fixed: this.widgets is undefined

https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/977

.github/workflows/publish.yml

@@ -0,0 +1,25 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'ltdrdata' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.gitignore

@@ -4,4 +4,8 @@ wildcards/**
 .vscode/
 .idea/
 subpack
-impact_subpack
+impact_subpack
+*.txt
+*.yaml
+!requirements.txt
+!LICENSE.txt

README.md

@@ -2,11 +2,21 @@
 
 # ComfyUI-Impact-Pack
 
-**Custom nodes pack for ComfyUI**
-This custom node helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more.
+**Custom node pack for ComfyUI**
+This node pack helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more.
 
+NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pack. To use the UltralyticsDetectorProvider node, please install the ComfyUI-Impact-Subpack separately.
 
-## NOTICE
+## NOTICE 
+* V8.0: The `Impact Subpack` is no longer installed automatically. To use `UltralyticsDetectorProvider` nodes, please install the `Impact Subpack` separately.
+* V7.6: Automatic installation is no longer supported. Please install using ComfyUI-Manager, or manually install requirements.txt and run install.py to complete the installation.
+* V7.0: Supports Switch based on Execution Model Inversion.
+* V6.0: Supports FLUX.1 model in Impact KSampler, Detailers, PreviewBridgeLatent
+* V5.0: It is no longer compatible with versions of ComfyUI before 2024.04.08. 
+* V4.87.4: Update to a version of ComfyUI after 2024.04.08 for proper functionality.
+* V4.85: Incompatible with the outdated **ComfyUI IPAdapter Plus**. (A version dated March 24th or later is required.)
+* V4.77: Compatibility patch applied. Requires ComfyUI version (Oct. 8th) or later.
+* V4.73.3: ControlNetApply (SEGS) supports AnimateDiff
 * V4.20.1: Due to the feature update in `RegionalSampler`, the parameter order has changed, causing malfunctions in previously created `RegionalSamplers`. Please adjust the parameters accordingly.
 * V4.12: `MASKS` is changed to `MASK`.
 * V4.7.2 isn't compatible with old version of `ControlNet Auxiliary Preprocessor`. If you will use `MediaPipe FaceMesh to SEGS` update to latest version(Sep. 17th).  
@@ -22,182 +32,268 @@ This custom node helps to conveniently enhance images through Detector, Detailer
 * 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.
 
 
+## How To Install
+
+### **Recommended**
+* Install via [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager).
+
+### **Manual**
+* Navigate to `ComfyUI/custom_nodes` in your terminal (cmd).
+* Clone the repository under the `custom_nodes` directory using the following command:
+  ```
+  git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack comfyui-impact-pack
+  cd comfyui-impact-pack
+  ```
+* Install dependencies in your Python environment.
+    * For Windows Portable, run the following command inside `ComfyUI\custom_nodes\comfyui-impact-pack`:
+        ```
+        ..\..\..\python_embeded\python.exe -m pip install -r requirements.txt
+        ```
+    * If using venv or conda, activate your Python environment first, then run:
+        ```
+        pip install -r requirements.txt
+        ```
+
+### Companion Pack
+* If you need the `Ultralytics Detector Provider` to use various YOLO detection models, you should also install [ComfyUI-Impact-Subpack](https://github.com/ltdrdata/ComfyUI-Impact-Subpack).
+
 ## Custom Nodes
-* [Detectors](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detectors.md)
-  * 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 BBOX_DETECTOR.
-  * CLIPSegDetectorProvider - Wrapper for CLIPSeg to provide BBOX_DETECTOR.
+### [Detector nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detectors.md)
+  * `SAMLoader` - Loads the SAM model.
+  * `ONNXDetectorProvider` - Loads the ONNX model to provide BBOX_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.
+  * `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.
-  * Simple Detector (SEGS) - Operating primarily with `BBOX_DETECTOR`, and with the additional provision of `SAM_MODEL` or `SEGM_DETECTOR`, this node internally generates improved SEGS through mask operations on both *bbox* and *silhouette*. It serves as a convenient tool to simplify a somewhat intricate workflow.
+  * `Simple Detector (SEGS)` - Operating primarily with `BBOX_DETECTOR`, and with the additional provision of `SAM_MODEL` or `SEGM_DETECTOR`, this node internally generates improved SEGS through mask operations on both *bbox* and *silhouette*. It serves as a convenient tool to simplify a somewhat intricate workflow.
 
-* ControlNetApply (SEGS) - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
+### ControlNet, IPAdapter
+  * `ControlNetApply (SEGS)` - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
+    * `segs_preprocessor` and `control_image` can be selectively applied. If a `control_image` is given, `segs_preprocessor` will be ignored.
+    * If set to `control_image`, you can preview the cropped cnet image through `SEGSPreview (CNET Image)`. Images generated by `segs_preprocessor` should be verified through the `cnet_images` output of each Detailer.
+    * The `segs_preprocessor` operates by applying preprocessing on-the-fly based on the cropped image during the detailing process, while `control_image` will be cropped and used as input to `ControlNetApply (SEGS)`.
+  * `ControlNetClear (SEGS)` - Clear applied ControlNet in SEGS
+  * `IPAdapterApply (SEGS)` - To apply IPAdapter in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
 
-* 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.
+### Mask operation
+  * `Pixelwise(SEGS & SEGS)` - Performs a 'pixelwise and' operation between two SEGS.
+  * `Pixelwise(SEGS - SEGS)` - Subtracts one SEGS from another.
+  * `Pixelwise(SEGS & MASK)` - Performs a pixelwise AND operation between SEGS and MASK.
+  * `Pixelwise(SEGS & MASKS ForEach)` - Performs a pixelwise AND operation between SEGS and MASKS.
+    * Please note that this operation is performed with batches of MASKS, not just a single MASK.
+  * `Pixelwise(MASK & MASK)` - Performs a 'pixelwise and' operation between two masks.
+  * `Pixelwise(MASK - MASK)` - Subtracts one mask from another.
+  * `Pixelwise(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.
+  * `Dilate Mask` - Dilate Mask.
+    * Support erosion for negative value.
+  * `Gaussian Blur Mask` - Apply Gaussian Blur to Mask. You can utilize this for mask feathering.
+  * `Mask Rect Area` - Create a rectangular mask defined by percentages with preview canvas.
+  * `Mask Rect Area (Advanced)` - Create a rectangular mask defined by pixels and image size. 
 
-* Detailer
-  * 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.
+### [Detailer nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detailers.md)
+  * `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.
-    * MASK to SEGS For AnimateDiff - Generates SEGS based on the mask for AnimateDiff.
-  * MediaPipe FaceMesh to SEGS - Separate each landmark from the mediapipe facemesh image to create labeled SEGS.
+  * `MASK to SEGS` - Generates SEGS based on the mask.
+  * `MASK to SEGS For AnimateDiff` - Generates SEGS based on the mask for AnimateDiff.
+    * When using a single mask, convert it to SEGS to apply it to the entire frame.
+    * When using a batch mask, the contour fill feature is disabled.
+  * `MediaPipe FaceMesh to SEGS` - Separate each landmark from the mediapipe facemesh image to create labeled SEGS.
     * Usually, the size of images created through the MediaPipe facemesh preprocessor is downscaled. It resizes the MediaPipe facemesh image to the original size given as reference_image_opt for matching sizes during processing. 
-  * 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 - This node converts the MASKS in batch form to a list of individual masks.
-  * Mask List to Masks - This node converts the MASK list to MASK batch form.
-  * 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).
-  * MaskDetailer (pipe) - This is a simple inpaint node that applies the Detailer to the mask area.
+  * `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` - This node converts the MASKS in batch form to a list of individual masks.
+  * `Mask List to Masks` - This node converts the MASK list to MASK batch form.
+  * `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).
+  * `MaskDetailer (pipe)` - This is a simple inpaint node that applies the Detailer to the mask area.
 
-* `FromDetailer (SDXL/pipe), BasicPipe -> DetailerPipe (SDXL), Edit DetailerPipe (SDXL)` - These are pipe functions used in Detailer for utilizing the refiner model of SDXL.
+  * `FromDetailer (SDXL/pipe)`, `BasicPipe -> DetailerPipe (SDXL)`, `Edit DetailerPipe (SDXL)` - These are pipe functions used in Detailer for utilizing the refiner model of SDXL.
+  * `Any PIPE -> BasicPipe` - Convert the PIPE Value of other custom nodes that are not BASIC_PIPE but internally have the same structure as BASIC_PIPE to BASIC_PIPE. If an incompatible type is applied, it may cause runtime errors.
 
-* SEGS Manipulation nodes 
-  * SEGSDetailer - Performs detailed work on SEGS without pasting it back onto the original image.
-  * SEGSPaste - Pastes the results of SEGS onto the original image.
+### SEGS Manipulation nodes
+  * `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.
     * This node can be used in conjunction with the processing results of AnimateDiff.
-  * SEGSPreview - Provides a preview of SEGS.
+  * `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.
      * This node can be used in conjunction with the processing results of AnimateDiff.
-  * 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 from segs2 will be ignored.
-  * Picker (SEGS) - Among the input SEGS, you can select a specific SEG through a dialog. If no SEG is selected, it outputs an empty SEGS. Increasing the batch_size of SEGSDetailer can be used for the purpose of selecting from the candidates.
-  * DecomposeSEGS - Decompose SEGS to allow for detailed manipulation.
-  * AssembleSEGS - Reassemble the decomposed SEGS.
-  * From SEG_ELT - Extract detailed information from SEG_ELT.
-  * Edit SEG_ELT - Modify some of the information in SEG_ELT.
-  * Dilate SEG_ELT - Dilate the mask of SEG_ELT.
-
-* Dilate Mask - Dilate Mask. 
-  * Support erosion for negative value.
+  * `SEGSPreview (CNET Image)` - Show images configured with `ControlNetApply (SEGS)` for debugging purposes.
+  * `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.
+  * `SEGS Filter (non max suppression)` - This node filters SEGS by removing those with high overlap based on the Intersection over Union (IoU) threshold, keeping only the most confident detections.
+  * `SEGS Filter (intersection)` - This node filters segs1, keeping only the SEGS that do not significantly overlap with any SEGS in segs2, based on the Intersection over Area (IoA) threshold.
+  * `SEGS Assign (label)` - Assign labels sequentially to SEGS. This node is useful when used with `[LAB]` of FaceDetailer.
+  * `SEGSConcat` - Concatenate segs1 and segs2. If source shape of segs1 and segs2 are different from segs2 will be ignored.
+  * `SEGS Merge` - SEGS contains multiple SEGs. SEGS Merge integrates several SEGs into a single merged SEG. The label is changed to `merged` and the confidence becomes the minimum confidence. The applied controlnet and cropped_image are removed.
+  * `Picker (SEGS)` - Among the input SEGS, you can select a specific SEG through a dialog. If no SEG is selected, it outputs an empty SEGS. Increasing the batch_size of SEGSDetailer can be used for the purpose of selecting from the candidates.
+  * `Set Default Image For SEGS` - Set a default image for SEGS. SEGS with images set this way do not need to have a fallback image set. When override is set to false, the original image is preserved.
+  * `Remove Image from SEGS` - Remove the image set for the SEGS that has been configured by "Set Default Image for SEGS" or SEGSDetailer. When the image for the SEGS is removed, the Detailer node will operate based on the currently processed image instead of the SEGS. 
+  * `Make Tile SEGS` - [experimental] Create SEGS in the form of tiles from an image to facilitate experiments for Tiled Upscale using the Detailer.
+    * The `filter_in_segs_opt` and `filter_out_segs_opt` are optional inputs. If these inputs are provided, when creating the tiles, the mask for each tile is generated by overlapping with the mask of `filter_in_segs_opt` and excluding the overlap with the mask of `filter_out_segs_opt`. Tiles with an empty mask will not be created as SEGS.
+  * `Dilate Mask (SEGS)` - Dilate/Erosion Mask in SEGS
+  * `Gaussian Blur Mask (SEGS)` - Apply Gaussian Blur to Mask in SEGS
+  * `SEGS_ELT Manipulation` - experimental nodes
+    * `DecomposeSEGS` - Decompose SEGS to allow for detailed manipulation.
+    * `AssembleSEGS` - Reassemble the decomposed SEGS.
+    * `From SEG_ELT` - Extract detailed information from SEG_ELT.
+    * `Edit SEG_ELT` - Modify some of the information in SEG_ELT.
+    * `Dilate SEG_ELT` - Dilate the mask of SEG_ELT.
+    * `From SEG_ELT` bbox - Extract coordinate from bbox in SEG_ELT
+    * `From SEG_ELT` crop_region - Extract coordinate from crop_region in SEG_ELT
+  * `Count Elt in SEGS` - Number of Elts ins SEGS
  
-* 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.
+### 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.
+* `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 [BlenderNeko/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.
-* NoiseInjectionHookProvider - During each iteration of IterativeUpscale, noise is injected into the latent space while varying the strength according to a schedule.
-  * You need to install the [BlenderNeko/ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) node extension.
-  * The seed serves as the initial value required for generating noise, and it increments by 1 with each iteration as the process unfolds.
-  * The source determines the types of CPU noise and GPU noise to be configured.
-  * Currently, there is only a simple schedule available, where the strength of the noise varies from start_strength to end_strength during the progression of each iteration.
-* NoiseInjectionDetailerHookProvider - The `detailer_hook` is a hook in the `Detailer` that injects noise during the processing of each SEGS.
-* CoreMLDetailerHookProvider - CoreML supports only 512x512, 512x768, 768x512, 768x768 size sampling. CoreMLDetailerHookProvider precisely fixes the upscale of the crop_region to this size. When using this hook, it will always be selected size, regardless of the guide_size. However, if the guide_size is too small, skipping will occur.
+### PK_HOOK
+  * `DenoiseScheduleHookProvider` - IterativeUpscale provides a hook that gradually changes the denoise to target_denoise as the iterative-step progresses.
+  * `CfgScheduleHookProvider` - IterativeUpscale provides a hook that gradually changes the cfg to target_cfg as the iterative-step progresses.
+  * `StepsScheduleHookProvider` - IterativeUpscale provides a hook that gradually changes the sampling-steps to target_steps as the iterative-step progresses.
+  * `NoiseInjectionHookProvider` - During each iteration of IterativeUpscale, noise is injected into the latent space while varying the strength according to a schedule.
+    * You need to install the [BlenderNeko/ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) node extension.
+    * The seed serves as the initial value required for generating noise, and it increments by 1 with each iteration as the process unfolds.
+    * The source determines the types of CPU noise and GPU noise to be configured.
+    * Currently, there is only a simple schedule available, where the strength of the noise varies from start_strength to end_strength during the progression of each iteration.
+  * `UnsamplerHookProvider` - Apply Unsampler during each iteration. To use this node, ComfyUI_Noise must be installed.
+  * `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.
+ 
+### DETAILER_HOOK
+  * `NoiseInjectionDetailerHookProvider` - The `detailer_hook` is a hook in the `Detailer` that injects noise during the processing of each SEGS.
+  * `UnsamplerDetailerHookProvider` - Apply Unsampler during each cycle. To use this node, ComfyUI_Noise must be installed.
+  * `DenoiseSchedulerDetailerHookProvider` - During the progress of the cycle, the detailer's denoise is altered up to the `target_denoise`. 
+  * `CoreMLDetailerHookProvider` - CoreML supports only 512x512, 512x768, 768x512, 768x768 size sampling. CoreMLDetailerHookProvider precisely fixes the upscale of the crop_region to this size. When using this hook, it will always be selected size, regardless of the guide_size. However, if the guide_size is too small, skipping will occur.
+  * `DetailerHookCombine` - This is used to connect two DETAILER_HOOKs. Similar to PixelKSampleHookCombine.
+  * `SEGSOrderedFilterDetailerHook`, SEGSRangeFilterDetailerHook, SEGSLabelFilterDetailerHook - There are a wrapper node that provides SEGSFilter nodes to be applied in FaceDetailer or Detector by creating DETAILER_HOOK.
+  * `PreviewDetailerHook` - Connecting this hook node helps provide assistance for viewing previews whenever SEGS Detailing tasks are completed. When working with a large number of SEGS, such as Make Tile SEGS, it allows for monitoring the situation as improvements progress incrementally.
+    * Since this is the hook applied when pasting onto the original image, it has no effect on nodes like `SEGSDetailer`.
+  * `VariationNoiseDetailerHookProvider` - Apply variation seed to the detailer. It can be applied in multiple stages through combine.
 
-* 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)'.
+### Iterative Upscale nodes
+  * `Iterative Upscale (Latent/on Pixel Space)` - 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.
+### TwoSamplers nodes
+* `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.
+* `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 [BlenderNeko/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.
+* `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, RegionalSampler.
+  * sigma_factor: By multiplying the denoise schedule by the sigma_factor, you can adjust the amount of denoising based on the configured denoise.
 
-* TwoSamplersForMaskUpscalerProvider - This is an Upscaler that extends TwoSamplersForMask to be used in Iterative Upscale.
+* `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.
+### Image Utils
+  * `PreviewBridge (image)` - This custom node can be used with a bridge for image when using the MaskEditor feature of Clipspace.
+  * `PreviewBridge (latent)` - This custom node can be used with a bridge for latent image when using the MaskEditor feature of Clipspace.
+    * If a latent with a mask is provided as input, it displays the mask. Additionally, the mask output provides the mask set in the latent.
+    * If a latent without a mask is provided as input, it outputs the original latent as is, but the mask output provides an output with the entire region set as a mask.
+    * When set mask through MaskEditor, a mask is applied to the latent, and the output includes the stored mask. The same mask is also output as the mask output.
+    * When connected to `vae_opt`, it takes higher priority than the `preview_method`.
+  * `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 nodes
-  * 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.
-  * Switch (Any) - This is a Switch node that takes an arbitrary number of inputs and produces a single output. Its type is determined when connected to any node, and connecting inputs increases the available slots for connections.
-  * Inversed Switch (Any) - In contrast to `Switch (Any)`, it takes a single input and outputs one of many. Due to ComfyUI's functional limitations, the value of `select` must be determined at the time of queuing a prompt, and while it can serve as a `Primitive Node` or `ImpactInt`, it cannot function properly when connected through other nodes. 
-  * Guide
-    * When the `Switch (Any)` and `Inversed Switch (Any)` selects are transformed into primitives, it's important to be cautious because the select range is not appropriately constrained, potentially leading to unintended behavior.   
-    * `Switch (image,mask)`, `Switch (latent)`, `Switch (SEGS)`, `Switch (Any)` supports `sel_mode` param. The `sel_mode` sets the moment at which the `select` parameter is determined. `select_on_prompt` determines the `select` at the time of queuing the prompt, while `select_on_execution` determines it during the execution of the workflow. While `select_on_execution` offers more flexibility, it can potentially trigger workflow execution errors due to running nodes that may be impossible to execute within the limitations of ComfyUI. `select_on_prompt` bypasses this constraint by treating any inputs not selected as if they were disconnected. However, please note that when using `select_on_prompt`, the `select` can only be used with widgets or `Primitive Nodes` determined at the queue prompt.
-    * There is an issue when connecting the built-in reroute node with the switch's input/output slots. it can lead to forced disconnections during workflow loading. Therefore, it is advisable not to use reroute for making connections in such cases. However, there are no issues when using the reroute node in Pythongossss.
+### Switch nodes
+  * `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.
+  * `Switch (Any)` - This is a Switch node that takes an arbitrary number of inputs and produces a single output. Its type is determined when connected to any node, and connecting inputs increases the available slots for connections.
+  * `Inversed Switch (Any)` - In contrast to `Switch (Any)`, it takes a single input and outputs one of many.
+  * NOTE: See this [tutorial](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/switch.md) 
 
-* [Wildcards](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md) - These are nodes that supports syntax in the form of `__wildcard-name__` and dynamic prompt syntax like `{a|b|c}`.
+### [Wildcards](http://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/ImpactWildcard.md) nodes
+  * These are nodes that supports syntax in the form of `__wildcard-name__` and dynamic prompt syntax like `{a|b|c}`.
   * Wildcard files can be used by placing `.txt` or `.yaml` files under either `ComfyUI-Impact-Pack/wildcards` or `ComfyUI-Impact-Pack/custom_wildcards` paths.
     * You can download and use [Wildcard YAML](https://civitai.com/models/138970/billions-of-wildcards-all-in-one) files in this format.
     * After the first execution, you can change the custom wildcards path in the `custom_wildcards` entry within the `ComfyUI-Impact-Pack/impact-pack.ini` file created.
-  * 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.
+  * `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.
-  * ImpactWildcardEncode - Similar to ImpactWildcardProcessor, this provides the loading functionality of LoRAs (e.g. `<lora:some_awesome_lora:0.7:1.2>`). Populated prompts are encoded using the clip after all the lora loading is done.
+  * `ImpactWildcardEncode` - Similar to ImpactWildcardProcessor, this provides the loading functionality of LoRAs (e.g. `<lora:some_awesome_lora:0.7:1.2>`). Populated prompts are encoded using the clip after all the lora loading is done.
     * If the `Inspire Pack` is installed, you can use **Lora Block Weight** in the form of `LBW=lbw spec;`
     * `<lora:chunli:1.0:1.0:LBW=B11:0,0,0,0,0,0,0,0,0,0,A,0,0,0,0,0,0;A=0.;>`, `<lora:chunli:1.0:1.0:LBW=0,0,0,0,0,0,0,0,0,0,A,B,0,0,0,0,0;A=0.5;B=0.2;>`, `<lora:chunli:1.0:1.0:LBW=SD-MIDD;>`
 
-* Regional Sampling - These nodes offer the capability to divide regions and perform partial sampling using a mask. Unlike TwoSamplersForMask, sampling for each region is applied during each step.
-  * RegionalPrompt - This node combines a **mask** for specifying regions and the **sampler** to apply to each region to create `REGIONAL_PROMPTS`.
-  * CombineRegionalPrompts - Combine multiple `REGIONAL_PROMPTS` to create a single `REGIONAL_PROMPTS`.
-  * RegionalSampler - This node performs sampling using a base sampler and regional prompts. Sampling by the base sampler is executed at each step, while sampling for each region is performed through the sampler bound to each region.
+### Regional Sampling
+  * These nodes offer the capability to divide regions and perform partial sampling using a mask. Unlike TwoSamplersForMask, sampling for each region is applied during each step.
+  * `RegionalPrompt` - This node combines a **mask** for specifying regions and the **sampler** to apply to each region to create `REGIONAL_PROMPTS`.
+  * `CombineRegionalPrompts` - Combine multiple `REGIONAL_PROMPTS` to create a single `REGIONAL_PROMPTS`.
+  * `RegionalSampler` - This node performs sampling using a base sampler and regional prompts. Sampling by the base sampler is executed at each step, while sampling for each region is performed through the sampler bound to each region.
     * overlap_factor - Specifies the amount of overlap for each region to blend well with the area outside the mask.
     * restore_latent - When sampling each region, restore the areas outside the mask to the base latent, preventing additional noise from being introduced outside the mask during region sampling.
-  * RegionalSamplerAdvanced - This is the Advanced version of the RegionalSampler. You can control it using `step` instead of `denoise`.
-  * NOTE: The `sde` sampler and `uni_pc` sampler introduce additional noise during each step of the sampling process. To mitigate this, when sampling each region, the `uni_pc` sampler applies additional `dpmpp_fast`, and the sde sampler applies the `dpmpp_2m` sampler as an additional measure.
+  * `RegionalSamplerAdvanced` - This is the Advanced version of the RegionalSampler. You can control it using `step` instead of `denoise`.
+    > NOTE: The `sde` sampler and `uni_pc` sampler introduce additional noise during each step of the sampling process. To mitigate this, when sampling each region, the `uni_pc` sampler applies additional `dpmpp_fast`, and the sde sampler applies the `dpmpp_2m` sampler as an additional measure.
 
-* KSampler (pipe), KSampler (advanced/pipe)
 
-* Image batch To Image List - Convert Image batch to Image List
-- You can use images generated in a multi batch to handle them
-* Make Image List - Convert multiple images into a single image list
-* Make Image Batch - Convert multiple images into a single image batch
-- The input of images can be scaled up as needed
+### Impact KSampler
+  * These samplers support basic_pipe and AYS/OSS/GITS scheduler
+  * `KSampler (pipe)` - pipe version of KSampler
+  * `KSampler (advanced/pipe)` - pipe version of KSamplerAdvacned
+  * When converting the scheduler widget to input, refer to the `Impact Scheduler Adapter` node to resolve compatibility issues.
+  * `GITSScheduler Func Provider` - provider scheduler function for GITSScheduler
+  
 
-* String Selector - It selects and returns a portion of the string. When `multiline` mode is disabled, it simply returns the string of the line pointed to by the selector. When `multiline` mode is enabled, it divides the string based on lines that start with `#` and returns them. If the `select` value is larger than the number of items, it will start counting from the first line again and return accordingly.
-* Combine Conditionings - It takes multiple conditionings as input and combines them into a single conditioning.
+### Batch/List Util
+  * `Image Batch to Image List` - Convert Image batch to Image List
+    - You can use images generated in a multi batch to handle them
+  * `Image List to Image Batch` - Convert Image List to Image Batch 
+  * `Make Image List` - Convert multiple images into a single image list
+  * `Make Image Batch` - Convert multiple images into a single image batch
+    - The input of images can be scaled up as needed
+  * `Masks to Mask List`, `Mask List to Masks`, `Make Mask List`, `Make Mask Batch` - It has the same functionality as the nodes above, but uses mask as input instead of image.
+  * `Flatten Mask Batch` - Flattens a Mask Batch into a single Mask. Normal operation is not guaranteed for non-binary masks. 
+  * `Make List (Any)` - Create a list with arbitrary values.
+  * `Select Nth Item (Any list)` - Selects the Nth item from a list. If the index is out of range, it returns the last item in the list. 
 
-* Logics (experimental) - These nodes are experimental nodes designed to implement the logic for loops and dynamic switching.
-  * ImpactCompare, ImpactConditionalBranch, ImpactInt, ImpactValueSender, ImpactValueReceiver, ImpactImageInfo, ImpactMinMax, ImpactNeg, ImpactConditionalStopIteration
-  * ImpactIsNotEmptySEGS - This node returns `true` only if the input SEGS is not empty. 
-  * Queue Trigger - When this node is executed, it adds a new queue to assist with repetitive tasks. It will only execute if the signal's status changes.
-  * Queue Trigger (Countdown) - Like the Queue Trigger, it adds a queue, but only adds it if it's greater than 1, and decrements the count by one each time it runs.
-  * Sleep - Waits for the specified time (in seconds).
-  * Set Widget Value - This node sets one of the optional inputs to the specified node's widget. An error may occur if the types do not match.
-  * Set Mute State - This node changes the mute state of a specific node.
-  * Control Bridge - Depending on whether the mode is set to `block` or `pass`, it changes the mute status of connected nodes. If there are nodes that require a change, the current execution is paused, the mute status is updated, and a new prompt queue is inserted.
+### Logics (experimental) 
+  * These nodes are experimental nodes designed to implement the logic for loops and dynamic switching.
+  * `ImpactCompare`, `ImpactConditionalBranch`, `ImpactConditionalBranchSelMode`, `ImpactInt`, `ImpactBoolean`, `ImpactValueSender`, `ImpactValueReceiver`, `ImpactImageInfo`, `ImpactMinMax`, `ImpactNeg`, `ImpactConditionalStopIteration`
+  * `ImpactIsNotEmptySEGS` - This node returns `true` only if the input SEGS is not empty. 
+  * `ImpactIfNone` - Returns `true` if any_input is None, and returns `false` if it is not None.
+  * `Queue Trigger` - When this node is executed, it adds a new queue to assist with repetitive tasks. It will only execute if the signal's status changes.
+  * `Queue Trigger (Countdown)` - Like the Queue Trigger, it adds a queue, but only adds it if it's greater than 1, and decrements the count by one each time it runs.
+  * `Sleep` - Waits for the specified time (in seconds).
+  * `Set Widget Value` - This node sets one of the optional inputs to the specified node's widget. An error may occur if the types do not match.
+  * `Set Mute State` - This node changes the mute state of a specific node.
+  * `Control Bridge` - This node modifies the state of the connected control nodes based on the `mode` and `behavior` . If there are nodes that require a change, the current execution is paused, the mute status is updated, and a new prompt queue is inserted. 
+    * When the `mode` is `active`, it makes the connected control nodes active regardless of the behavior. 
+    * When the `mode` is `Bypass/Mute`, it changes the state of the connected nodes based on whether the behavior is `Bypass` or `Mute`.
     * **Limitation**: Due to these characteristics, it does not function correctly when the batch count exceeds 1. Additionally, it does not guarantee proper operation when the seed is randomized or when the state of nodes is altered by actions such as `Queue Trigger`, `Set Widget Value`, `Set Mute`, before the Control Bridge.
     * When utilizing this node, please structure the workflow in such a way that `Queue Trigger`, `Set Widget Value`, `Set Mute State`, and similar actions are executed at the end of the workflow.
     * If you want to change the value of the seed at each iteration, please ensure that Set Widget Value is executed at the end of the workflow instead of using randomization.
       * It is not a problem if the seed changes due to randomization as long as it occurs after the Control Bridge section.
+  * `Remote Boolean (on prompt)`, `Remote Int (on prompt)` - At the start of the prompt, this node forcibly sets the `widget_value` of `node_id`. It is disregarded if the target widget type is different.
   * You can find the `node_id` by checking through [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) using the format `Badge: #ID Nickname`.
   * Experimental set of nodes for implementing loop functionality (tutorial to be prepared later / [example workflow](test/loop-test.json)).
 
-* HuggingFace - These nodes provide functionalities based on HuggingFace repository models.
+### HuggingFace nodes
+  * These nodes provide functionalities based on HuggingFace repository models.
+  * The path where the HuggingFace model cache is stored can be changed through the `HF_HOME` environment variable.
   * `HF Transformers Classifier Provider` - This is a node that provides a classifier based on HuggingFace's transformers models.
     * The 'repo id' parameter should contain HuggingFace's repo id. When `preset_repo_id` is set to `Manual repo id`, use the manually entered repo id in `manual_repo_id`.
     * e.g. 'rizvandwiki/gender-classification-2' is a repository that provides a model for gender classification.
@@ -207,79 +303,62 @@ This takes latent as input and outputs latent as the result.
       * For supported labels, please refer to the `config.json` of the respective HuggingFace repository.
     * `#Female` and `#Male` are symbols that group multiple labels such as `Female, women, woman, ...`, for convenience, rather than being single labels.
 
-## 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.
+
+### Etc nodes
+  * `Impact Scheduler Adapter` - With the addition of AYS to the scheduler of the Impact Pack and Inspire Pack, there is an issue of incompatibility when the existing scheduler widget is converted to input. The Impact Scheduler Adapter allows for an indirect connection to be possible.
+  * `StringListToString` - Convert String List to String
+  * `WildcardPromptFromString` - Create labeled wildcard for detailer from string. 
+    * This node works well when used with MakeTileSEGS. [[Link](https://github.com/ltdrdata/ComfyUI-Impact-Pack/pull/536#discussion_r1586060779)]
+
+  * `String Selector` - It selects and returns a portion of the string. When `multiline` mode is disabled, it simply returns the string of the line pointed to by the selector. When `multiline` mode is enabled, it divides the string based on lines that start with `#` and returns them. If the `select` value is larger than the number of items, it will start counting from the first line again and return accordingly.
+  * `Combine Conditionings` - It takes multiple conditionings as input and combines them into a single conditioning.
+  * `Concat Conditionings` - It takes multiple conditionings as input and concat them into a single conditioning.
+  * `Negative Cond Placeholder` - Models like FLUX.1 do not use Negative Conditioning. This is a placeholder node for them. You can use FLUX.1 by replacing the Negative Conditioning used in Impact KSampler, KSampler (Inspire), and Detailer with this node.
+  * `Execution Order Controller` - A helper node that can forcibly control the execution order of nodes.
+    * Connect the output of the node that should be executed first to the signal, and make the input of the node that should be executed later pass through this node.
+  * `List Bridge` - When passing the list output through this node, it collects and organizes the data before forwarding it, which ensures that the previous stage's sub-workflow has been completed.
 
 
 ## 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)'.
+* `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)'.
 * Providing a feature to detect errors that occur when mixing models and clips from checkpoints such as `SDXL Base`, `SDXL Refiner`, `SD1.x`, `SD2.x` during sample execution, and reporting appropriate errors.
 
-## Deprecated
-* The following nodes have been kept only for compatibility with existing workflows, and are no longer supported. Please replace them with new nodes.
-   * ONNX Detector (SEGS) - BBOX Detector (SEGS)
-   * 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 Install?
 
-## Ultralytics models
-* huggingface.co/Bingsu/[adetailer](https://github.com/ultralytics/assets/releases/) - You can download face, people detection models, and clothing detection models.
-* ultralytics/[assets](https://github.com/ultralytics/assets/releases/) - You can download various types of detection models other than faces or people.
-
-## 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
+### Install via ComfyUI-Manager (Recommended)
+* Search `ComfyUI Impact Pack` in ComfyUI-Manager and click `Install` button.
 
+### Manual Install (Not Recommended)
 1. `cd custom_nodes`
-1. `git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack.git`
+2. `git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack`
 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_embeded\python.exe install.py` to run the installation script.
+4. `pip install -r requirements.txt`
+    * **IMPORTANT**:
+        * You must install it within the Python environment where ComfyUI is running.
+        * For the portable version, use `<installed path>\python_embeded\python.exe -m pip` instead of `pip`. For a `venv`, activate the `venv` first and then use `pip`.
+5. Restart ComfyUI
 
+* NOTE1: If an error occurs during the installation process, please refer to [Troubleshooting Page](troubleshooting/TROUBLESHOOTING.md) for assistance. 
+* NOTE2: 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.
+* NOTE3: If you create an empty file named `skip_download_model` in the `ComfyUI/custom_nodes/` directory, it will skip the model download step during the installation of the impact pack.
 
