("coco")
+
+    n, m = bboxes.shape
+    if n == 0:
+        return [[], [], [], []]
+    labels = mmdet_results.labels
+    filter_inds = np.where(mmdet_results.scores > conf_thres)[0]
+    results = [[], [], [], []]
+    for i in filter_inds:
+        results[0].append(lab + "-" + classes[labels[i]])
+        results[1].append(bboxes[i])
+        results[2].append(segms[i])
+        results[3].append(scores[i])
+
+    return results
+
+
+def inference_bbox(modelname, image, conf_threshold):
+    image = image.numpy()[0] * 255
+    label = "A"
+    output = inference_detector(modelname, image).pred_instances
+    cv2_image = np.array(image)
+    cv2_image = cv2_image[:, :, ::-1].copy()
+    cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY)
+
+    segms = []
+    for x0, y0, x1, y1 in output.bboxes:
+        cv2_mask = np.zeros(cv2_gray.shape, np.uint8)
+        cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1)
+        cv2_mask_bool = cv2_mask.astype(bool)
+        segms.append(cv2_mask_bool)
+
+    n, m = output.bboxes.shape
+    if n == 0:
+        return [[], [], [], []]
+
+    bboxes = output.bboxes.numpy()
+    scores = output.scores.numpy()
+    filter_idxs = np.where(scores > conf_threshold)[0]
+    results = [[], [], [], []]
+    for i in filter_idxs:
+        results[0].append(label)
+        results[1].append(bboxes[i])
+        results[2].append(segms[i])
+        results[3].append(scores[i])
+
+    return results
+
+
+class BBoxDetector:
+    bbox_model = None
+
+    def __init__(self, bbox_model):
+        self.bbox_model = bbox_model
+
+    def detect(self, image, threshold, dilation, crop_factor, drop_size=1):
+        drop_size = max(drop_size, 1)
+        mmdet_results = inference_bbox(self.bbox_model, image, threshold)
+        segmasks = create_segmasks(mmdet_results)
+
+        if dilation > 0:
+            segmasks = dilate_masks(segmasks, dilation)
+
+        items = []
+        h = image.shape[1]
+        w = image.shape[2]
+
+        for x in segmasks:
+            item_bbox = x[0]
+            item_mask = x[1]
+
+            y1, x1, y2, x2 = item_bbox
+
+            if x2 - x1 > drop_size and y2 - y1 > drop_size:  # minimum dimension must be (2,2) to avoid squeeze issue
+                crop_region = make_crop_region(w, h, item_bbox, crop_factor)
+                cropped_image = crop_image(image, crop_region)
+                cropped_mask = crop_ndarray2(item_mask, crop_region)
+                confidence = x[2]
+                # bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
+
+                item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox)
+
+                items.append(item)
+
+        shape = image.shape[1], image.shape[2]
+        return shape, items
+
+    def detect_combined(self, image, threshold, dilation):
+        mmdet_results = inference_bbox(self.bbox_model, image, threshold)
+        segmasks = create_segmasks(mmdet_results)
+        if dilation > 0:
+            segmasks = dilate_masks(segmasks, dilation)
+
+        return combine_masks(segmasks)
+
+    def setAux(self, x):
+        pass
+
+
+class SegmDetector(BBoxDetector):
+    segm_model = None
+
+    def __init__(self, segm_model):
+        self.segm_model = segm_model
+
+    def detect(self, image, threshold, dilation, crop_factor, drop_size=1):
+        drop_size = max(drop_size, 1)
+        mmdet_results = inference_segm(image, self.segm_model, threshold)
+        segmasks = create_segmasks(mmdet_results)
+
+        if dilation > 0:
+            segmasks = dilate_masks(segmasks, dilation)
+
+        items = []
+        h = image.shape[1]
+        w = image.shape[2]
+        for x in segmasks:
+            item_bbox = x[0]
+            item_mask = x[1]
+
+            y1, x1, y2, x2 = item_bbox
+
+            if x2 - x1 > drop_size and y2 - y1 > drop_size:  # minimum dimension must be (2,2) to avoid squeeze issue
+                crop_region = make_crop_region(w, h, item_bbox, crop_factor)
+                cropped_image = crop_image(image, crop_region)
+                cropped_mask = crop_ndarray2(item_mask, crop_region)
+                confidence = x[2]
+
+                item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox)
+                items.append(item)
+
+        return image.shape, items
+
+    def detect_combined(self, image, threshold, dilation):
+        mmdet_results = inference_bbox(self.bbox_model, image, threshold)
+        segmasks = create_segmasks(mmdet_results)
+        if dilation > 0:
+            segmasks = dilate_masks(segmasks, dilation)
+
+        return combine_masks(segmasks)
+
+    def setAux(self, x):
+        pass
+
+
+class MMDetDetectorProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("mmdets_bbox")]
+        segms = ["segm/"+x for x in folder_paths.get_filename_list("mmdets_segm")]
+        return {"required": {"model_name": (bboxs + segms, )}}
+    RETURN_TYPES = ("BBOX_DETECTOR", "SEGM_DETECTOR")
+    FUNCTION = "load_mmdet"
+
+    CATEGORY = "ImpactPack"
+
+    def load_mmdet(self, model_name):
+        mmdet_path = folder_paths.get_full_path("mmdets", model_name)
+        model = load_mmdet(mmdet_path)
+
+        if model_name.startswith("bbox"):
+            return BBoxDetector(model), NO_SEGM_DETECTOR()
+        else:
+            return NO_BBOX_DETECTOR(), model

ComfyUI-Impact-Pack/modules/impact/onnx.py

@@ -0,0 +1,38 @@
+import impact.additional_dependencies
+from impact.utils import *
+
+impact.additional_dependencies.ensure_onnx_package()
+
+try:
+    import onnxruntime
+
+    def onnx_inference(image, onnx_model):
+        # prepare image
+        pil = tensor2pil(image)
+        image = np.ascontiguousarray(pil)
+        image = image[:, :, ::-1]  # to BGR image
+        image = image.astype(np.float32)
+        image -= [103.939, 116.779, 123.68]  # 'caffe' mode image preprocessing
+
+        # do detection
+        onnx_model = onnxruntime.InferenceSession(onnx_model, providers=["CPUExecutionProvider"])
+        outputs = onnx_model.run(
+            [s_i.name for s_i in onnx_model.get_outputs()],
+            {onnx_model.get_inputs()[0].name: np.expand_dims(image, axis=0)},
+        )
+
+        labels = [op for op in outputs if op.dtype == "int32"][0]
+        scores = [op for op in outputs if isinstance(op[0][0], np.float32)][0]
+        boxes = [op for op in outputs if isinstance(op[0][0], np.ndarray)][0]
+
+        # filter-out useless item
+        idx = np.where(labels[0] == -1)[0][0]
+
+        labels = labels[0][:idx]
+        scores = scores[0][:idx]
+        boxes = boxes[0][:idx].astype(np.uint32)
+
+        return labels, scores, boxes
+except Exception as e:
+    print("[ERROR] ComfyUI-Impact-Pack: 'onnxruntime' package doesn't support 'python 3.11', yet.")
+    print(f"\t{e}")

ComfyUI-Impact-Pack/modules/impact/pipe.py

@@ -0,0 +1,260 @@
+class ToDetailerPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                     "model": ("MODEL",),
+                     "clip": ("CLIP",),
+                     "vae": ("VAE",),
+                     "positive": ("CONDITIONING",),
+                     "negative": ("CONDITIONING",),
+                     "bbox_detector": ("BBOX_DETECTOR", ),
+                     "wildcard": ("STRING", {"multiline": True}),
+                     },
+                "optional": {
+                    "sam_model_opt": ("SAM_MODEL", ),
+                    "segm_detector_opt": ("SEGM_DETECTOR", ),
+                }}
+
+    RETURN_TYPES = ("DETAILER_PIPE", )
+    RETURN_NAMES = ("detailer_pipe", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, model, clip, vae, positive, negative, bbox_detector, wildcard, sam_model_opt=None, segm_detector_opt=None):
+        pipe = (model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt)
+        return (pipe, )
+
+
+class FromDetailerPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }, }
+
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING", "BBOX_DETECTOR", "SAM_MODEL", "SEGM_DETECTOR")
+    RETURN_NAMES = ("model", "clip", "vae", "positive", "negative", "bbox_detector", "sam_model_opt", "segm_detector_opt")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, detailer_pipe):
+        model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt = detailer_pipe
+        return model, clip, vae, positive, negative, bbox_detector, sam_model_opt, segm_detector_opt
+
+
+class FromDetailerPipe_v2:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }, }
+
+    RETURN_TYPES = ("DETAILER_PIPE", "MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING", "BBOX_DETECTOR", "SAM_MODEL", "SEGM_DETECTOR")
+    RETURN_NAMES = ("detailer_pipe", "model", "clip", "vae", "positive", "negative", "bbox_detector", "sam_model_opt", "segm_detector_opt")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, detailer_pipe):
+        model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt = detailer_pipe
+        return detailer_pipe, model, clip, vae, positive, negative, bbox_detector, sam_model_opt, segm_detector_opt
+
+
+class ToBasicPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                     "model": ("MODEL",),
+                     "clip": ("CLIP",),
+                     "vae": ("VAE",),
+                     "positive": ("CONDITIONING",),
+                     "negative": ("CONDITIONING",),
+                     },
+                }
+
+    RETURN_TYPES = ("BASIC_PIPE", )
+    RETURN_NAMES = ("basic_pipe", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, model, clip, vae, positive, negative):
+        pipe = (model, clip, vae, positive, negative)
+        return (pipe, )
+
+
+class FromBasicPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"basic_pipe": ("BASIC_PIPE",), }, }
+
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("model", "clip", "vae", "positive", "negative")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, basic_pipe):
+        model, clip, vae, positive, negative = basic_pipe
+        return model, clip, vae, positive, negative
+
+