-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
+   * piexif 
+   * opencv-python
+   * scipy
+   * numpy<2
+   * dill
+   * matplotlib
    * (optional) onnxruntime
+   * (deprecated) openmim      # for mim
+   * (deprecated) pycocotools  # for mim
    
-* mim install (optional)
-   * mmcv==2.0.0, mmdet==3.0.0, mmengine==0.7.2
-
 * linux packages (ubuntu)
   * libgl1-mesa-glx
   * libglib2.0-0
@@ -302,17 +381,16 @@ sam_editor_model = sam_vit_b_01ec64.pth
 ```
 
 
-## Other Materials (auto-download on initial startup)
+## Other Materials (auto-download when installing)
 
-* 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)
+## 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)
@@ -403,9 +481,10 @@ open-mmlab/[mmdetection](https://github.com/open-mmlab/mmdetection) - Object det
 
 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.
+BlenderNeok/[ComfyUI-TiledKSampler](https://github.com/BlenderNeko/ComfyUI_TiledKSampler) - The tile sampler allows high-resolution sampling even in places with low GPU VRAM.
 
-BlenderNeok/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - The noise injection feature relies on this function.
+BlenderNeok/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - The noise injection feature relies on this function and slerp code for noise variation
 
 WASasquatch/[was-node-suite-comfyui](https://github.com/WASasquatch/was-node-suite-comfyui) - A powerful custom node extensions of ComfyUI.
+
+Trung0246/[ComfyUI-0246](https://github.com/Trung0246/ComfyUI-0246) - Nice bypass hack!

__init__.py

@@ -13,45 +13,20 @@ import traceback
 
 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__), "impact_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)
 
-
 import impact.config
-import impact.sample_error_enhancer
 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 not os.path.exists(os.path.join(subpack_path, ".git")) and os.path.exists(subpack_path):
-    print(f"### CompfyUI-Impact-Pack: corrupted subpack detected.")
-    shutil.rmtree(subpack_path)
-
-if impact.config.get_config()['dependency_version'] < impact.config.dependency_version or not os.path.exists(subpack_path):
-    print(f"### ComfyUI-Impact-Pack: Updating dependencies [{impact.config.get_config()['dependency_version']} -> {impact.config.dependency_version}]")
-    do_install()
-
-sys.path.append(subpack_path)
-
 # Core
 # recheck dependencies for colab
 try:
-    import impact.subpack_nodes  # This import must be done before cv2.
-
     import folder_paths
     import torch
     import cv2
+    from cv2 import setNumThreads
     import numpy as np
     import comfy.samplers
     import comfy.sd
@@ -65,52 +40,32 @@ try:
         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()
+except Exception as e:
+    import logging
+    logging.error("[Impact Pack] Failed to import due to several dependencies are missing!!!!")
+    raise e
+
 
 import impact.impact_server  # to load server api
 
-def setup_js():
-    import nodes
-    js_dest_path = os.path.join(comfy_path, "web", "extensions", "impact-pack")
+from .modules.impact.impact_pack import *
+from .modules.impact.detectors import *
+from .modules.impact.pipe import *
+from .modules.impact.logics import *
+from .modules.impact.util_nodes import *
+from .modules.impact.segs_nodes import *
+from .modules.impact.special_samplers import *
+from .modules.impact.hf_nodes import *
+from .modules.impact.bridge_nodes import *
+from .modules.impact.hook_nodes import *
+from .modules.impact.animatediff_nodes import *
+from .modules.impact.segs_upscaler import *
 
-    if hasattr(nodes, "EXTENSION_WEB_DIRS"):
-        if os.path.exists(js_dest_path):
-            shutil.rmtree(js_dest_path)
-    else:
-        print(f"[WARN] ComfyUI-Impact-Pack: Your ComfyUI version is outdated. Please update to the latest version.")
-        # setup js
-        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)
+import threading
 
 
-setup_js()
+threading.Thread(target=impact.wildcards.wildcard_load).start()
 
-from impact.impact_pack import *
-from impact.detectors import *
-from impact.pipe import *
-from impact.logics import *
-from impact.util_nodes import *
-from impact.segs_nodes import *
-from impact.special_samplers import *
-from impact.hf_nodes import *
-
-impact.wildcards.read_wildcard_dict(wildcards_path)
-try:
-    impact.wildcards.read_wildcard_dict(impact.config.get_config()['custom_wildcards'])
-except Exception as e:
-    print(f"[Impact Pack] Failed to load custom wildcards directory.")
 
 NODE_CLASS_MAPPINGS = {
     "SAMLoader": SAMLoader,
@@ -124,6 +79,7 @@ NODE_CLASS_MAPPINGS = {
     "DetailerForEachDebug": DetailerForEachTest,
     "DetailerForEachPipe": DetailerForEachPipe,
     "DetailerForEachDebugPipe": DetailerForEachTestPipe,
+    "DetailerForEachPipeForAnimateDiff": DetailerForEachPipeForAnimateDiff,
 
     "SAMDetectorCombined": SAMDetectorCombined,
     "SAMDetectorSegmented": SAMDetectorSegmented,
@@ -137,6 +93,7 @@ NODE_CLASS_MAPPINGS = {
     "FromDetailerPipe": FromDetailerPipe,
     "FromDetailerPipe_v2": FromDetailerPipe_v2,
     "FromDetailerPipeSDXL": FromDetailerPipe_SDXL,
+    "AnyPipeToBasic": AnyPipeToBasic,
     "ToBasicPipe": ToBasicPipe,
     "FromBasicPipe": FromBasicPipe,
     "FromBasicPipe_v2": FromBasicPipe_v2,
@@ -159,17 +116,32 @@ NODE_CLASS_MAPPINGS = {
 
     "PixelKSampleHookCombine": PixelKSampleHookCombine,
     "DenoiseScheduleHookProvider": DenoiseScheduleHookProvider,
+    "StepsScheduleHookProvider": StepsScheduleHookProvider,
     "CfgScheduleHookProvider": CfgScheduleHookProvider,
     "NoiseInjectionHookProvider": NoiseInjectionHookProvider,
-    "NoiseInjectionDetailerHookProvider": NoiseInjectionDetailerHookProvider,
+    "UnsamplerHookProvider": UnsamplerHookProvider,
     "CoreMLDetailerHookProvider": CoreMLDetailerHookProvider,
+    "PreviewDetailerHookProvider": PreviewDetailerHookProvider,
+
+    "DetailerHookCombine": DetailerHookCombine,
+    "NoiseInjectionDetailerHookProvider": NoiseInjectionDetailerHookProvider,
+    "UnsamplerDetailerHookProvider": UnsamplerDetailerHookProvider,
+    "DenoiseSchedulerDetailerHookProvider": DenoiseSchedulerDetailerHookProvider,
+    "SEGSOrderedFilterDetailerHookProvider": SEGSOrderedFilterDetailerHookProvider,
+    "SEGSRangeFilterDetailerHookProvider": SEGSRangeFilterDetailerHookProvider,
+    "SEGSLabelFilterDetailerHookProvider": SEGSLabelFilterDetailerHookProvider,
+    "VariationNoiseDetailerHookProvider": VariationNoiseDetailerHookProvider,
+    # "CustomNoiseDetailerHookProvider": CustomNoiseDetailerHookProvider,
 
     "BitwiseAndMask": BitwiseAndMask,
     "SubtractMask": SubtractMask,
     "AddMask": AddMask,
+    "MaskRectArea": MaskRectArea,
+    "MaskRectAreaAdvanced": MaskRectAreaAdvanced,
     "ImpactSegsAndMask": SegsBitwiseAndMask,
     "ImpactSegsAndMaskForEach": SegsBitwiseAndMaskForEach,
     "EmptySegs": EmptySEGS,
+    "ImpactFlattenMask": FlattenMask,
 
     "MediaPipeFaceMeshToSEGS": MediaPipeFaceMeshToSEGS,
     "MaskToSEGS": MaskToSEGS,
@@ -177,7 +149,9 @@ NODE_CLASS_MAPPINGS = {
     "ToBinaryMask": ToBinaryMask,
     "MasksToMaskList": MasksToMaskList,
     "MaskListToMaskBatch": MaskListToMaskBatch,
-    "ImageListToImageBatch": ImageListToMaskBatch,
+    "ImageListToImageBatch": ImageListToImageBatch,
+    "SetDefaultImageForSEGS": DefaultImageForSEGS,
+    "RemoveImageFromSEGS": RemoveImageFromSEGS,
 
     "BboxDetectorSEGS": BboxDetectorForEach,
     "SegmDetectorSEGS": SegmDetectorForEach,
@@ -186,6 +160,9 @@ NODE_CLASS_MAPPINGS = {
     "ImpactSimpleDetectorSEGS": SimpleDetectorForEach,
     "ImpactSimpleDetectorSEGSPipe": SimpleDetectorForEachPipe,
     "ImpactControlNetApplySEGS": ControlNetApplySEGS,
+    "ImpactControlNetApplyAdvancedSEGS": ControlNetApplyAdvancedSEGS,
+    "ImpactControlNetClearSEGS": ControlNetClearSEGS,
+    "ImpactIPAdapterApplySEGS": IPAdapterApplySEGS,
 
     "ImpactDecomposeSEGS": DecomposeSEGS,
     "ImpactAssembleSEGS": AssembleSEGS,
@@ -193,7 +170,13 @@ NODE_CLASS_MAPPINGS = {
     "ImpactEdit_SEG_ELT": Edit_SEG_ELT,
     "ImpactDilate_Mask_SEG_ELT": Dilate_SEG_ELT,
     "ImpactDilateMask": DilateMask,
+    "ImpactGaussianBlurMask": GaussianBlurMask,
+    "ImpactDilateMaskInSEGS": DilateMaskInSEGS,
+    "ImpactGaussianBlurMaskInSEGS": GaussianBlurMaskInSEGS,
     "ImpactScaleBy_BBOX_SEG_ELT": SEG_ELT_BBOX_ScaleBy,
+    "ImpactFrom_SEG_ELT_bbox": From_SEG_ELT_bbox,
+    "ImpactFrom_SEG_ELT_crop_region": From_SEG_ELT_crop_region,
+    "ImpactCount_Elts_in_SEGS": Count_Elts_in_SEGS,
 
     "BboxDetectorCombined_v2": BboxDetectorCombined,
     "SegmDetectorCombined_v2": SegmDetectorCombined,
@@ -206,7 +189,10 @@ NODE_CLASS_MAPPINGS = {
     "KSamplerAdvancedProvider": KSamplerAdvancedProvider,
     "TwoAdvancedSamplersForMask": TwoAdvancedSamplersForMask,
 
+    "ImpactNegativeConditioningPlaceholder": NegativeConditioningPlaceholder,
+
     "PreviewBridge": PreviewBridge,
+    "PreviewBridgeLatent": PreviewBridgeLatent,
     "ImageSender": ImageSender,
     "ImageReceiver": ImageReceiver,
     "LatentSender": LatentSender,
@@ -220,14 +206,19 @@ NODE_CLASS_MAPPINGS = {
     "ImpactWildcardProcessor": ImpactWildcardProcessor,
     "ImpactWildcardEncode": ImpactWildcardEncode,
 
+    "SEGSUpscaler": SEGSUpscaler,
+    "SEGSUpscalerPipe": SEGSUpscalerPipe,
     "SEGSDetailer": SEGSDetailer,
     "SEGSPaste": SEGSPaste,
     "SEGSPreview": SEGSPreview,
+    "SEGSPreviewCNet": SEGSPreviewCNet,
     "SEGSToImageList": SEGSToImageList,
     "ImpactSEGSToMaskList": SEGSToMaskList,
     "ImpactSEGSToMaskBatch": SEGSToMaskBatch,
     "ImpactSEGSConcat": SEGSConcat,
     "ImpactSEGSPicker": SEGSPicker,
+    "ImpactMakeTileSEGS": MakeTileSEGS,
+    "ImpactSEGSMerge": SEGSMerge,
 
     "SEGSDetailerForAnimateDiff": SEGSDetailerForAnimateDiff,
 
@@ -240,6 +231,10 @@ NODE_CLASS_MAPPINGS = {
     "ImpactImageBatchToImageList": ImageBatchToImageList,
     "ImpactMakeImageList": MakeImageList,
     "ImpactMakeImageBatch": MakeImageBatch,
+    "ImpactMakeAnyList": MakeAnyList,
+    "ImpactMakeMaskList": MakeMaskList,
+    "ImpactMakeMaskBatch": MakeMaskBatch,
+    "ImpactSelectNthItemOfAnyList": NthItemOfAnyList,
 
     "RegionalSampler": RegionalSampler,
     "RegionalSamplerAdvanced": RegionalSamplerAdvanced,
@@ -247,22 +242,36 @@ NODE_CLASS_MAPPINGS = {
     "RegionalPrompt": RegionalPrompt,
 
     "ImpactCombineConditionings": CombineConditionings,
+    "ImpactConcatConditionings": ConcatConditionings,
 
+    "ImpactSEGSLabelAssign": SEGSLabelAssign,
     "ImpactSEGSLabelFilter": SEGSLabelFilter,
     "ImpactSEGSRangeFilter": SEGSRangeFilter,
     "ImpactSEGSOrderedFilter": SEGSOrderedFilter,
+    "ImpactSEGSIntersectionFilter": SEGSIntersectionFilter,
+    "ImpactSEGSNMSFilter": SEGSNMSFilter,
 
     "ImpactCompare": ImpactCompare,
     "ImpactConditionalBranch": ImpactConditionalBranch,
+    "ImpactConditionalBranchSelMode": ImpactConditionalBranchSelMode,
+    "ImpactIfNone": ImpactIfNone,
+    "ImpactConvertDataType": ImpactConvertDataType,
+    "ImpactLogicalOperators": ImpactLogicalOperators,
     "ImpactInt": ImpactInt,
-    # "ImpactFloat": ImpactFloat,
+    "ImpactFloat": ImpactFloat,
+    "ImpactBoolean": ImpactBoolean,
     "ImpactValueSender": ImpactValueSender,
     "ImpactValueReceiver": ImpactValueReceiver,
     "ImpactImageInfo": ImpactImageInfo,
+    "ImpactLatentInfo": ImpactLatentInfo,
     "ImpactMinMax": ImpactMinMax,
     "ImpactNeg": ImpactNeg,
     "ImpactConditionalStopIteration": ImpactConditionalStopIteration,
     "ImpactStringSelector": StringSelector,
+    "StringListToString": StringListToString,
+    "WildcardPromptFromString": WildcardPromptFromString,
+    "ImpactExecutionOrderController": ImpactExecutionOrderController,
+    "ImpactListBridge": ImpactListBridge,
 
     "RemoveNoiseMask": RemoveNoiseMask,
 
@@ -276,20 +285,29 @@ NODE_CLASS_MAPPINGS = {
     "ImpactControlBridge": ImpactControlBridge,
     "ImpactIsNotEmptySEGS": ImpactNotEmptySEGS,
     "ImpactSleep": ImpactSleep,
+    "ImpactRemoteBoolean": ImpactRemoteBoolean,
+    "ImpactRemoteInt": ImpactRemoteInt,
 
     "ImpactHFTransformersClassifierProvider": HF_TransformersClassifierProvider,
-    "ImpactSEGSClassify": SEGS_Classify
+    "ImpactSEGSClassify": SEGS_Classify,
+
+    "ImpactSchedulerAdapter": ImpactSchedulerAdapter,
+    "GITSSchedulerFuncProvider": GITSSchedulerFuncProvider
 }
 
 
 NODE_DISPLAY_NAME_MAPPINGS = {
+    "SAMLoader": "SAMLoader (Impact)",
+
     "BboxDetectorSEGS": "BBOX Detector (SEGS)",
     "SegmDetectorSEGS": "SEGM Detector (SEGS)",
     "ONNXDetectorSEGS": "ONNX Detector (SEGS/legacy) - use BBOXDetector",
     "ImpactSimpleDetectorSEGS_for_AD": "Simple Detector for AnimateDiff (SEGS)",
     "ImpactSimpleDetectorSEGS": "Simple Detector (SEGS)",
     "ImpactSimpleDetectorSEGSPipe": "Simple Detector (SEGS/pipe)",
-    "ImpactControlNetApplySEGS": "ControlNetApply (SEGS)",
+    "ImpactControlNetApplySEGS": "ControlNetApply (SEGS) - DEPRECATED",
+    "ImpactControlNetApplyAdvancedSEGS": "ControlNetApply (SEGS)",
+    "ImpactIPAdapterApplySEGS": "IPAdapterApply (SEGS)",
 
     "BboxDetectorCombined_v2": "BBOX Detector (combined)",
     "SegmDetectorCombined_v2": "SEGM Detector (combined)",
@@ -297,23 +315,29 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "MediaPipeFaceMeshToSEGS": "MediaPipe FaceMesh to SEGS",
     "MaskToSEGS": "MASK to SEGS",
     "MaskToSEGS_for_AnimateDiff": "MASK to SEGS for AnimateDiff",
-    "BitwiseAndMaskForEach": "Bitwise(SEGS & SEGS)",
-    "SubtractMaskForEach": "Bitwise(SEGS - SEGS)",
-    "ImpactSegsAndMask": "Bitwise(SEGS & MASK)",
-    "ImpactSegsAndMaskForEach": "Bitwise(SEGS & MASKS ForEach)",
-    "BitwiseAndMask": "Bitwise(MASK & MASK)",
-    "SubtractMask": "Bitwise(MASK - MASK)",
-    "AddMask": "Bitwise(MASK + MASK)",
+    "BitwiseAndMaskForEach": "Pixelwise(SEGS & SEGS)",
+    "SubtractMaskForEach": "Pixelwise(SEGS - SEGS)",
+    "ImpactSegsAndMask": "Pixelwise(SEGS & MASK)",
+    "ImpactSegsAndMaskForEach": "Pixelwise(SEGS & MASKS ForEach)",
+    "BitwiseAndMask": "Pixelwise(MASK & MASK)",
+    "SubtractMask": "Pixelwise(MASK - MASK)",
+    "AddMask": "Pixelwise(MASK + MASK)",
+    "MaskRectArea": "Mask Rect Area",
+    "MaskRectAreaAdvanced": "Mask Rect Area (Advanced)",
+    "ImpactFlattenMask": "Flatten Mask Batch",
     "DetailerForEach": "Detailer (SEGS)",
     "DetailerForEachPipe": "Detailer (SEGS/pipe)",
     "DetailerForEachDebug": "DetailerDebug (SEGS)",
     "DetailerForEachDebugPipe": "DetailerDebug (SEGS/pipe)",
-    "SEGSDetailerForAnimateDiff": "Detailer For AnimateDiff (SEGS/pipe)",
+    "SEGSDetailerForAnimateDiff": "SEGSDetailer For AnimateDiff (SEGS/pipe)",
+    "DetailerForEachPipeForAnimateDiff": "Detailer For AnimateDiff (SEGS/pipe)",
+    "SEGSUpscaler": "Upscaler (SEGS)",
+    "SEGSUpscalerPipe": "Upscaler (SEGS/pipe)",
 
     "SAMDetectorCombined": "SAMDetector (combined)",
     "SAMDetectorSegmented": "SAMDetector (segmented)",
     "FaceDetailerPipe": "FaceDetailer (pipe)",
-    "MaskDetailerPipe": "MaskDetailer (Pipe)",
+    "MaskDetailerPipe": "MaskDetailer (pipe)",
 
     "FromDetailerPipeSDXL": "FromDetailer (SDXL/pipe)",
     "BasicPipeToDetailerPipeSDXL": "BasicPipe -> DetailerPipe (SDXL)",
@@ -323,9 +347,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "DetailerPipeToBasicPipe": "DetailerPipe -> BasicPipe",
     "EditBasicPipe": "Edit BasicPipe",
     "EditDetailerPipe": "Edit DetailerPipe",
+    "AnyPipeToBasic": "Any PIPE -> BasicPipe",
 
     "LatentPixelScale": "Latent Scale (on Pixel Space)",
-    "IterativeLatentUpscale": "Iterative Upscale (Latent)",
+    "IterativeLatentUpscale": "Iterative Upscale (Latent/on Pixel Space)",
     "IterativeImageUpscale": "Iterative Upscale (Image)",
 
     "TwoSamplersForMaskUpscalerProvider": "TwoSamplersForMask Upscaler Provider",
@@ -336,41 +361,66 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 
     "ImpactKSamplerBasicPipe": "KSampler (pipe)",
     "ImpactKSamplerAdvancedBasicPipe": "KSampler (Advanced/pipe)",
+    "ImpactSEGSLabelAssign": "SEGS Assign (label)",
     "ImpactSEGSLabelFilter": "SEGS Filter (label)",
     "ImpactSEGSRangeFilter": "SEGS Filter (range)",
     "ImpactSEGSOrderedFilter": "SEGS Filter (ordered)",
+    "ImpactSEGSIntersectionFilter": "SEGS Filter (intersection)",
+    "ImpactSEGSNMSFilter": "SEGS Filter (non max suppression)",
     "ImpactSEGSConcat": "SEGS Concat",
     "ImpactSEGSToMaskList": "SEGS to Mask List",
     "ImpactSEGSToMaskBatch": "SEGS to Mask Batch",
     "ImpactSEGSPicker": "Picker (SEGS)",
+    "ImpactMakeTileSEGS": "Make Tile SEGS",
+    "ImpactSEGSMerge": "SEGS Merge",
 
     "ImpactDecomposeSEGS": "Decompose (SEGS)",
     "ImpactAssembleSEGS": "Assemble (SEGS)",
     "ImpactFrom_SEG_ELT": "From SEG_ELT",
     "ImpactEdit_SEG_ELT": "Edit SEG_ELT",
+    "ImpactFrom_SEG_ELT_bbox": "From SEG_ELT bbox",
+    "ImpactFrom_SEG_ELT_crop_region": "From SEG_ELT crop_region",
     "ImpactDilate_Mask_SEG_ELT": "Dilate Mask (SEG_ELT)",
     "ImpactScaleBy_BBOX_SEG_ELT": "ScaleBy BBOX (SEG_ELT)",
+    "ImpactCount_Elts_in_SEGS": "Count Elts in SEGS",
     "ImpactDilateMask": "Dilate Mask",
+    "ImpactGaussianBlurMask": "Gaussian Blur Mask",
+    "ImpactDilateMaskInSEGS": "Dilate Mask (SEGS)",
+    "ImpactGaussianBlurMaskInSEGS": "Gaussian Blur Mask (SEGS)",
 
-    "PreviewBridge": "Preview Bridge",
+    "PreviewBridge": "Preview Bridge (Image)",
+    "PreviewBridgeLatent": "Preview Bridge (Latent)",
     "ImageSender": "Image Sender",
     "ImageReceiver": "Image Receiver",
     "ImageMaskSwitch": "Switch (images, mask)",
     "ImpactSwitch": "Switch (Any)",
     "ImpactInversedSwitch": "Inversed Switch (Any)",
+    "ImpactExecutionOrderController": "Execution Order Controller",
+    "ImpactListBridge": "List Bridge",
 
-    "MasksToMaskList": "Masks to Mask List",
-    "MaskListToMaskBatch": "Mask List to Masks",
-    "ImpactImageBatchToImageList": "Image batch to Image List",
+    "MasksToMaskList": "Mask Batch to Mask List",
+    "MaskListToMaskBatch": "Mask List to Mask Batch",
+    "ImpactImageBatchToImageList": "Image Batch to Image List",
     "ImageListToImageBatch": "Image List to Image Batch",
+
     "ImpactMakeImageList": "Make Image List",
     "ImpactMakeImageBatch": "Make Image Batch",
+    "ImpactMakeMaskList": "Make Mask List",
+    "ImpactMakeMaskBatch": "Make Mask Batch",
+    "ImpactMakeAnyList": "Make List (Any)",
+    "ImpactSelectNthItemOfAnyList": "Select Nth Item (Any list)",
+
     "ImpactStringSelector": "String Selector",
+    "StringListToString": "String List to String",
+    "WildcardPromptFromString": "Wildcard Prompt from String",
     "ImpactIsNotEmptySEGS": "SEGS isn't Empty",
+    "SetDefaultImageForSEGS": "Set Default Image for SEGS",
+    "RemoveImageFromSEGS": "Remove Image from SEGS",
 
     "RemoveNoiseMask": "Remove Noise Mask",
 
     "ImpactCombineConditionings": "Combine Conditionings",
+    "ImpactConcatConditionings": "Concat Conditionings",
 
     "ImpactQueueTrigger": "Queue Trigger",
     "ImpactQueueTriggerCountdown": "Queue Trigger (Countdown)",
@@ -378,12 +428,20 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImpactNodeSetMuteState": "Set Mute State",
     "ImpactControlBridge": "Control Bridge",
     "ImpactSleep": "Sleep",
+    "ImpactRemoteBoolean": "Remote Boolean (on prompt)",
+    "ImpactRemoteInt": "Remote Int (on prompt)",
 
     "ImpactHFTransformersClassifierProvider": "HF Transformers Classifier Provider",
     "ImpactSEGSClassify": "SEGS Classify",
 
     "LatentSwitch": "Switch (latent/legacy)",
-    "SEGSSwitch": "Switch (SEGS/legacy)"
+    "SEGSSwitch": "Switch (SEGS/legacy)",
+
+    "SEGSPreviewCNet": "SEGSPreview (CNET Image)",
+
+    "ImpactSchedulerAdapter": "Impact Scheduler Adapter",
+    "GITSSchedulerFuncProvider": "GITSScheduler Func Provider",
+    "ImpactNegativeConditioningPlaceholder": "Negative Cond Placeholder"
 }
 
 if not impact.config.get_config()['mmdet_skip']:
@@ -410,19 +468,23 @@ if not impact.config.get_config()['mmdet_skip']:
         "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 Exception as e:
-    print("### ComfyUI-Impact-Pack: (IMPORT FAILED) Subpack\n")
-    print("  The module at the `custom_nodes/ComfyUI-Impact-Pack/impact_subpack` path appears to be incomplete.")
-    print("  Recommended to delete the path and restart ComfyUI.")
-    print("  If the issue persists, please report it to https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues.")
-    print("\n---------------------------------")
-    traceback.print_exc()
-    print("---------------------------------\n")
+# NOTE:  Inject directly into EXTENSION_WEB_DIRS instead of WEB_DIRECTORY
+#        Provide the js path fixed as ComfyUI-Impact-Pack instead of the path name, making it available for external use
+
+# WEB_DIRECTORY = "js"  -- deprecated method
+nodes.EXTENSION_WEB_DIRS["ComfyUI-Impact-Pack"] = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'js')
+
 
-WEB_DIRECTORY = "js"
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
+
+
+try:
+    import cm_global
+    cm_global.register_extension('ComfyUI-Impact-Pack',
+                                 {'version': config.version_code,
+                                  'name': 'Impact Pack',
+                                  'nodes': set(NODE_CLASS_MAPPINGS.keys()),
+                                  'description': 'This extension provides inpainting functionality based on the detector and detailer, along with convenient workflow features like wildcards and logics.', })
+except:
+    pass

example_workflows/2-MaskDetailer.json

@@ -0,0 +1,596 @@
+{
+  "last_node_id": 5,
+  "last_link_id": 5,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "LoadImage",
+      "pos": [
+        30,
+        210
+      ],
+      "size": [
+        390,
+        320
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "shape": 3,
+          "links": [
+            1
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "shape": 3,
+          "links": [
+            2
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "clipspace/clipspace-mask-609196.2000000011.png [input]",
+        "image"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "PreviewImage",
+      "pos": [
+        1230,
+        210
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 5
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 3,
+      "type": "workflow>Impact::MAKE_BASIC_PIPE",
+      "pos": [
+        20,
+        620
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "basic_pipe",
+          "type": "BASIC_PIPE",
+          "shape": 3,
+          "links": [
+            3
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "workflow/Impact::MAKE_BASIC_PIPE"
+      },
+      "widgets_values": [
+        "SD1.5/realcartoon3d_v13.safetensors",
+        "(best quality:1.4), fox girl",
+        "(worst quality:1.4), nsfw"
+      ]
+    },
+    {
+      "id": 2,
+      "type": "MaskDetailerPipe",
+      "pos": [
+        530,
+        210
+      ],
+      "size": [
+        569.4000244140625,
+        850
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 1
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 2
+        },
+        {
+          "name": "basic_pipe",
+          "type": "BASIC_PIPE",
+          "link": 3,
+          "slot_index": 2
+        },
+        {
+          "name": "refiner_basic_pipe_opt",
+          "type": "BASIC_PIPE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "detailer_hook",
+          "type": "DETAILER_HOOK",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "scheduler_func_opt",
+          "type": "SCHEDULER_FUNC",
+          "shape": 7,
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "shape": 3,
+          "links": [
+            5
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "cropped_refined",
+          "type": "IMAGE",
+          "shape": 6,
+          "links": null
+        },
+        {
+          "name": "cropped_enhanced_alpha",
+          "type": "IMAGE",
+          "shape": 6,
+          "links": [
+            4
+          ],
+          "slot_index": 2
+        },
+        {
+          "name": "basic_pipe",
+          "type": "BASIC_PIPE",
+          "shape": 3,
+          "links": null
+        },
+        {
+          "name": "refiner_basic_pipe_opt",
+          "type": "BASIC_PIPE",
+          "shape": 3,
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MaskDetailerPipe"
+      },
+      "widgets_values": [
+        512,
+        true,
+        1024,
+        true,
+        1003,
+        "fixed",
+        20,
+        8,
+        "euler",
+        "normal",
+        0.75,
+        5,
+        3,
+        10,
+        0.2,
+        1,
+        1,
+        false,
+        20,
+        false,
+        false
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 4,
+      "type": "PreviewImage",
+      "pos": [
+        1230,
+        560
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 4
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      2,
+      0,
+      "IMAGE"
+    ],
+    [
+      2,
+      1,
+      1,
+      2,
+      1,
+      "MASK"
+    ],
+    [
+      3,
+      3,
+      0,
+      2,
+      2,
+      "BASIC_PIPE"
+    ],
+    [
+      4,
+      2,
+      2,
+      4,
+      0,
+      "IMAGE"
+    ],
+    [
+      5,
+      2,
+      0,
+      5,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 1,
+      "offset": [
+        80,
+        -110
+      ]
+    },
+    "groupNodes": {
+      "Impact::MAKE_BASIC_PIPE": {
+        "author": "Dr.Lt.Data",
+        "category": "",
+        "config": {
+          "1": {
+            "input": {
+              "text": {
+                "name": "Positive prompt"
+              }
+            }
+          },
+          "2": {
+            "input": {
+              "text": {
+                "name": "Negative prompt"
+              }
+            }
+          }
+        },
+        "datetime": 1708272471445,
+        "external": [],
+        "links": [
+          [
+            0,
+            1,
+            1,
+            0,
+            1,
+            "CLIP"
+          ],
+          [
+            0,
+            1,
+            2,
+            0,
+            1,
+            "CLIP"
+          ],
+          [
+            0,
+            0,
+            3,
+            0,
+            1,
+            "MODEL"
+          ],
+          [
+            0,
+            1,
+            3,
+            1,
+            1,
+            "CLIP"
+          ],
+          [
+            0,
+            2,
+            3,
+            2,
+            1,
+            "VAE"
+          ],
+          [
+            1,
+            0,
+            3,
+            3,
+            3,
+            "CONDITIONING"
+          ],
+          [
+            2,
+            0,
+            3,
+            4,
+            4,
+            "CONDITIONING"
+          ]
+        ],
+        "nodes": [
+          {
+            "flags": {},
+            "index": 0,
+            "mode": 0,
+            "order": 0,
+            "outputs": [
+              {
+                "links": [],
+                "name": "MODEL",
+                "shape": 3,
+                "slot_index": 0,
+                "type": "MODEL",
+                "localized_name": "MODEL"
+              },
+              {
+                "links": [],
+                "name": "CLIP",
+                "shape": 3,
+                "slot_index": 1,
+                "type": "CLIP",
+                "localized_name": "CLIP"
+              },
+              {
+                "links": [],
+                "name": "VAE",
+                "shape": 3,
+                "slot_index": 2,
+                "type": "VAE",
+                "localized_name": "VAE"
+              }
+            ],
+            "pos": [
+              550,
+              360
+            ],
+            "properties": {
+              "Node name for S&R": "CheckpointLoaderSimple"
+            },
+            "size": {
+              "0": 315,
+              "1": 98
+            },
+            "type": "CheckpointLoaderSimple",
+            "widgets_values": [
+              "SDXL/sd_xl_base_1.0_0.9vae.safetensors"
+            ],
+            "inputs": []
+          },
+          {
+            "flags": {},
+            "index": 1,
+            "inputs": [
+              {
+                "link": null,
+                "name": "clip",
+                "type": "CLIP",
+                "localized_name": "clip"
+              }
+            ],
+            "mode": 0,
+            "order": 1,
+            "outputs": [
+              {
+                "links": [],
+                "name": "CONDITIONING",
+                "shape": 3,
+                "slot_index": 0,
+                "type": "CONDITIONING",
+                "localized_name": "CONDITIONING"
+              }
+            ],
+            "pos": [
+              940,
+              480
+            ],
+            "properties": {
+              "Node name for S&R": "CLIPTextEncode"
+            },
+            "size": {
+              "0": 263,
+              "1": 99
+            },
+            "title": "Positive",
+            "type": "CLIPTextEncode",
+            "widgets_values": [
+              ""
+            ]
+          },
+          {
+            "flags": {},
+            "index": 2,
+            "inputs": [
+              {
+                "link": null,
+                "name": "clip",
+                "type": "CLIP",
+                "localized_name": "clip"
+              }
+            ],
+            "mode": 0,
+            "order": 2,
+            "outputs": [
+              {
+                "links": [],
+                "name": "CONDITIONING",
+                "shape": 3,
+                "slot_index": 0,
+                "type": "CONDITIONING",
+                "localized_name": "CONDITIONING"
+              }
+            ],
+            "pos": [
+              940,
+              640
+            ],
+            "properties": {
+              "Node name for S&R": "CLIPTextEncode"
+            },
+            "size": {
+              "0": 263,
+              "1": 99
+            },
+            "title": "Negative",
+            "type": "CLIPTextEncode",
+            "widgets_values": [
+              ""
+            ]
+          },
+          {
+            "flags": {},
+            "index": 3,
+            "inputs": [
+              {
+                "link": null,
+                "name": "model",
+                "type": "MODEL",
+                "localized_name": "model"
+              },
+              {
+                "link": null,
+                "name": "clip",
+                "type": "CLIP",
+                "localized_name": "clip"
+              },
+              {
+                "link": null,
+                "name": "vae",
+                "type": "VAE",
+                "localized_name": "vae"
+              },
+              {
+                "link": null,
+                "name": "positive",
+                "type": "CONDITIONING",
+                "localized_name": "positive"
+              },
+              {
+                "link": null,
+                "name": "negative",
+                "type": "CONDITIONING",
+                "localized_name": "negative"
+              }
+            ],
+            "mode": 0,
+            "order": 3,
+            "outputs": [
+              {
+                "links": null,
+                "name": "basic_pipe",
+                "shape": 3,
+                "slot_index": 0,
+                "type": "BASIC_PIPE",
+                "localized_name": "basic_pipe"
+              }
+            ],
+            "pos": [
+              1320,
+              360
+            ],
+            "properties": {
+              "Node name for S&R": "ToBasicPipe"
+            },
+            "size": {
+              "0": 241.79998779296875,
+              "1": 106
+            },
+            "type": "ToBasicPipe"
+          }
+        ],
+        "packname": "Impact",
+        "version": "1.0"
+      }
+    },
+    "controller_panel": {
+      "controllers": {},
+      "hidden": true,
+      "highlight": true,
+      "version": 2,
+      "default_order": []
+    },
+    "node_versions": {
+      "comfy-core": "0.3.14",
+      "comfyui-impact-pack": "1ae7cae2df8cca06027edfa3a24512671239d6c4"
+    },
+    "ue_links": [],
+    "VHS_latentpreview": false,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

install.py

@@ -5,7 +5,6 @@ import subprocess
 import threading
 import locale
 import traceback
-import re
 
 
 if sys.argv[0] == 'install.py':
@@ -13,10 +12,24 @@ if sys.argv[0] == 'install.py':
 
 
 impact_path = os.path.join(os.path.dirname(__file__), "modules")
-old_subpack_path = os.path.join(os.path.dirname(__file__), "subpack")
-subpack_path = os.path.join(os.path.dirname(__file__), "impact_subpack")
-subpack_repo = "https://github.com/ltdrdata/ComfyUI-Impact-Subpack"
-comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+
+
+comfy_path = os.environ.get('COMFYUI_PATH')
+if comfy_path is None:
+    print(f"\nWARN: The `COMFYUI_PATH` environment variable is not set. Assuming `{os.path.dirname(__file__)}/../../` as the ComfyUI path.", file=sys.stderr)
+    comfy_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+
+model_path = os.environ.get('COMFYUI_MODEL_PATH')
+if model_path is None:
+    try:
+        import folder_paths
+        model_path = folder_paths.models_dir
+    except:
+        pass
+
+    if model_path is None:
+        model_path = os.path.abspath(os.path.join(comfy_path, 'models'))
+    print(f"\nWARN: The `COMFYUI_MODEL_PATH` environment variable is not set. Assuming `{model_path}` as the ComfyUI path.", file=sys.stderr)
 
 
 sys.path.append(impact_path)
@@ -34,9 +47,9 @@ def handle_stream(stream, is_stdout):
             print(msg, end="", file=sys.stderr)
             
 
-def process_wrap(cmd_str, cwd=None, handler=None):
+def process_wrap(cmd_str, cwd=None, handler=None, env=None):
     print(f"[Impact Pack] EXECUTE: {cmd_str} in '{cwd}'")
-    process = subprocess.Popen(cmd_str, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1)
+    process = subprocess.Popen(cmd_str, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env, text=True, bufsize=1)
 
     if handler is None:
         handler = handle_stream
@@ -54,222 +67,39 @@ def process_wrap(cmd_str, cwd=None, handler=None):
 # ---
 
 
-pip_list = None
-
-
-def get_installed_packages():
-    global pip_list
-
-    if pip_list is None:
-        try:
-            result = subprocess.check_output([sys.executable, '-m', 'pip', 'list'], universal_newlines=True)
-            pip_list = set([line.split()[0].lower() for line in result.split('\n') if line.strip()])
-        except subprocess.CalledProcessError as e:
-            print(f"[ComfyUI-Manager] Failed to retrieve the information of installed pip packages.")
-            return set()
-    
-    return pip_list
-    
-
-def is_installed(name):
-    name = name.strip()
-    pattern = r'([^<>!=]+)([<>!=]=?)'
-    match = re.search(pattern, name)
-    
-    if match:
-        name = match.group(1)
-        
-    result = name.lower() in get_installed_packages()
-    return result
-    
-
-def is_requirements_installed(file_path):
-    print(f"req_path: {file_path}")
-    if os.path.exists(file_path):
-        with open(file_path, 'r') as file:
-            lines = file.readlines()
-            for line in lines:
-                if not is_installed(line):
-                    return False
-                    
-    return True
-
 try:
     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
-        if os.path.exists(subpack_path):
-            try:
-                repo = git.Repo(subpack_path)
-                repo.remotes.origin.pull()
-            except:
-                traceback.print_exc()
-                if platform.system() == 'Windows':
-                    print(f"[ComfyUI-Impact-Pack] Please turn off ComfyUI and remove '{subpack_path}' and restart ComfyUI.")
-                else:
-                    shutil.rmtree(subpack_path)
-                    git.Repo.clone_from(subpack_repo, subpack_path)
-        else:
-            git.Repo.clone_from(subpack_repo, subpack_path)
-
-        if os.path.exists(old_subpack_path):
-            shutil.rmtree(old_subpack_path)
-
-
-    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) and not is_requirements_installed(subpack_req):
-            process_wrap(pip_install + ['-r', 'requirements.txt'], cwd=subpack_path)
-
-        if not impact.config.get_config()['mmdet_skip']:
-            process_wrap(pip_install + ['openmim'])
-
-            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. !!")
-
-                    process_wrap(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]
-                    process_wrap(pip_install + [url])
-
-
-    def ensure_pip_packages_last():
-        my_path = os.path.dirname(__file__)
-        requirements_path = os.path.join(my_path, "requirements.txt")
-        
-        if not is_requirements_installed(requirements_path):
-            process_wrap(pip_install + ['-r', requirements_path])
-            
-        # fallback
-        try:
-            import segment_anything
-            from skimage.measure import label, regionprops
-            import piexif
-        except Exception:
-            process_wrap(pip_install + ['-r', requirements_path])
-
-        # !! cv2 importing test must be very last !!
-        try:
-            import cv2
-        except Exception:
-            try:
-                if not is_installed('opencv-python'):
-                    process_wrap(pip_install + ['opencv-python'])
-                if not is_installed('opencv-python-headless'):
-                    process_wrap(pip_install + ['opencv-python-headless'])
-            except:
-                print(f"[ERROR] ComfyUI-Impact-Pack: failed to install 'opencv-python'. Please, install manually.")
-
-        try:
-            import git
-        except Exception:
-            if not is_installed('gitpython'):
-                process_wrap(pip_install + ['gitpython'])
-
-    def ensure_mmdet_package():
-        try:
-            import mmcv
-            import mmdet
-            from mmdet.evaluation import get_classes
-        except Exception:
-            process_wrap(pip_install + ['opendatalab==0.0.9'])
-            process_wrap(pip_install + ['-U', 'openmim'])
-            process_wrap(mim_install + ['mmcv>=2.0.0rc4, <2.1.0'])
-            process_wrap(mim_install + ['mmdet==3.0.0'])
-            process_wrap(mim_install + ['mmengine==0.7.4'])
-
-
     def install():
-        remove_olds()
-
-        subpack_install_script = os.path.join(subpack_path, "install.py")
-
-        print(f"### ComfyUI-Impact-Pack: Updating subpack")
-        ensure_subpack()  # The installation of the subpack must take place before ensure_pip. cv2 triggers a permission error.
-
-        if os.path.exists(subpack_install_script):
-            process_wrap([sys.executable, 'install.py'], cwd=subpack_path)
-            if not is_requirements_installed(os.path.join(subpack_path, 'requirements.txt')):
-                process_wrap(pip_install + ['-r', 'requirements.txt'], cwd=subpack_path)
-        else:
-            print(f"### ComfyUI-Impact-Pack: (Install Failed) Subpack\nFile not found: `{subpack_install_script}`")
-
-        ensure_pip_packages_first()
-
-        if not impact.config.get_config()['mmdet_skip']:
-            ensure_mmdet_package()
-
-        ensure_pip_packages_last()
+        new_env = os.environ.copy()
+        new_env["COMFYUI_PATH"] = comfy_path
+        new_env["COMFYUI_MODEL_PATH"] = model_path
 
         # 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(os.path.join(os.path.dirname(__file__), '..', 'skip_download_model')):
-            if not os.path.exists(bbox_path):
-                os.makedirs(bbox_path)
+            try:
+                if not impact.config.get_config()['mmdet_skip']:
+                    bbox_path = os.path.join(model_path, "mmdets", "bbox")
+                    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.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(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)
+                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)
+            except:
+                print(f"[Impact Pack] Failed to auto-download model files. Please download them manually.")
 
         if not os.path.exists(onnx_path):
             print(f"### ComfyUI-Impact-Pack: onnx model directory created ({onnx_path})")
@@ -277,6 +107,16 @@ try:
 
         impact.config.write_config()
 
+        # Remove legacy subpack
+        subpack_path = os.path.join(os.path.dirname(__file__), 'impact_subpack')
+        if os.path.exists(subpack_path):
+            shutil.rmtree(subpack_path)
+            print(f"Legacy subpack is detected. '{subpack_path}' is removed.")
+            
+        subpack_path = os.path.join(os.path.dirname(__file__), 'subpack')
+        if os.path.exists(subpack_path):
+            shutil.rmtree(subpack_path)
+            print(f"Legacy subpack is detected. '{subpack_path}' is removed.")
 
     install()
 

js/comboBoolMigration.js

@@ -1,31 +0,0 @@
-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; }
-				});
-			}
-		}
-	}
-});

js/common.js

@@ -3,6 +3,48 @@ import { app } from "../../scripts/app.js";
 
 let original_show = app.ui.dialog.show;
 
+export function customAlert(message) {
+	try {
+		app.extensionManager.toast.addAlert(message);
+	}
+	catch {
+		alert(message);
+	}
+}
+
+export function isBeforeFrontendVersion(compareVersion) {
+    try {
+        const frontendVersion = window['__COMFYUI_FRONTEND_VERSION__'];
+        if (typeof frontendVersion !== 'string') {
+            return false;
+        }
+
+        function parseVersion(versionString) {
+            const parts = versionString.split('.').map(Number);
+            return parts.length === 3 && parts.every(part => !isNaN(part)) ? parts : null;
+        }
+
+        const currentVersion = parseVersion(frontendVersion);
+        const comparisonVersion = parseVersion(compareVersion);
+
+        if (!currentVersion || !comparisonVersion) {
+            return false;
+        }
+
+        for (let i = 0; i < 3; i++) {
+            if (currentVersion[i] > comparisonVersion[i]) {
+                return false;
+            } else if (currentVersion[i] < comparisonVersion[i]) {
+                return true;
+            }
+        }
+
+        return false;
+    } catch {
+        return true;
+    }
+}
+
 function dialog_show_wrapper(html) {
 	if (typeof html === "string") {
 		if(html.includes("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE")) {
@@ -53,6 +95,7 @@ async function bridgeContinue(event) {
 	if(node) {
 		const mutes = new Set(event.detail.mutes);
 		const actives = new Set(event.detail.actives);
+		const bypasses = new Set(event.detail.bypasses);
 
 		for(let i in app.graph._nodes_by_id) {
 			let this_node = app.graph._nodes_by_id[i];
@@ -62,6 +105,9 @@ async function bridgeContinue(event) {
 			else if(actives.has(i)) {
 				this_node.mode = 0;
 			}
+			else if(bypasses.has(i)) {
+				this_node.mode = 4;
+			}
 		}
 
 		await app.queuePrompt(0, 1);
@@ -76,3 +122,16 @@ function addQueue(event) {
 }
 
 api.addEventListener("impact-add-queue", addQueue);
+
+
+function refreshPreview(event) {
+	let node_id = event.detail.node_id;
+	let item = event.detail.item;
+	let img = new Image();
+	img.src = `/view?filename=${item.filename}&subfolder=${item.subfolder}&type=${item.type}&no-cache=${Date.now()}`;
+	let node = app.graph._nodes_by_id[node_id];
+	if(node)
+		node.imgs = [img];
+}
+
+api.addEventListener("impact-preview", refreshPreview);

js/impact-image-util.js

@@ -9,31 +9,31 @@ function load_image(str) {
 
 function getFileItem(baseType, path) {
 	try {
-	    let pathType = baseType;
+		let pathType = baseType;
 
-	    if (path.endsWith("[output]")) {
-	        pathType = "output";
-	        path = path.slice(0, -9);
-	    } else if (path.endsWith("[input]")) {
-	        pathType = "input";
-	        path = path.slice(0, -8);
-	    } else if (path.endsWith("[temp]")) {
-	        pathType = "temp";
-	        path = path.slice(0, -7);
-	    }
+		if (path.endsWith("[output]")) {
+			pathType = "output";