+class FromBasicPipe_v2:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"basic_pipe": ("BASIC_PIPE",), }, }
+
+    RETURN_TYPES = ("BASIC_PIPE", "MODEL", "CLIP", "VAE", "CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("basic_pipe", "model", "clip", "vae", "positive", "negative")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, basic_pipe):
+        model, clip, vae, positive, negative = basic_pipe
+        return basic_pipe, model, clip, vae, positive, negative
+
+
+class BasicPipeToDetailerPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"basic_pipe": ("BASIC_PIPE",),
+                             "bbox_detector": ("BBOX_DETECTOR", ),
+                             "wildcard": ("STRING", {"multiline": True}),
+                             },
+                "optional": {
+                    "sam_model_opt": ("SAM_MODEL", ),
+                    "segm_detector_opt": ("SEGM_DETECTOR",),
+                    },
+                }
+
+    RETURN_TYPES = ("DETAILER_PIPE", )
+    RETURN_NAMES = ("detailer_pipe", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, basic_pipe, bbox_detector, wildcard, sam_model_opt=None, segm_detector_opt=None):
+        model, clip, vae, positive, negative = basic_pipe
+        pipe = model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt
+        return (pipe, )
+
+
+class DetailerPipeToBasicPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"detailer_pipe": ("DETAILER_PIPE",), }}
+
+    RETURN_TYPES = ("BASIC_PIPE", )
+    RETURN_NAMES = ("basic_pipe", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, detailer_pipe):
+        model, clip, vae, positive, negative, _, _, _, _ = detailer_pipe
+        pipe = model, clip, vae, positive, negative
+        return (pipe, )
+
+
+class EditBasicPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {"basic_pipe": ("BASIC_PIPE",), },
+                "optional": {
+                     "model": ("MODEL",),
+                     "clip": ("CLIP",),
+                     "vae": ("VAE",),
+                     "positive": ("CONDITIONING",),
+                     "negative": ("CONDITIONING",),
+                     },
+                }
+
+    RETURN_TYPES = ("BASIC_PIPE", )
+    RETURN_NAMES = ("basic_pipe", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, basic_pipe, model=None, clip=None, vae=None, positive=None, negative=None):
+        res_model, res_clip, res_vae, res_positive, res_negative = basic_pipe
+
+        if model is not None:
+            res_model = model
+
+        if clip is not None:
+            res_clip = clip
+
+        if vae is not None:
+            res_vae = vae
+
+        if positive is not None:
+            res_positive = positive
+
+        if negative is not None:
+            res_negative = negative
+
+        pipe = res_model, res_clip, res_vae, res_positive, res_negative
+
+        return (pipe, )
+
+
+class EditDetailerPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "detailer_pipe": ("DETAILER_PIPE",),
+                "wildcard": ("STRING", {"multiline": True}),
+            },
+            "optional": {
+                "model": ("MODEL",),
+                "clip": ("CLIP",),
+                "vae": ("VAE",),
+                "positive": ("CONDITIONING",),
+                "negative": ("CONDITIONING",),
+                "bbox_detector": ("BBOX_DETECTOR",),
+                "sam_model": ("SAM_MODEL",),
+                "segm_detector_opt": ("SEGM_DETECTOR",),
+            },
+        }
+
+    RETURN_TYPES = ("DETAILER_PIPE",)
+    RETURN_NAMES = ("detailer_pipe",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, detailer_pipe, wildcard, model=None, clip=None, vae=None, positive=None, negative=None,
+             bbox_detector=None, sam_model=None, segm_detector=None):
+        res_model, res_clip, res_vae, res_positive, res_negative, res_wildcard, res_bbox_detector, res_segm_detector, res_sam_model = detailer_pipe
+
+        if model is not None:
+            res_model = model
+
+        if clip is not None:
+            res_clip = clip
+
+        if vae is not None:
+            res_vae = vae
+
+        if positive is not None:
+            res_positive = positive
+
+        if negative is not None:
+            res_negative = negative
+
+        if bbox_detector is not None:
+            res_bbox_detector = bbox_detector
+
+        if segm_detector is not None:
+            res_segm_detector = segm_detector
+
+        if wildcard != "":
+            res_wildcard = wildcard
+
+        if sam_model is not None:
+            res_sam_model = sam_model
+
+        pipe = res_model, res_clip, res_vae, res_positive, res_negative, res_wildcard, res_bbox_detector, res_segm_detector, res_sam_model
+
+        return (pipe, )

ComfyUI-Impact-Pack/modules/impact/utils.py

@@ -0,0 +1,240 @@
+import torch
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw, ImageFilter
+
+LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+
+
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+
+def center_of_bbox(bbox):
+    w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
+    return bbox[0] + w/2, bbox[1] + h/2
+
+
+def combine_masks(masks):
+    if len(masks) == 0:
+        return None
+    else:
+        initial_cv2_mask = np.array(masks[0][1])
+        combined_cv2_mask = initial_cv2_mask
+
+        for i in range(1, len(masks)):
+            cv2_mask = np.array(masks[i][1])
+
+            if combined_cv2_mask.shape == cv2_mask.shape:
+                combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask)
+            else:
+                # do nothing - incompatible mask
+                pass
+
+        mask = torch.from_numpy(combined_cv2_mask)
+        return mask
+
+
+def combine_masks2(masks):
+    if len(masks) == 0:
+        return None
+    else:
+        initial_cv2_mask = np.array(masks[0]).astype(np.uint8)
+        combined_cv2_mask = initial_cv2_mask
+
+        for i in range(1, len(masks)):
+            cv2_mask = np.array(masks[i]).astype(np.uint8)
+
+            if combined_cv2_mask.shape == cv2_mask.shape:
+                combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask)
+            else:
+                # do nothing - incompatible mask
+                pass
+
+        mask = torch.from_numpy(combined_cv2_mask)
+        return mask
+
+
+def bitwise_and_masks(mask1, mask2):
+    mask1 = mask1.cpu()
+    mask2 = mask2.cpu()
+    cv2_mask1 = np.array(mask1)
+    cv2_mask2 = np.array(mask2)
+
+    if cv2_mask1.shape == cv2_mask2.shape:
+        cv2_mask = cv2.bitwise_and(cv2_mask1, cv2_mask2)
+        return torch.from_numpy(cv2_mask)
+    else:
+        # do nothing - incompatible mask shape: mostly empty mask
+        return mask1
+
+
+def to_binary_mask(mask, threshold=0):
+    mask = mask.clone().cpu()
+    mask[mask > threshold] = 1.
+    mask[mask <= threshold] = 0.
+    return mask
+
+
+def dilate_mask(mask, dilation_factor, iter=1):
+    if dilation_factor == 0:
+        return mask
+
+    kernel = np.ones((dilation_factor,dilation_factor), np.uint8)
+    return cv2.dilate(mask, kernel, iter)
+
+
+def dilate_masks(segmasks, dilation_factor, iter=1):
+    if dilation_factor == 0:
+        return segmasks
+
+    dilated_masks = []
+    kernel = np.ones((dilation_factor,dilation_factor), np.uint8)
+    for i in range(len(segmasks)):
+        cv2_mask = segmasks[i][1]
+        dilated_mask = cv2.dilate(cv2_mask, kernel, iter)
+        item = (segmasks[i][0], dilated_mask, segmasks[i][2])
+        dilated_masks.append(item)
+    return dilated_masks
+
+
+def feather_mask(mask, thickness):
+    pil_mask = Image.fromarray(np.uint8(mask * 255))
+
+    # Create a feathered mask by applying a Gaussian blur to the mask
+    blurred_mask = pil_mask.filter(ImageFilter.GaussianBlur(thickness))
+    feathered_mask = Image.new("L", pil_mask.size, 0)
+    feathered_mask.paste(blurred_mask, (0, 0), blurred_mask)
+    return feathered_mask
+
+
+def subtract_masks(mask1, mask2):
+    mask1 = mask1.cpu()
+    mask2 = mask2.cpu()
+    cv2_mask1 = np.array(mask1) * 255
+    cv2_mask2 = np.array(mask2) * 255
+
+    if cv2_mask1.shape == cv2_mask2.shape:
+        cv2_mask = cv2.subtract(cv2_mask1, cv2_mask2)
+        return torch.from_numpy(cv2_mask) / 255.0
+    else:
+        # do nothing - incompatible mask shape: mostly empty mask
+        return mask1
+
+
+def add_masks(mask1, mask2):
+    mask1 = mask1.cpu()
+    mask2 = mask2.cpu()
+    cv2_mask1 = np.array(mask1) * 255
+    cv2_mask2 = np.array(mask2) * 255
+
+    if cv2_mask1.shape == cv2_mask2.shape:
+        cv2_mask = cv2.add(cv2_mask1, cv2_mask2)
+        return torch.from_numpy(cv2_mask) / 255.0
+    else:
+        # do nothing - incompatible mask shape: mostly empty mask
+        return mask1
+
+
+def normalize_region(limit, startp, size):
+    if startp < 0:
+        new_endp = min(limit, size)
+        new_startp = 0
+    elif startp + size > limit:
+        new_startp = max(0, limit - size)
+        new_endp = limit
+    else:
+        new_startp = startp
+        new_endp = min(limit, startp+size)
+
+    return int(new_startp), int(new_endp)
+
+
+def make_crop_region(w, h, bbox, crop_factor):
+    x1 = bbox[0]
+    y1 = bbox[1]
+    x2 = bbox[2]
+    y2 = bbox[3]
+
+    bbox_w = x2 - x1
+    bbox_h = y2 - y1
+
+    crop_w = bbox_w * crop_factor
+    crop_h = bbox_h * crop_factor
+
+    kernel_x = x1 + bbox_w / 2
+    kernel_y = y1 + bbox_h / 2
+
+    new_x1 = int(kernel_x - crop_w / 2)
+    new_y1 = int(kernel_y - crop_h / 2)
+
+    # make sure position in (w,h)
+    new_x1, new_x2 = normalize_region(w, new_x1, crop_w)
+    new_y1, new_y2 = normalize_region(h, new_y1, crop_h)
+
+    return [new_x1, new_y1, new_x2, new_y2]
+
+
+def crop_ndarray4(npimg, crop_region):
+    x1 = crop_region[0]
+    y1 = crop_region[1]
+    x2 = crop_region[2]
+    y2 = crop_region[3]
+
+    cropped = npimg[:, y1:y2, x1:x2, :]
+
+    return cropped
+
+
+def crop_ndarray2(npimg, crop_region):
+    x1 = crop_region[0]
+    y1 = crop_region[1]
+    x2 = crop_region[2]
+    y2 = crop_region[3]
+
+    cropped = npimg[y1:y2, x1:x2]
+
+    return cropped
+
+
+def crop_image(image, crop_region):
+    return crop_ndarray4(np.array(image), crop_region)
+
+
+def to_latent_image(pixels, vae):
+    x = pixels.shape[1]
+    y = pixels.shape[2]
+    if pixels.shape[1] != x or pixels.shape[2] != y:
+        pixels = pixels[:, :x, :y, :]
+    t = vae.encode(pixels[:, :, :, :3])
+    return {"samples": t}
+
+
+def scale_tensor(w, h, image):
+    image = tensor2pil(image)
+    scaled_image = image.resize((w, h), resample=LANCZOS)
+    return pil2tensor(scaled_image)
+
+
+def scale_tensor_and_to_pil(w, h, image):
+    image = tensor2pil(image)
+    return image.resize((w, h), resample=LANCZOS)
+
+
+def empty_pil_tensor(w=64, h=64):
+    image = Image.new("RGB", (w, h))
+    draw = ImageDraw.Draw(image)
+    draw.rectangle((0, 0, w-1, h-1), fill=(0, 0, 0))
+    return pil2tensor(image)
+
+
+class NonListIterable:
+    def __init__(self, data):
+        self.data = data
+
+    def __getitem__(self, index):
+        return self.data[index]