+			path = path.slice(0, -9);
+		} else if (path.endsWith("[input]")) {
+			pathType = "input";
+			path = path.slice(0, -8);
+		} else if (path.endsWith("[temp]")) {
+			pathType = "temp";
+			path = path.slice(0, -7);
+		}
 
-	    const subfolder = path.substring(0, path.lastIndexOf('/'));
-	    const filename = path.substring(path.lastIndexOf('/') + 1);
+		const subfolder = path.substring(0, path.lastIndexOf('/'));
+		const filename = path.substring(path.lastIndexOf('/') + 1);
 
-	    return {
-	        filename: filename,
-	        subfolder: subfolder,
-	        type: pathType
-	    };
-    }
-    catch(exception) {
-        return null;
-    }
+		return {
+			filename: filename,
+			subfolder: subfolder,
+			type: pathType
+		};
+	}
+	catch(exception) {
+		return null;
+	}
 }
 
 async function loadImageFromUrl(image, node_id, v, need_to_load) {
@@ -46,7 +46,7 @@ async function loadImageFromUrl(image, node_id, v, need_to_load) {
 		if(res.status == 200) {
 			let pb_id = await res.text();
 			if(need_to_load) {;
-				image.src = `view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`;
+				image.src = api.apiURL(`/view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`);
 			}
 			return pb_id;
 		}
@@ -63,7 +63,7 @@ async function loadImageFromId(image, v) {
 	let res = await api.fetchApi('/impact/get/pb_id_image?id='+v, { cache: "no-store" });
 	if(res.status == 200) {
 		let item = await res.json();
-		image.src = `view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`;
+		image.src = api.apiURL(`/view?filename=${item.filename}&type=${item.type}&subfolder=${item.subfolder}`);
 		return true;
 	}
 
@@ -74,13 +74,16 @@ app.registerExtension({
 	name: "Comfy.Impact.img",
 
 	nodeCreated(node, app) {
-		if(node.comfyClass == "PreviewBridge") {
+		if(node.comfyClass == "PreviewBridge" || node.comfyClass == "PreviewBridgeLatent") {
 			let w = node.widgets.find(obj => obj.name === 'image');
 			node._imgs = [new Image()];
 			node.imageIndex = 0;
 
 			Object.defineProperty(w, 'value', {
 				async set(v) {
+					if(w._lock)
+						return;
+
 					const stackTrace = new Error().stack;
 					if(stackTrace.includes('presetText.js'))
 						return;
@@ -101,7 +104,9 @@ app.registerExtension({
 					}
 					else {
 						// from clipspace
+						w._lock = true;
 						w._value = await loadImageFromUrl(image, node.id, v, false);
+						w._lock = false;
 					}
 				},
 				get() {
@@ -176,7 +181,7 @@ app.registerExtension({
 
 			Object.defineProperty(node, 'imgs', {
 				set(v) {
-					if (!v[0].complete) {
+					if (v && !v[0].complete) {
 						let orig_onload = v[0].onload;
 						v[0].onload = function(v2) {
 							if(orig_onload)
@@ -204,7 +209,7 @@ app.registerExtension({
 
 							let res = api.fetchApi('/view/validate'+params, { cache: "no-store" }).then(response => response);
 							if(res.status == 200) {
-								image.src = 'view'+params;
+								image.src = api.apiURL('/view'+params);
 							}
 
 							this._img = [new Image()]; // placeholder
@@ -220,5 +225,5 @@ app.registerExtension({
 				}
 			});
 		}
-    }
-})
+	}
+})

js/impact-sam-editor.js

@@ -1,4 +1,5 @@
 import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
 import { ComfyDialog, $el } from "../../scripts/ui.js";
 import { ComfyApp } from "../../scripts/app.js";
 import { ClipspaceDialog } from "../../extensions/core/clipspace.js";
@@ -42,7 +43,7 @@ function loadedImageToBlob(image) {
 }
 
 async function uploadMask(filepath, formData) {
-	await fetch('/upload/mask', {
+	await api.fetchApi('/upload/mask', {
 		method: 'POST',
 		body: formData
 	}).then(response => {}).catch(error => {
@@ -261,7 +262,7 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			const pointsCanvas = document.createElement('canvas');
 
 			imgCanvas.id = "imageCanvas";
-			maskCanvas.id = "maskCanvas";
+			maskCanvas.id = "samEditorMaskCanvas";
 			pointsCanvas.id = "pointsCanvas";
 
 			this.setlayout(imgCanvas, maskCanvas, pointsCanvas);
@@ -352,13 +353,16 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			imgCtx.drawImage(orig_image, 0, 0, drawWidth, drawHeight);
 
 			// update mask
-			pointsCanvas.width = drawWidth;
-			pointsCanvas.height = drawHeight;
+			let w = (drawWidth * imgCanvas.clientWidth/imgCanvas.width) + "px";
+			let h = (drawHeight * imgCanvas.clientHeight/imgCanvas.height) + "px";
+
+			pointsCanvas.width = drawWidth * imgCanvas.clientWidth/imgCanvas.width;
+			pointsCanvas.height = drawHeight * imgCanvas.clientHeight/imgCanvas.height;
 			pointsCanvas.style.top = imgCanvas.offsetTop + "px";
 			pointsCanvas.style.left = imgCanvas.offsetLeft + "px";
 
-			maskCanvas.width = drawWidth;
-			maskCanvas.height = drawHeight;
+			maskCanvas.width = pointsCanvas.width;
+			maskCanvas.height = pointsCanvas.height;
 			maskCanvas.style.top = imgCanvas.offsetTop + "px";
 			maskCanvas.style.left = imgCanvas.offsetLeft + "px";
 
@@ -434,7 +438,7 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			ctx.arc(scaledX, scaledY, 3, 0, 3 * Math.PI);
 			ctx.fill();
 		}
-	}줘
+	}
 
 	invalidateMaskCanvas(self) {
 		if(self.mask_image) {
@@ -458,7 +462,7 @@ class ImpactSamEditorDialog extends ComfyDialog {
 				subfolder: subfolder
 			};
 
-		fetch('/sam/prepare', {
+		api.fetchApi('/sam/prepare', {
 			method: 'POST',
 			headers: { 'Content-Type': 'application/json' },
 			body: JSON.stringify(data)
@@ -472,8 +476,9 @@ class ImpactSamEditorDialog extends ComfyDialog {
 		for(const i in self.prompt_points) {
 			const [is_positive, x, y] = self.prompt_points[i];
 			const point = [x,y];
-			if(is_positive)
+			if(is_positive) {
 				positive_points.push(point);
+			}
 			else
 				negative_points.push(point);
 		}
@@ -484,7 +489,7 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			threshold: self.confidence/100
 		};
 		
-		const response = await fetch('/sam/detect', {
+		const response = await api.fetchApi('/sam/detect', {
 			method: 'POST',
 			headers: { 'Content-Type': 'image/png' },
 			body: JSON.stringify(data)
@@ -507,8 +512,8 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			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;
+			const originalX = x * self.image.width / self.pointsCanvas.clientWidth;
+			const originalY = y * self.image.height / self.pointsCanvas.clientHeight;
 
 			var point = null;
 			if (event.button == 0) {
@@ -617,7 +622,7 @@ app.registerExtension({
 	},
 
 	async beforeRegisterNodeDef(nodeType, nodeData, app) {
-		if (nodeData.output.includes("MASK") && nodeData.output.includes("IMAGE")) {
+		if (Array.isArray(nodeData.output) && (nodeData.output.includes("MASK") || nodeData.output.includes("IMAGE"))) {
 			addMenuHandler(nodeType, function (_, options) {
 				options.unshift({
 					content: "Open in SAM Detector",

js/mask-rect-area-advanced.js

@@ -0,0 +1,381 @@
+import { app } from "/scripts/app.js";
+function showPreviewCanvas(node, app) {
+
+    const widget = {
+        type: "customCanvas",
+        name: "mask-rect-area-canvas",
+        get value() {
+            return this.canvas.value;
+        },
+        set value(x) {
+            this.canvas.value = x;
+        },
+        draw: function (ctx, node, widgetWidth, widgetY) {
+
+            // If we are initially offscreen when created we wont have received a resize event
+            // Calculate it here instead
+            if (!node.canvasHeight) {
+                computeCanvasSize(node, node.size);
+            }
+
+            const visible = true;
+            const t = ctx.getTransform();
+            const margin = 12;
+            const border = 2;
+            const widgetHeight = node.canvasHeight;
+            const width = Math.round(node.properties["width"]);
+            const height = Math.round(node.properties["height"]);
+            const scale = Math.min((widgetWidth - margin * 3) / width, (widgetHeight - margin * 3) / height);
+            const blurRadius = node.properties["blur_radius"] || 0;
+            const index = 0;
+
+            Object.assign(this.canvas.style, {
+                left: `${t.e}px`,
+                top: `${t.f + (widgetY * t.d)}px`,
+                width: `${widgetWidth * t.a}px`,
+                height: `${widgetHeight * t.d}px`,
+                position: "absolute",
+                zIndex: 1,
+                fontSize: `${t.d * 10.0}px`,
+                pointerEvents: "none"
+            });
+
+            this.canvas.hidden = !visible;
+
+            let backgroundWidth = width * scale;
+            let backgroundHeight = height * scale;
+
+            let xOffset = margin;
+            if (backgroundWidth < widgetWidth) {
+                xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
+            }
+            let yOffset = (margin / 2);
+            if (backgroundHeight < widgetHeight) {
+                yOffset += (widgetHeight - backgroundHeight) / 2 - margin;
+            }
+
+            let widgetX = xOffset;
+            widgetY = widgetY + yOffset;
+
+            // Draw the background border
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
+            ctx.fillRect(widgetX - border, widgetY - border, backgroundWidth + border * 2, backgroundHeight + border * 2)
+
+            // Draw the main background area 
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR;
+            ctx.fillRect(widgetX, widgetY, backgroundWidth, backgroundHeight);
+
+            // Draw the conditioning zone
+            let [x, y, w, h] = getDrawArea(node, backgroundWidth, backgroundHeight);
+
+            ctx.fillStyle = getDrawColor(0, "80");
+            ctx.fillRect(widgetX + x, widgetY + y, w, h);
+            ctx.beginPath();
+            ctx.lineWidth = 1;
+
+            // Draw grid lines
+            for (let x = 0; x <= width / 64; x += 1) {
+                ctx.moveTo(widgetX + x * 64 * scale, widgetY);
+                ctx.lineTo(widgetX + x * 64 * scale, widgetY + backgroundHeight);
+            }
+
+            for (let y = 0; y <= height / 64; y += 1) {
+                ctx.moveTo(widgetX, widgetY + y * 64 * scale);
+                ctx.lineTo(widgetX + backgroundWidth, widgetY + y * 64 * scale);
+            }
+
+            ctx.strokeStyle = "#66666650";
+            ctx.stroke();
+            ctx.closePath();
+
+            // Draw current zone
+            let [sx, sy, sw, sh] = getDrawArea(node, backgroundWidth, backgroundHeight);
+
+            ctx.fillStyle = getDrawColor(0, "80");
+            ctx.fillRect(widgetX + sx, widgetY + sy, sw, sh);
+
+            ctx.fillStyle = getDrawColor(0, "40");
+            ctx.fillRect(widgetX + sx + border, widgetY + sy + border, sw - border * 2, sh - border * 2);
+
+            // Draw white border around the current zone
+            ctx.strokeStyle = globalThis.LiteGraph.NODE_SELECTED_TITLE_COLOR;
+            ctx.lineWidth = 2;
+            ctx.strokeRect(widgetX + sx, widgetY + sy, sw, sh);
+
+            // Display
+            ctx.beginPath();
+
+            ctx.arc(LiteGraph.NODE_SLOT_HEIGHT * 0.5, LiteGraph.NODE_SLOT_HEIGHT * (index + 0.5) + 4, 4, 0, Math.PI * 2);
+            ctx.fill();
+
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = "white";
+            ctx.stroke();
+
+            ctx.lineWidth = 1;
+            ctx.closePath();
+
+            // Draw progress bar canvas
+            if (backgroundWidth < widgetWidth) {
+                xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
+            }
+
+            // Ajustar las coordenadas X e Y
+            const barHeight = 8;
+            let widgetYBar = widgetY + backgroundHeight + margin;
+
+            // Dibujar el borde negro alrededor de la barra
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
+            ctx.fillRect(
+                    widgetX - border,
+                    widgetYBar - border,
+                    backgroundWidth + border * 2,
+                    barHeight + border * 2
+                    );
+
+            // Dibujar el área principal de la barra (fondo)
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; // Mismo color de fondo que el canvas
+            ctx.fillRect(
+                    widgetX,
+                    widgetYBar,
+                    backgroundWidth,
+                    barHeight
+                    );
+
+
+            // Draw progress bar grid
+            ctx.beginPath();
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = "#66666650";
+
+            // Calcular el número de líneas en función del tamaño de la barra
+            const numLines = Math.floor(backgroundWidth / 64);
+
+            // Dibujar líneas del grid
+            for (let x = 0; x <= width / 64; x += 1) {
+                ctx.moveTo(widgetX + x * 64 * scale, widgetYBar);
+                ctx.lineTo(widgetX + x * 64 * scale, widgetYBar + barHeight);
+            }
+            ctx.stroke();
+            ctx.closePath();
+
+            // Dibujar progreso (basado en blur_radius)
+            const progress = Math.min(blurRadius / 255, 1);
+            ctx.fillStyle = "rgba(0, 120, 255, 0.5)";
+
+            ctx.fillRect(
+                    widgetX,
+                    widgetYBar,
+                    backgroundWidth * progress,
+                    barHeight
+                    );
+        }
+    };
+
+    widget.canvas = document.createElement("canvas");
+    widget.canvas.className = "mask-rect-area-canvas";
+    widget.parent = node;
+
+    document.body.appendChild(widget.canvas);
+    node.addCustomWidget(widget);
+
+    app.canvas.onDrawBackground = function () {
+        // Draw node isnt fired once the node is off the screen
+        // if it goes off screen quickly, the input may not be removed
+        // this shifts it off screen so it can be moved back if the node is visible.
+        for (let n in app.graph._nodes) {
+            n = app.graph._nodes[n];
+            for (let w in n.widgets) {
+                let wid = n.widgets[w];
+                if (Object.hasOwn(wid, "canvas")) {
+                    wid.canvas.style.left = -8000 + "px";
+                    wid.canvas.style.position = "absolute";
+                }
+            }
+        }
+    };
+
+    node.onResize = function (size) {
+        computeCanvasSize(node, size);
+    };
+
+    return {minWidth: 200, minHeight: 200, widget};
+}
+
+app.registerExtension({
+    name: 'drltdata.MaskRectAreaAdvanced',
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if (nodeData.name === "MaskRectAreaAdvanced") {
+            const onNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = function () {
+                const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;
+
+                this.setProperty("width", 512);
+                this.setProperty("height", 512);
+                this.setProperty("x", 0);
+                this.setProperty("y", 0);
+                this.setProperty("w", 256);
+                this.setProperty("h", 256);
+                this.setProperty("blur_radius", 0);
+
+                this.selected = false;
+                this.index = 3;
+                this.serialize_widgets = true;
+
+                CUSTOM_INT(this, "x", 0, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["x"] = this.value;
+                });
+                CUSTOM_INT(this, "y", 0, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["y"] = this.value;
+                });
+                CUSTOM_INT(this, "width", 256, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["w"] = this.value;
+                });
+                CUSTOM_INT(this, "height", 256, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["h"] = this.value;
+                });
+                CUSTOM_INT(this, "image_width", 512, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["width"] = this.value;
+                });
+                CUSTOM_INT(this, "image_height", 512, function (v, _, node) {
+                    const s = this.options.step / 10;
+                    this.value = Math.round(v / s) * s;
+                    node.properties["height"] = this.value;
+                });
+                CUSTOM_INT(this, "blur_radius", 0, function (v, _, node) {
+                    this.value = Math.round(v) || 0;
+                    node.properties["blur_radius"] = this.value;
+                },
+                        {"min": 0, "max": 255, "step": 10}
+                );
+
+                showPreviewCanvas(this, app);
+
+                this.onSelected = function () {
+                    this.selected = true;
+                };
+                this.onDeselected = function () {
+                    this.selected = false;
+                };
+
+                return r;
+            };
+        }
+    }
+});
+
+// Calculate the drawing area using individual properties.
+function getDrawArea(node, backgroundWidth, backgroundHeight) {
+    let x = node.properties["x"] * backgroundWidth / node.properties["width"];
+    let y = node.properties["y"] * backgroundHeight / node.properties["height"];
+    let w = node.properties["w"] * backgroundWidth / node.properties["width"];
+    let h = node.properties["h"] * backgroundHeight / node.properties["height"];
+
+    if (x > backgroundWidth) {
+        x = backgroundWidth;
+    }
+    if (y > backgroundHeight) {
+        y = backgroundHeight;
+    }
+
+    if (x + w > backgroundWidth) {
+        w = Math.max(0, backgroundWidth - x);
+    }
+
+    if (y + h > backgroundHeight) {
+        h = Math.max(0, backgroundHeight - y);
+    }
+
+    return [x, y, w, h];
+}
+
+function CUSTOM_INT(node, inputName, val, func, config = {}) {
+    return {
+        widget: node.addWidget(
+                "number",
+                inputName,
+                val,
+                func,
+                Object.assign({}, {min: 0, max: 4096, step: 640, precision: 0}, config)
+                )
+    };
+}
+
+function getDrawColor(percent, alpha) {
+    let h = 360 * percent;
+    let s = 50;
+    let l = 50;
+    l /= 100;
+    const a = s * Math.min(l, 1 - l) / 100;
+    const f = n => {
+        const k = (n + h / 30) % 12;
+        const color = l - a * Math.max(Math.min(k - 3, 9 - k, 1), -1);
+        return Math.round(255 * color).toString(16).padStart(2, '0');   // convert to Hex and prefix "0" if needed
+    };
+    return `#${f(0)}${f(8)}${f(4)}${alpha}`;
+}
+
+function computeCanvasSize(node, size) {
+    if (node.widgets[0].last_y == null) {
+        return;
+    }
+
+    const MIN_HEIGHT = 220;
+    const MIN_WIDTH = 240;
+
+    let y = LiteGraph.NODE_WIDGET_HEIGHT * Math.max(node.inputs.length, node.outputs.length) + 5;
+    let freeSpace = size[1] - y;
+
+    // Compute the height of all non-customCanvas widgets
+    let widgetHeight = 0;
+    for (let i = 0; i < node.widgets.length; i++) {
+        const w = node.widgets[i];
+        if (w.type !== "customCanvas") {
+            if (w.computeSize) {
+                widgetHeight += w.computeSize()[1] + 4;
+            } else {
+                widgetHeight += LiteGraph.NODE_WIDGET_HEIGHT + 5;
+            }
+        }
+    }
+
+    // Ensure there is enough vertical space
+    freeSpace -= widgetHeight;
+
+    // Adjust the height of the node if needed
+    if (freeSpace < MIN_HEIGHT) {
+        freeSpace = MIN_HEIGHT;
+        node.size[1] = y + widgetHeight + freeSpace;
+        node.graph.setDirtyCanvas(true);
+    }
+
+    // Ensure the node width meets the minimum width requirement
+    if (node.size[0] < MIN_WIDTH) {
+        node.size[0] = MIN_WIDTH;
+        node.graph.setDirtyCanvas(true);
+    }
+
+    // Position each of the widgets
+    for (const w of node.widgets) {
+        w.y = y;
+        if (w.type === "customCanvas") {
+            y += freeSpace;
+        } else if (w.computeSize) {
+            y += w.computeSize()[1] + 4;
+        } else {
+            y += LiteGraph.NODE_WIDGET_HEIGHT + 4;
+        }
+    }
+
+    node.canvasHeight = freeSpace;
+}

js/mask-rect-area.js

@@ -0,0 +1,366 @@
+import { app } from "/scripts/app.js";
+function showPreviewCanvas(node, app) {
+
+    const widget = {
+        type: "customCanvas",
+        name: "mask-rect-area-canvas",
+        get value() {
+            return this.canvas.value;
+        },
+        set value(x) {
+            this.canvas.value = x;
+        },
+        draw: function (ctx, node, widgetWidth, widgetY) {
+
+            // If we are initially offscreen when created we wont have received a resize event
+            // Calculate it here instead
+            if (!node.canvasHeight) {
+                computeCanvasSize(node, node.size);
+            }
+
+            const visible = true;
+            const t = ctx.getTransform();
+            const margin = 12;
+            const border = 2;
+            const widgetHeight = node.canvasHeight;
+            const width = 512;
+            const height = 512;
+            const scale = Math.min((widgetWidth - margin * 3) / width, (widgetHeight - margin * 3) / height);
+            const blurRadius = node.properties["blur_radius"] || 0;
+            const index = 0;
+
+            Object.assign(this.canvas.style, {
+                left: `${t.e}px`,
+                top: `${t.f + (widgetY * t.d)}px`,
+                width: `${widgetWidth * t.a}px`,
+                height: `${widgetHeight * t.d}px`,
+                position: "absolute",
+                zIndex: 1,
+                fontSize: `${t.d * 10.0}px`,
+                pointerEvents: "none"
+            });
+
+            this.canvas.hidden = !visible;
+
+            let backgroundWidth = width * scale;
+            let backgroundHeight = height * scale;
+            let xOffset = margin;
+            if (backgroundWidth < widgetWidth) {
+                xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
+            }
+            let yOffset = (margin / 2);
+            if (backgroundHeight < widgetHeight) {
+                yOffset += (widgetHeight - backgroundHeight) / 2 - margin;
+            }
+
+            let widgetX = xOffset;
+            widgetY = widgetY + yOffset;
+
+            // Draw the background border
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
+            ctx.fillRect(widgetX - border, widgetY - border, backgroundWidth + border * 2, backgroundHeight + border * 2);
+
+            // Draw the main background area 
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR;
+            ctx.fillRect(widgetX, widgetY, backgroundWidth, backgroundHeight);
+
+            // Draw the conditioning zone
+            let [x, y, w, h] = getDrawArea(node, backgroundWidth, backgroundHeight);
+
+            ctx.fillStyle = getDrawColor(0, "80");
+            ctx.fillRect(widgetX + x, widgetY + y, w, h);
+            ctx.beginPath();
+            ctx.lineWidth = 1;
+
+            // Draw grid lines
+            for (let x = 0; x <= width / 64; x += 1) {
+                ctx.moveTo(widgetX + x * 64 * scale, widgetY);
+                ctx.lineTo(widgetX + x * 64 * scale, widgetY + backgroundHeight);
+            }
+
+            for (let y = 0; y <= height / 64; y += 1) {
+                ctx.moveTo(widgetX, widgetY + y * 64 * scale);
+                ctx.lineTo(widgetX + backgroundWidth, widgetY + y * 64 * scale);
+            }
+
+            ctx.strokeStyle = "#66666650";
+            ctx.stroke();
+            ctx.closePath();
+
+            // Draw current zone
+            let [sx, sy, sw, sh] = getDrawArea(node, backgroundWidth, backgroundHeight);
+
+            ctx.fillStyle = getDrawColor(0, "80");
+            ctx.fillRect(widgetX + sx, widgetY + sy, sw, sh);
+
+            ctx.fillStyle = getDrawColor(0, "40");
+            ctx.fillRect(widgetX + sx + border, widgetY + sy + border, sw - border * 2, sh - border * 2);
+
+            // Draw white border around the current zone
+            ctx.strokeStyle = globalThis.LiteGraph.NODE_SELECTED_TITLE_COLOR;
+            ctx.lineWidth = 2;
+            ctx.strokeRect(widgetX + sx, widgetY + sy, sw, sh);
+            //ctx.strokeRect(finalSX, finalSY, finalSW, finalSH);
+
+            // Display
+            ctx.beginPath();
+
+            ctx.arc(LiteGraph.NODE_SLOT_HEIGHT * 0.5, LiteGraph.NODE_SLOT_HEIGHT * (index + 0.5) + 4, 4, 0, Math.PI * 2);
+            ctx.fill();
+
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = "white";
+            ctx.stroke();
+            ctx.lineWidth = 1;
+            ctx.closePath();
+
+            // Draw progress bar canvas
+            if (backgroundWidth < widgetWidth) {
+                xOffset += (widgetWidth - backgroundWidth) / 2 - margin;
+            }
+
+            const barHeight = 8;
+            let widgetYBar = widgetY + backgroundHeight + margin;
+
+            // Draw progress bar border
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_OUTLINE_COLOR;
+            ctx.fillRect(
+                    widgetX - border,
+                    widgetYBar - border,
+                    backgroundWidth + border * 2,
+                    barHeight + border * 2
+                    );
+
+            // Draw progress bar area
+            ctx.fillStyle = globalThis.LiteGraph.WIDGET_BGCOLOR; // Mismo color de fondo que el canvas
+            ctx.fillRect(
+                    widgetX,
+                    widgetYBar,
+                    backgroundWidth,
+                    barHeight
+                    );
+
+            // Draw progress bar grid
+            ctx.beginPath();
+            ctx.lineWidth = 1;
+            ctx.strokeStyle = "#66666650";
+
+            // Determine max lines
+            const numLines = Math.floor(backgroundWidth / 64);
+
+            // Draw progress bar grid
+            for (let x = 0; x <= width / 64; x += 1) {
+                ctx.moveTo(widgetX + x * 64 * scale, widgetYBar);
+                ctx.lineTo(widgetX + x * 64 * scale, widgetYBar + barHeight);
+            }
+            ctx.stroke();
+            ctx.closePath();
+
+            // Draw progress bar
+            const progress = Math.min(blurRadius / 255, 1);
+            ctx.fillStyle = "rgba(0, 120, 255, 0.5)";
+
+            ctx.fillRect(
+                    widgetX,
+                    widgetYBar,
+                    backgroundWidth * progress,
+                    barHeight
+                    );
+        }
+    };
+
+    widget.canvas = document.createElement("canvas");
+    widget.canvas.className = "mask-rect-area-canvas";
+    widget.parent = node;
+
+    document.body.appendChild(widget.canvas);
+    node.addCustomWidget(widget);
+
+    app.canvas.onDrawBackground = function () {
+        // Draw node isnt fired once the node is off the screen
+        // if it goes off screen quickly, the input may not be removed
+        // this shifts it off screen so it can be moved back if the node is visible.
+        for (let n in app.graph._nodes) {
+            n = app.graph._nodes[n];
+            for (let w in n.widgets) {
+                let wid = n.widgets[w];
+                if (Object.hasOwn(wid, "canvas")) {
+                    wid.canvas.style.left = -8000 + "px";
+                    wid.canvas.style.position = "absolute";
+                }
+            }
+        }
+    };
+
+    node.onResize = function (size) {
+        computeCanvasSize(node, size);
+    };
+
+    return {minWidth: 200, minHeight: 200, widget};
+}
+
+app.registerExtension({
+    name: 'drltdata.MaskRectArea',
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if (nodeData.name === "MaskRectArea") {
+            const onNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = function () {
+                const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;
+
+                this.setProperty("width", 512);
+                this.setProperty("height", 512);
+                this.setProperty("x", 0);
+                this.setProperty("y", 0);
+                this.setProperty("w", 50);
+                this.setProperty("h", 50);
+                this.setProperty("blur_radius", 0);
+
+                this.selected = false;
+                this.index = 3;
+                this.serialize_widgets = true;
+
+                CUSTOM_INT(this, "x", 0, function (v, _, node) {
+                    this.value = Math.max(0, Math.min(100, Math.round(v))); // Limitar entre 0 y 100
+                    node.properties["x"] = this.value;
+                });
+                CUSTOM_INT(this, "y", 0, function (v, _, node) {
+                    this.value = Math.max(0, Math.min(100, Math.round(v)));
+                    node.properties["y"] = this.value;
+                });
+                CUSTOM_INT(this, "w", 50, function (v, _, node) {
+                    this.value = Math.max(0, Math.min(100, Math.round(v)));
+                    node.properties["w"] = this.value;
+                });
+                CUSTOM_INT(this, "h", 50, function (v, _, node) {
+                    this.value = Math.max(0, Math.min(100, Math.round(v)));
+                    node.properties["h"] = this.value;
+                });
+                CUSTOM_INT(this, "blur_radius", 0, function (v, _, node) {
+                    this.value = Math.round(v) || 0;
+                    node.properties["blur_radius"] = this.value;
+                },
+                        {"min": 0, "max": 255, "step": 10}
+                );
+
+                showPreviewCanvas(this, app);
+
+                this.onSelected = function () {
+                    this.selected = true;
+                };
+                this.onDeselected = function () {
+                    this.selected = false;
+                };
+
+                return r;
+            };
+        }
+    }
+});
+
+// Calculate the drawing area using percentage-based properties.
+function getDrawArea(node, backgroundWidth, backgroundHeight) {
+    // Convert percentages to actual pixel values based on the background dimensions
+    let x = (node.properties["x"] / 100) * backgroundWidth;
+    let y = (node.properties["y"] / 100) * backgroundHeight;
+    let w = (node.properties["w"] / 100) * backgroundWidth;
+    let h = (node.properties["h"] / 100) * backgroundHeight;
+
+    // Ensure the values do not exceed the background boundaries
+    if (x > backgroundWidth) {
+        x = backgroundWidth;
+    }
+    if (y > backgroundHeight) {
+        y = backgroundHeight;
+    }
+
+    // Adjust width and height to fit within the background dimensions
+    if (x + w > backgroundWidth) {
+        w = Math.max(0, backgroundWidth - x);
+    }
+    if (y + h > backgroundHeight) {
+        h = Math.max(0, backgroundHeight - y);
+    }
+
+    return [x, y, w, h];
+}
+
+function CUSTOM_INT(node, inputName, val, func, config = {}) {
+    return {
+        widget: node.addWidget(
+                "number",
+                inputName,
+                val,
+                func,
+                Object.assign({}, {min: 0, max: 100, step: 10, precision: 0}, config)
+                )
+    };
+}
+
+function getDrawColor(percent, alpha) {
+    let h = 360 * percent;
+    let s = 50;
+    let l = 50;
+    l /= 100;
+    const a = s * Math.min(l, 1 - l) / 100;
+    const f = n => {
+        const k = (n + h / 30) % 12;
+        const color = l - a * Math.max(Math.min(k - 3, 9 - k, 1), -1);
+        return Math.round(255 * color).toString(16).padStart(2, '0');   // convert to Hex and prefix "0" if needed
+    };
+    return `#${f(0)}${f(8)}${f(4)}${alpha}`;
+}
+
+function computeCanvasSize(node, size) {
+    if (node.widgets[0].last_y == null) {
+        return;
+    }
+
+    const MIN_HEIGHT = 200;
+    const MIN_WIDTH = 200;
+
+    let y = LiteGraph.NODE_WIDGET_HEIGHT * Math.max(node.inputs.length, node.outputs.length) + 5;
+    let freeSpace = size[1] - y;
+
+    // Compute the height of all non-customCanvas widgets
+    let widgetHeight = 0;
+    for (let i = 0; i < node.widgets.length; i++) {
+        const w = node.widgets[i];
+        if (w.type !== "customCanvas") {
+            if (w.computeSize) {
+                widgetHeight += w.computeSize()[1] + 4;
+            } else {
+                widgetHeight += LiteGraph.NODE_WIDGET_HEIGHT + 5;
+            }
+        }
+    }
+
+    // Ensure there is enough vertical space
+    freeSpace -= widgetHeight;
+
+    // Adjust the height of the node if needed
+    if (freeSpace < MIN_HEIGHT) {
+        freeSpace = MIN_HEIGHT;
+        node.size[1] = y + widgetHeight + freeSpace;
+        node.graph.setDirtyCanvas(true);
+    }
+
+    // Ensure the node width meets the minimum width requirement
+    if (node.size[0] < MIN_WIDTH) {
+        node.size[0] = MIN_WIDTH;
+        node.graph.setDirtyCanvas(true);
+    }
+
+    // Position each of the widgets
+    for (const w of node.widgets) {
+        w.y = y;
+        if (w.type === "customCanvas") {
+            y += freeSpace;
+        } else if (w.computeSize) {
+            y += w.computeSize()[1] + 4;
+        } else {
+            y += LiteGraph.NODE_WIDGET_HEIGHT + 4;
+        }
+    }
+
+    node.canvasHeight = freeSpace;
+}

modules/impact/animatediff_nodes.py

@@ -0,0 +1,197 @@
+from nodes import MAX_RESOLUTION
+from impact.utils import *
+import impact.core as core
+from impact.core import SEG
+from impact.segs_nodes import SEGSPaste
+
+
+try:
+    from comfy_extras import nodes_differential_diffusion
+except Exception:
+    print(f"\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n")
+    raise Exception("[Impact Pack] ComfyUI is an outdated version.")
+
+
+class SEGSDetailerForAnimateDiff:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                     "image_frames": ("IMAGE", ),
+                     "segs": ("SEGS", ),
+                     "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+                     "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
+                     "max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+                     "scheduler": (core.SCHEDULERS,),
+                     "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
+                     "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
+                     "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
+                     },
+                "optional": {
+                     "refiner_basic_pipe_opt": ("BASIC_PIPE",),
+                     "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
+                     "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                     }
+                }
+
+    RETURN_TYPES = ("SEGS", "IMAGE")
+    RETURN_NAMES = ("segs", "cnet_images")
+    OUTPUT_IS_LIST = (False, True)
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detailer"
+
+    DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node.\nAs a specialized detailer node for improving video details, such as in AnimateDiff, this node can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
+
+    @staticmethod
+    def do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
+                  denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, noise_mask_feather=0, scheduler_func_opt=None):
+
+        model, clip, vae, positive, negative = basic_pipe
+        if refiner_basic_pipe_opt is None:
+            refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None
+        else:
+            refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt
+
+        segs = core.segs_scale_match(segs, image_frames.shape)
+
+        new_segs = []
+        cnet_image_list = []
+
+        if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+            model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
+
+        for seg in segs[1]:
+            cropped_image_frames = None
+
+            for image in image_frames:
+                image = image.unsqueeze(0)
+                cropped_image = seg.cropped_image if seg.cropped_image is not None else crop_tensor4(image, seg.crop_region)
+                cropped_image = to_tensor(cropped_image)
+                if cropped_image_frames is None:
+                    cropped_image_frames = cropped_image
+                else:
+                    cropped_image_frames = torch.concat((cropped_image_frames, cropped_image), dim=0)
+
+            cropped_image_frames = cropped_image_frames.cpu().numpy()
+
+            # It is assumed that AnimateDiff does not support conditioning masks based on test results, but it will be added for future consideration.
+            cropped_positive = [
+                [condition, {
+                    k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v
+                    for k, v in details.items()
+                }]
+                for condition, details in positive
+            ]
+
+            cropped_negative = [
+                [condition, {
+                    k: core.crop_condition_mask(v, cropped_image_frames, seg.crop_region) if k == "mask" else v
+                    for k, v in details.items()
+                }]
+                for condition, details in negative
+            ]
+
+            if not (isinstance(model, str) and model == "DUMMY"):
+                enhanced_image_tensor, cnet_images = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size,
+                                                                                         seg.bbox, seed, steps, cfg, sampler_name, scheduler,
+                                                                                         cropped_positive, cropped_negative, denoise, seg.cropped_mask,
+                                                                                         refiner_ratio=refiner_ratio, refiner_model=refiner_model,
+                                                                                         refiner_clip=refiner_clip, refiner_positive=refiner_positive,
+                                                                                         refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
+                                                                                         noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
+            else:
+                enhanced_image_tensor = cropped_image_frames
+                cnet_images = None
+
+            if cnet_images is not None:
+                cnet_image_list.extend(cnet_images)
+
+            if enhanced_image_tensor is None:
+                new_cropped_image = cropped_image_frames
+            else:
+                new_cropped_image = enhanced_image_tensor.cpu().numpy()
+
+            new_seg = SEG(new_cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None)
+            new_segs.append(new_seg)
+
+        return (segs[0], new_segs), cnet_image_list
+
+    def doit(self, image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
+             denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+
+        segs, cnet_images = SEGSDetailerForAnimateDiff.do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
+                                                                 scheduler, denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt,
+                                                                 noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+
+        if len(cnet_images) == 0:
+            cnet_images = [empty_pil_tensor()]
+
+        return (segs, cnet_images)
+
+
+class DetailerForEachPipeForAnimateDiff:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                      "image_frames": ("IMAGE", ),
+                      "segs": ("SEGS", ),
+                      "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                      "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
+                      "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                      "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                      "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+                      "scheduler": (core.SCHEDULERS,),
+                      "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
+                      "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
+                      "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
+                      "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
+                      },
+                "optional": {
+                      "detailer_hook": ("DETAILER_HOOK",),
+                      "refiner_basic_pipe_opt": ("BASIC_PIPE",),
+                      "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
+                      "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                      }
+                }
+
+    RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE")
+    RETURN_NAMES = ("image", "segs", "basic_pipe", "cnet_images")
+    OUTPUT_IS_LIST = (False, False, False, True)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Detailer"
+
+    DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is a specialized detailer node for enhancing video details, such as in AnimateDiff. It can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
+
+    @staticmethod
+    def doit(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
+             denoise, feather, basic_pipe, refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
+             noise_mask_feather=0, scheduler_func_opt=None):
+
+        enhanced_segs = []
+        cnet_image_list = []
+
+        for sub_seg in segs[1]:
+            single_seg = segs[0], [sub_seg]
+            enhanced_seg, cnet_images = SEGSDetailerForAnimateDiff().do_detail(image_frames, single_seg, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
+                                                                               denoise, basic_pipe, refiner_ratio, refiner_basic_pipe_opt, noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+
+            image_frames = SEGSPaste.doit(image_frames, enhanced_seg, feather, alpha=255)[0]
+
+            if cnet_images is not None:
+                cnet_image_list.extend(cnet_images)
+
+            if detailer_hook is not None:
+                image_frames = detailer_hook.post_paste(image_frames)
+
+            enhanced_segs += enhanced_seg[1]
+
+        new_segs = segs[0], enhanced_segs
+        return image_frames, new_segs, basic_pipe, cnet_image_list

modules/impact/bridge_nodes.py

@@ -0,0 +1,390 @@
+import os
+from PIL import ImageOps
+from impact.utils import *
+import latent_preview
+
+
+# NOTE: this should not be `from . import core`.
+# I don't know why but... 'from .' and 'from impact' refer to different core modules.
+# This separates global variables of the core module and breaks the preview bridge.
+from impact import core
+# <--
+import random
+
+
+class PreviewBridge:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "images": ("IMAGE",),
+                    "image": ("STRING", {"default": ""}),
+                    },
+                "optional": {
+                    "block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped."}),
+                    "restore_mask": (["never", "always", "if_same_size"], {"tooltip": "if_same_size: If the changed input image is the same size as the previous image, restore using the last saved mask\nalways: Whenever the input image changes, always restore using the last saved mask\nnever: Do not restore the mask.\n`restore_mask` has higher priority than `block`"}),
+                    },
+                "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ("IMAGE", "MASK", )
+
+    FUNCTION = "doit"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "ImpactPack/Util"
+
+    DESCRIPTION = "This is a feature that allows you to edit and send a Mask over a image.\nIf the block is set to 'is_empty_mask', the execution is stopped when the mask is empty."
+
+    def __init__(self):
+        super().__init__()
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+        self.prev_hash = None
+
+    @staticmethod
+    def load_image(pb_id):
+        is_fail = False
+        if pb_id not in core.preview_bridge_image_id_map:
+            is_fail = True
+
+        image_path, ui_item = core.preview_bridge_image_id_map[pb_id]
+
+        if not os.path.isfile(image_path):
+            is_fail = True
+
+        if not is_fail:
+            i = Image.open(image_path)
+            i = ImageOps.exif_transpose(i)
+            image = i.convert("RGB")
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+        else:
+            image = empty_pil_tensor()