ComfyUI-Impact-Pack/modules/impact/wildcards.py

@@ -0,0 +1,140 @@
+import re
+import random
+import os
+import nodes
+import folder_paths
+
+wildcard_dict = {}
+
+
+def read_wildcard_dict(wildcard_path):
+    global wildcard_dict
+    for root, directories, files in os.walk(wildcard_path):
+        for file in files:
+            if file.endswith('.txt'):
+                file_path = os.path.join(root, file)
+                rel_path = os.path.relpath(file_path, wildcard_path)
+                key = os.path.splitext(rel_path)[0].replace('\\', '/')
+
+                with open(file_path, 'r', encoding="UTF-8") as f:
+                    lines = f.read().splitlines()
+                    wildcard_dict[key] = lines
+
+    return wildcard_dict
+
+
+def process(text):
+    def replace_options(string):
+        replacements_found = False
+
+        def replace_option(match):
+            nonlocal replacements_found
+            options = match.group(1).split('|')
+            replacement = random.choice(options)
+            replacements_found = True
+            return replacement
+
+        pattern = r'{([^{}]*?)}'
+        replaced_string = re.sub(pattern, replace_option, string)
+
+        pattern = r'\[([^[\]]*?)\]'
+        replaced_string = re.sub(pattern, replace_option, replaced_string)
+
+        return replaced_string, replacements_found
+
+    def replace_wildcard(string):
+        global wildcard_dict
+        pattern = r"__([\w.-/]+)__"
+        matches = re.findall(pattern, string)
+
+        replacements_found = False
+
+        for match in matches:
+            if match in wildcard_dict:
+                replacement = random.choice(wildcard_dict[match])
+                replacements_found = True
+                string = string.replace(f"__{match}__", replacement, 1)
+
+        return string, replacements_found
+
+    replace_depth = 100
+    stop_unwrap = False
+    while not stop_unwrap and replace_depth > 1:
+        replace_depth -= 1  # prevent infinite loop
+
+        # pass1: replace options
+        pass1, is_replaced1 = replace_options(text)
+
+        while is_replaced1:
+            pass1, is_replaced1 = replace_options(pass1)
+
+        # pass2: replace wildcards
+        text, is_replaced2 = replace_wildcard(pass1)
+        stop_unwrap = not is_replaced1 and not is_replaced2
+
+    return text
+
+
+def safe_float(x):
+    try:
+        return float(x)
+    except:
+        return 1.0
+
+
+def extract_lora_values(string):
+    pattern = r'<lora:([^>]+)>'
+    matches = re.findall(pattern, string)
+    items = [match.strip(':') for match in matches]
+
+    result = {}
+    for item in items:
+        item = item.split(':')
+
+        lora = None
+        a = 1.0
+        b = 1.0
+        if len(item) == 1:
+            lora = item[0]
+        elif len(item) == 2:
+            lora = item[0]
+            a = safe_float(item[1])
+        elif len(item) >= 3:
+            lora = item[0]
+            if item[1] != '':
+                a = safe_float(item[1])
+            b = safe_float(item[2])
+
+        if lora is not None:
+            result[lora] = a, b
+
+    return result
+
+
+def remove_lora_tags(string):
+    pattern = r'<lora:[^>]+>'
+    result = re.sub(pattern, '', string)
+
+    return result
+
+
+def process_with_loras(wildcard_opt, model, clip):
+    pass1 = process(wildcard_opt)
+    loras = extract_lora_values(pass1)
+    pass2 = remove_lora_tags(pass1)
+
+    for lora_name, (model_weight, clip_weight) in loras.items():
+        if (lora_name.split('.')[-1]) not in folder_paths.supported_pt_extensions:
+            lora_name = lora_name+".safetensors"
+
+        path = folder_paths.get_full_path("loras", lora_name)
+
+        if path is not None:
+            print(f"LOAD LORA: {lora_name}: {model_weight}, {clip_weight}")
+            model, clip = nodes.LoraLoader().load_lora(model, clip, lora_name, model_weight, clip_weight)
+        else:
+            print(f"LORA NOT FOUND: {lora_name}")
+
+    print(f"CLIP: {pass2}")
+    return model, nodes.CLIPTextEncode().encode(clip, pass2)[0]
+

ComfyUI-Impact-Pack/notebook/comfyui_colab_impact_pack.ipynb

@@ -0,0 +1,172 @@
+{
+  "cells": [
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aaaaaaaaaa"
+      },
+      "source": [
+        "Git clone the repo and install the requirements. (ignore the pip errors about protobuf)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bbbbbbbbbb"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Environment Setup\n",
+        "\n",
+        "from pathlib import Path\n",
+        "\n",
+        "OPTIONS = {}\n",
+        "\n",
+        "WORKSPACE = 'ComfyUI'\n",
+        "USE_GOOGLE_DRIVE = True  #@param {type:\"boolean\"}\n",
+        "UPDATE_COMFY_UI = True  #@param {type:\"boolean\"}\n",
+        "\n",
+        "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n",
+        "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n",
+        "\n",
+        "if OPTIONS['USE_GOOGLE_DRIVE']:\n",
+        "    !echo \"Mounting Google Drive...\"\n",
+        "    %cd /\n",
+        "    \n",
+        "    from google.colab import drive\n",
+        "    drive.mount('/content/drive')\n",
+        "\n",
+        "    WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n",
+        "    \n",
+        "    %cd /content/drive/MyDrive\n",
+        "\n",
+        "![ ! -d $WORKSPACE ] && echo \"-= Initial setup ComfyUI (Original)=-\" && git clone https://github.com/comfyanonymous/ComfyUI\n",
+        "%cd $WORKSPACE\n",
+        "\n",
+        "if OPTIONS['UPDATE_COMFY_UI']:\n",
+        "  !echo \"-= Updating ComfyUI =-\"\n",
+        "  !git pull\n",
+        "  !rm \"/content/drive/MyDrive/ComfyUI/custom_nodes/comfyui-impact-pack.py\"\n",
+        "\n",
+        "%cd custom_nodes\n",
+        "!git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack\n",
+        "%cd $WORKSPACE\n",
+        "\n",
+        "!echo -= Install dependencies =-\n",
+        "!pip -q install xformers -r requirements.txt\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kkkkkkkkkkkkkk"
+      },
+      "source": [
+        "### Run ComfyUI with localtunnel (Recommended Way)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jjjjjjjjjjjjj",
+        "outputId": "83be9411-d939-4813-e6c1-80e75bf8e80d"
+      },
+      "outputs": [],
+      "source": [
+        "!npm install -g localtunnel\n",
+        "\n",
+        "import subprocess\n",
+        "import threading\n",
+        "import time\n",
+        "import socket\n",
+        "def iframe_thread(port):\n",
+        "  while True:\n",
+        "      time.sleep(0.5)\n",
+        "      sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
+        "      result = sock.connect_ex(('127.0.0.1', port))\n",
+        "      if result == 0:\n",
+        "        break\n",
+        "      sock.close()\n",
+        "  print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\")\n",
+        "  p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n",
+        "  for line in p.stdout:\n",
+        "    print(line.decode(), end='')\n",
+        "\n",
+        "\n",
+        "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n",
+        "\n",
+        "!python main.py --dont-print-server"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gggggggggg"
+      },
+      "source": [
+        "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n",
+        "\n",
+        "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n",
+        "\n",
+        "If you want to open it in another window use the link.\n",
+        "\n",
+        "Note that some UI features like live image previews won't work because the colab iframe blocks websockets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hhhhhhhhhh"
+      },
+      "outputs": [],
+      "source": [
+        "import threading\n",
+        "import time\n",
+        "import socket\n",
+        "def iframe_thread(port):\n",
+        "  while True:\n",
+        "      time.sleep(0.5)\n",
+        "      sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
+        "      result = sock.connect_ex(('127.0.0.1', port))\n",
+        "      if result == 0:\n",
+        "        break\n",
+        "      sock.close()\n",
+        "  from google.colab import output\n",
+        "  output.serve_kernel_port_as_iframe(port, height=1024)\n",
+        "  print(\"to open it in a window you can open this link here:\")\n",
+        "  output.serve_kernel_port_as_window(port)\n",
+        "\n",
+        "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n",
+        "\n",
+        "!python main.py --dont-print-server"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

ComfyUI-Impact-Pack/requirements.txt

@@ -0,0 +1,3 @@
+segment-anything
+scikit-image
+piexif

ComfyUI-Impact-Pack/subpack/.gitignore

@@ -0,0 +1,5 @@
+__pycache__
+*.ini
+wildcards/**
+.vscode/
+.idea/

ComfyUI-Impact-Pack/subpack/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
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+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

ComfyUI-Impact-Pack/subpack/README.md

@@ -0,0 +1,18 @@
+# ComfyUI-Impact-Subpack
+This extension serves as a complement to the Impact Pack, offering features that are not deemed suitable for inclusion by default in the ComfyUI Impact Pack
+
+The nodes in this repository cannot be used standalone and depend on [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack).
+
+## Nodes
+* UltralyticsDetectorProvider - This node provides an object detection detector based on Ultralystics.
+    * By using this Detector Provider, you can replace the existing mmdet-based detector.
+
+
+## Credits
+
+ComfyUI/[ComfyUI](https://github.com/comfyanonymous/ComfyUI) - A powerful and modular stable diffusion GUI.
+
+Bing-su/[adetailer](https://github.com/Bing-su/adetailer/) - This repo sitoryprovides an object detection model and features based on Ultralystics.
+
+huggingface/Bingsu/[adetailer](https://huggingface.co/Bingsu/adetailer/tree/main) - This repository offers various models based on Ultralystics.
+* You can download other models supported by the UltralyticsDetectorProvider from here.

ComfyUI-Impact-Pack/subpack/impact/subcore.py

@@ -0,0 +1,182 @@
+from pathlib import Path
+from PIL import Image
+
+import impact.core as core
+import cv2
+import numpy as np
+from torchvision.transforms.functional import to_pil_image
+import torch
+
+from ultralytics import YOLO
+
+def load_yolo(model_path: str):
+    try:
+        return YOLO(model_path)
+    except ModuleNotFoundError:
+        # https://github.com/ultralytics/ultralytics/issues/3856
+        YOLO("yolov8n.pt")
+        return YOLO(model_path)
+
+
+def inference_bbox(
+    model,
+    image: Image.Image,
+    confidence: float = 0.3,
+    device: str = "",
+):
+    pred = model(image, conf=confidence, device=device)
+
+    bboxes = pred[0].boxes.xyxy.cpu().numpy()
+    cv2_image = np.array(image)
+    cv2_image = cv2_image[:, :, ::-1].copy()
+    cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY)
+
+    segms = []
+    for x0, y0, x1, y1 in bboxes:
+        cv2_mask = np.zeros(cv2_gray.shape, np.uint8)
+        cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1)
+        cv2_mask_bool = cv2_mask.astype(bool)
+        segms.append(cv2_mask_bool)
+
+    n, m = bboxes.shape
+    if n == 0:
+        return [[], [], [], []]
+
+    results = [[], [], [], []]
+    for i in range(len(bboxes)):
+        results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())])
+        results[1].append(bboxes[i])
+        results[2].append(segms[i])
+        results[3].append(pred[0].boxes[i].conf.cpu().numpy())
+
+    return results
+
+
+def inference_segm(
+    model,
+    image: Image.Image,
+    confidence: float = 0.3,
+    device: str = "",
+):
+    pred = model(image, conf=confidence, device=device)
+    bboxes = pred[0].boxes.xyxy.cpu().numpy()
+    segms = pred[0].masks.data.cpu().numpy()
+
+    n, m = bboxes.shape
+    if n == 0:
+        return [[], [], [], []]
+
+    results = [[], [], [], []]
+    for i in range(len(bboxes)):
+        results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())])
+        results[1].append(bboxes[i])
+
+        mask = torch.from_numpy(segms[i])
+        scaled_mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(image.size[1], image.size[0]),
+                                                      mode='bilinear', align_corners=False)
+        scaled_mask = scaled_mask.squeeze().squeeze()
+
+        results[2].append(scaled_mask.numpy())
+        results[3].append(pred[0].boxes[i].conf.cpu().numpy())
+
+    return results
+
+
+class UltraBBoxDetector:
+    bbox_model = None
+
+    def __init__(self, bbox_model):
+        self.bbox_model = bbox_model
+
+    def detect(self, image, threshold, dilation, crop_factor, drop_size=1):
+        drop_size = max(drop_size, 1)
+        detected_results = inference_bbox(self.bbox_model, core.tensor2pil(image), threshold)
+        segmasks = core.create_segmasks(detected_results)
+
+        if dilation > 0:
+            segmasks = core.dilate_masks(segmasks, dilation)
+
+        items = []
+        h = image.shape[1]
+        w = image.shape[2]
+
+        for x, label in zip(segmasks, detected_results[0]):
+            item_bbox = x[0]
+            item_mask = x[1]
+
+            y1, x1, y2, x2 = item_bbox
+
+            if x2 - x1 > drop_size and y2 - y1 > drop_size:  # minimum dimension must be (2,2) to avoid squeeze issue
+                crop_region = core.make_crop_region(w, h, item_bbox, crop_factor)
+                cropped_image = core.crop_image(image, crop_region)
+                cropped_mask = core.crop_ndarray2(item_mask, crop_region)
+                confidence = x[2]
+                # bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
+
+                item = core.SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label)
+
+                items.append(item)
+
+        shape = image.shape[1], image.shape[2]
+        return shape, items
+
+    def detect_combined(self, image, threshold, dilation):
+        detected_results = inference_bbox(self.bbox_model, image, threshold)
+        segmasks = core.create_segmasks(detected_results)
+        if dilation > 0:
+            segmasks = core.dilate_masks(segmasks, dilation)
+
+        return core.combine_masks(segmasks)
+
+    def setAux(self, x):
+        pass
+
+
+class UltraSegmDetector:
+    bbox_model = None
+
+    def __init__(self, bbox_model):
+        self.bbox_model = bbox_model
+
+    def detect(self, image, threshold, dilation, crop_factor, drop_size=1):
+        drop_size = max(drop_size, 1)
+        detected_results = inference_segm(self.bbox_model, core.tensor2pil(image), threshold)
+        segmasks = core.create_segmasks(detected_results)
+
+        if dilation > 0:
+            segmasks = core.dilate_masks(segmasks, dilation)
+
+        items = []
+        h = image.shape[1]
+        w = image.shape[2]
+
+        for x, label in zip(segmasks, detected_results[0]):
+            item_bbox = x[0]
+            item_mask = x[1]
+
+            y1, x1, y2, x2 = item_bbox
+
+            if x2 - x1 > drop_size and y2 - y1 > drop_size:  # minimum dimension must be (2,2) to avoid squeeze issue
+                crop_region = core.make_crop_region(w, h, item_bbox, crop_factor)
+                cropped_image = core.crop_image(image, crop_region)
+                cropped_mask = core.crop_ndarray2(item_mask, crop_region)
+                confidence = x[2]
+                # bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
+
+                item = core.SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label)
+
+                items.append(item)
+
+        shape = image.shape[1], image.shape[2]
+        return shape, items
+
+    def detect_combined(self, image, threshold, dilation):
+        detected_results = inference_bbox(self.bbox_model, image, threshold)
+        segmasks = core.create_segmasks(detected_results)
+        if dilation > 0:
+            segmasks = core.dilate_masks(segmasks, dilation)
+
+        return core.combine_masks(segmasks)
+
+    def setAux(self, x):
+        pass