+            mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            ui_item = {
+                "filename": 'empty.png',
+                "subfolder": '',
+                "type": 'temp'
+            }
+
+        return image, mask.unsqueeze(0), ui_item
+
+    def doit(self, images, image, unique_id, block=False, restore_mask="never", prompt=None, extra_pnginfo=None):
+        need_refresh = False
+
+        if unique_id not in core.preview_bridge_cache:
+            need_refresh = True
+
+        elif core.preview_bridge_cache[unique_id][0] is not images:
+            need_refresh = True
+
+        if not need_refresh:
+            pixels, mask, path_item = PreviewBridge.load_image(image)
+            image = [path_item]
+        else:
+            if restore_mask != "never":
+                mask = core.preview_bridge_last_mask_cache.get(unique_id)
+                if mask is None or (restore_mask != "always" and mask.shape[1:] != images.shape[1:3]):
+                    mask = None
+            else:
+                mask = None
+
+            if mask is None:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+                res = nodes.PreviewImage().save_images(images, filename_prefix="PreviewBridge/PB-", prompt=prompt, extra_pnginfo=extra_pnginfo)
+            else:
+                masked_images = tensor_convert_rgba(images)
+                resized_mask = resize_mask(mask, (images.shape[1], images.shape[2])).unsqueeze(3)
+                resized_mask = 1 - resized_mask
+                tensor_putalpha(masked_images, resized_mask)
+                res = nodes.PreviewImage().save_images(masked_images, filename_prefix="PreviewBridge/PB-", prompt=prompt, extra_pnginfo=extra_pnginfo)
+
+            image2 = res['ui']['images']
+            pixels = images
+
+            path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', image2[0]['filename'])
+            core.set_previewbridge_image(unique_id, path, image2[0])
+            core.preview_bridge_image_id_map[image] = (path, image2[0])
+            core.preview_bridge_image_name_map[unique_id, path] = (image, image2[0])
+            core.preview_bridge_cache[unique_id] = (images, image2)
+
+            image = image2
+
+        is_empty_mask = torch.all(mask == 0)
+
+        if block and is_empty_mask and core.is_execution_model_version_supported():
+            from comfy_execution.graph import ExecutionBlocker
+            result = ExecutionBlocker(None), ExecutionBlocker(None)
+        elif block and is_empty_mask:
+            print(f"[Impact Pack] PreviewBridge: ComfyUI is outdated - blocking feature is disabled.")
+            result = pixels, mask
+        else:
+            result = pixels, mask
+
+        if not is_empty_mask:
+            core.preview_bridge_last_mask_cache[unique_id] = mask
+
+        return {
+            "ui": {"images": image},
+            "result": result,
+        }
+
+
+def decode_latent(latent, preview_method, vae_opt=None):
+    if vae_opt is not None:
+        image = nodes.VAEDecode().decode(vae_opt, latent)[0]
+        return image
+
+    from comfy.cli_args import LatentPreviewMethod
+    import comfy.latent_formats as latent_formats
+
+    if preview_method.startswith("TAE"):
+        decoder_name = None
+
+        if preview_method == "TAESD15":
+            decoder_name = "taesd"
+        elif preview_method == 'TAESDXL':
+            decoder_name = "taesdxl"
+        elif preview_method == 'TAESD3':
+            decoder_name = "taesd3"
+        elif preview_method == 'TAEF1':
+            decoder_name = "taef1"
+
+        if decoder_name:
+            vae = nodes.VAELoader().load_vae(decoder_name)[0]
+            image = nodes.VAEDecode().decode(vae, latent)[0]
+            return image
+
+    if preview_method == "Latent2RGB-SD15":
+        latent_format = latent_formats.SD15()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SDXL":
+        latent_format = latent_formats.SDXL()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SD3":
+        latent_format = latent_formats.SD3()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SD-X4":
+        latent_format = latent_formats.SD_X4()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-Playground-2.5":
+        latent_format = latent_formats.SDXL_Playground_2_5()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SC-Prior":
+        latent_format = latent_formats.SC_Prior()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-SC-B":
+        latent_format = latent_formats.SC_B()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-FLUX.1":
+        latent_format = latent_formats.Flux()
+        method = LatentPreviewMethod.Latent2RGB
+    elif preview_method == "Latent2RGB-LTXV":
+        latent_format = latent_formats.LTXV()
+        method = LatentPreviewMethod.Latent2RGB
+    else:
+        print(f"[Impact Pack] PreviewBridgeLatent: '{preview_method}' is unsupported preview method.")
+        latent_format = latent_formats.SD15()
+        method = LatentPreviewMethod.Latent2RGB
+
+    previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method)
+    samples = latent_format.process_in(latent['samples'])
+
+    pil_image = previewer.decode_latent_to_preview(samples)
+    pixels_size = pil_image.size[0]*8, pil_image.size[1]*8
+    resized_image = pil_image.resize(pixels_size, resample=LANCZOS)
+
+    return to_tensor(resized_image).unsqueeze(0)
+
+
+class PreviewBridgeLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "latent": ("LATENT",),
+                    "image": ("STRING", {"default": ""}),
+                    "preview_method": (["Latent2RGB-FLUX.1",
+                                        "Latent2RGB-SDXL", "Latent2RGB-SD15", "Latent2RGB-SD3",
+                                        "Latent2RGB-SD-X4", "Latent2RGB-Playground-2.5",
+                                        "Latent2RGB-SC-Prior", "Latent2RGB-SC-B",
+                                        "Latent2RGB-LTXV",
+                                        "TAEF1", "TAESDXL", "TAESD15", "TAESD3"],),
+                    },
+                "optional": {
+                    "vae_opt": ("VAE", ),
+                    "block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped. Instead, it returns a white mask."}),
+                    "restore_mask": (["never", "always", "if_same_size"], {"tooltip": "if_same_size: If the changed input latent is the same size as the previous latent, restore using the last saved mask\nalways: Whenever the input latent changes, always restore using the last saved mask\nnever: Do not restore the mask.\n`restore_mask` has higher priority than `block`\nIf the input latent already has a mask, do not restore mask."}),
+                },
+                "hidden": {"unique_id": "UNIQUE_ID", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ("LATENT", "MASK", )
+
+    FUNCTION = "doit"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "ImpactPack/Util"
+
+    DESCRIPTION = "This is a feature that allows you to edit and send a Mask over a latent image.\nIf the block is set to 'is_empty_mask', the execution is stopped when the mask is empty."
+
+    def __init__(self):
+        super().__init__()
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+        self.prev_hash = None
+        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+
+    @staticmethod
+    def load_image(pb_id):
+        is_fail = False
+        if pb_id not in core.preview_bridge_image_id_map:
+            is_fail = True
+
+        image_path, ui_item = core.preview_bridge_image_id_map[pb_id]
+
+        if not os.path.isfile(image_path):
+            is_fail = True
+
+        if not is_fail:
+            i = Image.open(image_path)
+            i = ImageOps.exif_transpose(i)
+            image = i.convert("RGB")
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = None
+        else:
+            image = empty_pil_tensor()
+            mask = None
+            ui_item = {
+                "filename": 'empty.png',
+                "subfolder": '',
+                "type": 'temp'
+            }
+
+        return image, mask, ui_item
+
+    def doit(self, latent, image, preview_method, vae_opt=None, block=False, unique_id=None, restore_mask='never', prompt=None, extra_pnginfo=None):
+        latent_channels = latent['samples'].shape[1]
+
+        if 'SD3' in preview_method or 'SC-Prior' in preview_method or 'FLUX.1' in preview_method or 'TAEF1' == preview_method:
+            preview_method_channels = 16
+        elif 'LTXV' in preview_method:
+            preview_method_channels = 128
+        else:
+            preview_method_channels = 4
+
+        if vae_opt is None and latent_channels != preview_method_channels:
+            print(f"[PreviewBridgeLatent] The version of latent is not compatible with preview_method.\nSD3, SD1/SD2, SDXL, SC-Prior, SC-B and FLUX.1 are not compatible with each other.")
+            raise Exception("The version of latent is not compatible with preview_method.<BR>SD3, SD1/SD2, SDXL, SC-Prior, SC-B and FLUX.1 are not compatible with each other.")
+
+        need_refresh = False
+
+        if unique_id not in core.preview_bridge_cache:
+            need_refresh = True
+
+        elif (core.preview_bridge_cache[unique_id][0] is not latent
+              or (vae_opt is None and core.preview_bridge_cache[unique_id][2] is not None)
+              or (vae_opt is None and core.preview_bridge_cache[unique_id][1] != preview_method)
+              or (vae_opt is not None and core.preview_bridge_cache[unique_id][2] is not vae_opt)):
+            need_refresh = True
+
+        if not need_refresh:
+            pixels, mask, path_item = PreviewBridge.load_image(image)
+
+            if mask is None:
+                mask = torch.ones(latent['samples'].shape[2:], dtype=torch.float32, device="cpu").unsqueeze(0)
+                if 'noise_mask' in latent:
+                    res_latent = latent.copy()
+                    del res_latent['noise_mask']
+                else:
+                    res_latent = latent
+
+                is_empty_mask = True
+            else:
+                res_latent = latent.copy()
+                res_latent['noise_mask'] = mask
+
+                is_empty_mask = torch.all(mask == 1)
+
+            res_image = [path_item]
+        else:
+            decoded_image = decode_latent(latent, preview_method, vae_opt)
+
+            if 'noise_mask' in latent:
+                mask = latent['noise_mask'].squeeze(0)  # 4D mask -> 3D mask
+
+                decoded_pil = to_pil(decoded_image)
+
+                inverted_mask = 1 - mask  # invert
+                resized_mask = resize_mask(inverted_mask, (decoded_image.shape[1], decoded_image.shape[2]))
+                result_pil = apply_mask_alpha_to_pil(decoded_pil, resized_mask)
+
+                full_output_folder, filename, counter, _, _ = folder_paths.get_save_image_path("PreviewBridge/PBL-"+self.prefix_append, folder_paths.get_temp_directory(), result_pil.size[0], result_pil.size[1])
+                file = f"{filename}_{counter}.png"
+                result_pil.save(os.path.join(full_output_folder, file), compress_level=4)
+                res_image = [{
+                                'filename': file,
+                                'subfolder': 'PreviewBridge',
+                                'type': 'temp',
+                            }]
+
+                is_empty_mask = False
+            else:
+                if restore_mask != "never":
+                    mask = core.preview_bridge_last_mask_cache.get(unique_id)
+                    if mask is None or (restore_mask != "always" and mask.shape[1:] != decoded_image.shape[1:3]):
+                        mask = None
+                else:
+                    mask = None
+
+                if mask is None:
+                    mask = torch.ones(latent['samples'].shape[2:], dtype=torch.float32, device="cpu").unsqueeze(0)
+                    res = nodes.PreviewImage().save_images(decoded_image, filename_prefix="PreviewBridge/PBL-", prompt=prompt, extra_pnginfo=extra_pnginfo)
+                else:
+                    masked_images = tensor_convert_rgba(decoded_image)
+                    resized_mask = resize_mask(mask, (decoded_image.shape[1], decoded_image.shape[2])).unsqueeze(3)
+                    resized_mask = 1 - resized_mask
+                    tensor_putalpha(masked_images, resized_mask)
+                    res = nodes.PreviewImage().save_images(masked_images, filename_prefix="PreviewBridge/PBL-", prompt=prompt, extra_pnginfo=extra_pnginfo)
+
+                res_image = res['ui']['images']
+
+            is_empty_mask = torch.all(mask == 1)
+
+            path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', res_image[0]['filename'])
+            core.set_previewbridge_image(unique_id, path, res_image[0])
+            core.preview_bridge_image_id_map[image] = (path, res_image[0])
+            core.preview_bridge_image_name_map[unique_id, path] = (image, res_image[0])
+            core.preview_bridge_cache[unique_id] = (latent, preview_method, vae_opt, res_image)
+
+            res_latent = latent
+
+        if block and is_empty_mask and core.is_execution_model_version_supported():
+            from comfy_execution.graph import ExecutionBlocker
+            result = ExecutionBlocker(None), ExecutionBlocker(None)
+        elif block and is_empty_mask:
+            print(f"[Impact Pack] PreviewBridgeLatent: ComfyUI is outdated - blocking feature is disabled.")
+            result = res_latent, mask
+        else:
+            result = res_latent, mask
+
+        if not is_empty_mask:
+            core.preview_bridge_last_mask_cache[unique_id] = mask
+
+        return {
+            "ui": {"images": res_image},
+            "result": result,
+        }

modules/impact/config.py

@@ -1,18 +1,16 @@
 import configparser
 import os
 
+version_code = [8, 14, 1]
+version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
 
-version = "V4.38.2"
-
-dependency_version = 19
+dependency_version = 24
 
 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()
@@ -34,11 +32,15 @@ def read_config():
         config.read(config_path)
         default_conf = config['default']
 
+        if not os.path.exists(default_conf['custom_wildcards']):
+            print(f"[WARN] ComfyUI-Impact-Pack: custom_wildcards path not found: {default_conf['custom_wildcards']}. Using default path.")
+            default_conf['custom_wildcards'] = os.path.join(my_path, "..", "..", "custom_wildcards")
+
         return {
                     'dependency_version': int(default_conf['dependency_version']),
                     'mmdet_skip': default_conf['mmdet_skip'].lower() == 'true' if 'mmdet_skip' in default_conf else True,
                     'sam_editor_cpu': default_conf['sam_editor_cpu'].lower() == 'true' if 'sam_editor_cpu' in default_conf else False,
-                    'sam_editor_model': 'sam_vit_b_01ec64.pth',
+                    'sam_editor_model': default_conf['sam_editor_model'].lower() if 'sam_editor_model' else 'sam_vit_b_01ec64.pth',
                     'custom_wildcards': default_conf['custom_wildcards'] if 'custom_wildcards' in default_conf else os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "custom_wildcards")),
                     'disable_gpu_opencv': default_conf['disable_gpu_opencv'].lower() == 'true' if 'disable_gpu_opencv' in default_conf else True
                }

modules/impact/defs.py

@@ -0,0 +1,17 @@
+detection_labels = [
+    'hand', 'face', 'mouth', 'eyes', 'eyebrows', 'pupils',
+    'left_eyebrow', 'left_eye', 'left_pupil', 'right_eyebrow', 'right_eye', 'right_pupil',
+    'short_sleeved_shirt', 'long_sleeved_shirt', 'short_sleeved_outwear', 'long_sleeved_outwear',
+    'vest', 'sling', 'shorts', 'trousers', 'skirt', 'short_sleeved_dress', 'long_sleeved_dress', 'vest_dress', 'sling_dress',
+    "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
+    "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
+    "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
+    "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
+    "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
+    "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
+    "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza",
+    "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet",
+    "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
+    "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
+    "hair drier", "toothbrush"
+ ]

modules/impact/detectors.py

@@ -1,23 +1,35 @@
 import impact.core as core
-from impact.config import MAX_RESOLUTION
+from nodes import MAX_RESOLUTION
 import impact.segs_nodes as segs_nodes
-import numpy as np
 import impact.utils as utils
+import torch
+from impact.core import SEG
+
+SAM_MODEL_TOOLTIP = {"tooltip": "Segment Anything Model for Silhouette Detection.\nBe sure to use the SAM_MODEL loaded through the SAMLoader (Impact) node as input."}
+SAM_MODEL_TOOLTIP_OPTIONAL = {"tooltip": "[OPTIONAL]\nSegment Anything Model for Silhouette Detection.\nBe sure to use the SAM_MODEL loaded through the SAMLoader (Impact) node as input.\nGiven this input, it refines the rectangular areas detected by BBOX_DETECTOR into silhouette shapes through SAM.\nsam_model_opt takes priority over segm_detector_opt."}
+
+MASK_HINT_THRESHOLD_TOOLTIP = "When detection_hint is mask-area, the mask of SEGS is used as a point hint for SAM (Segment Anything).\nIn this case, only the areas of the mask with brightness values equal to or greater than mask_hint_threshold are used as hints."
+MASK_HINT_USE_NEGATIVE_TOOLTIP = "When detecting with SAM (Segment Anything), negative hints are applied as follows:\nSmall: When the SEGS is smaller than 10 pixels in size\nOuter: Sampling the image area outside the SEGS region at regular intervals"
+
+DILATION_TOOLTIP = "Set the value to dilate the result mask. If the value is negative, it erodes the mask."
+DETECTION_HINT_TOOLTIP = {"tooltip": "It is recommended to use only center-1.\nWhen refining the mask of SEGS with the SAM (Segment Anything) model, center-1 uses only the rectangular area of SEGS and a single point at the exact center as hints.\nOther options were added during the experimental stage and do not work well."}
+
+BBOX_EXPANSION_TOOLTIP = "When performing SAM (Segment Anything) detection within the SEGS area, the rectangular area of SEGS is expanded and used as a hint."
 
 class SAMDetectorCombined:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                        "sam_model": ("SAM_MODEL", ),
-                        "segs": ("SEGS", ),
-                        "image": ("IMAGE", ),
+                        "sam_model": ("SAM_MODEL", SAM_MODEL_TOOLTIP),
+                        "segs": ("SEGS", {"tooltip": "This is the segment information detected by the detector.\nIt refines the Mask through the SAM (Segment Anything) detector for all areas pointed to by SEGS, and combines all Masks to return as a single Mask."}),
+                        "image": ("IMAGE", {"tooltip": "It is assumed that segs contains only the information about the detected areas, and does not include the image. SAM (Segment Anything) operates by referencing this 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": -512, "max": 512, "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"], )
+                                            "mask-points", "mask-point-bbox", "none"], DETECTION_HINT_TOOLTIP),
+                        "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1, "tooltip": DILATION_TOOLTIP}),
+                        "threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Set the sensitivity threshold for the mask detected by SAM (Segment Anything). A higher value generates a more specific mask with a narrower range. For example, when pointing to a person's area, it might detect clothes, which is a narrower range, instead of the entire person."}),
+                        "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": BBOX_EXPANSION_TOOLTIP}),
+                        "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": MASK_HINT_THRESHOLD_TOOLTIP}),
+                        "mask_hint_use_negative": (["False", "Small", "Outter"], {"tooltip": MASK_HINT_USE_NEGATIVE_TOOLTIP})
                       }
                 }
 
@@ -36,16 +48,16 @@ class SAMDetectorSegmented:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                        "sam_model": ("SAM_MODEL", ),
-                        "segs": ("SEGS", ),
-                        "image": ("IMAGE", ),
+                        "sam_model": ("SAM_MODEL", SAM_MODEL_TOOLTIP),
+                        "segs": ("SEGS", {"tooltip": "This is the segment information detected by the detector.\nFor the SEGS region, the masks detected by SAM (Segment Anything) are created as a unified mask and a batch of individual masks."}),
+                        "image": ("IMAGE", {"tooltip": "It is assumed that segs contains only the information about the detected areas, and does not include the image. SAM (Segment Anything) operates by referencing this 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": -512, "max": 512, "step": 1}),
+                                            "mask-points", "mask-point-bbox", "none"], DETECTION_HINT_TOOLTIP),
+                        "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1, "tooltip": DILATION_TOOLTIP}),
                         "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"], )
+                        "bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": BBOX_EXPANSION_TOOLTIP}),
+                        "mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": MASK_HINT_THRESHOLD_TOOLTIP}),
+                        "mask_hint_use_negative": (["False", "Small", "Outter"], {"tooltip": MASK_HINT_USE_NEGATIVE_TOOLTIP})
                       }
                 }
 
@@ -84,6 +96,9 @@ class BboxDetectorForEach:
     CATEGORY = "ImpactPack/Detector"
 
     def doit(self, bbox_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None):
+        if len(image) > 1:
+            raise Exception('[Impact Pack] ERROR: BboxDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
+
         segs = bbox_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook)
 
         if labels is not None and labels != '':
@@ -115,6 +130,9 @@ class SegmDetectorForEach:
     CATEGORY = "ImpactPack/Detector"
 
     def doit(self, segm_detector, image, threshold, dilation, crop_factor, drop_size, labels=None, detailer_hook=None):
+        if len(image) > 1:
+            raise Exception('[Impact Pack] ERROR: SegmDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
+
         segs = segm_detector.detect(image, threshold, dilation, crop_factor, drop_size, detailer_hook)
 
         if labels is not None and labels != '':
@@ -143,7 +161,11 @@ class SegmDetectorCombined:
 
     def doit(self, segm_detector, image, threshold, dilation):
         mask = segm_detector.detect_combined(image, threshold, dilation)
-        return (mask,)
+
+        if mask is None:
+            mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
+
+        return (mask.unsqueeze(0),)
 
 
 class BboxDetectorCombined(SegmDetectorCombined):
@@ -159,7 +181,11 @@ class BboxDetectorCombined(SegmDetectorCombined):
 
     def doit(self, bbox_detector, image, threshold, dilation):
         mask = bbox_detector.detect_combined(image, threshold, dilation)
-        return (mask,)
+
+        if mask is None:
+            mask = torch.zeros((image.shape[2], image.shape[1]), dtype=torch.float32, device="cpu")
+
+        return (mask.unsqueeze(0),)
 
 
 class SimpleDetectorForEach:
@@ -170,21 +196,22 @@ class SimpleDetectorForEach:
                         "image": ("IMAGE", ),
 
                         "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                        "bbox_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
+                        "bbox_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
 
                         "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
                         "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
 
                         "sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                        "sub_dilation": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
+                        "sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
                         "sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
 
                         "sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
                       },
                 "optional": {
-                        "sam_model_opt": ("SAM_MODEL", ),
+                        "post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
+                        "sam_model_opt": ("SAM_MODEL", SAM_MODEL_TOOLTIP_OPTIONAL),
                         "segm_detector_opt": ("SEGM_DETECTOR", ),
-                 }
+                      }
                 }
 
     RETURN_TYPES = ("SEGS",)
@@ -194,29 +221,39 @@ class SimpleDetectorForEach:
 
     @staticmethod
     def detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
-             sub_threshold, sub_dilation, sub_bbox_expansion,
-             sam_mask_hint_threshold, sam_model_opt=None, segm_detector_opt=None):
-        segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size)
+               sub_threshold, sub_dilation, sub_bbox_expansion,
+               sam_mask_hint_threshold, post_dilation=0, sam_model_opt=None, segm_detector_opt=None,
+               detailer_hook=None):
+        if len(image) > 1:
+            raise Exception('[Impact Pack] ERROR: SimpleDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
 
-        if sam_model_opt is not None:
-            mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation,
-                                      sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False)
-            segs = core.segs_bitwise_and_mask(segs, mask)
-        elif segm_detector_opt is not None:
-            segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size)
-            mask = core.segs_to_combined_mask(segm_segs)
-            segs = core.segs_bitwise_and_mask(segs, mask)
+        if segm_detector_opt is not None and hasattr(segm_detector_opt, 'bbox_detector') and segm_detector_opt.bbox_detector == bbox_detector:
+            # Better segm support for YOLO-World detector
+            segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
+        else:
+            segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
+
+            if sam_model_opt is not None:
+                mask = core.make_sam_mask(sam_model_opt, segs, image, "center-1", sub_dilation,
+                                          sub_threshold, sub_bbox_expansion, sam_mask_hint_threshold, False)
+                segs = core.segs_bitwise_and_mask(segs, mask)
+            elif segm_detector_opt is not None:
+                segm_segs = segm_detector_opt.detect(image, sub_threshold, sub_dilation, crop_factor, drop_size, detailer_hook=detailer_hook)
+                mask = core.segs_to_combined_mask(segm_segs)
+                segs = core.segs_bitwise_and_mask(segs, mask)
+
+        segs = core.dilate_segs(segs, post_dilation)
 
         return (segs,)
 
-
     def doit(self, bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
              sub_threshold, sub_dilation, sub_bbox_expansion,
-             sam_mask_hint_threshold, sam_model_opt=None, segm_detector_opt=None):
+             sam_mask_hint_threshold, post_dilation=0, sam_model_opt=None, segm_detector_opt=None):
 
         return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
                                             sub_threshold, sub_dilation, sub_bbox_expansion,
-                                            sam_mask_hint_threshold, sam_model_opt, segm_detector_opt)
+                                            sam_mask_hint_threshold, post_dilation=post_dilation,
+                                            sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt)
 
 
 class SimpleDetectorForEachPipe:
@@ -227,17 +264,20 @@ class SimpleDetectorForEachPipe:
                         "image": ("IMAGE", ),
 
                         "bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                        "bbox_dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
+                        "bbox_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
 
                         "crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 100, "step": 0.1}),
                         "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
 
                         "sub_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                        "sub_dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
+                        "sub_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
                         "sub_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
 
                         "sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
                       },
+                "optional": {
+                        "post_dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
+                      }
                 }
 
     RETURN_TYPES = ("SEGS",)
@@ -246,13 +286,17 @@ class SimpleDetectorForEachPipe:
     CATEGORY = "ImpactPack/Detector"
 
     def doit(self, detailer_pipe, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
-             sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold):
+             sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold, post_dilation=0):
+
+        if len(image) > 1:
+            raise Exception('[Impact Pack] ERROR: SimpleDetectorForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
 
         model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe
 
         return SimpleDetectorForEach.detect(bbox_detector, image, bbox_threshold, bbox_dilation, crop_factor, drop_size,
                                             sub_threshold, sub_dilation, sub_bbox_expansion,
-                                            sam_mask_hint_threshold, sam_model_opt, segm_detector_opt)
+                                            sam_mask_hint_threshold, post_dilation=post_dilation, sam_model_opt=sam_model_opt, segm_detector_opt=segm_detector_opt,
+                                            detailer_hook=detailer_hook)
 
 
 class SimpleDetectorForAnimateDiff:
@@ -275,7 +319,9 @@ class SimpleDetectorForAnimateDiff:
                         "sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
                       },
                 "optional": {
-                        "sam_model_opt": ("SAM_MODEL", ),
+                        "masking_mode": (["Pivot SEGS", "Combine neighboring frames", "Don't combine"],),
+                        "segs_pivot": (["Combined mask", "1st frame mask"],),
+                        "sam_model_opt": ("SAM_MODEL", SAM_MODEL_TOOLTIP_OPTIONAL),
                         "segm_detector_opt": ("SEGM_DETECTOR", ),
                  }
                 }
@@ -287,11 +333,14 @@ class SimpleDetectorForAnimateDiff:
 
     @staticmethod
     def detect(bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
-               sub_threshold, sub_dilation, sub_bbox_expansion,
-               sam_mask_hint_threshold, sam_model_opt=None, segm_detector_opt=None):
+               sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold,
+               masking_mode="Pivot SEGS", segs_pivot="Combined mask", sam_model_opt=None, segm_detector_opt=None):
+
+        h = image_frames.shape[1]
+        w = image_frames.shape[2]
 
         # gather segs for all frames
-        all_segs = []
+        segs_by_frames = []
         for image in image_frames:
             image = image.unsqueeze(0)
             segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, crop_factor, drop_size)
@@ -305,25 +354,112 @@ class SimpleDetectorForAnimateDiff:
                 mask = core.segs_to_combined_mask(segm_segs)
                 segs = core.segs_bitwise_and_mask(segs, mask)
 
-            all_segs.append(segs)
+            segs_by_frames.append(segs)
 
-        # create merged masks
-        all_masks = []
-        for segs in all_segs:
-            all_masks += segs_nodes.SEGSToMaskList().doit(segs)[0]
+        def get_masked_frames():
+            masks_by_frame = []
+            for i, segs in enumerate(segs_by_frames):
+                masks_in_frame = segs_nodes.SEGSToMaskList().doit(segs)[0]
+                current_frame_mask = (masks_in_frame[0] * 255).to(torch.uint8)
 
-        result_mask = all_masks[0]
-        for mask in all_masks[1:]:
-            result_mask += mask
+                for mask in masks_in_frame[1:]:
+                    current_frame_mask |= (mask * 255).to(torch.uint8)
 
-        result_mask = utils.to_binary_mask(result_mask, 0.1)
+                current_frame_mask = (current_frame_mask/255.0).to(torch.float32)
+                current_frame_mask = utils.to_binary_mask(current_frame_mask, 0.1)[0]
 
-        return segs_nodes.MaskToSEGS().doit(result_mask, False, crop_factor, False, drop_size)
+                masks_by_frame.append(current_frame_mask)
+
+            return masks_by_frame
+
+        def get_empty_mask():
+            return torch.zeros((h, w), dtype=torch.float32, device="cpu")
+
+        def get_neighboring_mask_at(i, masks_by_frame):
+            prv = masks_by_frame[i-1] if i > 1 else get_empty_mask()
+            cur = masks_by_frame[i]
+            nxt = masks_by_frame[i-1] if i > 1 else get_empty_mask()
+
+            prv = prv if prv is not None else get_empty_mask()
+            cur = cur.clone() if cur is not None else get_empty_mask()
+            nxt = nxt if nxt is not None else get_empty_mask()
+
+            return prv, cur, nxt
+
+        def get_merged_neighboring_mask(masks_by_frame):
+            if len(masks_by_frame) <= 1:
+                return masks_by_frame
+
+            result = []
+            for i in range(0, len(masks_by_frame)):
+                prv, cur, nxt = get_neighboring_mask_at(i, masks_by_frame)
+                cur = (cur * 255).to(torch.uint8)
+                cur |= (prv * 255).to(torch.uint8)
+                cur |= (nxt * 255).to(torch.uint8)
+                cur = (cur / 255.0).to(torch.float32)
+                cur = utils.to_binary_mask(cur, 0.1)[0]
+                result.append(cur)
+
+            return result
+
+        def get_whole_merged_mask():
+            all_masks = []
+            for segs in segs_by_frames:
+                all_masks += segs_nodes.SEGSToMaskList().doit(segs)[0]
+
+            merged_mask = (all_masks[0] * 255).to(torch.uint8)
+            for mask in all_masks[1:]:
+                merged_mask |= (mask * 255).to(torch.uint8)
+
+            merged_mask = (merged_mask / 255.0).to(torch.float32)
+            merged_mask = utils.to_binary_mask(merged_mask, 0.1)[0]
+            return merged_mask
+
+        def get_pivot_segs():
+            if segs_pivot == "1st frame mask":
+                return segs_by_frames[0][1]
+            else:
+                merged_mask = get_whole_merged_mask()
+                return segs_nodes.MaskToSEGS.doit(merged_mask, False, crop_factor, False, drop_size, contour_fill=True)[0]
+
+        def get_segs(merged_neighboring=False):
+            pivot_segs = get_pivot_segs()
+
+            masks_by_frame = get_masked_frames()
+            if merged_neighboring:
+                masks_by_frame = get_merged_neighboring_mask(masks_by_frame)
+
+            new_segs = []
+            for seg in pivot_segs[1]:
+                cropped_mask = torch.zeros(seg.cropped_mask.shape, dtype=torch.float32, device="cpu").unsqueeze(0)
+                pivot_mask = torch.from_numpy(seg.cropped_mask)
+                x1, y1, x2, y2 = seg.crop_region
+                for mask in masks_by_frame:
+                    cropped_mask_at_frame = (mask[y1:y2, x1:x2] * pivot_mask).unsqueeze(0)
+                    cropped_mask = torch.cat((cropped_mask, cropped_mask_at_frame), dim=0)
+
+                if len(cropped_mask) > 1:
+                    cropped_mask = cropped_mask[1:]
+
+                new_seg = SEG(seg.cropped_image, cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
+                new_segs.append(new_seg)
+
+            return pivot_segs[0], new_segs
+
+        # create result mask
+        if masking_mode == "Pivot SEGS":
+            return (get_pivot_segs(), )
+
+        elif masking_mode == "Combine neighboring frames":
+            return (get_segs(merged_neighboring=True), )
+
+        else: # elif masking_mode == "Don't combine":
+            return (get_segs(merged_neighboring=False), )
 
     def doit(self, bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
-             sub_threshold, sub_dilation, sub_bbox_expansion,
-             sam_mask_hint_threshold, sam_model_opt=None, segm_detector_opt=None):
+             sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold,
+             masking_mode="Pivot SEGS", segs_pivot="Combined mask", sam_model_opt=None, segm_detector_opt=None):
 
         return SimpleDetectorForAnimateDiff.detect(bbox_detector, image_frames, bbox_threshold, bbox_dilation, crop_factor, drop_size,
-                                                   sub_threshold, sub_dilation, sub_bbox_expansion,
-                                                   sam_mask_hint_threshold, sam_model_opt, segm_detector_opt)
+                                                   sub_threshold, sub_dilation, sub_bbox_expansion, sam_mask_hint_threshold,
+                                                   masking_mode, segs_pivot, sam_model_opt, segm_detector_opt)

modules/impact/hf_nodes.py

@@ -1,4 +1,3 @@
-from transformers import pipeline
 import comfy
 import re
 from impact.utils import *
@@ -31,6 +30,8 @@ class HF_TransformersClassifierProvider:
     CATEGORY = "ImpactPack/HuggingFace"
 
     def doit(self, preset_repo_id, manual_repo_id, device_mode):
+        from transformers import pipeline
+
         if preset_repo_id == 'Manual repo id':
             url = manual_repo_id
         else:
@@ -41,7 +42,7 @@ class HF_TransformersClassifierProvider:
         else:
             device = "cpu"
 
-        classifier = pipeline(model=url, device=device)
+        classifier = pipeline('image-classification', model=url, device=device)
 
         return (classifier,)
 
@@ -82,8 +83,9 @@ class SEGS_Classify:
                     }
                 }
 
-    RETURN_TYPES = ("SEGS", "SEGS",)
-    RETURN_NAMES = ("filtered_SEGS", "remained_SEGS",)
+    RETURN_TYPES = ("SEGS", "SEGS", "STRING")
+    RETURN_NAMES = ("filtered_SEGS", "remained_SEGS", "detected_labels")
+    OUTPUT_IS_LIST = (False, False, True)
 
     FUNCTION = "doit"
 
@@ -116,7 +118,7 @@ class SEGS_Classify:
         match = re.match(classify_expr_pattern, expr_str)
 
         if match is None:
-            return ((segs[0], []), segs)
+            return (segs[0], []), segs, []
 
         a = match.group(1)
         op = match.group(2)
@@ -127,6 +129,7 @@ class SEGS_Classify:
 
         classified = []
         remained_SEGS = []
+        provided_labels = set()
 
         for seg in segs[1]:
             cropped_image = None
@@ -138,9 +141,12 @@ class SEGS_Classify:
                 cropped_image = crop_image(ref_image_opt, seg.crop_region)
 
             if cropped_image is not None:
-                cropped_image = Image.fromarray(np.clip(255. * cropped_image.squeeze(), 0, 255).astype(np.uint8))
+                cropped_image = to_pil(cropped_image)
                 res = classifier(cropped_image)
                 classified.append((seg, res))
+
+                for x in res:
+                    provided_labels.add(x['label'])
             else:
                 remained_SEGS.append(seg)
 
@@ -179,4 +185,4 @@ class SEGS_Classify:
             else:
                 remained_SEGS.append(seg)
 
-        return ((segs[0], filtered_SEGS), (segs[0], remained_SEGS))
+        return (segs[0], filtered_SEGS), (segs[0], remained_SEGS), list(provided_labels)

modules/impact/hook_nodes.py

@@ -0,0 +1,85 @@
+import sys
+from . import hooks
+from . import defs
+
+
+class SEGSOrderedFilterDetailerHookProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2"],),
+                        "order": ("BOOLEAN", {"default": True, "label_on": "descending", "label_off": "ascending"}),
+                        "take_start": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
+                        "take_count": ("INT", {"default": 1, "min": 0, "max": sys.maxsize, "step": 1}),
+                     },
+                }
+
+    RETURN_TYPES = ("DETAILER_HOOK", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, target, order, take_start, take_count):
+        hook = hooks.SEGSOrderedFilterDetailerHook(target, order, take_start, take_count)
+        return (hook, )
+
+
+class SEGSRangeFilterDetailerHookProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "target": (["area(=w*h)", "width", "height", "x1", "y1", "x2", "y2", "length_percent"],),
+                        "mode": ("BOOLEAN", {"default": True, "label_on": "inside", "label_off": "outside"}),
+                        "min_value": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
+                        "max_value": ("INT", {"default": 67108864, "min": 0, "max": sys.maxsize, "step": 1}),
+                     },
+                }
+
+    RETURN_TYPES = ("DETAILER_HOOK", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, target, mode, min_value, max_value):
+        hook = hooks.SEGSRangeFilterDetailerHook(target, mode, min_value, max_value)
+        return (hook, )
+
+
+class SEGSLabelFilterDetailerHookProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "segs": ("SEGS", ),
+                        "preset": (['all'] + defs.detection_labels,),
+                        "labels": ("STRING", {"multiline": True, "placeholder": "List the types of segments to be allowed, separated by commas"}),
+                     },
+                }
+
+    RETURN_TYPES = ("DETAILER_HOOK", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, preset, labels):
+        hook = hooks.SEGSLabelFilterDetailerHook(labels)
+        return (hook, )
+
+
+class PreviewDetailerHookProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {"quality": ("INT", {"default": 95, "min": 20, "max": 100})},
+            "hidden": {"unique_id": "UNIQUE_ID"},
+        }
+
+    RETURN_TYPES = ("DETAILER_HOOK", "UPSCALER_HOOK")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    NOT_IDEMPOTENT = True
+
+    def doit(self, quality, unique_id):
+        hook = hooks.PreviewDetailerHook(unique_id, quality)
+        return hook, hook

modules/impact/hooks.py

@@ -0,0 +1,518 @@
+import copy
+import torch
+import nodes
+from impact import utils
+from . import segs_nodes
+from thirdparty import noise_nodes
+from server import PromptServer
+import asyncio
+import folder_paths
+import os
+from comfy_extras import nodes_custom_sampler
+import math
+
+
+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_decode(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
+
+    def post_crop_region(self, w, h, item_bbox, crop_region):
+        return crop_region
+
+    def touch_scaled_size(self, w, h):
+        return w, h
+
+
+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 pre_decode(self, samples):
+        return self.hook2.pre_decode(self.hook1.pre_decode(samples))
+
+    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 post_crop_region(self, w, h, item_bbox, crop_region):
+        crop_region = self.hook1.post_crop_region(w, h, item_bbox, crop_region)
+        return self.hook2.post_crop_region(w, h, item_bbox, crop_region)
+
+    def touch_scaled_size(self, w, h):
+        w, h = self.hook1.touch_scaled_size(w, h)
+        return self.hook2.touch_scaled_size(w, h)
+
+    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 DetailerHookCombine(PixelKSampleHookCombine):
+    def cycle_latent(self, latent):
+        latent = self.hook1.cycle_latent(latent)
+        latent = self.hook2.cycle_latent(latent)
+        return latent
+
+    def post_detection(self, segs):
+        segs = self.hook1.post_detection(segs)
+        segs = self.hook2.post_detection(segs)
+        return segs