ComfyUI-Impact-Pack/subpack/impact/subpack_nodes.py

@@ -0,0 +1,42 @@
+import os
+import folder_paths
+import impact.core as core
+import impact.subcore as subcore
+
+print(f"### Loading: ComfyUI-Impact-Pack (Subpack: V0.2)")
+
+model_path = folder_paths.models_dir
+folder_paths.folder_names_and_paths["ultralytics_bbox"] = ([os.path.join(model_path, "ultralytics", "bbox")], folder_paths.supported_pt_extensions)
+folder_paths.folder_names_and_paths["ultralytics_segm"] = ([os.path.join(model_path, "ultralytics", "segm")], folder_paths.supported_pt_extensions)
+folder_paths.folder_names_and_paths["ultralytics"] = ([os.path.join(model_path, "ultralytics")], folder_paths.supported_pt_extensions)
+
+
+class UltralyticsDetectorProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("ultralytics_bbox")]
+        segms = ["segm/"+x for x in folder_paths.get_filename_list("ultralytics_segm")]
+        return {"required": {"model_name": (bboxs + segms, )}}
+    RETURN_TYPES = ("BBOX_DETECTOR", "SEGM_DETECTOR")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack"
+
+    def doit(self, model_name):
+        model_path = folder_paths.get_full_path("ultralytics", model_name)
+        model = subcore.load_yolo(model_path)
+
+        if model_name.startswith("bbox"):
+            return subcore.UltraBBoxDetector(model), core.NO_SEGM_DETECTOR()
+        else:
+            return subcore.UltraBBoxDetector(model), subcore.UltraSegmDetector(model)
+
+
+NODE_CLASS_MAPPINGS = {
+    "UltralyticsDetectorProvider": UltralyticsDetectorProvider
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+
+}

ComfyUI-Impact-Pack/subpack/install.py

@@ -0,0 +1,31 @@
+import os
+import sys
+from torchvision.datasets.utils import download_url
+
+subpack_path = os.path.join(os.path.dirname(__file__))
+comfy_path = os.path.join(subpack_path, '..', '..', '..')
+
+sys.path.append(comfy_path)
+
+import folder_paths
+model_path = folder_paths.models_dir
+ultralytics_bbox_path = os.path.join(model_path, "ultralytics", "bbox")
+ultralytics_segm_path = os.path.join(model_path, "ultralytics", "segm")
+
+if not os.path.exists(ultralytics_bbox_path):
+    os.makedirs(ultralytics_bbox_path)
+
+if not os.path.exists(ultralytics_segm_path):
+    os.makedirs(ultralytics_segm_path)
+
+if not os.path.exists(os.path.join(ultralytics_bbox_path, "face_yolov8m.pt")):
+    download_url("https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8m.pt",
+                 ultralytics_bbox_path)
+
+if not os.path.exists(os.path.join(ultralytics_bbox_path, "hand_yolov8s.pt")):
+    download_url("https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8s.pt",
+                 ultralytics_bbox_path)
+
+if not os.path.exists(os.path.join(ultralytics_segm_path, "person_yolov8m-seg.pt")):
+    download_url("https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8m-seg.pt",
+                 ultralytics_segm_path)

ComfyUI-Impact-Pack/subpack/requirements.txt

@@ -0,0 +1 @@
+ultralytics

ComfyUI-Impact-Pack/test/advanced-sampler.json

@@ -0,0 +1,976 @@
+{
+  "last_node_id": 27,
+  "last_link_id": 46,
+  "nodes": [
+    {
+      "id": 11,
+      "type": "EditBasicPipe",
+      "pos": [
+        1260,
+        590
+      ],
+      "size": {
+        "0": 267,
+        "1": 126
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "basic_pipe",
+          "type": "BASIC_PIPE",
+          "link": 15
+        },
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": null
+        },
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 17
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "basic_pipe",
+          "type": "BASIC_PIPE",
+          "links": [
+            20
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EditBasicPipe"
+      }
+    },
+    {
+      "id": 12,
+      "type": "CLIPTextEncode",
+      "pos": [
+        420,
+        670
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 16
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            17
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "photorealistic:1.4, best quality:1.4, masterpiece, 1girl is sitting in the cafe terrace, (colorful hair:1.1)"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        415,
+        186
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            13
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "photorealistic:1.4, best quality:1.4, masterpiece, 1girl is sitting in the cafe terrace"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        413,
+        389
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            14
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "text, watermark, low quality:1.4, worst quality:1.4"
+      ]
+    },
+    {
+      "id": 10,
+      "type": "ToBasicPipe",
+      "pos": [
+        952,
+        189
+      ],
+      "size": {
+        "0": 241.79998779296875,
+        "1": 106
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 10
+        },
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 11
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 12
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 13
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 14
+        }
+      ],
+      "outputs": [
+        {
+          "name": "basic_pipe",
+          "type": "BASIC_PIPE",
+          "links": [
+            15,
+            19,
+            33
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ToBasicPipe"
+      }
+    },
+    {
+      "id": 22,
+      "type": "FromBasicPipe",
+      "pos": [
+        880,
+        1040
+      ],
+      "size": {
+        "0": 241.79998779296875,
+        "1": 106
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "basic_pipe",
+          "type": "BASIC_PIPE",
+          "link": 33
+        }
+      ],
+      "outputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "links": [
+            34
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "links": [
+            40
+          ],
+          "shape": 3,
+          "slot_index": 2
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [
+            35
+          ],
+          "shape": 3,
+          "slot_index": 3
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [
+            36
+          ],
+          "shape": 3,
+          "slot_index": 4
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "FromBasicPipe"
+      }
+    },
+    {
+      "id": 24,
+      "type": "VAEDecode",
+      "pos": [
+        1938,
+        935
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 46
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 40
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            41
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -5,
+        212
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            10
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5,
+            11,
+            16
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ComfyUI-Impact-Pack/test/detailer-pipe-test.json

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ComfyUI-Impact-Pack/test/loop-test.json

@@ -0,0 +1,1114 @@
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+}

ComfyUI-Impact-Pack/test/masks.json

@@ -0,0 +1,622 @@
+{
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+  "last_link_id": 52,
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+          "shape": 6,
+          "slot_index": 0
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+          "type": "MASK",
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+          "type": "SEGS",
+          "links": [
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+            46
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+          "slot_index": 0
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ComfyUI-Impact-Pack/test/regional_prompt.json

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+      "LATENT"
+    ],
+    [
+      20,
+      6,
+      1,
+      18,
+      0,
+      "CLIP"
+    ],
+    [
+      21,
+      18,
+      0,
+      17,
+      4,
+      "CONDITIONING"
+    ],
+    [
+      22,
+      5,
+      0,
+      17,
+      0,
+      "BASIC_PIPE"
+    ],
+    [
+      23,
+      21,
+      0,
+      20,
+      1,
+      "KSAMPLER_ADVANCED"
+    ],
+    [
+      24,
+      17,
+      0,
+      21,
+      0,
+      "BASIC_PIPE"
+    ],
+    [
+      25,
+      12,
+      0,
+      22,
+      0,
+      "REGIONAL_PROMPTS"
+    ],
+    [
+      26,
+      20,
+      0,
+      22,
+      1,
+      "REGIONAL_PROMPTS"
+    ],
+    [
+      27,
+      22,
+      0,
+      2,
+      2,
+      "REGIONAL_PROMPTS"
+    ],
+    [
+      28,
+      15,
+      1,
+      20,
+      0,
+      "MASK"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ComfyUI-Impact-Pack/troubleshooting/TROUBLESHOOTING.md

@@ -0,0 +1,25 @@
+## When a permission error occurs during the installation process (on Windows)
+
+* There are cases where the package you are trying to install is already being used by another custom node that has been loaded.
+    * This issue occurs only on Windows.
+* Please close ComfyUI and execute install.py directly using Python in the custom_nodes/ComfyUI-Impact-Pack directory.
+    * In case **portable** version:
+        1. goto **ComfyUI_windows_portable** directory in **cmd**
+        2. execute ```.\python_embeded\python -s -m custom_nodes\ComfyUI-Impact-Pack\install.py```
+    * In case **venv**:
+        1. activate venv
+        2. execute ```python -s -m custom_nodes\ComfyUI-Impact-Pack\install.py```
+    * Others:
+        1. Please modify the path of 'python' according to your Python environment.
+        2. execute ```(YOUR PYTHON) -s -m custom_nodes\ComfyUI-Impact-Pack\install.py```
+
+
+
+## Destortion on Detailer
+
+* Please also note that this issue may be caused by a bug in xformers 0.0.18. If you encounter this problem, please try adjusting the guide_size parameter.
+
+![example](black1.png)
+
+![example](black2.png)
+* guide_size changed from 256 -> 192

ComfyUI-Impact-Pack/uninstall.py

@@ -0,0 +1,38 @@
+import os
+import sys
+import time
+import platform
+import shutil
+import subprocess
+
+comfy_path = '../..'
+
+def rmtree(path):
+    retry_count = 3
+
+    while True:
+        try:
+            retry_count -= 1
+
+            if platform.system() == "Windows":
+                subprocess.check_call(['attrib', '-R', path + '\\*', '/S'])
+
+            shutil.rmtree(path)
+
+            return True
+
+        except Exception as ex:
+            print(f"ex: {ex}")
+            time.sleep(3)
+
+            if retry_count < 0:
+                raise ex
+
+            print(f"Uninstall retry({retry_count})")
+
+js_dest_path = os.path.join(comfy_path, "web", "extensions", "impact-pack")
+
+if os.path.exists(js_dest_path):
+    rmtree(js_dest_path)
+        
+

ComfyUI-Impact-Pack/wildcards/samples/flower.txt

@@ -0,0 +1,9 @@
+rose
+orchid
+iris
+carnation
+lily
+daisy
+chrysanthemum
+daffodil
+dahlia

ComfyUI-Impact-Pack/wildcards/samples/jewel.txt

@@ -0,0 +1,9 @@
+diamond
+emerald
+sapphire
+opal
+ruby
+topaz
+pearl
+rubyamethyst
+aquamarine