+
+    def post_paste(self, image):
+        image = self.hook1.post_paste(image)
+        image = self.hook2.post_paste(image)
+        return image
+
+    def get_custom_noise(self, seed, noise, is_touched):
+        noise_1st, is_touched = self.hook1.get_custom_noise(seed, noise, is_touched)
+        noise_2nd, is_touched = self.hook2.get_custom_noise(seed, noise, is_touched)
+        return noise, is_touched
+
+
+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):
+        if self.total_step > 1:
+            progress = self.cur_step / (self.total_step - 1)
+            gap = self.target_cfg - cfg
+            current_cfg = int(cfg + gap * progress)
+        else:
+            current_cfg = self.target_cfg
+
+        return model, seed, steps, current_cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
+
+
+class SimpleDenoiseScheduleHook(PixelKSampleHook):
+    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):
+        if self.total_step > 1:
+            progress = self.cur_step / (self.total_step - 1)
+            gap = self.target_denoise - denoise
+            current_denoise = denoise + gap * progress
+        else:
+            current_denoise = self.target_denoise
+
+        return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, current_denoise
+
+
+class SimpleStepsScheduleHook(PixelKSampleHook):
+    def __init__(self, target_steps):
+        super().__init__()
+        self.target_steps = target_steps
+
+    def pre_ksample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise):
+        if self.total_step > 1:
+            progress = self.cur_step / (self.total_step - 1)
+            gap = self.target_steps - steps
+            current_steps = int(steps + gap * progress)
+        else:
+            current_steps = self.target_steps
+
+        return model, seed, current_steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise
+
+
+class DetailerHook(PixelKSampleHook):
+    def cycle_latent(self, latent):
+        return latent
+
+    def post_detection(self, segs):
+        return segs
+
+    def post_paste(self, image):
+        return image
+
+    def get_custom_noise(self, seed, noise, is_touched):
+        return noise, is_touched
+
+
+# class CustomNoiseDetailerHookProvider(DetailerHook):
+#     def __init__(self, noise):
+#         super().__init__()
+#         self.noise = noise
+#
+#     def get_custom_noise(self, seed, noise, is_start):
+#         return self.noise
+
+
+class VariationNoiseDetailerHookProvider(DetailerHook):
+    def __init__(self, variation_seed, variation_strength):
+        super().__init__()
+        self.variation_seed = variation_seed
+        self.variation_strength = variation_strength
+
+    def get_custom_noise(self, seed, noise, is_touched):
+        empty_noise = {'samples': torch.zeros(noise.size())}
+        if not is_touched:
+            noise = nodes_custom_sampler.Noise_RandomNoise(seed).generate_noise(empty_noise)
+        noise_2nd = nodes_custom_sampler.Noise_RandomNoise(self.variation_seed).generate_noise(empty_noise)
+
+        mixed_noise = ((1 - self.variation_strength) * noise + self.variation_strength * noise_2nd)
+
+        # NOTE: Since the variance of the Gaussian noise in mixed_noise has changed, it must be corrected through scaling.
+        scale_factor = math.sqrt((1 - self.variation_strength) ** 2 + self.variation_strength ** 2)
+        corrected_noise = mixed_noise / scale_factor  # Scale the noise to maintain variance of 1
+
+        return corrected_noise, True
+
+
+class SimpleDetailerDenoiseSchedulerHook(DetailerHook):
+    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, latent, denoise):
+        if self.total_step > 1:
+            progress = self.cur_step / (self.total_step - 1)
+            gap = self.target_denoise - denoise
+            current_denoise = denoise + gap * progress
+        else:
+            # ignore hook if total cycle <= 1
+            current_denoise = denoise
+
+        return model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, current_denoise
+
+
+class CoreMLHook(DetailerHook):
+    def __init__(self, mode):
+        super().__init__()
+        resolution = mode.split('x')
+
+        self.w = int(resolution[0])
+        self.h = int(resolution[1])
+
+        self.override_bbox_by_segm = False
+
+    def pre_decode(self, samples):
+        new_samples = copy.deepcopy(samples)
+        new_samples['samples'] = samples['samples'][0].unsqueeze(0)
+        return new_samples
+
+    def post_encode(self, samples):
+        new_samples = copy.deepcopy(samples)
+        new_samples['samples'] = samples['samples'].repeat(2, 1, 1, 1)
+        return new_samples
+
+    def post_crop_region(self, w, h, item_bbox, crop_region):
+        x1, y1, x2, y2 = crop_region
+        bx1, by1, bx2, by2 = item_bbox
+        crop_w = x2-x1
+        crop_h = y2-y1
+
+        crop_ratio = crop_w/crop_h
+        target_ratio = self.w/self.h
+        if crop_ratio < target_ratio:
+            # shrink height
+            top_gap = by1 - y1
+            bottom_gap = y2 - by2
+
+            gap_ratio = top_gap / bottom_gap
+
+            target_height = 1/target_ratio*crop_w
+            delta_height = crop_h - target_height
+
+            new_y1 = int(y1 + delta_height*gap_ratio)
+            new_y2 = int(new_y1 + target_height)
+            crop_region = x1, new_y1, x2, new_y2
+
+        elif crop_ratio > target_ratio:
+            # shrink width
+            left_gap = bx1 - x1
+            right_gap = x2 - bx2
+
+            gap_ratio = left_gap / right_gap
+
+            target_width = target_ratio*crop_h
+            delta_width = crop_w - target_width
+
+            new_x1 = int(x1 + delta_width*gap_ratio)
+            new_x2 = int(new_x1 + target_width)
+            crop_region = new_x1, y1, new_x2, y2
+
+        return crop_region
+
+    def touch_scaled_size(self, w, h):
+        return self.w, self.h
+
+
+# REQUIREMENTS: BlenderNeko/ComfyUI Noise
+class InjectNoiseHook(PixelKSampleHook):
+    def __init__(self, source, seed, start_strength, end_strength):
+        super().__init__()
+        self.source = source
+        self.seed = seed
+        self.start_strength = start_strength
+        self.end_strength = end_strength
+
+    def post_encode(self, samples):
+        cur_step = self.cur_step
+
+        size = samples['samples'].shape
+        seed = cur_step + self.seed + cur_step
+
+        if "BNK_NoisyLatentImage" in nodes.NODE_CLASS_MAPPINGS and "BNK_InjectNoise" in nodes.NODE_CLASS_MAPPINGS:
+            NoisyLatentImage = nodes.NODE_CLASS_MAPPINGS["BNK_NoisyLatentImage"]
+            InjectNoise = nodes.NODE_CLASS_MAPPINGS["BNK_InjectNoise"]
+        else:
+            utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_Noise',
+                                          "To use 'NoiseInjectionHookProvider', 'ComfyUI Noise' extension is required.")
+            raise Exception("'BNK_NoisyLatentImage', 'BNK_InjectNoise' nodes are not installed.")
+
+        noise = NoisyLatentImage().create_noisy_latents(self.source, seed, size[3] * 8, size[2] * 8, size[0])[0]
+
+        # inj noise
+        mask = None
+        if 'noise_mask' in samples:
+            mask = samples['noise_mask']
+
+        strength = self.start_strength + (self.end_strength - self.start_strength) * cur_step / self.total_step
+        samples = InjectNoise().inject_noise(samples, strength, noise, mask)[0]
+        print(f"[Impact Pack] InjectNoiseHook: strength = {strength}")
+
+        if mask is not None:
+            samples['noise_mask'] = mask
+
+        return samples
+
+
+class UnsamplerHook(PixelKSampleHook):
+    def __init__(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
+                 scheduler, normalize, positive, negative):
+        super().__init__()
+        self.model = model
+        self.cfg = cfg
+        self.sampler_name = sampler_name
+        self.steps = steps
+        self.start_end_at_step = start_end_at_step
+        self.end_end_at_step = end_end_at_step
+        self.scheduler = scheduler
+        self.normalize = normalize
+        self.positive = positive
+        self.negative = negative
+
+    def post_encode(self, samples):
+        cur_step = self.cur_step
+
+        Unsampler = noise_nodes.Unsampler
+
+        end_at_step = self.start_end_at_step + (self.end_end_at_step - self.start_end_at_step) * cur_step / self.total_step
+        end_at_step = int(end_at_step)
+
+        print(f"[Impact Pack] UnsamplerHook: end_at_step = {end_at_step}")
+
+        # inj noise
+        mask = None
+        if 'noise_mask' in samples:
+            mask = samples['noise_mask']
+
+        samples = Unsampler().unsampler(self.model, self.cfg, self.sampler_name, self.steps, end_at_step,
+                                        self.scheduler, self.normalize, self.positive, self.negative, samples)[0]
+
+        if mask is not None:
+            samples['noise_mask'] = mask
+
+        return samples
+
+
+class InjectNoiseHookForDetailer(DetailerHook):
+    def __init__(self, source, seed, start_strength, end_strength, from_start=False):
+        super().__init__()
+        self.source = source
+        self.seed = seed
+        self.start_strength = start_strength
+        self.end_strength = end_strength
+        self.from_start = from_start
+
+    def inject_noise(self, samples):
+        cur_step = self.cur_step if self.from_start else self.cur_step - 1
+        total_step = self.total_step if self.from_start else self.total_step - 1
+
+        size = samples['samples'].shape
+        seed = cur_step + self.seed + cur_step
+
+        if "BNK_NoisyLatentImage" in nodes.NODE_CLASS_MAPPINGS and "BNK_InjectNoise" in nodes.NODE_CLASS_MAPPINGS:
+            NoisyLatentImage = nodes.NODE_CLASS_MAPPINGS["BNK_NoisyLatentImage"]
+            InjectNoise = nodes.NODE_CLASS_MAPPINGS["BNK_InjectNoise"]
+        else:
+            utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_Noise',
+                                          "To use 'NoiseInjectionDetailerHookProvider', 'ComfyUI Noise' extension is required.")
+            raise Exception("'BNK_NoisyLatentImage', 'BNK_InjectNoise' nodes are not installed.")
+
+        noise = NoisyLatentImage().create_noisy_latents(self.source, seed, size[3] * 8, size[2] * 8, size[0])[0]
+
+        # inj noise
+        mask = None
+        if 'noise_mask' in samples:
+            mask = samples['noise_mask']
+
+        strength = self.start_strength + (self.end_strength - self.start_strength) * cur_step / total_step
+        samples = InjectNoise().inject_noise(samples, strength, noise, mask)[0]
+
+        if mask is not None:
+            samples['noise_mask'] = mask
+
+        return samples
+
+    def cycle_latent(self, latent):
+        if self.cur_step == 0 and not self.from_start:
+            return latent
+        else:
+            return self.inject_noise(latent)
+
+
+class UnsamplerDetailerHook(DetailerHook):
+    def __init__(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
+                 scheduler, normalize, positive, negative, from_start=False):
+        super().__init__()
+        self.model = model
+        self.cfg = cfg
+        self.sampler_name = sampler_name
+        self.steps = steps
+        self.start_end_at_step = start_end_at_step
+        self.end_end_at_step = end_end_at_step
+        self.scheduler = scheduler
+        self.normalize = normalize
+        self.positive = positive
+        self.negative = negative
+        self.from_start = from_start
+
+    def unsample(self, samples):
+        cur_step = self.cur_step if self.from_start else self.cur_step - 1
+        total_step = self.total_step if self.from_start else self.total_step - 1
+
+        Unsampler = noise_nodes.Unsampler
+
+        end_at_step = self.start_end_at_step + (self.end_end_at_step - self.start_end_at_step) * cur_step / total_step
+        end_at_step = int(end_at_step)
+
+        # inj noise
+        mask = None
+        if 'noise_mask' in samples:
+            mask = samples['noise_mask']
+
+        samples = Unsampler().unsampler(self.model, self.cfg, self.sampler_name, self.steps, end_at_step,
+                                        self.scheduler, self.normalize, self.positive, self.negative, samples)[0]
+
+        if mask is not None:
+            samples['noise_mask'] = mask
+
+        return samples
+
+    def cycle_latent(self, latent):
+        if self.cur_step == 0 and not self.from_start:
+            return latent
+        else:
+            return self.unsample(latent)
+
+
+class SEGSOrderedFilterDetailerHook(DetailerHook):
+    def __init__(self, target, order, take_start, take_count):
+        super().__init__()
+        self.target = target
+        self.order = order
+        self.take_start = take_start
+        self.take_count = take_count
+
+    def post_detection(self, segs):
+        return segs_nodes.SEGSOrderedFilter().doit(segs, self.target, self.order, self.take_start, self.take_count)[0]
+
+
+class SEGSRangeFilterDetailerHook(DetailerHook):
+    def __init__(self, target, mode, min_value, max_value):
+        super().__init__()
+        self.target = target
+        self.mode = mode
+        self.min_value = min_value
+        self.max_value = max_value
+
+    def post_detection(self, segs):
+        return segs_nodes.SEGSRangeFilter().doit(segs, self.target, self.mode, self.min_value, self.max_value)[0]
+
+
+class SEGSLabelFilterDetailerHook(DetailerHook):
+    def __init__(self, labels):
+        super().__init__()
+        self.labels = labels
+
+    def post_detection(self, segs):
+        return segs_nodes.SEGSLabelFilter().doit(segs, "", self.labels)[0]
+
+
+class PreviewDetailerHook(DetailerHook):
+    def __init__(self, node_id, quality):
+        super().__init__()
+        self.node_id = node_id
+        self.quality = quality
+
+    async def send(self, image):
+        if len(image) > 0:
+            image = image[0].unsqueeze(0)
+        img = utils.tensor2pil(image)
+
+        temp_path = os.path.join(folder_paths.get_temp_directory(), 'pvhook')
+
+        if not os.path.exists(temp_path):
+            os.makedirs(temp_path)
+
+        fullpath = os.path.join(temp_path, f"{self.node_id}.webp")
+        img.save(fullpath, quality=self.quality)
+
+        item = {
+                "filename": f"{self.node_id}.webp",
+                "subfolder": 'pvhook',
+                "type": 'temp'
+                }
+
+        PromptServer.instance.send_sync("impact-preview", {'node_id': self.node_id, 'item': item})
+
+    def post_paste(self, image):
+        asyncio.run(self.send(image))
+        return image

modules/impact/impact_sampling.py

@@ -0,0 +1,321 @@
+import nodes
+from comfy.k_diffusion import sampling as k_diffusion_sampling
+from comfy import samplers
+from comfy_extras import nodes_custom_sampler
+import latent_preview
+import comfy
+import torch
+import math
+import comfy.model_management as mm
+
+
+try:
+    from comfy_extras.nodes_custom_sampler import Noise_EmptyNoise, Noise_RandomNoise
+    import node_helpers
+except:
+    print(f"\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n")
+    raise Exception("[Impact Pack] ComfyUI is an outdated version.")
+
+
+def calculate_sigmas(model, sampler, scheduler, steps):
+    discard_penultimate_sigma = False
+    if sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']:
+        steps += 1
+        discard_penultimate_sigma = True
+
+    if scheduler.startswith('AYS'):
+        sigmas = nodes.NODE_CLASS_MAPPINGS['AlignYourStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0]
+    elif scheduler.startswith('GITS[coeff='):
+        sigmas = nodes.NODE_CLASS_MAPPINGS['GITSScheduler']().get_sigmas(float(scheduler[11:-1]), steps, denoise=1.0)[0]
+    elif scheduler == 'LTXV[default]':
+        sigmas = nodes.NODE_CLASS_MAPPINGS['LTXVScheduler']().get_sigmas(20, 2.05, 0.95, True, 0.1)[0]
+    elif scheduler.startswith('OSS'):
+        sigmas = nodes.NODE_CLASS_MAPPINGS['OptimalStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0]
+    else:
+        sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps)
+
+    if discard_penultimate_sigma:
+        sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+    return sigmas
+
+
+def get_noise_sampler(x, cpu, total_sigmas, **kwargs):
+    if 'extra_args' in kwargs and 'seed' in kwargs['extra_args']:
+        sigma_min, sigma_max = total_sigmas[total_sigmas > 0].min(), total_sigmas.max()
+        seed = kwargs['extra_args'].get("seed", None)
+        return k_diffusion_sampling.BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=cpu)
+    return None
+
+
+def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
+    if sampler_name in ["dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu"]:
+        if sampler_name == "dpmpp_sde":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde
+        elif sampler_name == "dpmpp_sde_gpu":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde_gpu
+        elif sampler_name == "dpmpp_2m_sde":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde
+        elif sampler_name == "dpmpp_2m_sde_gpu":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde_gpu
+        elif sampler_name == "dpmpp_3m_sde":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde
+        elif sampler_name == "dpmpp_3m_sde_gpu":
+            orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde_gpu
+
+        def sampler_function_wrapper(model, x, sigmas, **kwargs):
+            if 'noise_sampler' not in kwargs:
+                kwargs['noise_sampler'] = get_noise_sampler(x, 'gpu' not in sampler_name, total_sigmas, **kwargs)
+
+            return orig_sampler_function(model, x, sigmas, **kwargs)
+
+        sampler_function = sampler_function_wrapper
+
+    else:
+        return comfy.samplers.sampler_object(sampler_name)
+
+    return samplers.KSAMPLER(sampler_function, extra_options, inpaint_options)
+
+
+# modified version of SamplerCustom.sample
+def sample_with_custom_noise(model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image, noise=None, callback=None):
+    latent = latent_image
+    latent_image = latent["samples"]
+
+    if hasattr(comfy.sample, 'fix_empty_latent_channels'):
+        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+
+    out = latent.copy()
+    out['samples'] = latent_image
+
+    if noise is None:
+        if not add_noise:
+            noise = Noise_EmptyNoise().generate_noise(out)
+        else:
+            noise = Noise_RandomNoise(noise_seed).generate_noise(out)
+
+    noise_mask = None
+    if "noise_mask" in latent:
+        noise_mask = latent["noise_mask"]
+
+    x0_output = {}
+    preview_callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+
+    if callback is not None:
+        def touched_callback(step, x0, x, total_steps):
+            callback(step, x0, x, total_steps)
+            preview_callback(step, x0, x, total_steps)
+    else:
+        touched_callback = preview_callback
+
+    disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+
+    device = mm.get_torch_device()
+
+    noise = noise.to(device)
+    latent_image = latent_image.to(device)
+    if noise_mask is not None:
+        noise_mask = noise_mask.to(device)
+
+    if negative != 'NegativePlaceholder':
+        # This way is incompatible with Advanced ControlNet, yet.
+        # guider = comfy.samplers.CFGGuider(model)
+        # guider.set_conds(positive, negative)
+        # guider.set_cfg(cfg)
+        samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image,
+                                             noise_mask=noise_mask, callback=touched_callback,
+                                             disable_pbar=disable_pbar, seed=noise_seed)
+    else:
+        guider = nodes_custom_sampler.Guider_Basic(model)
+        positive = node_helpers.conditioning_set_values(positive, {"guidance": cfg})
+        guider.set_conds(positive)
+        samples = guider.sample(noise, latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=touched_callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+    samples = samples.to(comfy.model_management.intermediate_device())
+
+    out["samples"] = samples
+    if "x0" in x0_output:
+        out_denoised = latent.copy()
+        out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+    else:
+        out_denoised = out
+    return out, out_denoised
+
+
+# When sampling one step at a time, it mitigates the problem. (especially for _sde series samplers)
+def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative,
+                     latent_image, start_at_step, end_at_step, return_with_leftover_noise, sigma_ratio=1.0, sampler_opt=None, noise=None, callback=None, scheduler_func=None):
+
+    if scheduler_func is not None:
+        total_sigmas = scheduler_func(model, sampler_name, steps)
+    else:
+        if sampler_opt is None:
+            total_sigmas = calculate_sigmas(model, sampler_name, scheduler, steps)
+        else:
+            total_sigmas = calculate_sigmas(model, "", scheduler, steps)
+
+    sigmas = total_sigmas
+
+    if end_at_step is not None and end_at_step < (len(total_sigmas) - 1):
+        sigmas = total_sigmas[:end_at_step + 1]
+        if not return_with_leftover_noise:
+            sigmas[-1] = 0
+
+    if start_at_step is not None:
+        if start_at_step < (len(sigmas) - 1):
+            sigmas = sigmas[start_at_step:] * sigma_ratio
+        else:
+            if latent_image is not None:
+                return latent_image
+            else:
+                return {'samples': torch.zeros_like(noise)}
+
+    if sampler_opt is None:
+        impact_sampler = ksampler(sampler_name, total_sigmas)
+    else:
+        impact_sampler = sampler_opt
+
+    if len(sigmas) == 0 or (len(sigmas) == 1 and sigmas[0] == 0):
+        return latent_image
+    
+    res = sample_with_custom_noise(model, add_noise, seed, cfg, positive, negative, impact_sampler, sigmas, latent_image, noise=noise, callback=callback)
+
+    if return_with_leftover_noise:
+        return res[0]
+    else:
+        return res[1]
+
+
+def impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, sigma_ratio=1.0, sampler_opt=None, noise=None, scheduler_func=None):
+    advanced_steps = math.floor(steps / denoise)
+    start_at_step = advanced_steps - steps
+    end_at_step = start_at_step + steps
+    return separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+                            start_at_step, end_at_step, False, scheduler_func=scheduler_func)
+
+
+def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise,
+                     refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0, noise=None, scheduler_func=None):
+
+    if refiner_ratio is None or refiner_model is None or refiner_clip is None or refiner_positive is None or refiner_negative is None:
+        # Use separated_sample instead of KSampler for `AYS scheduler`
+        # refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise * sigma_factor)[0]
+
+        advanced_steps = math.floor(steps / denoise)
+        start_at_step = advanced_steps - steps
+        end_at_step = start_at_step + steps
+
+        refined_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
+                                          positive, negative, latent_image, start_at_step, end_at_step, False,
+                                          sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
+    else:
+        advanced_steps = math.floor(steps / denoise)
+        start_at_step = advanced_steps - steps
+        end_at_step = start_at_step + math.floor(steps * (1.0 - refiner_ratio))
+
+        # print(f"pre: {start_at_step} .. {end_at_step} / {advanced_steps}")
+        temp_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
+                                       positive, negative, latent_image, start_at_step, end_at_step, True,
+                                       sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
+
+        if 'noise_mask' in latent_image:
+            # noise_latent = \
+            #     impact_sampling.separated_sample(refiner_model, "enable", seed, advanced_steps, cfg, sampler_name,
+            #                                      scheduler, refiner_positive, refiner_negative, latent_image, end_at_step,
+            #                                      end_at_step, "enable")
+
+            latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
+            temp_latent = latent_compositor.composite(latent_image, temp_latent, 0, 0, False, latent_image['noise_mask'])[0]
+
+        # print(f"post: {end_at_step} .. {advanced_steps + 1} / {advanced_steps}")
+        refined_latent = separated_sample(refiner_model, False, seed, advanced_steps, cfg, sampler_name, scheduler,
+                                          refiner_positive, refiner_negative, temp_latent, end_at_step, advanced_steps + 1, False,
+                                          sigma_ratio=sigma_factor, scheduler_func=scheduler_func)
+
+    return refined_latent
+
+
+class KSamplerAdvancedWrapper:
+    params = None
+
+    def __init__(self, model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=None, sigma_factor=1.0, scheduler_func=None):
+        self.params = model, cfg, sampler_name, scheduler, positive, negative, sigma_factor
+        self.sampler_opt = sampler_opt
+        self.scheduler_func = scheduler_func
+
+    def clone_with_conditionings(self, positive, negative):
+        model, cfg, sampler_name, scheduler, _, _, _ = self.params
+        return KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, self.sampler_opt)
+
+    def sample_advanced(self, add_noise, seed, steps, latent_image, start_at_step, end_at_step, return_with_leftover_noise, hook=None,
+                        recovery_mode="ratio additional", recovery_sampler="AUTO", recovery_sigma_ratio=1.0, noise=None):
+
+        model, cfg, sampler_name, scheduler, positive, negative, sigma_factor = self.params
+        # steps, start_at_step, end_at_step = self.compensate_denoise(steps, start_at_step, end_at_step)
+
+        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)
+
+        if recovery_mode != 'DISABLE' and sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']:
+            base_image = latent_image.copy()
+            if recovery_mode == "ratio between":
+                sigma_ratio = 1.0 - recovery_sigma_ratio
+            else:
+                sigma_ratio = 1.0
+        else:
+            base_image = None
+            sigma_ratio = 1.0
+
+        try:
+            if sigma_ratio > 0:
+                latent_image = separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
+                                                positive, negative, latent_image, start_at_step, end_at_step,
+                                                return_with_leftover_noise, sigma_ratio=sigma_ratio * sigma_factor,
+                                                sampler_opt=self.sampler_opt, noise=noise, scheduler_func=self.scheduler_func)
+        except ValueError as e:
+            if str(e) == 'sigma_min and sigma_max must not be 0':
+                print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
+                return latent_image
+
+        if (recovery_sigma_ratio > 0 and recovery_mode != 'DISABLE' and
+                sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']):
+            compensate = 0 if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu'] else 2
+            if recovery_sampler == "AUTO":
+                recovery_sampler = 'dpm_fast' if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu'] else 'dpmpp_2m'
+
+            latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
+
+            noise_mask = latent_image['noise_mask']
+
+            if len(noise_mask.shape) == 4:
+                noise_mask = noise_mask.squeeze(0).squeeze(0)
+
+            latent_image = latent_compositor.composite(base_image, latent_image, 0, 0, False, noise_mask)[0]
+
+            try:
+                latent_image = separated_sample(model, add_noise, seed, steps, cfg, recovery_sampler, scheduler,
+                                                positive, negative, latent_image, start_at_step-compensate, end_at_step, return_with_leftover_noise,
+                                                sigma_ratio=recovery_sigma_ratio * sigma_factor, sampler_opt=self.sampler_opt, scheduler_func=self.scheduler_func)
+            except ValueError as e:
+                if str(e) == 'sigma_min and sigma_max must not be 0':
+                    print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
+
+        return latent_image
+
+
+class KSamplerWrapper:
+    params = None
+
+    def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, scheduler_func=None):
+        self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
+        self.scheduler_func = scheduler_func
+
+    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 impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=self.scheduler_func)

modules/impact/impact_server.py

@@ -1,54 +1,27 @@
 import os
 import threading
+import traceback
 
 from aiohttp import web
 
 import impact
-import server
 import folder_paths
 
+import torchvision
+
 import impact.core as core
 import impact.impact_pack as impact_pack
+from impact.utils import to_tensor
 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
 from io import BytesIO
 import random
-
-
-@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)
+from server import PromptServer
+import logging
 
 
 sam_predictor = None
@@ -87,7 +60,7 @@ def async_prepare_sam(image_dir, model_name, filename):
         sam_predictor.model.cpu()
 
 
-@server.PromptServer.instance.routes.post("/sam/prepare")
+@PromptServer.instance.routes.post("/sam/prepare")
 async def sam_prepare(request):
     global sam_predictor
     global last_prepare_data
@@ -104,9 +77,13 @@ async def sam_prepare(request):
         if data['sam_model_name'] == 'auto':
             model_name = impact.config.get_config()['sam_editor_model']
 
-        model_name = os.path.join(impact_pack.model_path, "sams", model_name)
+        model_path = folder_paths.get_full_path("sams", model_name)
 
-        print(f"[INFO] ComfyUI-Impact-Pack: Loading SAM model '{impact_pack.model_path}'")
+        if model_path is None:
+            logging.error(f"[Impact Pack] The '{model_name}' model file cannot be found in any sams model path.")
+            return web.Response(status=400)
+
+        logging.info(f"[Impact Pack] Loading SAM model '{model_path}'")
 
         filename, image_dir = folder_paths.annotated_filepath(data["filename"])
 
@@ -119,13 +96,14 @@ async def sam_prepare(request):
         if image_dir is None:
             return web.Response(status=400)
 
-        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_name, filename,))
+        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_path, filename,))
         thread.start()
 
-        print(f"[INFO] ComfyUI-Impact-Pack: SAM model loaded. ")
+        logging.info("[Impact Pack] SAM model loaded. ")
+    return web.Response(status=200)
 
 
-@server.PromptServer.instance.routes.post("/sam/release")
+@PromptServer.instance.routes.post("/sam/release")
 async def release_sam(request):
     global sam_predictor
 
@@ -133,10 +111,10 @@ async def release_sam(request):
         del sam_predictor
         sam_predictor = None
 
-    print(f"[INFO] ComfyUI-Impact-Pack: unloading SAM model")
+    logging.info("[Impact Pack]: unloading SAM model")
 
 
-@server.PromptServer.instance.routes.post("/sam/detect")
+@PromptServer.instance.routes.post("/sam/detect")
 async def sam_detect(request):
     global sam_predictor
     with sam_lock:
@@ -189,22 +167,28 @@ async def sam_detect(request):
             return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/wildcards/list")
+@PromptServer.instance.routes.get("/impact/wildcards/refresh")
+async def wildcards_refresh(request):
+    impact.wildcards.wildcard_load()
+    return web.Response(status=200)
+
+
+@PromptServer.instance.routes.get("/impact/wildcards/list")
 async def wildcards_list(request):
     data = {'data': impact.wildcards.get_wildcard_list()}
     return web.json_response(data)
 
 
-@server.PromptServer.instance.routes.post("/impact/wildcards")
+@PromptServer.instance.routes.post("/impact/wildcards")
 async def populate_wildcards(request):
     data = await request.json()
-    populated = wildcards.process(data['text'], data.get('seed', None))
+    populated = impact.wildcards.process(data['text'], data.get('seed', None))
     return web.json_response({"text": populated})
 
 
 segs_picker_map = {}
 
-@server.PromptServer.instance.routes.get("/impact/segs/picker/count")
+@PromptServer.instance.routes.get("/impact/segs/picker/count")
 async def segs_picker_count(request):
     node_id = request.rel_url.query.get('id', '')
 
@@ -215,24 +199,24 @@ async def segs_picker_count(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/segs/picker/view")
+@PromptServer.instance.routes.get("/impact/segs/picker/view")
 async def segs_picker(request):
     node_id = request.rel_url.query.get('id', '')
     idx = int(request.rel_url.query.get('idx', ''))
 
     if node_id in segs_picker_map and idx < len(segs_picker_map[node_id]):
-        pil = segs_picker_map[node_id][idx]
+        img = to_tensor(segs_picker_map[node_id][idx]).permute(0, 3, 1, 2).squeeze(0)
+        pil = torchvision.transforms.ToPILImage('RGB')(img)
 
         image_bytes = BytesIO()
         pil.save(image_bytes, format="PNG")
         image_bytes.seek(0)
-
         return web.Response(status=200, body=image_bytes, content_type='image/png', headers={"Content-Disposition": f"filename={node_id}{idx}.png"})
 
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/view/validate")
+@PromptServer.instance.routes.get("/view/validate")
 async def view_validate(request):
     if "filename" in request.rel_url.query:
         filename = request.rel_url.query["filename"]
@@ -253,7 +237,7 @@ async def view_validate(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/validate/pb_id_image")
+@PromptServer.instance.routes.get("/impact/validate/pb_id_image")
 async def view_validate(request):
     if "id" in request.rel_url.query:
         pb_id = request.rel_url.query["id"]
@@ -268,40 +252,43 @@ async def view_validate(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/set/pb_id_image")
+@PromptServer.instance.routes.get("/impact/set/pb_id_image")
 async def set_previewbridge_image(request):
-    if "filename" in request.rel_url.query:
-        node_id = request.rel_url.query["node_id"]
-        filename = request.rel_url.query["filename"]
-        path_type = request.rel_url.query["type"]
-        subfolder = request.rel_url.query["subfolder"]
-        filename, output_dir = folder_paths.annotated_filepath(filename)
+    try:
+        if "filename" in request.rel_url.query:
+            node_id = request.rel_url.query["node_id"]
+            filename = request.rel_url.query["filename"]
+            path_type = request.rel_url.query["type"]
+            subfolder = request.rel_url.query["subfolder"]
+            filename, output_dir = folder_paths.annotated_filepath(filename)
 
-        if filename == '' or filename[0] == '/' or '..' in filename:
-            return web.Response(status=400)
+            if filename == '' or filename[0] == '/' or '..' in filename:
+                return web.Response(status=400)
 
-        if output_dir is None:
-            if path_type == 'input':
-                output_dir = folder_paths.get_input_directory()
-            elif path_type == 'output':
-                output_dir = folder_paths.get_output_directory()
-            else:
-                output_dir = folder_paths.get_temp_directory()
+            if output_dir is None:
+                if path_type == 'input':
+                    output_dir = folder_paths.get_input_directory()
+                elif path_type == 'output':
+                    output_dir = folder_paths.get_output_directory()
+                else:
+                    output_dir = folder_paths.get_temp_directory()
 
-        file = os.path.join(output_dir, subfolder, filename)
-        item = {
-            'filename': filename,
-            'type': path_type,
-            'subfolder': subfolder,
-        }
-        pb_id = core.set_previewbridge_image(node_id, file, item)
+            file = os.path.join(output_dir, subfolder, filename)
+            item = {
+                'filename': filename,
+                'type': path_type,
+                'subfolder': subfolder,
+            }
+            pb_id = core.set_previewbridge_image(node_id, file, item)
 
-        return web.Response(status=200, text=pb_id)
+            return web.Response(status=200, text=pb_id)
+    except Exception:
+        traceback.print_exc()
 
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/get/pb_id_image")
+@PromptServer.instance.routes.get("/impact/get/pb_id_image")
 async def get_previewbridge_image(request):
     if "id" in request.rel_url.query:
         pb_id = request.rel_url.query["id"]
@@ -313,7 +300,7 @@ async def get_previewbridge_image(request):
     return web.Response(status=400)
 
 
-@server.PromptServer.instance.routes.get("/impact/view/pb_id_image")
+@PromptServer.instance.routes.get("/impact/view/pb_id_image")
 async def view_previewbridge_image(request):
     if "id" in request.rel_url.query:
         pb_id = request.rel_url.query["id"]
@@ -331,6 +318,9 @@ async def view_previewbridge_image(request):
 def onprompt_for_switch(json_data):
     inversed_switch_info = {}
     onprompt_switch_info = {}
+    onprompt_cond_branch_info = {}
+    disabled_switch = set()
+
 
     for k, v in json_data['prompt'].items():
         if 'class_type' not in v:
@@ -338,17 +328,24 @@ def onprompt_for_switch(json_data):
 
         cls = v['class_type']
         if cls == 'ImpactInversedSwitch':
-            select_input = v['inputs']['select']
-            if isinstance(select_input, list) and len(select_input) == 2:
-                input_node = json_data['prompt'][select_input[0]]
-                if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']:
-                    inversed_switch_info[k] = input_node['inputs']['value']
-            else:
-                inversed_switch_info[k] = select_input
+            # if 'sel_mode' is 'select_on_prompt'
+            if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']:
+                select_input = v['inputs']['select']
+                # if 'select' is converted input
+                if isinstance(select_input, list) and len(select_input) == 2:
+                    input_node = json_data['prompt'][select_input[0]]
+                    if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']:
+                        inversed_switch_info[k] = input_node['inputs']['value']
+                    else:
+                        logging.warning(f"\n##### ##### #####\n[Impact Pack] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactInversedSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
+                else:
+                    inversed_switch_info[k] = select_input
 
         elif cls in ['ImpactSwitch', 'LatentSwitch', 'SEGSSwitch', 'ImpactMakeImageList']:
-            if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode']:
+            # if 'sel_mode' is 'select_on_prompt'
+            if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']:
                 select_input = v['inputs']['select']
+                # if 'select' is converted input
                 if isinstance(select_input, list) and len(select_input) == 2:
                     input_node = json_data['prompt'][select_input[0]]
                     if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']:
@@ -357,10 +354,29 @@ def onprompt_for_switch(json_data):
                         if isinstance(input_node['inputs']['select'], int):
                             onprompt_switch_info[k] = input_node['inputs']['select']
                         else:
-                            print(f"\n##### ##### #####\n[WARN] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs.\n##### ##### #####\n")
+                            logging.warning(f"\n##### ##### #####\n[Impact Pack] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
                 else:
                     onprompt_switch_info[k] = select_input
 
+                if k in onprompt_switch_info and f'input{onprompt_switch_info[k]}' not in v['inputs']:
+                    # disconnect output
+                    disabled_switch.add(k)
+
+        elif cls == 'ImpactConditionalBranchSelMode':
+            if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'cond' in v['inputs']:
+                cond_input = v['inputs']['cond']
+                if isinstance(cond_input, list) and len(cond_input) == 2:
+                    input_node = json_data['prompt'][cond_input[0]]
+                    if (input_node['class_type'] == 'ImpactValueReceiver' and 'inputs' in input_node
+                            and 'value' in input_node['inputs'] and 'typ' in input_node['inputs']):
+                        if 'BOOLEAN' == input_node['inputs']['typ']:
+                            try:
+                                onprompt_cond_branch_info[k] = input_node['inputs']['value'].lower() == "true"
+                            except:
+                                pass
+                else:
+                    onprompt_cond_branch_info[k] = cond_input
+
     for k, v in json_data['prompt'].items():
         disable_targets = set()
 
@@ -369,6 +385,11 @@ def onprompt_for_switch(json_data):
                 if vv[0] in inversed_switch_info:
                     if vv[1] + 1 != inversed_switch_info[vv[0]]:
                         disable_targets.add(kk)
+                    else:
+                        del inversed_switch_info[k]
+
+                if vv[0] in disabled_switch:
+                    disable_targets.add(kk)
 
         if k in onprompt_switch_info:
             selected_slot_name = f"input{onprompt_switch_info[k]}"
@@ -376,10 +397,18 @@ def onprompt_for_switch(json_data):
                 if kk != selected_slot_name and kk.startswith('input'):
                     disable_targets.add(kk)
 
+        if k in onprompt_cond_branch_info:
+            selected_slot_name = "tt_value" if onprompt_cond_branch_info[k] else "ff_value"
+            for kk, vv in v['inputs'].items():
+                if kk in ['tt_value', 'ff_value'] and kk != selected_slot_name:
+                    disable_targets.add(kk)
+
         for kk in disable_targets:
             del v['inputs'][kk]
 
-    return json_data
+    # inversed_switch - select out of range
+    for target in inversed_switch_info.keys():
+        del json_data['prompt'][target]['inputs']['input']
 
 
 def onprompt_for_pickers(json_data):
@@ -404,9 +433,14 @@ def gc_preview_bridge_cache(json_data):
 
     for key in list(core.preview_bridge_cache.keys()):
         if key not in prompt_keys:
-            print(f"key deleted: {key}")
+            # print(f"key deleted [PB]: {key}")
             del core.preview_bridge_cache[key]
 
+    for key in list(core.preview_bridge_last_mask_cache.keys()):
+        if key not in prompt_keys:
+            # print(f"key deleted [PB_last_mask]: {key}")
+            del core.preview_bridge_last_mask_cache[key]
+
 
 def workflow_imagereceiver_update(json_data):
     prompt = json_data['prompt']
@@ -437,20 +471,110 @@ def regional_sampler_seed_update(json_data):
                 new_seed = random.randint(0, 1125899906842624)
 
             if new_seed is not None:
-                server.PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "seed_2nd", "type": "INT", "value": new_seed})
+                PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "seed_2nd", "type": "INT", "value": new_seed})
+
+
+def onprompt_populate_wildcards(json_data):
+    prompt = json_data['prompt']
+
+    updated_widget_values = {}
+    for k, v in prompt.items():
+        if 'class_type' in v and (v['class_type'] == 'ImpactWildcardEncode' or v['class_type'] == 'ImpactWildcardProcessor'):
+            inputs = v['inputs']
+
+            # legacy adapter
+            if isinstance(inputs['mode'], bool):
+                if inputs['mode']:
+                    new_mode = 'populate'
+                else:
+                    new_mode = 'fixed'
+
+                inputs['mode'] = new_mode
+
+            if inputs['mode'] == 'populate' and isinstance(inputs['populated_text'], str):
+                if isinstance(inputs['seed'], list):
+                    try:
+                        input_node = prompt[inputs['seed'][0]]
+                        if input_node['class_type'] == 'ImpactInt':
+                            input_seed = int(input_node['inputs']['value'])
+                            if not isinstance(input_seed, int):
+                                continue
+                        if input_node['class_type'] == 'Seed (rgthree)':
+                            input_seed = int(input_node['inputs']['seed'])
+                            if not isinstance(input_seed, int):
+                                continue
+                        else:
+                            logging.info(f"[Impact Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
+                            continue
+                    except:
+                        continue
+                else:
+                    input_seed = int(inputs['seed'])
+
+                inputs['populated_text'] = impact.wildcards.process(inputs['wildcard_text'], input_seed)
+                inputs['mode'] = 'reproduce'
+
+                PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "STRING", "value": inputs['populated_text']})
+                updated_widget_values[k] = inputs['populated_text']
+            
+            if inputs['mode'] == 'reproduce':
+                PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "mode", "type": "STRING", "value": 'populate'})
+
+
+
+    if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']:
+        for node in json_data['extra_data']['extra_pnginfo']['workflow']['nodes']:
+            key = str(node['id'])
+            if key in updated_widget_values:
+                node['widgets_values'][1] = updated_widget_values[key]
+                node['widgets_values'][2] = 'reproduce'
+
+
+def onprompt_for_remote(json_data):
+    prompt = json_data['prompt']
+
+    for v in prompt.values():
+        if 'class_type' in v:
+            cls = v['class_type']
+            if cls == 'ImpactRemoteBoolean' or cls == 'ImpactRemoteInt':
+                inputs = v['inputs']
+                node_id = str(inputs['node_id'])
+
+                if node_id not in prompt:
+                    continue
+
+                target_inputs = prompt[node_id]['inputs']
+
+                widget_name = inputs['widget_name']
+                if widget_name in target_inputs:
+                    widget_type = None
+                    if cls == 'ImpactRemoteBoolean' and isinstance(target_inputs[widget_name], bool):
+                        widget_type = 'BOOLEAN'
+
+                    elif cls == 'ImpactRemoteInt' and (isinstance(target_inputs[widget_name], int) or isinstance(target_inputs[widget_name], float)):
+                        widget_type = 'INT'
+
+                    if widget_type is None:
+                        break
+
+                    target_inputs[widget_name] = inputs['value']
+                    PromptServer.instance.send_sync("impact-node-feedback", {"node_id": node_id, "widget_name": widget_name, "type": widget_type, "value": inputs['value']})
 
 
 def onprompt(json_data):
     try:
-        json_data = onprompt_for_switch(json_data)
+        onprompt_for_remote(json_data)  # NOTE: top priority
+        onprompt_for_switch(json_data)
         onprompt_for_pickers(json_data)
+        onprompt_populate_wildcards(json_data)
         gc_preview_bridge_cache(json_data)
         workflow_imagereceiver_update(json_data)
         regional_sampler_seed_update(json_data)
+        core.current_prompt = json_data
     except Exception as e:
-        print(f"[WARN] ComfyUI-Impact-Pack: Error on prompt - several features will not work.\n{e}")
+        logging.warning(f"[Impact Pack] ComfyUI-Impact-Pack: Error on prompt - several features will not work.\n{e}")
 
     return json_data
 
 
-server.PromptServer.instance.add_on_prompt_handler(onprompt)
+PromptServer.instance.add_on_prompt_handler(onprompt)

modules/impact/legacy_nodes.py

@@ -25,6 +25,8 @@ class MMDetLoader:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     def load_mmdet(self, model_name):
         mmdet_path = folder_paths.get_full_path("mmdets", model_name)
         model = mmdet_nodes.load_mmdet(mmdet_path)
@@ -52,6 +54,8 @@ class BboxDetectorForEach:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     @staticmethod
     def detect(bbox_model, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
         mmdet_results = mmdet_nodes.inference_bbox(bbox_model, image, threshold)
@@ -102,6 +106,8 @@ class SegmDetectorCombined:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     def doit(self, segm_model, image, threshold, dilation):
         mmdet_results = mmdet_nodes.inference_segm(image, segm_model, threshold)
         segmasks = core.create_segmasks(mmdet_results)
@@ -150,6 +156,8 @@ class SegmDetectorForEach:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     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)
@@ -190,6 +198,8 @@ class SegsMaskCombine:
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     @staticmethod
     def combine(segs, image):
         h = image.shape[1]
@@ -226,6 +236,8 @@ class MaskPainter(nodes.PreviewImage):
 
     CATEGORY = "ImpactPack/Legacy"
 
+    DEPRECATED = True
+
     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):

modules/impact/logics.py

@@ -2,11 +2,12 @@ import sys
 import time
 
 import execution
-import folder_paths
 import impact.impact_server
 from server import PromptServer
 from impact.utils import any_typ
 import impact.core as core
+import re
+import nodes
 
 
 class ImpactCompare:
@@ -63,7 +64,46 @@ class ImpactConditionalBranch:
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "cond": ("BOOLEAN", {"forceInput": True}),
+                "cond": ("BOOLEAN",),
+                "tt_value": (any_typ,{"lazy": True}),
+                "ff_value": (any_typ,{"lazy": True}),
+            },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = (any_typ, )
+
+    def check_lazy_status(self, cond, tt_value=None, ff_value=None):
+        if cond and tt_value is None:
+            return ["tt_value"]
+        if not cond and ff_value is None:
+            return ["ff_value"]
+
+    def doit(self, cond, tt_value=None, ff_value=None):
+        if cond:
+            return (tt_value,)
+        else:
+            return (ff_value,)
+
+
+class ImpactConditionalBranchSelMode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        if not core.is_execution_model_version_supported():
+            required_inputs = {
+                "cond": ("BOOLEAN",),
+                "sel_mode": ("BOOLEAN", {"default": True, "label_on": "select_on_prompt", "label_off": "select_on_execution"}),
+                }
+        else:
+            required_inputs = {
+                "cond": ("BOOLEAN",),
+                }
+
+        return {
+            "required": required_inputs,
+            "optional": {
                 "tt_value": (any_typ,),
                 "ff_value": (any_typ,),
             },
@@ -74,13 +114,92 @@ class ImpactConditionalBranch:
 
     RETURN_TYPES = (any_typ, )
 
-    def doit(self, cond, tt_value, ff_value):
+    def doit(self, cond, tt_value=None, ff_value=None, **kwargs):
+        print(f'tt={tt_value is None}\nff={ff_value is None}')
         if cond:
             return (tt_value,)
         else:
             return (ff_value,)
 
 
+class ImpactConvertDataType:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"value": (any_typ,)}}
+
+    RETURN_TYPES = ("STRING", "FLOAT", "INT", "BOOLEAN")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic"
+
+    @staticmethod
+    def is_number(string):
+        pattern = re.compile(r'^[-+]?[0-9]*\.?[0-9]+$')
+        return bool(pattern.match(string))
+
+    def doit(self, value):
+        if self.is_number(str(value)):
+            num = value
+        else:
+            if str.lower(str(value)) != "false":
+                num = 1
+            else:
+                num = 0
+        return (str(value), float(num), int(float(num)), bool(float(num)), )
+
+
+class ImpactIfNone:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {},
+            "optional": {"signal": (any_typ,), "any_input": (any_typ,), }
+        }
+
+    RETURN_TYPES = (any_typ, "BOOLEAN")
+    RETURN_NAMES = ("signal_opt", "bool")
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic"
+
+    def doit(self, signal=None, any_input=None):
+        if any_input is None:
+            return (signal, False, )
+        else:
+            return (signal, True, )
+
+
+class ImpactLogicalOperators:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "operator": (['and', 'or', 'xor'],),
+                "bool_a": ("BOOLEAN", {"forceInput": True}),
+                "bool_b": ("BOOLEAN", {"forceInput": True}),
+            },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ("BOOLEAN", )
+
+    def doit(self, operator, bool_a, bool_b):
+        if operator == "and":
+            return (bool_a and bool_b, )
+        elif operator == "or":
+            return (bool_a or bool_b, )
+        else:
+            return (bool_a != bool_b, )
+
+
 class ImpactConditionalStopIteration:
     @classmethod
     def INPUT_TYPES(cls):
@@ -153,6 +272,24 @@ class ImpactFloat:
         return (value, )
 
 
+class ImpactBoolean:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    FUNCTION = "doit"
+    CATEGORY = "ImpactPack/Logic"
+
+    RETURN_TYPES = ("BOOLEAN", )
+
+    def doit(self, value):
+        return (value, )
+
+
 class ImpactValueSender:
     @classmethod
     def INPUT_TYPES(cls):
@@ -224,7 +361,7 @@ class ImpactValueReceiver:
         elif typ == "FLOAT":
             return (float(value), )
         elif typ == "BOOLEAN":
-            return (bool(value), )
+            return (value.lower() == "true", )
         else:
             return (value, )
 
@@ -248,6 +385,26 @@ class ImpactImageInfo:
         return (value.shape[0], value.shape[1], value.shape[2], value.shape[3])
 
 
+class ImpactLatentInfo:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "value": ("LATENT", ),
+                    },
+                }
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic/_for_test"
+
+    RETURN_TYPES = ("INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("batch", "height", "width", "channel")
+
+    def doit(self, value):
+        shape = value['samples'].shape
+        return (shape[0], shape[2] * 8, shape[3] * 8, shape[1])
+
+
 class ImpactMinMax:
     @classmethod
     def INPUT_TYPES(cls):
@@ -298,26 +455,33 @@ class ImpactQueueTriggerCountdown:
     @classmethod
     def INPUT_TYPES(cls):
         return {"required": {
-                    "signal": (any_typ,),
-                    "count": ("INT", {"default": 10, "min": 0, "max": 0xffffffffffffffff})
+                    "count": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "total": ("INT", {"default": 10, "min": 1, "max": 0xffffffffffffffff}),
+                    "mode": ("BOOLEAN", {"default": True, "label_on": "Trigger", "label_off": "Don't trigger"}),
                     },
+                "optional": {"signal": (any_typ,),},
                 "hidden": {"unique_id": "UNIQUE_ID"}
                 }
 
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Logic/_for_test"
-    RETURN_TYPES = (any_typ, "INT")
-    RETURN_NAMES = ("signal_opt", "count")
+    RETURN_TYPES = (any_typ, "INT", "INT")
+    RETURN_NAMES = ("signal_opt", "count", "total")
     OUTPUT_NODE = True
 
-    def doit(self, signal, count, unique_id):
-        if count > 0:
-            PromptServer.instance.send_sync("impact-node-feedback",
-                                            {"node_id": unique_id, "widget_name": "count", "type": "int", "value": count-1})
-            PromptServer.instance.send_sync("impact-add-queue", {})
+    def doit(self, count, total, mode, unique_id, signal=None):
+        if (mode):
+            if count < total - 1:
+                PromptServer.instance.send_sync("impact-node-feedback",
+                                                {"node_id": unique_id, "widget_name": "count", "type": "int", "value": count+1})
+                PromptServer.instance.send_sync("impact-add-queue", {})
+            if count >= total - 1:
+                PromptServer.instance.send_sync("impact-node-feedback",
+                                                {"node_id": unique_id, "widget_name": "count", "type": "int", "value": 0})
+
+        return (signal, count, total)
 
-        return (signal, count)
 
 
 class ImpactSetWidgetValue:
@@ -410,27 +574,6 @@ class ImpactSleep:
         return (signal,)
 
 
-error_skip_flag = False
-try:
-    import sys
-    def filter_message(str):
-        global error_skip_flag
-
-        if "IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE" in str:
-            return True
-        elif error_skip_flag and "ERROR:root:!!! Exception during processing !!!\n" == str:
-            error_skip_flag = False
-            return True
-        else:
-            return False
-
-    sys.__comfyui_manager_register_message_collapse(filter_message)
-
-except Exception as e:
-    print(f"[WARN] ComfyUI-Impact-Pack: `ComfyUI` or `ComfyUI-Manager` is an outdated version.")
-    pass
-
-
 def workflow_to_map(workflow):
     nodes = {}
     links = {}
@@ -442,53 +585,195 @@ def workflow_to_map(workflow):
     return nodes, links
 
 
+class ImpactRemoteBoolean:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "widget_name": ("STRING", {"multiline": False}),
+                    "value": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}),
+                    }}
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic/_for_test"
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+
+    def doit(self, **kwargs):
+        return {}
+
+
+class ImpactRemoteInt:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "node_id": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "widget_name": ("STRING", {"multiline": False}),
+                    "value": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff}),
+                    }}
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Logic/_for_test"
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+
+    def doit(self, **kwargs):
+        return {}
+
 class ImpactControlBridge:
     @classmethod
     def INPUT_TYPES(cls):
         return {"required": {
                       "value": (any_typ,),
-                      "mode": ("BOOLEAN", {"default": True, "label_on": "pass", "label_off": "block"}),
+                      "mode": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Stop/Mute/Bypass"}),
+                      "behavior": (["Stop", "Mute", "Bypass"], ),
                     },
                 "hidden": {"unique_id": "UNIQUE_ID", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}
                 }
 
     FUNCTION = "doit"
 
-    CATEGORY = "ImpactPack/Logic/_for_test"
+    CATEGORY = "ImpactPack/Logic"
     RETURN_TYPES = (any_typ,)
     RETURN_NAMES = ("value",)
     OUTPUT_NODE = True
 
-    def doit(self, value, mode, unique_id, prompt, extra_pnginfo):
+    DESCRIPTION = ("When behavior is Stop and mode is active, the input value is passed directly to the output.\n"
+                   "When behavior is Mute/Bypass and mode is active, the node connected to the output is changed to active state.\n"
+                   "When behavior is Stop and mode is Stop/Mute/Bypass, the workflow execution of the current node is halted.\n"
+                   "When behavior is Mute/Bypass and mode is Stop/Mute/Bypass, the node connected to the output is changed to Mute/Bypass state.")
+
+    @classmethod
+    def IS_CHANGED(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None):
+        if behavior == "Stop":
+            return value, mode, behavior
+        else:
+            # NOTE: extra_pnginfo is not populated for IS_CHANGED.
+            #       so extra_pnginfo is useless in here
+            try:
+                workflow = core.current_prompt['extra_data']['extra_pnginfo']['workflow']
+            except:
+                print(f"[Impact Pack] core.current_prompt['extra_data']['extra_pnginfo']['workflow']")
+                return 0
+
+            nodes, links = workflow_to_map(workflow)
+            next_nodes = []
+
+            for link in nodes[unique_id]['outputs'][0]['links']:
+                node_id = str(links[link][2])
+                impact.utils.collect_non_reroute_nodes(nodes, links, next_nodes, node_id)
+
+        return next_nodes
+
+    def doit(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None):
         global error_skip_flag
 
-        nodes, links = workflow_to_map(extra_pnginfo['workflow'])
-
-        outputs = [str(links[link][2]) for link in nodes[unique_id]['outputs'][0]['links']]
-
-        prompt_set = set(prompt.keys())
-        output_set = set(outputs)
-
-        if mode:
-            should_active_but_muted = output_set - prompt_set
-            if len(should_active_but_muted) > 0:
-                PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'actives': list(should_active_but_muted)})
-                error_skip_flag = True
-                raise Exception("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE\nIf you see this message, your ComfyUI-Manager is outdated. Please update it.")
+        if core.is_execution_model_version_supported():
+            from comfy_execution.graph import ExecutionBlocker
         else:
-            should_muted_but_active = prompt_set.intersection(output_set)
-            if len(should_muted_but_active) > 0:
-                PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'mutes': list(should_muted_but_active)})
-                error_skip_flag = True
-                raise Exception("IMPACT-PACK-SIGNAL: STOP CONTROL BRIDGE\nIf you see this message, your ComfyUI-Manager is outdated. Please update it.")
+            print("[Impact Pack] ImpactControlBridge: ComfyUI is outdated. The 'Stop' behavior cannot function properly.")
 
-        return (value, )
+        if behavior == "Stop":
+            if mode:
+                return (value, )
+            else:
+                return (ExecutionBlocker(None), )
+        elif extra_pnginfo is None:
+            logging.warn(f"[Impact Pack] limitation: '{behavior}' behavior cannot be used in API execution.")
+            return (value,)
+        else:
+            workflow_nodes, links = workflow_to_map(extra_pnginfo['workflow'])
+
+            active_nodes = []
+            mute_nodes = []
+            bypass_nodes = []
+
+            for link in workflow_nodes[unique_id]['outputs'][0]['links']:
+                node_id = str(links[link][2])
+
+                next_nodes = []
+                impact.utils.collect_non_reroute_nodes(workflow_nodes, links, next_nodes, node_id)
+
+                for next_node_id in next_nodes:
+                    node_mode = workflow_nodes[next_node_id]['mode']
+
+                    if node_mode == 0:
+                        active_nodes.append(next_node_id)
+                    elif node_mode == 2:
+                        mute_nodes.append(next_node_id)
+                    elif node_mode == 4:
+                        bypass_nodes.append(next_node_id)
+
+            if mode:
+                # active
+                should_be_active_nodes = mute_nodes + bypass_nodes
+                if len(should_be_active_nodes) > 0:
+                    PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'actives': list(should_be_active_nodes)})
+                    nodes.interrupt_processing()
+
+            elif behavior == "Mute" or behavior == True:
+                # mute
+                should_be_mute_nodes = active_nodes + bypass_nodes
+                if len(should_be_mute_nodes) > 0:
+                    PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'mutes': list(should_be_mute_nodes)})
+                    nodes.interrupt_processing()
+
+            else:
+                # bypass
+                should_be_bypass_nodes = active_nodes + mute_nodes
+                if len(should_be_bypass_nodes) > 0:
+                    PromptServer.instance.send_sync("impact-bridge-continue", {"node_id": unique_id, 'bypasses': list(should_be_bypass_nodes)})
+                    nodes.interrupt_processing()
+
+            return (value, )
+
+
+class ImpactExecutionOrderController:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "signal": (any_typ,),
+                    "value": (any_typ,),
+                    }}
+
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+    RETURN_TYPES = (any_typ, any_typ)
+    RETURN_NAMES = ("signal", "value")
+
+    def doit(self, signal, value):
+        return signal, value
+
+
+class ImpactListBridge:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+                    "list_input": (any_typ,),
+                    }}
+
+    FUNCTION = "doit"
+
+    DESCRIPTION = "When passing the list output through this node, it collects and organizes the data before forwarding it, which ensures that the previous stage's sub-workflow has been completed."
+
+    CATEGORY = "ImpactPack/Util"
+    RETURN_TYPES = (any_typ, )
+    RETURN_NAMES = ("list_output", )
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True, )
+
+    @staticmethod
+    def doit(list_input):
+        return (list_input,)
 
 
 original_handle_execution = execution.PromptExecutor.handle_execution_error
 
 
 def handle_execution_error(**kwargs):
-    print(f" handled")
     execution.PromptExecutor.handle_execution_error(**kwargs)
 

modules/impact/mmdet_nodes.py

@@ -1,5 +1,6 @@
 import folder_paths
 from impact.core import *
+import os
 
 import mmcv
 from mmdet.apis import (inference_detector, init_detector)
@@ -178,7 +179,12 @@ class SegmDetector(BBoxDetector):
                 item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, None, None)
                 items.append(item)
 
-        return image.shape, items
+        segs = image.shape, items
+
+        if detailer_hook is not None and hasattr(detailer_hook, "post_detection"):
+            segs = detailer_hook.post_detection(segs)
+
+        return segs
 
     def detect_combined(self, image, threshold, dilation):
         mmdet_results = inference_bbox(self.bbox_model, image, threshold)

modules/impact/pipe.py

@@ -1,5 +1,7 @@
 import folder_paths
 import impact.wildcards
+from impact.utils import any_typ
+
 
 class ToDetailerPipe:
     @classmethod
@@ -13,7 +15,7 @@ class ToDetailerPipe:
                      "bbox_detector": ("BBOX_DETECTOR", ),
                      "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                      "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
-                     "Select to add Wildcard": (["Select the Wildcard to add to the text"] + impact.wildcards.get_wildcard_list(),),
+                     "Select to add Wildcard": (["Select the Wildcard to add to the text"], ),
                      },
                 "optional": {
                     "sam_model_opt": ("SAM_MODEL",),
@@ -51,7 +53,7 @@ class ToDetailerPipeSDXL(ToDetailerPipe):
                      "bbox_detector": ("BBOX_DETECTOR", ),
                      "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                      "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
-                     "Select to add Wildcard": (["Select the Wildcard to add to the text"] + impact.wildcards.get_wildcard_list(),),
+                     "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
                      },
                 "optional": {
                     "sam_model_opt": ("SAM_MODEL",),
@@ -108,6 +110,23 @@ class FromDetailerPipe_SDXL:
         return detailer_pipe, model, clip, vae, positive, negative, bbox_detector, sam_model_opt, segm_detector_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative
 
 
+class AnyPipeToBasic:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {"any_pipe": (any_typ,)},
+        }
+
+    RETURN_TYPES = ("BASIC_PIPE", )
+    RETURN_NAMES = ("basic_pipe", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Pipe"
+
+    def doit(self, any_pipe):
+        return (any_pipe[:5], )
+
+
 class ToBasicPipe:
     @classmethod
     def INPUT_TYPES(s):
@@ -170,7 +189,7 @@ class BasicPipeToDetailerPipe:
                              "bbox_detector": ("BBOX_DETECTOR", ),
                              "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                              "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
-                             "Select to add Wildcard": (["Select the Wildcard to add to the text"] + impact.wildcards.get_wildcard_list(),),
+                             "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
                              },
                 "optional": {
                     "sam_model_opt": ("SAM_MODEL", ),
@@ -206,7 +225,7 @@ class BasicPipeToDetailerPipeSDXL:
                              "bbox_detector": ("BBOX_DETECTOR", ),
                              "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                              "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
-                             "Select to add Wildcard": (["Select the Wildcard to add to the text"] + impact.wildcards.get_wildcard_list(),),
+                             "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
                              },
                 "optional": {
                     "sam_model_opt": ("SAM_MODEL", ),
@@ -305,7 +324,7 @@ class EditDetailerPipe:
                 "detailer_pipe": ("DETAILER_PIPE",),
                 "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                 "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
-                "Select to add Wildcard": (["Select the Wildcard to add to the text"] + impact.wildcards.get_wildcard_list(),),
+                "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
             },
             "optional": {
                 "model": ("MODEL",),
@@ -402,7 +421,7 @@ class EditDetailerPipeSDXL(EditDetailerPipe):
                 "detailer_pipe": ("DETAILER_PIPE",),
                 "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                 "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
-                "Select to add Wildcard": (["Select the Wildcard to add to the text"] + impact.wildcards.get_wildcard_list(),),
+                "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
             },
             "optional": {
                 "model": ("MODEL",),

modules/impact/sample_error_enhancer.py

@@ -1,25 +0,0 @@
-import comfy.sample
-import traceback
-
-original_sample = comfy.sample.sample
-
-
-def informative_sample(*args, **kwargs):
-    try:
-        return original_sample(*args, **kwargs)  # This code helps interpret error messages that occur within exceptions but does not have any impact on other operations.
-    except RuntimeError as e:
-        is_model_mix_issue = False
-        try:
-            if 'mat1 and mat2 shapes cannot be multiplied' in e.args[0]:
-                if 'torch.nn.functional.linear' in traceback.format_exc().strip().split('\n')[-3]:
-                    is_model_mix_issue = True
-        except:
-            pass
-
-        if is_model_mix_issue:
-            raise RuntimeError("\n\n#### It seems that models and clips are mixed and interconnected between SDXL Base, SDXL Refiner, SD1.x, and SD2.x. Please verify. ####\n\n")
-        else:
-            raise e
-
-
-comfy.sample.sample = informative_sample

modules/impact/segs_upscaler.py

@@ -0,0 +1,137 @@
+from impact.utils import *
+from impact import impact_sampling
+from comfy import model_management
+from comfy.cli_args import args
+import nodes
+
+try:
+    from comfy_extras import nodes_differential_diffusion
+except Exception:
+    print(f"[Impact Pack] ComfyUI is an outdated version. The DifferentialDiffusion feature will be disabled.")
+
+
+# Implementation based on `https://github.com/lingondricka2/Upscaler-Detailer`
+
+# code from comfyroll --->
+# https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/blob/main/nodes/functions_upscale.py
+
+def upscale_with_model(upscale_model, image):
+    device = model_management.get_torch_device()
+    upscale_model.to(device)
+    in_img = image.movedim(-1, -3).to(device)
+    free_memory = model_management.get_free_memory(device)
+
+    tile = 512
+    overlap = 32
+
+    oom = True
+    while oom:
+        try:
+            steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
+            pbar = comfy.utils.ProgressBar(steps)
+            s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
+            oom = False
+        except model_management.OOM_EXCEPTION as e:
+            tile //= 2
+            if tile < 128:
+                raise e
+
+    s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0)
+    return s
+
+
+def apply_resize_image(image: Image.Image, original_width, original_height, rounding_modulus, mode='scale', supersample='true', factor: int = 2, width: int = 1024, height: int = 1024,
+                       resample='bicubic'):
+    # Calculate the new width and height based on the given mode and parameters
+    if mode == 'rescale':
+        new_width, new_height = int(original_width * factor), int(original_height * factor)
+    else:
+        m = rounding_modulus
+        original_ratio = original_height / original_width
+        height = int(width * original_ratio)
+
+        new_width = width if width % m == 0 else width + (m - width % m)
+        new_height = height if height % m == 0 else height + (m - height % m)
+
+    # Define a dictionary of resampling filters
+    resample_filters = {'nearest': 0, 'bilinear': 2, 'bicubic': 3, 'lanczos': 1}
+
+    # Apply supersample
+    if supersample == 'true':
+        image = image.resize((new_width * 8, new_height * 8), resample=Image.Resampling(resample_filters[resample]))
+
+    # Resize the image using the given resampling filter
+    resized_image = image.resize((new_width, new_height), resample=Image.Resampling(resample_filters[resample]))
+
+    return resized_image
+
+
+def upscaler(image, upscale_model, rescale_factor, resampling_method, supersample, rounding_modulus):
+    if upscale_model is not None:
+        up_image = upscale_with_model(upscale_model, image)
+    else:
+        up_image = image
+
+    pil_img = tensor2pil(image)
+    original_width, original_height = pil_img.size
+    scaled_image = pil2tensor(apply_resize_image(tensor2pil(up_image), original_width, original_height, rounding_modulus, 'rescale',
+                                                 supersample, rescale_factor, 1024, resampling_method))
+    return scaled_image
+
+# <---
+
+
+def img2img_segs(image, model, clip, vae, seed, steps, cfg, sampler_name, scheduler,
+                 positive, negative, denoise, noise_mask, control_net_wrapper=None,
+                 inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+
+    original_image_size = image.shape[1:3]
+
+    # Match to original image size
+    if original_image_size[0] % 8 > 0 or original_image_size[1] % 8 > 0:
+        scale = 8/min(original_image_size[0], original_image_size[1]) + 1
+        w = int(original_image_size[1] * scale)
+        h = int(original_image_size[0] * scale)
+        image = tensor_resize(image, w, h)
+
+    if noise_mask is not None:
+        noise_mask = tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
+        noise_mask = noise_mask.squeeze(3)
+
+        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+            model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
+
+    if control_net_wrapper is not None:
+        positive, negative, _ = control_net_wrapper.apply(positive, negative, image, noise_mask)
+
+    # prepare mask
+    if noise_mask is not None and inpaint_model:
+        imc_encode = nodes.InpaintModelConditioning().encode
+        if 'noise_mask' in inspect.signature(imc_encode).parameters:
+            positive, negative, latent_image = imc_encode(positive, negative, image, vae, mask=noise_mask, noise_mask=True)
+        else:
+            print(f"[Impact Pack] ComfyUI is an outdated version.")
+            positive, negative, latent_image = imc_encode(positive, negative, image, vae, noise_mask)
+    else:
+        latent_image = to_latent_image(image, vae)
+        if noise_mask is not None:
+            latent_image['noise_mask'] = noise_mask
+
+    refined_latent = latent_image
+
+    # ksampler
+    refined_latent = impact_sampling.ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, refined_latent, denoise, scheduler_func=scheduler_func_opt)
+
+    # non-latent downscale - latent downscale cause bad quality
+    refined_image = vae.decode(refined_latent['samples'])
+
+    # prevent mixing of device
+    refined_image = refined_image.cpu()
+
+    # Match to original image size
+    if refined_image.shape[1:3] != original_image_size:
+        refined_image = tensor_resize(refined_image, original_image_size[1], original_image_size[0])
+
+    # don't convert to latent - latent break image
+    # preserving pil is much better
+    return refined_image

modules/impact/special_samplers.py

@@ -1,34 +1,37 @@
-import time
-
-import comfy
 import math
 import impact.core as core
+from comfy_extras.nodes_custom_sampler import Noise_RandomNoise
 from impact.utils import *
 from nodes import MAX_RESOLUTION
 import nodes
+from impact.impact_sampling import KSamplerWrapper, KSamplerAdvancedWrapper, separated_sample, impact_sample
+import comfy
 
 class TiledKSamplerProvider:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                    "tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
-                    "tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
-                    "tiling_strategy": (["random", "padded", 'simple'], ),
-                    "basic_pipe": ("BASIC_PIPE", )
+                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}),
+                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}),
+                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "noise schedule"}),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                    "tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64, "tooltip": "Sets the width of the tile to be used in TiledKSampler."}),
+                    "tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64, "tooltip": "Sets the height of the tile to be used in TiledKSampler."}),
+                    "tiling_strategy": (["random", "padded", 'simple'], {"tooltip": "Sets the tiling strategy for TiledKSampler."} ),
+                    "basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"})
                     }}
 
+    OUTPUT_TOOLTIPS = ("sampler wrapper. (Can be used when generating a regional_prompt.)", )
+
     RETURN_TYPES = ("KSAMPLER",)
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
-    def doit(self, seed, steps, cfg, sampler_name, scheduler, denoise,
+    @staticmethod
+    def doit(seed, steps, cfg, sampler_name, scheduler, denoise,
              tile_width, tile_height, tiling_strategy, basic_pipe):
         model, _, _, positive, negative = basic_pipe
         sampler = core.TiledKSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
@@ -40,24 +43,30 @@ class KSamplerProvider:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                                "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                                "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                                "basic_pipe": ("BASIC_PIPE", )
+                                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                                "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}),
+                                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}),
+                                "scheduler": (core.SCHEDULERS, {"tooltip": "noise schedule"}),
+                                "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                                "basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"})
                              },
+                "optional": {
+                    "scheduler_func_opt": ("SCHEDULER_FUNC", {"tooltip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}),
+                    }
                 }
 
+    OUTPUT_TOOLTIPS = ("sampler wrapper. (Can be used when generating a regional_prompt.)",)
+
     RETURN_TYPES = ("KSAMPLER",)
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
-    def doit(self, seed, steps, cfg, sampler_name, scheduler, denoise, basic_pipe):
+    @staticmethod
+    def doit(seed, steps, cfg, sampler_name, scheduler, denoise, basic_pipe, scheduler_func_opt=None):
         model, _, _, positive, negative = basic_pipe
-        sampler = core.KSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise)
+        sampler = KSamplerWrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, scheduler_func=scheduler_func_opt)
         return (sampler, )
 
 
@@ -65,21 +74,29 @@ class KSamplerAdvancedProvider:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                                "basic_pipe": ("BASIC_PIPE", )
+                                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "toolip": "classifier free guidance value"}),
+                                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"toolip": "sampler"}),
+                                "scheduler": (core.SCHEDULERS, {"toolip": "noise schedule"}),
+                                "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "toolip": "Multiplier of noise schedule"}),
+                                "basic_pipe": ("BASIC_PIPE", {"toolip": "basic_pipe input for sampling"})
                              },
+                "optional": {
+                                "sampler_opt": ("SAMPLER", {"toolip": "[OPTIONAL] Uses the passed sampler instead of internal impact_sampler."}),
+                                "scheduler_func_opt": ("SCHEDULER_FUNC", {"toolip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}),
+                            }
                 }
 
+    OUTPUT_TOOLTIPS = ("sampler wrapper. (Can be used when generating a regional_prompt.)", )
+
     RETURN_TYPES = ("KSAMPLER_ADVANCED",)
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
-    def doit(self, cfg, sampler_name, scheduler, basic_pipe):
+    @staticmethod
+    def doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=1.0, sampler_opt=None, scheduler_func_opt=None):
         model, _, _, positive, negative = basic_pipe
-        sampler = core.KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative)
+        sampler = KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=sampler_opt, sigma_factor=sigma_factor, scheduler_func=scheduler_func_opt)
         return (sampler, )
 
 
@@ -87,19 +104,22 @@ class TwoSamplersForMask:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "latent_image": ("LATENT", ),
-                     "base_sampler": ("KSAMPLER", ),
-                     "mask_sampler": ("KSAMPLER", ),
-                     "mask": ("MASK", )
+                     "latent_image": ("LATENT", {"tooltip": "input latent image"}),
+                     "base_sampler": ("KSAMPLER", {"tooltip": "Sampler to apply to the region outside the mask."}),
+                     "mask_sampler": ("KSAMPLER", {"tooltip": "Sampler to apply to the masked region."}),
+                     "mask": ("MASK", {"tooltip": "region mask"})
                      },
                 }
 
+    OUTPUT_TOOLTIPS = ("result latent", )
+
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
-    def doit(self, latent_image, base_sampler, mask_sampler, mask):
+    @staticmethod
+    def doit(latent_image, base_sampler, mask_sampler, mask):
         inv_mask = torch.where(mask != 1.0, torch.tensor(1.0), torch.tensor(0.0))
 
         latent_image['noise_mask'] = inv_mask
@@ -117,82 +137,59 @@ class TwoAdvancedSamplersForMask:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                     "samples": ("LATENT", ),
-                     "base_sampler": ("KSAMPLER_ADVANCED", ),
-                     "mask_sampler": ("KSAMPLER_ADVANCED", ),
-                     "mask": ("MASK", ),
-                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000})
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                     "samples": ("LATENT", {"tooltip": "input latent image"}),
+                     "base_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "Sampler to apply to the region outside the mask."}),
+                     "mask_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "Sampler to apply to the masked region."}),
+                     "mask": ("MASK", {"tooltip": "region mask"}),
+                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000, "tooltip": "To smooth the seams of the region boundaries, expand the mask by the overlap_factor amount to overlap with other regions."})
                      },
                 }
 
+    OUTPUT_TOOLTIPS = ("result latent", )
+
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Sampler"
 
     @staticmethod
-    def mask_erosion(samples, mask, grow_mask_by):
-        mask = mask.clone()
+    def doit(seed, steps, denoise, samples, base_sampler, mask_sampler, mask, overlap_factor):
+        regional_prompts = RegionalPrompt().doit(mask=mask, advanced_sampler=mask_sampler)[0]
 
-        w = samples['samples'].shape[3]
-        h = samples['samples'].shape[2]
-
-        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()
-
-    def doit(self, seed, steps, denoise, samples, base_sampler, mask_sampler, mask, overlap_factor):
-
-        inv_mask = torch.where(mask != 1.0, torch.tensor(1.0), torch.tensor(0.0))
-
-        adv_steps = int(steps / denoise)
-        start_at_step = adv_steps - steps
-
-        new_latent_image = samples.copy()
-
-        mask_erosion = TwoAdvancedSamplersForMask.mask_erosion(samples, mask, overlap_factor)
-
-        for i in range(start_at_step, adv_steps):
-            add_noise = "enable" if i == start_at_step else "disable"
-            return_with_leftover_noise = "enable" if i+1 != adv_steps else "disable"
-
-            new_latent_image['noise_mask'] = inv_mask
-            new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable", recover_special_sampler=True)
-
-            new_latent_image['noise_mask'] = mask_erosion
-            new_latent_image = mask_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, i, i + 1, return_with_leftover_noise, recover_special_sampler=True)
-
-        del new_latent_image['noise_mask']
-
-        return (new_latent_image, )
+        return RegionalSampler().doit(seed=seed, seed_2nd=0, seed_2nd_mode="ignore", steps=steps, base_only_steps=1,
+                                      denoise=denoise, samples=samples, base_sampler=base_sampler,
+                                      regional_prompts=regional_prompts, overlap_factor=overlap_factor,
+                                      restore_latent=True, additional_mode="ratio between",
+                                      additional_sampler="AUTO", additional_sigma_ratio=0.3)
 
 
 class RegionalPrompt:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "mask": ("MASK", ),
-                     "advanced_sampler": ("KSAMPLER_ADVANCED", ),
-                     },
+                    "mask": ("MASK", {"tooltip": "region mask"}),
+                    "advanced_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "sampler for specified region"}),
+                    },
+                "optional": {
+                    "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Sets the extra seed to be used for noise variation."}),
+                    "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Sets the strength of the noise variation."}),
+                    "variation_method": (["linear", "slerp"], {"tooltip": "Sets how the original noise and extra noise are blended together."}),
+                    }
                 }
 
+    OUTPUT_TOOLTIPS = ("regional prompts. (Can be used in the RegionalSampler.)", )
+
     RETURN_TYPES = ("REGIONAL_PROMPTS", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Regional"
 
-    def doit(self, mask, advanced_sampler):
-        regional_prompt = core.REGIONAL_PROMPT(mask, advanced_sampler)
+    @staticmethod
+    def doit(mask, advanced_sampler, variation_seed=0, variation_strength=0.0, variation_method="linear"):
+        regional_prompt = core.REGIONAL_PROMPT(mask, advanced_sampler, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method)
         return ([regional_prompt], )
 
 
@@ -200,16 +197,19 @@ class CombineRegionalPrompts:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "regional_prompts1": ("REGIONAL_PROMPTS", ),
+                     "regional_prompts1": ("REGIONAL_PROMPTS", {"tooltip": "input regional_prompts. (Connecting to the input slot increases the number of additional slots.)"}),
                      },
                 }
 
+    OUTPUT_TOOLTIPS = ("Combined REGIONAL_PROMPTS", )
+
     RETURN_TYPES = ("REGIONAL_PROMPTS", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Regional"
 
-    def doit(self, **kwargs):
+    @staticmethod
+    def doit(**kwargs):
         res = []
         for k, v in kwargs.items():
             res += v
@@ -221,47 +221,96 @@ class CombineConditionings:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "conditioning1": ("CONDITIONING", ),
+                     "conditioning1": ("CONDITIONING", { "tooltip": "input conditionings. (Connecting to the input slot increases the number of additional slots.)" }),
                      },
                 }
 
+    OUTPUT_TOOLTIPS = ("Combined conditioning", )
+
     RETURN_TYPES = ("CONDITIONING", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, **kwargs):
+    @staticmethod
+    def doit(**kwargs):
         res = []
         for k, v in kwargs.items():
             res += v
 
         return (res, )
+    
 
+class ConcatConditionings:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                     "conditioning1": ("CONDITIONING", { "tooltip": "input conditionings. (Connecting to the input slot increases the number of additional slots.)" }),
+                     },
+                }
 
+    OUTPUT_TOOLTIPS = ("Concatenated conditioning", )
+
+    RETURN_TYPES = ("CONDITIONING", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    @staticmethod
+    def doit(**kwargs):
+        conditioning_to = list(kwargs.values())[0]
+
+        for k, conditioning_from in list(kwargs.items())[1:]:
+            out = []
+            if len(conditioning_from) > 1:
+                print("Warning: ConcatConditionings {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.")
+
+            cond_from = conditioning_from[0][0]
+
+            for i in range(len(conditioning_to)):
+                t1 = conditioning_to[i][0]
+                tw = torch.cat((t1, cond_from), 1)
+                n = [tw, conditioning_to[i][1].copy()]
+                out.append(n)
+
+            conditioning_to = out
+
+        return (out, )
+    
+    
 class RegionalSampler:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "seed_2nd": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "seed_2nd_mode": (["ignore", "fixed", "seed+seed_2nd", "seed-seed_2nd", "increment", "decrement", "randomize"], ),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "base_only_steps": ("INT", {"default": 2, "min": 0, "max": 10000}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                     "samples": ("LATENT", ),
-                     "base_sampler": ("KSAMPLER_ADVANCED", ),
-                     "regional_prompts": ("REGIONAL_PROMPTS", ),
-                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000}),
-                     "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "seed_2nd": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Additional noise seed. The behavior is determined by seed_2nd_mode."}),
+                     "seed_2nd_mode": (["ignore", "fixed", "seed+seed_2nd", "seed-seed_2nd", "increment", "decrement", "randomize"], {"tooltip": "application method of seed_2nd. 1) ignore: Do not use seed_2nd. In the base only sampling stage, the seed is applied as a noise seed, and in the regional sampling stage, denoising is performed as it is without additional noise. 2) Others: In the base only sampling stage, the seed is applied as a noise seed, and once it is closed so that there is no leftover noise, new noise is added with seed_2nd and the regional samping stage is performed. a) fixed: Use seed_2nd as it is as an additional noise seed. b) seed+seed_2nd: Apply the value of seed+seed_2nd as an additional noise seed. c) seed-seed_2nd: Apply the value of seed-seed_2nd as an additional noise seed. d) increment: Not implemented yet. Same with fixed. e) decrement: Not implemented yet. Same with fixed. f) randomize: Not implemented yet. Same with fixed."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "base_only_steps": ("INT", {"default": 2, "min": 0, "max": 10000, "tooltip": "total sampling steps"}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                     "samples": ("LATENT", {"tooltip": "input latent image"}),
+                     "base_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "The sampler applied outside the area set by the regional_prompt."}),
+                     "regional_prompts": ("REGIONAL_PROMPTS", {"tooltip": "The prompt applied to each region"}),
+                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000, "tooltip": "To smooth the seams of the region boundaries, expand the mask set in regional_prompts by the overlap_factor amount to overlap with other regions."}),
+                     "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled", "tooltip": "At each step, restore the noise outside the mask area to its original state, as per the principle of inpainting. This option is provided for backward compatibility, and it is recommended to always set it to true."}),
+                     "additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between", "tooltip": "..._sde or uni_pc and other special samplers are used, the region is not properly denoised, and it causes a phenomenon that destroys the overall harmony. To compensate for this, a recovery operation is performed using another sampler. This requires a longer time for sampling because a second sampling is performed at each step in each region using a special sampler. 1) DISABLE: Disable this feature. 2) ratio additional: After performing the denoise amount to be performed in the step with the sampler set in the region, the recovery sampler is additionally applied by the additional_sigma_ratio. If you use this option, the total denoise amount increases by additional_sigma_ratio. 3) ratio between: The denoise amount to be performed in the step with the sampler set in the region and the denoise amount to be applied to the recovery sampler are divided by additional_sigma_ratio, and denoise is performed for each denoise amount. If you use this option, the total denoise amount does not change."}),
+                     "additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"], {"tooltip": "1) AUTO: Automatically set the recovery sampler. If the sampler is uni_pc, uni_pc_bh2, dpmpp_sde, dpmpp_sde_gpu, the dpm_fast sampler is selected If the sampler is dpmpp_2m_sde, dpmpp_2m_sde_gpu, dpmpp_3m_sde, dpmpp_3m_sde_gpu, the dpmpp_2m sampler is selected. 2) Others: Manually set the recovery sampler."}),
+                     "additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Multiplier of noise schedule to be applied according to additional_mode."}),
                      },
-                 "hidden": {"unique_id": "UNIQUE_ID"},
+                "hidden": {"unique_id": "UNIQUE_ID"},
                 }
 
+    OUTPUT_TOOLTIPS = ("result latent", )
+
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Regional"
 
+    @staticmethod
+    def separated_sample(*args, **kwargs):
+        return separated_sample(*args, **kwargs)
+
     @staticmethod
     def mask_erosion(samples, mask, grow_mask_by):
         mask = mask.clone()
@@ -280,7 +329,13 @@ class RegionalSampler:
 
         return mask_erosion[:, :, :w, :h].round()
 
-    def doit(self, seed, seed_2nd, seed_2nd_mode, steps, base_only_steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, restore_latent, unique_id=None):
+    @staticmethod
+    def doit(seed, seed_2nd, seed_2nd_mode, steps, base_only_steps, denoise, samples, base_sampler, regional_prompts, overlap_factor, restore_latent,
+             additional_mode, additional_sampler, additional_sigma_ratio, unique_id=None):
+
+        samples = samples.copy()
+        samples['samples'] = comfy.sample.fix_empty_latent_channels(base_sampler.params[0], samples['samples'])
+
         if restore_latent:
             latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
         else:
@@ -298,12 +353,17 @@ class RegionalSampler:
         region_len = len(regional_prompts)
         total = steps*region_len
 
-        leftover_noise = 'disable'
+        leftover_noise = False
         if base_only_steps > 0:
             if seed_2nd_mode == 'ignore':
-                leftover_noise = 'enable'
+                leftover_noise = True
 
-            samples = base_sampler.sample_advanced("enable", seed, adv_steps, samples, start_at_step, start_at_step + base_only_steps, leftover_noise, recover_special_sampler=False)
+            noise = Noise_RandomNoise(seed).generate_noise(samples)
+
+            for rp in regional_prompts:
+                noise = rp.touch_noise(noise)
+
+            samples = base_sampler.sample_advanced(True, seed, adv_steps, samples, start_at_step, start_at_step + base_only_steps, leftover_noise, recovery_mode="DISABLE", noise=noise)
 
         if seed_2nd_mode == "seed+seed_2nd":
             seed += seed_2nd
@@ -319,16 +379,23 @@ class RegionalSampler:
         new_latent_image = samples.copy()
         base_latent_image = None
 
-        if leftover_noise != 'enable':
-            add_noise = "enable"
+        if not leftover_noise:
+            add_noise = True
+            noise = Noise_RandomNoise(seed).generate_noise(samples)
+
+            for rp in regional_prompts:
+                noise = rp.touch_noise(noise)
         else:
-            add_noise = "disable"
+            add_noise = False
+            noise = None
 
         for i in range(start_at_step+base_only_steps, adv_steps):
             core.update_node_status(unique_id, f"{i}/{steps} steps  |         ", ((i-start_at_step)*region_len)/total)
 
             new_latent_image['noise_mask'] = inv_mask
-            new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, "enable", recover_special_sampler=True)
+            new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image,
+                                                            start_at_step=i, end_at_step=i + 1, return_with_leftover_noise=True,
+                                                            recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio, noise=noise)
 
             if restore_latent:
                 if 'noise_mask' in new_latent_image:
@@ -345,8 +412,8 @@ class RegionalSampler:
                 region_mask = regional_prompt.get_mask_erosion(overlap_factor).squeeze(0).squeeze(0)
 
                 new_latent_image['noise_mask'] = region_mask
-                new_latent_image = regional_prompt.sampler.sample_advanced("disable", seed, adv_steps, new_latent_image,
-                                                                           i, i + 1, "enable", recover_special_sampler=True)
+                new_latent_image = regional_prompt.sampler.sample_advanced(False, seed, adv_steps, new_latent_image, i, i + 1, True,
+                                                                           recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
 
                 if restore_latent:
                     del new_latent_image['noise_mask']
@@ -355,7 +422,7 @@ class RegionalSampler:
 
                 j += 1
 
-            add_noise = 'disable'
+            add_noise = False
 
         # finalize
         core.update_node_status(unique_id, f"finalize")
@@ -365,7 +432,8 @@ class RegionalSampler:
             base_latent_image = new_latent_image
 
         new_latent_image['noise_mask'] = inv_mask
-        new_latent_image = base_sampler.sample_advanced("disable", seed, adv_steps, new_latent_image, adv_steps, adv_steps+1, "disable", recover_special_sampler=False)
+        new_latent_image = base_sampler.sample_advanced(False, seed, adv_steps, new_latent_image, adv_steps, adv_steps+1, False,
+                                                        recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
 
         core.update_node_status(unique_id, f"{steps}/{steps} steps", total)
         core.update_node_status(unique_id, "", None)
@@ -383,28 +451,38 @@ class RegionalSamplerAdvanced:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000}),
-                     "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
-                     "latent_image": ("LATENT", ),
-                     "base_sampler": ("KSAMPLER_ADVANCED", ),
-                     "regional_prompts": ("REGIONAL_PROMPTS", ),
+                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled", "tooltip": "Whether to add noise"}),
+                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000, "tooltip": "The starting step of the sampling to be applied at this node within the range of 'steps'."}),
+                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000, "tooltip": "The step at which sampling applied at this node will stop within the range of steps (if greater than steps, sampling will continue only up to steps)."}),
+                     "overlap_factor": ("INT", {"default": 10, "min": 0, "max": 10000, "tooltip": "To smooth the seams of the region boundaries, expand the mask set in regional_prompts by the overlap_factor amount to overlap with other regions."}),
+                     "restore_latent": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled", "tooltip": "At each step, restore the noise outside the mask area to its original state, as per the principle of inpainting. This option is provided for backward compatibility, and it is recommended to always set it to true."}),
+                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled", "tooltip": "Whether to return the latent with noise remaining if the noise has not been completely removed according to the noise schedule, or to completely remove the noise before returning it."}),
+                     "latent_image": ("LATENT", {"tooltip": "input latent image"}),
+                     "base_sampler": ("KSAMPLER_ADVANCED", {"tooltip": "The sampler applied outside the area set by the regional_prompt."}),
+                     "regional_prompts": ("REGIONAL_PROMPTS", {"tooltip": "The prompt applied to each region"}),
+                     "additional_mode": (["DISABLE", "ratio additional", "ratio between"], {"default": "ratio between", "tooltip": "..._sde or uni_pc and other special samplers are used, the region is not properly denoised, and it causes a phenomenon that destroys the overall harmony. To compensate for this, a recovery operation is performed using another sampler. This requires a longer time for sampling because a second sampling is performed at each step in each region using a special sampler. 1) DISABLE: Disable this feature. 2) ratio additional: After performing the denoise amount to be performed in the step with the sampler set in the region, the recovery sampler is additionally applied by the additional_sigma_ratio. If you use this option, the total denoise amount increases by additional_sigma_ratio. 3) ratio between: The denoise amount to be performed in the step with the sampler set in the region and the denoise amount to be applied to the recovery sampler are divided by additional_sigma_ratio, and denoise is performed for each denoise amount. If you use this option, the total denoise amount does not change."}),
+                     "additional_sampler": (["AUTO", "euler", "heun", "heunpp2", "dpm_2", "dpm_fast", "dpmpp_2m", "ddpm"], {"tooltip": "1) AUTO: Automatically set the recovery sampler. If the sampler is uni_pc, uni_pc_bh2, dpmpp_sde, dpmpp_sde_gpu, the dpm_fast sampler is selected If the sampler is dpmpp_2m_sde, dpmpp_2m_sde_gpu, dpmpp_3m_sde, dpmpp_3m_sde_gpu, the dpmpp_2m sampler is selected. 2) Others: Manually set the recovery sampler."}),
+                     "additional_sigma_ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Multiplier of noise schedule to be applied according to additional_mode."}),
                      },
                  "hidden": {"unique_id": "UNIQUE_ID"},
                 }
 
+    OUTPUT_TOOLTIPS = ("result latent", )
+
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Regional"
 
-    def doit(self, add_noise, noise_seed, steps, start_at_step, end_at_step, overlap_factor, restore_latent,
-             return_with_leftover_noise, latent_image, base_sampler, regional_prompts, unique_id):
+    @staticmethod
+    def doit(add_noise, noise_seed, steps, start_at_step, end_at_step, overlap_factor, restore_latent, return_with_leftover_noise, latent_image, base_sampler, regional_prompts,
+             additional_mode, additional_sampler, additional_sigma_ratio, unique_id):
+
+        new_latent_image = latent_image.copy()
+        new_latent_image['samples'] = comfy.sample.fix_empty_latent_channels(base_sampler.params[0], new_latent_image['samples'])
+
         if restore_latent:
             latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
         else:
@@ -420,17 +498,24 @@ class RegionalSamplerAdvanced:
         end_at_step = min(steps, end_at_step)
         total = (end_at_step - start_at_step) * region_len
 
-        new_latent_image = latent_image.copy()
         base_latent_image = None
         region_masks = {}
 
-        for i in range(start_at_step, end_at_step):
+        for i in range(start_at_step, end_at_step-1):
             core.update_node_status(unique_id, f"{start_at_step+i}/{end_at_step} steps  |         ", ((i-start_at_step)*region_len)/total)
 
-            cur_add_noise = "enable" if i == start_at_step and add_noise else "disable"
+            cur_add_noise = True if i == start_at_step and add_noise else False
+
+            if cur_add_noise:
+                noise = Noise_RandomNoise(noise_seed).generate_noise(new_latent_image)
+                for rp in regional_prompts:
+                    noise = rp.touch_noise(noise)
+            else:
+                noise = None
 
             new_latent_image['noise_mask'] = inv_mask
-            new_latent_image = base_sampler.sample_advanced(cur_add_noise, noise_seed, steps, new_latent_image, i, i + 1, "enable", recover_special_sampler=True)
+            new_latent_image = base_sampler.sample_advanced(cur_add_noise, noise_seed, steps, new_latent_image, i, i + 1, True,
+                                                            recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio, noise=noise)
 
             if restore_latent:
                 del new_latent_image['noise_mask']
@@ -450,8 +535,8 @@ class RegionalSamplerAdvanced:
                     region_mask = region_masks[j]
 
                 new_latent_image['noise_mask'] = region_mask
-                new_latent_image = regional_prompt.sampler.sample_advanced("disable", noise_seed, steps, new_latent_image,
-                                                                           i, i + 1, "enable", recover_special_sampler=True)
+                new_latent_image = regional_prompt.sampler.sample_advanced(False, noise_seed, steps, new_latent_image, i, i + 1, True,
+                                                                           recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
 
                 if restore_latent:
                     del new_latent_image['noise_mask']
@@ -468,7 +553,8 @@ class RegionalSamplerAdvanced:
             base_latent_image = new_latent_image
 
         new_latent_image['noise_mask'] = inv_mask
-        new_latent_image = base_sampler.sample_advanced("disable", noise_seed, steps, new_latent_image, end_at_step, end_at_step+1, return_with_leftover_noise, recover_special_sampler=False)
+        new_latent_image = base_sampler.sample_advanced(False, noise_seed, steps, new_latent_image, end_at_step-1, end_at_step, return_with_leftover_noise,
+                                                        recovery_mode=additional_mode, recovery_sampler=additional_sampler, recovery_sigma_ratio=additional_sigma_ratio)
 
         core.update_node_status(unique_id, f"{end_at_step}/{end_at_step} steps", total)
         core.update_node_status(unique_id, "", None)
@@ -486,63 +572,112 @@ class KSamplerBasicPipe:
     @classmethod
     def INPUT_TYPES(s):
         return {"required":
-                    {"basic_pipe": ("BASIC_PIPE",),
-                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                     "latent_image": ("LATENT", ),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                     }
+                    {"basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"}),
+                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}),
+                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}),
+                     "scheduler": (core.SCHEDULERS, {"tooltip": "noise schedule"}),
+                     "latent_image": ("LATENT", {"tooltip": "input latent image"}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of noise to remove. This amount is the noise added at the start, and the higher it is, the more the input latent will be modified before being returned."}),
+                     },
+                "optional":
+                    {
+                        "scheduler_func_opt": ("SCHEDULER_FUNC", {"tooltip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}),
+                    }
                 }
 
+    OUTPUT_TOOLTIPS = ("passthrough input basic_pipe", "result latent", "VAE in basic_pipe")
+
     RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE")
     FUNCTION = "sample"
 
-    CATEGORY = "sampling"
+    CATEGORY = "ImpactPack/sampling"
 
-    def sample(self, basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0):
+    @staticmethod
+    def sample(basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0, scheduler_func_opt=None):
         model, clip, vae, positive, negative = basic_pipe
-        latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)[0]
-        return (basic_pipe, latent, vae)
+        latent = impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=scheduler_func_opt)
+        return basic_pipe, latent, vae
 
 
 class KSamplerAdvancedBasicPipe:
     @classmethod
     def INPUT_TYPES(s):
         return {"required":
-                    {"basic_pipe": ("BASIC_PIPE",),
-                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
-                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
-                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                     "latent_image": ("LATENT", ),
-                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
-                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
-                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
-                     }
+                    {"basic_pipe": ("BASIC_PIPE", {"tooltip": "basic_pipe input for sampling"}),
+                     "add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable", "tooltip": "Whether to add noise"}),
+                     "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed to use for generating CPU noise for sampling."}),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "total sampling steps"}),
+                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "tooltip": "classifier free guidance value"}),
+                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "sampler"}),
+                     "scheduler": (core.SCHEDULERS, {"tooltip": "noise schedule"}),
+                     "latent_image": ("LATENT", {"tooltip": "input latent image"}),
+                     "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000, "tooltip": "The starting step of the sampling to be applied at this node within the range of 'steps'."}),
+                     "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000, "tooltip": "The step at which sampling applied at this node will stop within the range of steps (if greater than steps, sampling will continue only up to steps)."}),
+                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable", "tooltip": "Whether to return the latent with noise remaining if the noise has not been completely removed according to the noise schedule, or to completely remove the noise before returning it."}),
+                     },
+                "optional":
+                    {
+                        "scheduler_func_opt": ("SCHEDULER_FUNC", {"tooltip": "[OPTIONAL] Noise schedule generation function. If this is set, the scheduler widget will be ignored."}),
+                    }
                 }
 
+    OUTPUT_TOOLTIPS = ("passthrough input basic_pipe", "result latent", "VAE in basic_pipe")
+
     RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE")
     FUNCTION = "sample"
 
-    CATEGORY = "sampling"
+    CATEGORY = "ImpactPack/sampling"
 
-    def sample(self, basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
+    @staticmethod
+    def sample(basic_pipe, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, scheduler_func_opt=None):
         model, clip, vae, positive, negative = basic_pipe
 
-        if add_noise:
-            add_noise = "enable"
-        else:
-            add_noise = "disable"
+        latent = separated_sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, scheduler_func=scheduler_func_opt)
+        return basic_pipe, latent, vae
 
-        if return_with_leftover_noise:
-            return_with_leftover_noise = "enable"
-        else:
-            return_with_leftover_noise = "disable"
 
-        latent = nodes.KSamplerAdvanced().sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise)[0]
-        return (basic_pipe, latent, vae)
+class GITSSchedulerFuncProvider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05, "tooltip": "coeff factor of GITS Scheduler"}),
+                        "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "denoise amount for noise schedule"}),
+                    }
+                }
+
+    OUTPUT_TOOLTIPS = ("Returns a function that generates a noise schedule using GITSScheduler. This can be used in place of a predetermined noise schedule to dynamically generate a noise schedule based on the steps.",)
+
+    RETURN_TYPES = ("SCHEDULER_FUNC",)
+    CATEGORY = "ImpactPack/sampling"
+
+    FUNCTION = "doit"
+
+    @staticmethod
+    def doit(coeff, denoise):
+        def f(model, sampler, steps):
+            if 'GITSScheduler' not in nodes.NODE_CLASS_MAPPINGS:
+                raise Exception("[Impact Pack] ComfyUI is an outdated version. Cannot use GITSScheduler.")
+
+            scheduler = nodes.NODE_CLASS_MAPPINGS['GITSScheduler']()
+            return scheduler.get_sigmas(coeff, steps, denoise)[0]
+
+        return (f, )
+
+
+class NegativeConditioningPlaceholder:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {}}
+
+    OUTPUT_TOOLTIPS = ("This is a Placeholder for the FLUX model that does not use Negative Conditioning.",)
+
+    RETURN_TYPES = ("CONDITIONING",)
+    CATEGORY = "ImpactPack/sampling"
+
+    FUNCTION = "doit"
+
+    @staticmethod
+    def doit():
+        return ("NegativePlaceholder", )

modules/impact/util_nodes.py

@@ -1,75 +1,115 @@
-from impact.utils import any_typ
+from impact.utils import any_typ, ByPassTypeTuple, make_3d_mask
 import comfy_extras.nodes_mask
 from nodes import MAX_RESOLUTION
+import torch
+import comfy
+import sys
+import nodes
+import re
+import impact.core as core
+from server import PromptServer
+import inspect
+
 
 class GeneralSwitch:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {
-                    "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1}),
-                    "sel_mode": ("BOOLEAN", {"default": True, "label_on": "select_on_prompt", "label_off": "select_on_execution", "forceInput": False}),
-                    },
-                "optional": {
-                    "input1": (any_typ,),
+        dyn_inputs = {"input1": (any_typ, {"lazy": True, "tooltip": "Any input. When connected, one more input slot is added."}), }
+        if core.is_execution_model_version_supported():
+            stack = inspect.stack()
+            if stack[2].function == 'get_input_info':
+                # bypass validation
+                class AllContainer:
+                    def __contains__(self, item):
+                        return True
+
+                    def __getitem__(self, key):
+                        return any_typ, {"lazy": True}
+
+                dyn_inputs = AllContainer()
+
+        inputs = {"required": {
+                    "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1, "tooltip": "The input number you want to output among the inputs"}),
+                    "sel_mode": ("BOOLEAN", {"default": False, "label_on": "select_on_prompt", "label_off": "select_on_execution", "forceInput": False,
+                                             "tooltip": "In the case of 'select_on_execution', the selection is dynamically determined at the time of workflow execution. 'select_on_prompt' is an option that exists for older versions of ComfyUI, and it makes the decision before the workflow execution."}),
                     },
+                "optional": dyn_inputs,
                 "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"}
                 }
 
+        return inputs
+
     RETURN_TYPES = (any_typ, "STRING", "INT")
     RETURN_NAMES = ("selected_value", "selected_label", "selected_index")
+    OUTPUT_TOOLTIPS = ("Output is generated only from the input chosen by the 'select' value.", "Slot label of the selected input slot", "Outputs the select value as is")
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, *args, **kwargs):
+    def check_lazy_status(self, *args, **kwargs):
+        selected_index = int(kwargs['select'])
+        input_name = f"input{selected_index}"
+
+        print(f"SELECTED: {input_name}")
+
+        if input_name in kwargs:
+            return [input_name]
+        else:
+            return []
+
+    @staticmethod
+    def doit(*args, **kwargs):
         selected_index = int(kwargs['select'])
         input_name = f"input{selected_index}"
 
         selected_label = input_name
         node_id = kwargs['unique_id']
-        nodelist = kwargs['extra_pnginfo']['workflow']['nodes']
-        for node in nodelist:
-            if str(node['id']) == node_id:
-                inputs = node['inputs']
 
-                for slot in inputs:
-                    if slot['name'] == input_name and 'label' in slot:
-                        selected_label = slot['label']
+        if 'extra_pnginfo' in kwargs and kwargs['extra_pnginfo'] is not None:
+            nodelist = kwargs['extra_pnginfo']['workflow']['nodes']
+            for node in nodelist:
+                if str(node['id']) == node_id:
+                    inputs = node['inputs']
 
-                break
+                    for slot in inputs:
+                        if slot['name'] == input_name and 'label' in slot:
+                            selected_label = slot['label']
+
+                    break
+        else:
+            print(f"[Impact-Pack] The switch node does not guarantee proper functioning in API mode.")
 
         if input_name in kwargs:
-            return (kwargs[input_name], selected_label, selected_index)
+            return kwargs[input_name], selected_label, selected_index
         else:
             print(f"ImpactSwitch: invalid select index (ignored)")
-            return (None, "", selected_index)
+            return None, "", selected_index
 
-
-class GeneralInversedSwitch:
+class LatentSwitch:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                    "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1}),
-                    "input": (any_typ,),
+                    "select": ("INT", {"default": 1, "min": 1, "max": 99999, "step": 1}),
+                    "latent1": ("LATENT",),
                     },
-                "hidden": {"unique_id": "UNIQUE_ID"},
                 }
 
-    RETURN_TYPES = tuple([any_typ] * 100)
+    RETURN_TYPES = ("LATENT", )
+
+    OUTPUT_NODE = True
+
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, select, input, unique_id):
-        res = []
+    def doit(self, *args, **kwargs):
+        input_name = f"latent{int(kwargs['select'])}"
 
-        for i in range(0, select):
-            if select == i+1:
-                res.append(input)
-            else:
-                res.append(None)
-
-        return res
+        if input_name in kwargs:
+            return (kwargs[input_name],)
+        else:
+            print(f"LatentSwitch: invalid select index ('latent1' is selected)")
+            return (kwargs['latent1'],)
 
 
 class ImageMaskSwitch:
@@ -92,6 +132,9 @@ class ImageMaskSwitch:
         }
 
     RETURN_TYPES = ("IMAGE", "MASK",)
+
+    OUTPUT_NODE = True
+
     FUNCTION = "doit"
 
     CATEGORY = "ImpactPack/Util"
@@ -108,6 +151,54 @@ class ImageMaskSwitch:
             return images4_opt, mask4_opt,
 
 
+class GeneralInversedSwitch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "select": ("INT", {"default": 1, "min": 1, "max": 999999, "step": 1, "tooltip": "The output number you want to send from the input"}),
+                    "input": (any_typ, {"tooltip": "Any input. When connected, one more input slot is added."}),
+
+                    },
+                "optional": {
+                    "sel_mode": ("BOOLEAN", {"default": False, "label_on": "select_on_prompt", "label_off": "select_on_execution", "forceInput": False,
+                                             "tooltip": "In the case of 'select_on_execution', the selection is dynamically determined at the time of workflow execution. 'select_on_prompt' is an option that exists for older versions of ComfyUI, and it makes the decision before the workflow execution."}),
+                    },
+                "hidden": {"prompt": "PROMPT", "unique_id": "UNIQUE_ID"},
+                }
+
+    RETURN_TYPES = ByPassTypeTuple((any_typ, ))
+    OUTPUT_TOOLTIPS = ("Output occurs only from the output selected by the 'select' value.\nWhen slots are connected, additional slots are created.", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, select, prompt, unique_id, input, **kwargs):
+        if core.is_execution_model_version_supported():
+            from comfy_execution.graph import ExecutionBlocker
+        else:
+            print("[Impact Pack] InversedSwitch: ComfyUI is outdated. The 'select_on_execution' mode cannot function properly.")
+
+        res = []
+
+        # search max output count in prompt
+        cnt = 0
+        for x in prompt.values():
+            for y in x.get('inputs', {}).values():
+                if isinstance(y, list) and len(y) == 2:
+                    if y[0] == unique_id:
+                        cnt = max(cnt, y[1])
+
+        for i in range(0, cnt + 1):
+            if select == i+1:
+                res.append(input)
+            elif core.is_execution_model_version_supported():
+                res.append(ExecutionBlocker(None))
+            else:
+                res.append(None)
+
+        return res
+
+
 class RemoveNoiseMask:
     @classmethod
     def INPUT_TYPES(s):
@@ -155,9 +246,10 @@ class ImpactLogger:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                        "data": (any_typ, ""),
+                        "data": (any_typ,),
+                        "text": ("STRING", {"multiline": True}),
                     },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"},
                 }
 
     CATEGORY = "ImpactPack/Debug"
@@ -167,7 +259,7 @@ class ImpactLogger:
     RETURN_TYPES = ()
     FUNCTION = "doit"
 
-    def doit(self, data, prompt, extra_pnginfo):
+    def doit(self, data, text, prompt, extra_pnginfo, unique_id):
         shape = ""
         if hasattr(data, "shape"):
             shape = f"{data.shape} / "
@@ -178,12 +270,13 @@ class ImpactLogger:
 
         # for x in prompt:
         #     if 'inputs' in x and 'populated_text' in x['inputs']:
-        #         print(f"PROMP: {x['10']['inputs']['populated_text']}")
+        #         print(f"PROMPT: {x['10']['inputs']['populated_text']}")
         #
         # for x in extra_pnginfo['workflow']['nodes']:
         #     if x['type'] == 'ImpactWildcardProcessor':
         #         print(f" WV : {x['widgets_values'][1]}\n")
 
+        PromptServer.instance.send_sync("impact-node-feedback", {"node_id": unique_id, "widget_name": "text", "type": "TEXT", "value": f"{data}"})
         return {}
 
 
@@ -199,3 +292,450 @@ class ImpactDummyInput:
 
     def doit(self):
         return ("DUMMY",)
+
+
+class MasksToMaskList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "masks": ("MASK", ),
+                      }
+                }
+
+    RETURN_TYPES = ("MASK", )
+    OUTPUT_IS_LIST = (True, )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Operation"
+
+    def doit(self, masks):
+        if masks is None:
+            empty_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            return ([empty_mask], )
+
+        res = []
+
+        for mask in masks:
+            res.append(mask)
+
+        print(f"mask len: {len(res)}")
+
+        res = [make_3d_mask(x) for x in res]
+
+        return (res, )
+
+
+class MaskListToMaskBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "mask": ("MASK", ),
+                      }
+                }
+
+    INPUT_IS_LIST = True
+
+    RETURN_TYPES = ("MASK", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Operation"
+
+    def doit(self, mask):
+        if len(mask) == 1:
+            mask = make_3d_mask(mask[0])
+            return (mask,)
+        elif len(mask) > 1:
+            mask1 = make_3d_mask(mask[0])
+
+            for mask2 in mask[1:]:
+                mask2 = make_3d_mask(mask2)
+                if mask1.shape[1:] != mask2.shape[1:]:
+                    mask2 = comfy.utils.common_upscale(mask2.movedim(-1, 1), mask1.shape[2], mask1.shape[1], "lanczos", "center").movedim(1, -1)
+                mask1 = torch.cat((mask1, mask2), dim=0)
+
+            return (mask1,)
+        else:
+            empty_mask = torch.zeros((1, 64, 64), dtype=torch.float32, device="cpu").unsqueeze(0)
+            return (empty_mask,)
+
+
+class ImageListToImageBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "images": ("IMAGE", ),
+                      }
+                }
+
+    INPUT_IS_LIST = True
+
+    RETURN_TYPES = ("IMAGE", )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Operation"
+
+    def doit(self, images):
+        if len(images) <= 1:
+            return (images[0],)
+        else:
+            image1 = images[0]
+            for image2 in images[1:]:
+                if image1.shape[1:] != image2.shape[1:]:
+                    image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "lanczos", "center").movedim(1, -1)
+                image1 = torch.cat((image1, image2), dim=0)
+            return (image1,)
+
+
+class ImageBatchToImageList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image": ("IMAGE",), }}
+
+    RETURN_TYPES = ("IMAGE",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, image):
+        images = [image[i:i + 1, ...] for i in range(image.shape[0])]
+        return (images, )
+
+
+class MakeAnyList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {},
+            "optional": {"value1": (any_typ,), }
+        }
+
+    RETURN_TYPES = (any_typ,)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, **kwargs):
+        values = []
+
+        for k, v in kwargs.items():
+            if v is not None:
+                values.append(v)
+
+        return (values, )
+
+
+class MakeMaskList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"mask1": ("MASK",), }}
+
+    RETURN_TYPES = ("MASK",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, **kwargs):
+        masks = []
+
+        for k, v in kwargs.items():
+            masks.append(v)
+
+        return (masks, )
+
+
+class NthItemOfAnyList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":  {
+                    "any_list": (any_typ,),
+                    "index": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1, "tooltip": "The index of the item you want to select from the list."}),
+                    }
+        }
+
+    RETURN_TYPES = (any_typ,)
+    INPUT_IS_LIST = True
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    DESCRIPTION = "Selects the Nth item from a list. If the index is out of range, it returns the last item in the list."
+
+    def doit(self, any_list, index):
+        i = index[0]
+        if i >= len(any_list):
+            return (any_list[-1],)
+        else:
+            return (any_list[i],)
+
+
+class MakeImageList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image1": ("IMAGE",), }}
+
+    RETURN_TYPES = ("IMAGE",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, **kwargs):
+        images = []
+
+        for k, v in kwargs.items():
+            images.append(v)
+
+        return (images, )
+
+
+class MakeImageBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image1": ("IMAGE",), }}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, **kwargs):
+        image1 = kwargs['image1']
+        del kwargs['image1']
+        images = [value for value in kwargs.values()]
+
+        if len(images) == 0:
+            return (image1,)
+        else:
+            for image2 in images:
+                if image1.shape[1:] != image2.shape[1:]:
+                    image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "lanczos", "center").movedim(1, -1)
+                image1 = torch.cat((image1, image2), dim=0)
+            return (image1,)
+
+
+class MakeMaskBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"mask1": ("MASK",), }}
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, **kwargs):
+        mask1 = kwargs['mask1']
+        del kwargs['mask1']
+        masks = [make_3d_mask(value) for value in kwargs.values()]
+
+        if len(masks) == 0:
+            return (mask1,)
+        else:
+            for mask2 in masks:
+                if mask1.shape[1:] != mask2.shape[1:]:
+                    mask2 = comfy.utils.common_upscale(mask2.movedim(-1, 1), mask1.shape[2], mask1.shape[1], "lanczos", "center").movedim(1, -1)
+                mask1 = torch.cat((mask1, mask2), dim=0)
+            return (mask1,)
+
+
+class ReencodeLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "samples": ("LATENT", ),
+                        "tile_mode": (["None", "Both", "Decode(input) only", "Encode(output) only"],),
+                        "input_vae": ("VAE", ),
+                        "output_vae": ("VAE", ),
+                        "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
+                    },
+                "optional": {
+                    "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32, "tooltip": "This setting applies when 'tile_mode' is enabled."}),
+                    }
+                }
+
+    CATEGORY = "ImpactPack/Util"
+
+    RETURN_TYPES = ("LATENT", )
+    FUNCTION = "doit"
+
+    def doit(self, samples, tile_mode, input_vae, output_vae, tile_size=512, overlap=64):
+        if tile_mode in ["Both", "Decode(input) only"]:
+            decoder = nodes.VAEDecodeTiled()
+            if 'overlap' in inspect.signature(decoder.decode).parameters:
+                pixels = decoder.decode(input_vae, samples, tile_size, overlap=overlap)[0]
+            else:
+                pixels = decoder.decode(input_vae, samples, tile_size, overlap=overlap)[0]
+        else:
+            pixels = nodes.VAEDecode().decode(input_vae, samples)[0]
+
+        if tile_mode in ["Both", "Encode(output) only"]:
+            encoder = nodes.VAEEncodeTiled()
+            if 'overlap' in inspect.signature(encoder.encode).parameters:
+                return encoder.encode(output_vae, pixels, tile_size, overlap=overlap)
+            else:
+                return encoder.encode(output_vae, pixels, tile_size)
+        else:
+            return nodes.VAEEncode().encode(output_vae, pixels)
+
+
+class ReencodeLatentPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "samples": ("LATENT", ),
+                        "tile_mode": (["None", "Both", "Decode(input) only", "Encode(output) only"],),
+                        "input_basic_pipe": ("BASIC_PIPE", ),
+                        "output_basic_pipe": ("BASIC_PIPE", ),
+                    },
+                }
+
+    CATEGORY = "ImpactPack/Util"
+
+    RETURN_TYPES = ("LATENT", )
+    FUNCTION = "doit"
+
+    def doit(self, samples, tile_mode, input_basic_pipe, output_basic_pipe):
+        _, _, input_vae, _, _ = input_basic_pipe
+        _, _, output_vae, _, _ = output_basic_pipe
+        return ReencodeLatent().doit(samples, tile_mode, input_vae, output_vae)
+
+
+class StringSelector:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "strings": ("STRING", {"multiline": True}),
+            "multiline": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+            "select": ("INT", {"min": 0, "max": sys.maxsize, "step": 1, "default": 0}),
+        }}
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, strings, multiline, select):
+        lines = strings.split('\n')
+
+        if multiline:
+            result = []
+            current_string = ""
+
+            for line in lines:
+                if line.startswith("#"):
+                    if current_string:
+                        result.append(current_string.strip())
+                        current_string = ""
+                current_string += line + "\n"
+
+            if current_string:
+                result.append(current_string.strip())
+
+            if len(result) == 0:
+                selected = strings
+            else:
+                selected = result[select % len(result)]
+
+            if selected.startswith('#'):
+                selected = selected[1:]
+        else:
+            if len(lines) == 0:
+                selected = strings
+            else:
+                selected = lines[select % len(lines)]
+
+        return (selected, )
+
+
+class StringListToString:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "join_with": ("STRING", {"default": "\\n"}),
+                "string_list": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, join_with, string_list):
+        # convert \\n to newline character
+        if join_with[0] == "\\n":
+            join_with[0] = "\n"
+
+        joined_text = join_with[0].join(string_list)
+
+        return (joined_text,)
+
+
+class WildcardPromptFromString:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": ("STRING", {"forceInput": True}),
+                "delimiter": ("STRING", {"multiline": False, "default": "\\n" }),
+                "prefix_all": ("STRING", {"multiline": False}),
+                "postfix_all": ("STRING", {"multiline": False}),
+                "restrict_to_tags": ("STRING", {"multiline": False}),
+                "exclude_tags": ("STRING", {"multiline": False})
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING",)
+    RETURN_NAMES = ("wildcard", "segs_labels",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    def doit(self, string, delimiter, prefix_all, postfix_all, restrict_to_tags, exclude_tags):
+        # convert \\n to newline character
+        if delimiter == "\\n":
+            delimiter = "\n"
+
+        # some sanity checks and normalization for later processing
+        if prefix_all is None:
+            prefix_all = ""
+        if postfix_all is None:
+            postfix_all = ""
+        if restrict_to_tags is None:
+            restrict_to_tags = ""
+        if exclude_tags is None:
+            exclude_tags = ""
+
+        restrict_to_tags = restrict_to_tags.split(", ")
+        exclude_tags = exclude_tags.split(", ")
+
+        # build the wildcard prompt per list entry
+        output = ["[LAB]"]
+        labels = []
+        for x in string.split(delimiter):
+            label = str(len(labels) + 1)
+            labels.append(label)
+            x = x.split(", ")
+            # restrict to tags
+            if restrict_to_tags != [""]:
+                x = list(set(x) & set(restrict_to_tags))
+            # remove tags
+            if exclude_tags != [""]:
+                x = list(set(x) - set(exclude_tags))
+            # next row: <LABEL> <PREFIX> <TAGS> <POSTFIX>
+            prompt_for_seg = f'[{label}] {prefix_all} {", ".join(x)} {postfix_all}'.strip()
+            output.append(prompt_for_seg)
+        output = "\n".join(output)
+
+        # clean string: fixup double spaces, commas etc.
+        output = re.sub(r' ,', ',', output)
+        output = re.sub(r'  +', ' ', output)
+        output = re.sub(r',,+', ',', output)
+        output = re.sub(r'\n, ', '\n', output)
+
+        return output, ", ".join(labels)

modules/impact/utils.py

@@ -1,23 +1,249 @@
 import torch
+import torchvision
 import cv2
 import numpy as np
-from PIL import Image, ImageDraw, ImageFilter
 import folder_paths
+import nodes
 from . import config
+from PIL import Image
+import comfy
+import time
 
+
+class TensorBatchBuilder:
+    def __init__(self):
+        self.tensor = None
+
+    def concat(self, new_tensor):
+        if self.tensor is None:
+            self.tensor = new_tensor
+        else:
+            self.tensor = torch.concat((self.tensor, new_tensor), dim=0)
+
+
+def tensor_convert_rgba(image, prefer_copy=True):
+    """Assumes NHWC format tensor with 1, 3 or 4 channels."""
+    _tensor_check_image(image)
+    n_channel = image.shape[-1]
+    if n_channel == 4:
+        return image
+
+    if n_channel == 3:
+        alpha = torch.ones((*image.shape[:-1], 1))
+        return torch.cat((image, alpha), axis=-1)
+
+    if n_channel == 1:
+        if prefer_copy:
+            image = image.repeat(1, -1, -1, 4)
+        else:
+            image = image.expand(1, -1, -1, 3)
+        return image
+
+    # NOTE: Similar error message as in PIL, for easier googling :P
+    raise ValueError(f"illegal conversion (channels: {n_channel} -> 4)")
+
+
+def tensor_convert_rgb(image, prefer_copy=True):
+    """Assumes NHWC format tensor with 1, 3 or 4 channels."""
+    _tensor_check_image(image)
+    n_channel = image.shape[-1]
+    if n_channel == 3:
+        return image
+
+    if n_channel == 4:
+        image = image[..., :3]
+        if prefer_copy:
+            image = image.copy()
+        return image
+
+    if n_channel == 1:
+        if prefer_copy:
+            image = image.repeat(1, -1, -1, 4)
+        else:
+            image = image.expand(1, -1, -1, 3)
+        return image
+
+    # NOTE: Same error message as in PIL, for easier googling :P
+    raise ValueError(f"illegal conversion (channels: {n_channel} -> 3)")
+
+
+def general_tensor_resize(image, w: int, h: int):
+    _tensor_check_image(image)
+    image = image.permute(0, 3, 1, 2)
+    image = torch.nn.functional.interpolate(image, size=(h, w), mode="bilinear")
+    image = image.permute(0, 2, 3, 1)
+    return image
+
+
+# TODO: Sadly, we need LANCZOS
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+def tensor_resize(image, w: int, h: int):
+    _tensor_check_image(image)
+    if image.shape[3] >= 3:
+        scaled_images = TensorBatchBuilder()
+        for single_image in image:
+            single_image = single_image.unsqueeze(0)
+            single_pil = tensor2pil(single_image)
+            scaled_pil = single_pil.resize((w, h), resample=LANCZOS)
+
+            single_image = pil2tensor(scaled_pil)
+            scaled_images.concat(single_image)
+
+        return scaled_images.tensor
+    else:
+        return general_tensor_resize(image, w, h)
 
 
-def pil2numpy(image):
-    return (np.array(image).astype(np.float32) / 255.0)[np.newaxis, :, :, :]
+def tensor_get_size(image):
+    """Mimicking `PIL.Image.size`"""
+    _tensor_check_image(image)
+    _, h, w, _ = image.shape
+    return (w, h)
+
+
+def tensor2pil(image):
+    _tensor_check_image(image)
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(0), 0, 255).astype(np.uint8))
 
 
 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 numpy2pil(image):
+    return Image.fromarray(np.clip(255. * image.squeeze(0), 0, 255).astype(np.uint8))
+
+
+def to_pil(image):
+    if isinstance(image, Image.Image):
+        return image
+    if isinstance(image, torch.Tensor):
+        return tensor2pil(image)
+    if isinstance(image, np.ndarray):
+        return numpy2pil(image)
+    raise ValueError(f"Cannot convert {type(image)} to PIL.Image")
+
+
+def to_tensor(image):
+    if isinstance(image, Image.Image):
+        return torch.from_numpy(np.array(image)) / 255.0
+    if isinstance(image, torch.Tensor):
+        return image
+    if isinstance(image, np.ndarray):
+        return torch.from_numpy(image)
+    raise ValueError(f"Cannot convert {type(image)} to torch.Tensor")
+
+
+def to_numpy(image):
+    if isinstance(image, Image.Image):
+        return np.array(image)
+    if isinstance(image, torch.Tensor):
+        return image.numpy()
+    if isinstance(image, np.ndarray):
+        return image
+    raise ValueError(f"Cannot convert {type(image)} to numpy.ndarray")
+    
+
+
+def tensor_putalpha(image, mask):
+    _tensor_check_image(image)
+    _tensor_check_mask(mask)
+    image[..., -1] = mask[..., 0]
+
+
+def _tensor_check_image(image):
+    if image.ndim != 4:
+        raise ValueError(f"Expected NHWC tensor, but found {image.ndim} dimensions")
+    if image.shape[-1] not in (1, 3, 4):
+        raise ValueError(f"Expected 1, 3 or 4 channels for image, but found {image.shape[-1]} channels")
+    return
+
+
+def _tensor_check_mask(mask):
+    if mask.ndim != 4:
+        raise ValueError(f"Expected NHWC tensor, but found {mask.ndim} dimensions")
+    if mask.shape[-1] != 1:
+        raise ValueError(f"Expected 1 channel for mask, but found {mask.shape[-1]} channels")
+    return
+
+
+def tensor_crop(image, crop_region):
+    _tensor_check_image(image)
+    return crop_ndarray4(image, crop_region)
+
+
+def tensor2numpy(image):
+    _tensor_check_image(image)
+    return image.numpy()
+
+
+def tensor_paste(image1, image2, left_top, mask):
+    """
+    Pastes image2 onto image1 at position left_top using mask.
+    Supports both RGB and RGBA images.
+    """
+    _tensor_check_image(image1)
+    _tensor_check_image(image2)
+    _tensor_check_mask(mask)
+    
+    if image2.shape[1:3] != mask.shape[1:3]:
+        mask = resize_mask(mask.squeeze(dim=3), image2.shape[1:3]).unsqueeze(dim=3)
+    
+    x, y = left_top
+    _, h1, w1, c1 = image1.shape
+    _, h2, w2, c2 = image2.shape
+    
+    # Calculate image patch size
+    w = min(w1, x + w2) - x
+    h = min(h1, y + h2) - y
+    
+    # If the patch is out of bound, nothing to do!
+    if w <= 0 or h <= 0:
+        return
+    
+    mask = mask[:, :h, :w, :]
+    
+    # Get the region to be modified
+    region1 = image1[:, y:y+h, x:x+w, :]
+    region2 = image2[:, :h, :w, :]
+    
+    # Handle RGB and RGBA cases
+    if c1 == 3 and c2 == 3:
+        # Both RGB - simple case
+        image1[:, y:y+h, x:x+w, :] = (1 - mask) * region1 + mask * region2
+    
+    elif c1 == 4 and c2 == 4:
+        # Both RGBA - need to handle alpha channel separately
+        # RGB channels
+        image1[:, y:y+h, x:x+w, :3] = (
+            (1 - mask) * region1[:, :, :, :3] +
+            mask * region2[:, :, :, :3]
+        )
+        
+        # Alpha channel - use "over" composition
+        a1 = region1[:, :, :, 3:4]
+        a2 = region2[:, :, :, 3:4] * mask
+        new_alpha = a1 + a2 * (1 - a1)
+        image1[:, y:y+h, x:x+w, 3:4] = new_alpha
+    
+    elif c1 == 4 and c2 == 3:
+        # Target is RGBA, source is RGB - assume source is fully opaque
+        image1[:, y:y+h, x:x+w, :3] = (
+            (1 - mask) * region1[:, :, :, :3] +
+            mask * region2
+        )
+        # Alpha channel - reduce alpha where mask is applied
+        image1[:, y:y+h, x:x+w, 3:4] = region1[:, :, :, 3:4] * (1 - mask) + mask
+    
+    elif c1 == 3 and c2 == 4:
+        # Target is RGB, source is RGBA - apply source alpha to mask
+        effective_mask = mask * region2[:, :, :, 3:4]
+        image1[:, y:y+h, x:x+w, :] = (
+            (1 - effective_mask) * region1 +
+            effective_mask * region2[:, :, :, :3]
+        )
+    
+    return
 
 
 def center_of_bbox(bbox):
@@ -80,8 +306,7 @@ def bitwise_and_masks(mask1, mask2):
 
 
 def to_binary_mask(mask, threshold=0):
-    if len(mask.shape) == 3:
-        mask = mask.squeeze(0)
+    mask = make_3d_mask(mask)
 
     mask = mask.clone().cpu()
     mask[mask > threshold] = 1.
@@ -95,10 +320,9 @@ def use_gpu_opencv():
 
 def dilate_mask(mask, dilation_factor, iter=1):
     if dilation_factor == 0:
-        return mask
+        return make_2d_mask(mask)
 
-    if len(mask.shape) == 3:
-        mask = mask.squeeze(0)
+    mask = make_2d_mask(mask)
 
     kernel = np.ones((abs(dilation_factor), abs(dilation_factor)), np.uint8)
 
@@ -146,15 +370,65 @@ def dilate_masks(segmasks, dilation_factor, iter=1):
 
     return dilated_masks
 
+import torch.nn.functional as F
+def feather_mask(mask, thickness):
+    mask = mask.permute(0, 3, 1, 2)
 
-def feather_mask(mask, thickness, base_alpha=255):
-    pil_mask = Image.fromarray(np.uint8(mask * base_alpha))
+    # Gaussian kernel for blurring
+    kernel_size = 2 * int(thickness) + 1
+    sigma = thickness / 3  # Adjust the sigma value as needed
+    blur_kernel = _gaussian_kernel(kernel_size, sigma).to(mask.device, mask.dtype)
 
-    # 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
+    # Apply blur to the mask
+    blurred_mask = F.conv2d(mask, blur_kernel.unsqueeze(0).unsqueeze(0), padding=thickness)
+
+    blurred_mask = blurred_mask.permute(0, 2, 3, 1)
+
+    return blurred_mask
+
+def _gaussian_kernel(kernel_size, sigma):
+    # Generate a 1D Gaussian kernel
+    kernel = torch.exp(-(torch.arange(kernel_size) - kernel_size // 2)**2 / (2 * sigma**2))
+    return kernel / kernel.sum()
+
+
+def tensor_gaussian_blur_mask(mask, kernel_size, sigma=10.0):
+    """Return NHWC torch.Tenser from ndim == 2 or 4 `np.ndarray` or `torch.Tensor`"""
+    if isinstance(mask, np.ndarray):
+        mask = torch.from_numpy(mask)
+
+    if mask.ndim == 2:
+        mask = mask[None, ..., None]
+    elif mask.ndim == 3:
+        mask = mask[..., None]
+
+    _tensor_check_mask(mask)
+
+    if kernel_size <= 0:
+        return mask
+
+    kernel_size = kernel_size*2+1
+
+    shortest = min(mask.shape[1], mask.shape[2])
+    if shortest <= kernel_size:
+        kernel_size = int(shortest/2)
+        if kernel_size % 2 == 0:
+            kernel_size += 1
+        if kernel_size < 3:
+            return mask  # skip feathering
+
+    prev_device = mask.device
+    device = comfy.model_management.get_torch_device()
+    mask.to(device)
+
+    # apply gaussian blur
+    mask = mask[:, None, ..., 0]
+    blurred_mask = torchvision.transforms.GaussianBlur(kernel_size=kernel_size, sigma=sigma)(mask)
+    blurred_mask = blurred_mask[:, 0, ..., None]
+
+    blurred_mask.to(prev_device)
+
+    return blurred_mask
 
 
 def subtract_masks(mask1, mask2):
@@ -239,6 +513,20 @@ def crop_ndarray4(npimg, crop_region):
     return cropped
 
 
+crop_tensor4 = crop_ndarray4
+
+
+def crop_ndarray3(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]
@@ -251,34 +539,124 @@ def crop_ndarray2(npimg, crop_region):
 
 
 def crop_image(image, crop_region):
-    return crop_ndarray4(np.array(image), crop_region)
+    return crop_tensor4(image, crop_region)
 
 
-def to_latent_image(pixels, vae):
+def to_latent_image(pixels, vae, vae_tiled_encode=False):
     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}
 
+    start = time.time()
+    if vae_tiled_encode:
+        encoded = nodes.VAEEncodeTiled().encode(vae, pixels, 512, overlap=64)[0] # using default settings
+        print(f"[Impact Pack] vae encoded (tiled) in {time.time() - start:.1f}s")
+    else:
+        encoded = nodes.VAEEncode().encode(vae, pixels)[0]
+        print(f"[Impact Pack] vae encoded in {time.time() - start:.1f}s")
 
-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)
+    return encoded
 
 
 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)
+    return torch.zeros((1, h, w, 3), dtype=torch.float32)
+
+
+def make_2d_mask(mask):
+    if len(mask.shape) == 4:
+        return mask.squeeze(0).squeeze(0)
+
+    elif len(mask.shape) == 3:
+        return mask.squeeze(0)
+
+    return mask
+
+
+def make_3d_mask(mask):
+    if len(mask.shape) == 4:
+        return mask.squeeze(0)
+
+    elif len(mask.shape) == 2:
+        return mask.unsqueeze(0)
+
+    return mask
+
+
+def make_4d_mask(mask):
+    if len(mask.shape) == 3:
+        return mask.unsqueeze(0)
+
+    elif len(mask.shape) == 2:
+        return mask.unsqueeze(0).unsqueeze(0)
+
+    return mask
+
+
+def is_same_device(a, b):
+    a_device = torch.device(a) if isinstance(a, str) else a
+    b_device = torch.device(b) if isinstance(b, str) else b
+    return a_device.type == b_device.type and a_device.index == b_device.index
+
+
+def collect_non_reroute_nodes(node_map, links, res, node_id):
+    if node_map[node_id]['type'] != 'Reroute' and node_map[node_id]['type'] != 'Reroute (rgthree)':
+        res.append(node_id)
+    else:
+        for link in node_map[node_id]['outputs'][0]['links']:
+            next_node_id = str(links[link][2])
+            collect_non_reroute_nodes(node_map, links, res, next_node_id)
+
+
+from torchvision.transforms.functional import to_pil_image
+
+
+def resize_mask(mask, size):
+    mask = make_4d_mask(mask)
+    resized_mask = torch.nn.functional.interpolate(mask, size=size, mode='bilinear', align_corners=False)
+    return resized_mask.squeeze(0)
+
+
+def apply_mask_alpha_to_pil(decoded_pil, mask):
+    decoded_rgba = decoded_pil.convert('RGBA')
+    mask_pil = to_pil_image(mask)
+    decoded_rgba.putalpha(mask_pil)
+
+    return decoded_rgba
+
+
+def flatten_mask(all_masks):
+    merged_mask = (all_masks[0] * 255).to(torch.uint8)
+    for mask in all_masks[1:]:
+        merged_mask |= (mask * 255).to(torch.uint8)
+
+    return merged_mask
+
+
+def try_install_custom_node(custom_node_url, msg):
+    try:
+        import cm_global
+        cm_global.try_call(api='cm.try-install-custom-node',
+                           sender="Impact Pack", custom_node_url=custom_node_url, msg=msg)
+    except Exception:
+        print(msg)
+        print(f"[Impact Pack] ComfyUI-Manager is outdated. The custom node installation feature is not available.")
+
+
+# author: Trung0246 --->
+class TautologyStr(str):
+    def __ne__(self, other):
+        return False
+
+
+class ByPassTypeTuple(tuple):
+    def __getitem__(self, index):
+        if index > 0:
+            index = 0
+        item = super().__getitem__(index)
+        if isinstance(item, str):
+            return TautologyStr(item)
+        return item
 
 
 class NonListIterable:
@@ -289,7 +667,6 @@ class NonListIterable:
         return self.data[index]
 
 
-# author: Trung0246
 def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
     # Iterate over the list of full folder paths
     for full_folder_path in full_folder_paths:
@@ -309,7 +686,7 @@ def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
         # Now we just need to update the set of extensions as it would be an empty set
         # Also ensure that all paths are included (since add_model_folder_path adds only one path at a time)
         folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions)
-
+# <---
 
 # wildcard trick is taken from pythongossss's
 class AnyType(str):

modules/impact/wildcards.py

@@ -4,16 +4,32 @@ import os
 import nodes
 import folder_paths
 import yaml
+import numpy as np
+import threading
+from impact import utils
+from impact import config
 
+
+wildcards_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "wildcards"))
+
+RE_WildCardQuantifier = re.compile(r"(?P<quantifier>\d+)#__(?P<keyword>[\w.\-+/*\\]+?)__", re.IGNORECASE)
+wildcard_lock = threading.Lock()
 wildcard_dict = {}
 
 
 def get_wildcard_list():
-    return [f"__{x}__" for x in wildcard_dict.keys()]
+    with wildcard_lock:
+        return [f"__{x}__" for x in wildcard_dict.keys()]
+
+
+def get_wildcard_dict():
+    global wildcard_dict
+    with wildcard_lock:
+        return wildcard_dict
 
 
 def wildcard_normalize(x):
-    return x.replace("\\", "/").lower()
+    return x.replace("\\", "/").replace(' ', '-').lower()
 
 
 def read_wildcard(k, v):
@@ -25,7 +41,12 @@ def read_wildcard(k, v):
             new_key = f"{k}/{k2}"
             new_key = wildcard_normalize(new_key)
             read_wildcard(new_key, v2)
-
+    elif isinstance(v, str):
+        k = wildcard_normalize(k)
+        wildcard_dict[k] = [v]
+    elif isinstance(v, (int, float)):
+        k = wildcard_normalize(k)
+        wildcard_dict[k] = [str(v)]
 
 def read_wildcard_dict(wildcard_path):
     global wildcard_dict
@@ -34,30 +55,61 @@ def read_wildcard_dict(wildcard_path):
             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('\\', '/').lower()
+                key = wildcard_normalize(os.path.splitext(rel_path)[0])
 
                 try:
                     with open(file_path, 'r', encoding="ISO-8859-1") as f:
                         lines = f.read().splitlines()
-                        wildcard_dict[key] = lines
-                except UnicodeDecodeError:
+                        wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
+                except yaml.reader.ReaderError:
                     with open(file_path, 'r', encoding="UTF-8", errors="ignore") as f:
                         lines = f.read().splitlines()
-                        wildcard_dict[key] = lines
-            elif file.endswith('.yaml'):
+                        wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
+            elif file.endswith('.yaml') or file.endswith('.yml'):
                 file_path = os.path.join(root, file)
-                with open(file_path, 'r') as f:
-                    yaml_data = yaml.load(f, Loader=yaml.FullLoader)
 
-                    for k, v in yaml_data.items():
-                        read_wildcard(k, v)
+                try:
+                    with open(file_path, 'r', encoding="ISO-8859-1") as f:
+                        yaml_data = yaml.load(f, Loader=yaml.FullLoader)
+                except yaml.reader.ReaderError as e:
+                    with open(file_path, 'r', encoding="UTF-8", errors="ignore") as f:
+                        yaml_data = yaml.load(f, Loader=yaml.FullLoader)
+
+                for k, v in yaml_data.items():
+                    read_wildcard(k, v)
 
     return wildcard_dict
 
 
+def process_comment_out(text):
+    lines = text.split('\n')
+
+    lines0 = []
+    flag = False
+    for line in lines:
+        if line.lstrip().startswith('#'):
+            flag = True
+            continue
+
+        if len(lines0) == 0:
+            lines0.append(line)
+        elif flag:
+            lines0[-1] += ' ' + line
+            flag = False
+        else:
+            lines0.append(line)
+
+    return '\n'.join(lines0)
+
+
 def process(text, seed=None):
+    text = process_comment_out(text)
+
     if seed is not None:
         random.seed(seed)
+    random_gen = np.random.default_rng(seed)
+
+    local_wildcard_dict = get_wildcard_dict()
 
     def replace_options(string):
         replacements_found = False
@@ -71,6 +123,7 @@ def process(text, seed=None):
             select_sep = ' '
             range_pattern = r'(\d+)(-(\d+))?'
             range_pattern2 = r'-(\d+)'
+            wildcard_pattern = r"__([\w.\-+/*\\]+?)__"
 
             if len(multi_select_pattern) > 1:
                 r = re.match(range_pattern, options[0])
@@ -81,10 +134,11 @@ def process(text, seed=None):
                     b = r.group(1).strip()
                 else:
                     a = r.group(1).strip()
-                    try:
-                        b = r.group(3).strip()
-                    except:
-                        b = None
+                    b = r.group(3)
+                    if b is not None:
+                        b = b.strip()
+                    else:
+                        b = "-1"
                         
                 if r is not None:
                     if b is not None and is_numeric_string(a) and is_numeric_string(b):
@@ -95,20 +149,32 @@ def process(text, seed=None):
                         x = int(a)
                         select_range = (x, x)
 
+                    # Expand wildcard path or return the string after $$
+                    def expand_wildcard_or_return_string(options, pattern, wildcard_pattern):
+                        matches = re.findall(wildcard_pattern, pattern)
+                        if len(options) == 1 and matches:
+                            # $$<single wildcard>
+                            return get_wildcard_options(pattern)
+                        else:
+                            # $$opt1|opt2|...
+                            options[0] = pattern
+                            return options
+
                     if select_range is not None and len(multi_select_pattern) == 2:
                         # PATTERN: count$$
-                        options[0] = multi_select_pattern[1]
+                        options = expand_wildcard_or_return_string(options, multi_select_pattern[1], wildcard_pattern )
                     elif select_range is not None and len(multi_select_pattern) == 3:
                         # PATTERN: count$$ sep $$
                         select_sep = multi_select_pattern[1]
-                        options[0] = multi_select_pattern[2]
+                        options = expand_wildcard_or_return_string(options, multi_select_pattern[2], wildcard_pattern )
 
             adjusted_probabilities = []
 
             total_prob = 0
 
             for option in options:
-                parts = option.split('::', 1)
+                parts = option.split('::', 1) if isinstance(option, str) else f"{option}".split('::', 1)
+
                 if len(parts) == 2 and is_numeric_string(parts[0].strip()):
                     config_value = float(parts[0].strip())
                 else:
@@ -122,22 +188,30 @@ def process(text, seed=None):
             if select_range is None:
                 select_count = 1
             else:
-                select_count = random.randint(select_range[0], select_range[1])
+                def calculate_max(_options_length, _max_select_range):
+                    return min(_max_select_range + 1, _options_length + 1) if _max_select_range > 0 else _options_length + 1
 
-            if select_count > len(options):
+                def calculate_select_count(_max_value, _min_select_range, random_gen):
+                    if max(_max_value, _min_select_range) <= 0:
+                        return 0
+                    # fix: low >= high
+                    elif _max_value == _min_select_range:
+                        return _max_value
+                    else:
+                        # fix: low >= high
+                        _low_value = min(_min_select_range, _max_value)
+                        _high_value = max(_min_select_range, _max_value)
+                        return random_gen.integers(low=_low_value, high=_high_value, size=1)
+                select_count = calculate_select_count(calculate_max(len(options), select_range[1]), select_range[0], random_gen)
+
+            if select_count > len(options) or total_prob <= 1:
+                random_gen.shuffle(options)
                 selected_items = options
             else:
-                selected_items = random.choices(options, weights=normalized_probabilities, k=select_count)
-                selected_items = set(selected_items)
+                selected_items = random_gen.choice(options, p=normalized_probabilities, size=select_count, replace=False)
 
-                try_count = 0
-                while len(selected_items) < select_count and try_count < 10:
-                    remaining_count = select_count - len(selected_items)
-                    additional_items = random.choices(options, weights=normalized_probabilities, k=remaining_count)
-                    selected_items |= set(additional_items)
-                    try_count += 1
-
-            selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', x, 1) for x in selected_items]
+            # x may be numpy.int32, convert to string
+            selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', str(x), 1) for x in selected_items]
             replacement = select_sep.join(selected_items2)
             if '::' in replacement:
                 pass
@@ -150,9 +224,36 @@ def process(text, seed=None):
 
         return replaced_string, replacements_found
 
+    def get_wildcard_options(string):
+        pattern = r"__([\w.\-+/*\\]+?)__"
+        matches = re.findall(pattern, string)
+
+        options = []
+
+        for match in matches:
+            keyword = match.lower()
+            keyword = wildcard_normalize(keyword)
+            if keyword in local_wildcard_dict:
+                options.extend(local_wildcard_dict[keyword])
+            elif '*' in keyword:
+                subpattern = keyword.replace('*', '.*').replace('+', '\\+')
+                total_patterns = []
+                found = False
+                for k, v in local_wildcard_dict.items():
+                    if re.match(subpattern, k) is not None or re.match(subpattern, k+'/') is not None:
+                        total_patterns += v
+                        found = True
+
+                if found:
+                    options.extend(total_patterns)
+            elif '/' not in keyword:
+                string_fallback = string.replace(f"__{match}__", f"__*/{match}__", 1)
+                options.extend(get_wildcard_options(string_fallback))
+
+        return options
+
     def replace_wildcard(string):
-        global wildcard_dict
-        pattern = r"__([\w.\-+/*\\]+)__"
+        pattern = r"__([\w.\-+/*\\]+?)__"
         matches = re.findall(pattern, string)
 
         replacements_found = False
@@ -160,21 +261,37 @@ def process(text, seed=None):
         for match in matches:
             keyword = match.lower()
             keyword = wildcard_normalize(keyword)
-            if keyword in wildcard_dict:
-                replacement = random.choice(wildcard_dict[keyword])
+            if keyword in local_wildcard_dict:
+                # look for adjusted probability
+                adjusted_probabilities = []
+                total_prob = 0
+                options=local_wildcard_dict[keyword]
+                for option in options:
+                    parts = option.split('::', 1)
+                    if len(parts) == 2 and is_numeric_string(parts[0].strip()):
+                        config_value = float(parts[0].strip())
+                    else:
+                        config_value = 1  # Default value if no configuration is provided
+
+                    adjusted_probabilities.append(config_value)
+                    total_prob += config_value
+
+                normalized_probabilities = [prob / total_prob for prob in adjusted_probabilities]
+                selected_item = random_gen.choice(options, p=normalized_probabilities, replace=False)
+                replacement = re.sub(r'^\s*[0-9.]+::', '', selected_item, 1)
                 replacements_found = True
                 string = string.replace(f"__{match}__", replacement, 1)
             elif '*' in keyword:
-                subpattern = keyword.replace('*', '.*').replace('+','\+')
+                subpattern = keyword.replace('*', '.*').replace('+', '\\+')
                 total_patterns = []
                 found = False
-                for k, v in wildcard_dict.items():
-                    if re.match(subpattern, k) is not None:
+                for k, v in local_wildcard_dict.items():
+                    if re.match(subpattern, k) is not None or re.match(subpattern, k+'/') is not None:
                         total_patterns += v
                         found = True
 
                 if found:
-                    replacement = random.choice(total_patterns)
+                    replacement = random_gen.choice(total_patterns)
                     replacements_found = True
                     string = string.replace(f"__{match}__", replacement, 1)
             elif '/' not in keyword:
@@ -187,6 +304,15 @@ def process(text, seed=None):
     stop_unwrap = False
     while not stop_unwrap and replace_depth > 1:
         replace_depth -= 1  # prevent infinite loop
+        
+        option_quantifier = [e.groupdict() for e in RE_WildCardQuantifier.finditer(text)]
+        for match in option_quantifier:
+            keyword = match['keyword'].lower()
+            quantifier = int(match['quantifier']) if match['quantifier'] else 1
+            replacement = '__|__'.join([keyword,] * quantifier)
+            wilder_keyword = keyword.replace('*', '\\*')
+            RE_TEMP = re.compile(fr"(?P<quantifier>\d+)#__(?P<keyword>{wilder_keyword})__", re.IGNORECASE)
+            text = RE_TEMP.sub(f"__{replacement}__", text)
 
         # pass1: replace options
         pass1, is_replaced1 = replace_options(text)
@@ -202,7 +328,7 @@ def process(text, seed=None):
 
 
 def is_numeric_string(input_str):
-    return re.match(r'^-?\d+(\.\d+)?$', input_str) is not None
+    return re.match(r'^-?(\d*\.?\d+|\d+\.?\d*)$', input_str) is not None
 
 
 def safe_float(x):
@@ -254,7 +380,7 @@ def extract_lora_values(string):
         if a is None:
             a = 1.0
         if b is None:
-            b = 1.0
+            b = a
 
         if lora is not None and lora not in added:
             result.append((lora, a, b, lbw, lbw_a, lbw_b))
@@ -282,20 +408,37 @@ def resolve_lora_name(lora_name_cache, name):
                 return x
 
 
-def process_with_loras(wildcard_opt, model, clip, clip_encoder=None):
+def process_with_loras(wildcard_opt, model, clip, clip_encoder=None, seed=None, processed=None):
+    """
+    process wildcard text including loras
+
+    :param wildcard_opt: wildcard text
+    :param model: model
+    :param clip: clip
+    :param clip_encoder: you can pass custom encoder such as adv_cliptext_encode
+    :param seed: seed for populating
+    :param processed: output variable - [pass1, pass2, pass3] will be saved into passed list
+    :return: model, clip, conditioning
+    """
+
     lora_name_cache = []
 
-    pass1 = process(wildcard_opt)
+    pass1 = process(wildcard_opt, seed)
     loras = extract_lora_values(pass1)
     pass2 = remove_lora_tags(pass1)
 
     for lora_name, model_weight, clip_weight, lbw, lbw_a, lbw_b in loras:
-        if (lora_name.split('.')[-1]) not in folder_paths.supported_pt_extensions:
+        lora_name_ext = lora_name.split('.')
+        if ('.'+lora_name_ext[-1]) not in folder_paths.supported_pt_extensions:
             lora_name = lora_name+".safetensors"
 
+        orig_lora_name = lora_name
         lora_name = resolve_lora_name(lora_name_cache, lora_name)
 
-        path = folder_paths.get_full_path("loras", lora_name)
+        if lora_name is not None:
+            path = folder_paths.get_full_path("loras", lora_name)
+        else:
+            path = None
 
         if path is not None:
             print(f"LOAD LORA: {lora_name}: {model_weight}, {clip_weight}, LBW={lbw}, A={lbw_a}, B={lbw_b}")
@@ -305,6 +448,10 @@ def process_with_loras(wildcard_opt, model, clip, clip_encoder=None):
 
             if lbw is not None:
                 if 'LoraLoaderBlockWeight //Inspire' not in nodes.NODE_CLASS_MAPPINGS:
+                    utils.try_install_custom_node(
+                        'https://github.com/ltdrdata/ComfyUI-Inspire-Pack',
+                        "To use 'LBW=' syntax in wildcards, 'Inspire Pack' extension is required.")
+
                     print(f"'LBW(Lora Block Weight)' is given, but the 'Inspire Pack' is not installed. The LBW= attribute is being ignored.")
                     model, clip = default_lora()
                 else:
@@ -313,19 +460,41 @@ def process_with_loras(wildcard_opt, model, clip, clip_encoder=None):
             else:
                 model, clip = default_lora()
         else:
-            print(f"LORA NOT FOUND: {lora_name}")
+            print(f"LORA NOT FOUND: {orig_lora_name}")
 
-    print(f"CLIP: {pass2}")
+    pass3 = [x.strip() for x in pass2.split("BREAK")]
+    pass3 = [x for x in pass3 if x != '']
 
-    if clip_encoder is None:
-        return model, clip, nodes.CLIPTextEncode().encode(clip, pass2)[0]
-    else:
-        return model, clip, clip_encoder.encode(clip, pass2)[0]
+    if len(pass3) == 0:
+        pass3 = ['']
+
+    pass3_str = [f'[{x}]' for x in pass3]
+    print(f"CLIP: {str.join(' + ', pass3_str)}")
+
+    result = None
+
+    for prompt in pass3:
+        if clip_encoder is None:
+            cur = nodes.CLIPTextEncode().encode(clip, prompt)[0]
+        else:
+            cur = clip_encoder.encode(clip, prompt)[0]
+
+        if result is not None:
+            result = nodes.ConditioningConcat().concat(result, cur)[0]
+        else:
+            result = cur
+
+    if processed is not None:
+        processed.append(pass1)
+        processed.append(pass2)
+        processed.append(pass3)
+
+    return model, clip, result
 
 
 def starts_with_regex(pattern, text):
     regex = re.compile(pattern)
-    return bool(regex.match(text))
+    return regex.match(text)
 
 
 def split_to_dict(text):
@@ -370,6 +539,31 @@ class WildcardChooserDict:
         return text
 
 
+def split_string_with_sep(input_string):
+    sep_pattern = r'\[SEP(?:\:\w+)?\]'
+
+    substrings = re.split(sep_pattern, input_string)
+
+    result_list = [None]
+    matches = re.findall(sep_pattern, input_string)
+    for i, substring in enumerate(substrings):
+        result_list.append(substring)
+        if i < len(matches):
+            if matches[i] == '[SEP]':
+                result_list.append(None)
+            elif matches[i] == '[SEP:R]':
+                result_list.append(random.randint(0, 1125899906842624))
+            else:
+                try:
+                    seed = int(matches[i][5:-1])
+                except:
+                    seed = None
+                result_list.append(seed)
+
+    iterable = iter(result_list)
+    return list(zip(iterable, iterable))
+
+
 def process_wildcard_for_segs(wildcard):
     if wildcard.startswith('[LAB]'):
         raw_items = split_to_dict(wildcard)
@@ -382,21 +576,33 @@ def process_wildcard_for_segs(wildcard):
 
         return 'LAB', WildcardChooserDict(items)
 
-    elif starts_with_regex(r"\[(ASC|DSC|RND)\]", wildcard):
-        mode = wildcard[1:4]
-        raw_items = wildcard[5:].split('[SEP]')
-
-        items = []
-        for x in raw_items:
-            x = x.strip()
-            if x != '':
-                items.append(x)
-
-        if mode == 'RND':
-            random.shuffle(items)
-            return mode, WildcardChooser(items, True)
-        else:
-            return mode, WildcardChooser(items, False)
-
     else:
-        return None, WildcardChooser([wildcard], False)
+        match = starts_with_regex(r"\[(ASC-SIZE|DSC-SIZE|ASC|DSC|RND)\]", wildcard)
+
+        if match:
+            mode = match[1]
+            items = split_string_with_sep(wildcard[len(match[0]):])
+
+            if mode == 'RND':
+                random.shuffle(items)
+                return mode, WildcardChooser(items, True)
+            else:
+                return mode, WildcardChooser(items, False)
+
+        else:
+            return None, WildcardChooser([(None, wildcard)], False)
+
+
+def wildcard_load():
+    global wildcard_dict
+    wildcard_dict = {}
+
+    with wildcard_lock:
+        read_wildcard_dict(wildcards_path)
+
+        try:
+            read_wildcard_dict(config.get_config()['custom_wildcards'])
+        except Exception as e:
+            print(f"[Impact Pack] Failed to load custom wildcards directory.")
+
+        print(f"[Impact Pack] Wildcards loading done.")

modules/thirdparty/noise_nodes.py

@@ -0,0 +1,86 @@
+# Due to the current lack of maintenance for the `ComfyUI_Noise` extension,
+# I have copied the code from the applied PR.
+# https://github.com/BlenderNeko/ComfyUI_Noise/pull/13/files
+
+import comfy
+import torch
+
+from comfy import sampler_helpers
+
+
+class Unsampler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "end_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                     "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
+                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+                     "normalize": (["disable", "enable"],),
+                     "positive": ("CONDITIONING",),
+                     "negative": ("CONDITIONING",),
+                     "latent_image": ("LATENT",),
+                     }}
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "unsampler"
+
+    CATEGORY = "sampling"
+
+    def unsampler(self, model, cfg, sampler_name, steps, end_at_step, scheduler, normalize, positive, negative,
+                  latent_image):
+        normalize = normalize == "enable"
+        device = comfy.model_management.get_torch_device()
+        latent = latent_image
+        latent_image = latent["samples"]
+
+        end_at_step = min(end_at_step, steps - 1)
+        end_at_step = steps - end_at_step
+
+        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+        noise_mask = None
+        if "noise_mask" in latent:
+            noise_mask = comfy.sampler_helpers.prepare_mask(latent["noise_mask"], noise.shape, device)
+
+        noise = noise.to(device)
+        latent_image = latent_image.to(device)
+
+        conds0 = \
+            {"positive": comfy.sampler_helpers.convert_cond(positive),
+             "negative": comfy.sampler_helpers.convert_cond(negative)}
+
+        conds = {}
+        for k in conds0:
+            conds[k] = list(map(lambda a: a.copy(), conds0[k]))
+
+        models, inference_memory = comfy.sampler_helpers.get_additional_models(conds, model.model_dtype())
+
+        comfy.model_management.load_models_gpu([model] + models, model.memory_required(noise.shape) + inference_memory)
+
+        sampler = comfy.samplers.KSampler(model, steps=steps, device=device, sampler=sampler_name,
+                                          scheduler=scheduler, denoise=1.0, model_options=model.model_options)
+
+        sigmas = sampler.sigmas.flip(0) + 0.0001
+
+        pbar = comfy.utils.ProgressBar(steps)
+
+        def callback(step, x0, x, total_steps):
+            pbar.update_absolute(step + 1, total_steps)
+
+        samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image,
+                                 force_full_denoise=False, denoise_mask=noise_mask, sigmas=sigmas, start_step=0,
+                                 last_step=end_at_step, callback=callback)
+        if normalize:
+            # technically doesn't normalize because unsampling is not guaranteed to end at a std given by the schedule
+            samples -= samples.mean()
+            samples /= samples.std()
+        samples = samples.cpu()
+
+        comfy.sampler_helpers.cleanup_additional_models(models)
+
+        out = latent.copy()
+        out["samples"] = samples
+        return (out,)
+

pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "comfyui-impact-pack"
+description = "This node pack offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
+version = "8.14.1"
+license = { file = "LICENSE.txt" }
+dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]
+
+[project.urls]
+Repository = "https://github.com/ltdrdata/ComfyUI-Impact-Pack"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "drltdata"
+DisplayName = "ComfyUI Impact Pack"
+Icon = ""

requirements.txt

@@ -3,3 +3,7 @@ scikit-image
 piexif
 transformers
 opencv-python-headless
+scipy>=1.11.4
+numpy<2
+dill
+matplotlib