hotfix: front - robustness fix

https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/886#issuecomment-2600716281
version marker
2025-01-19 18:08:59 +09:00 · 2025-01-19 03:17:25 +09:00 · 2025-01-19 03:16:56 +09:00 · 2025-01-14 00:47:27 +09:00 · 2025-01-10 00:49:38 +09:00 · 2025-01-10 00:49:06 +09:00
25 changed files with 1659 additions and 588 deletions
@@ -7,3 +7,5 @@ subpack
 impact_subpack
 *.txt
 *.yaml
+!requirements.txt
+!LICENSE.txt
@@ -2,11 +2,14 @@

 # 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 
+* 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. 
@@ -32,9 +35,6 @@ This custom node helps to conveniently enhance images through Detector, Detailer
 ## Custom Nodes
 ### [Detector nodes](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.
    * You need to install the ComfyUI-CLIPSeg node extension.
@@ -48,7 +48,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer

 ### 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 an `control_image` is given, `segs_preprocessor` will be ignored.
+    * `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
@@ -68,6 +68,8 @@ This custom node helps to conveniently enhance images through Detector, Detailer
  * `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 nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detailers.md)
  * `Detailer (SEGS)` - Refines the image based on SEGS.
@@ -89,6 +91,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
  * `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.
+  * `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.
@@ -106,6 +109,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
  * `SEGS Filter (range)` - This node retrieves only SEGs from SEGS that have a size and position within a certain range.
  * `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. 
@@ -236,11 +240,11 @@ This custom node helps to conveniently enhance images through Detector, Detailer
    - 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.

 ### Logics (experimental) 
  * These nodes are experimental nodes designed to implement the logic for loops and dynamic switching.
-  * `ImpactCompare`, `ImpactConditionalBranch`, `ImpactConditionalBranchSelMode`, `ImpactInt`, `ImpactValueSender`, `ImpactValueReceiver`, `ImpactImageInfo`, `ImpactMinMax`, `ImpactNeg`, `ImpactConditionalStopIteration`
+  * `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.
@@ -271,6 +275,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
      * 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.

+
 ### 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
@@ -283,11 +288,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
  * `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.
-
-
-## MMDet nodes (DEPRECATED) - Don't use these 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.
+  * `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
@@ -295,72 +296,41 @@ This custom node helps to conveniently enhance images through Detector, Detailer
 * 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?

+### Install via ComfyUI-Manager (Recommended)
+* Search `ComfyUI Impact Pack` in ComfyUI-Manager and click `Install` button.

-## 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.
-* civitai/[adetailer](https://civitai.com/search/models?sortBy=models_v5&query=adetailer) - You can download various types detection models....Many models are associated with NSFW content.
-
-## How to activate 'MMDet usage' (DEPRECATED)
-* 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
-
+### Manual Install (Not Recommended)
 1. `cd custom_nodes`
-2. `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 clone https://github.com/ltdrdata/ComfyUI-Impact-Subpack impact_subpack`
-   * 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.
-
-
-6. Restart ComfyUI
+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.

+
 ## 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 (deprecated)
-   * mmcv==2.0.0, mmdet==3.0.0, mmengine==0.7.2
-
 * linux packages (ubuntu)
  * libgl1-mesa-glx
  * libglib2.0-0
@@ -383,17 +353,16 @@ 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)
@@ -13,7 +13,6 @@ 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")

 sys.path.append(modules_path)
@@ -22,30 +21,9 @@ 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
@@ -63,10 +41,10 @@ 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
@@ -116,6 +94,7 @@ NODE_CLASS_MAPPINGS = {
    "FromDetailerPipe": FromDetailerPipe,
    "FromDetailerPipe_v2": FromDetailerPipe_v2,
    "FromDetailerPipeSDXL": FromDetailerPipe_SDXL,
+    "AnyPipeToBasic": AnyPipeToBasic,
    "ToBasicPipe": ToBasicPipe,
    "FromBasicPipe": FromBasicPipe,
    "FromBasicPipe_v2": FromBasicPipe_v2,
@@ -158,6 +137,8 @@ NODE_CLASS_MAPPINGS = {
    "BitwiseAndMask": BitwiseAndMask,
    "SubtractMask": SubtractMask,
    "AddMask": AddMask,
+    "MaskRectArea": MaskRectArea,
+    "MaskRectAreaAdvanced": MaskRectAreaAdvanced,
    "ImpactSegsAndMask": SegsBitwiseAndMask,
    "ImpactSegsAndMaskForEach": SegsBitwiseAndMaskForEach,
    "EmptySegs": EmptySEGS,
@@ -238,6 +219,7 @@ NODE_CLASS_MAPPINGS = {
    "ImpactSEGSConcat": SEGSConcat,
    "ImpactSEGSPicker": SEGSPicker,
    "ImpactMakeTileSEGS": MakeTileSEGS,
+    "ImpactSEGSMerge": SEGSMerge,

    "SEGSDetailerForAnimateDiff": SEGSDetailerForAnimateDiff,

@@ -250,6 +232,7 @@ NODE_CLASS_MAPPINGS = {
    "ImpactImageBatchToImageList": ImageBatchToImageList,
    "ImpactMakeImageList": MakeImageList,
    "ImpactMakeImageBatch": MakeImageBatch,
+    "ImpactMakeAnyList": MakeAnyList,
    "ImpactMakeMaskList": MakeMaskList,
    "ImpactMakeMaskBatch": MakeMaskBatch,

@@ -274,6 +257,7 @@ NODE_CLASS_MAPPINGS = {
    "ImpactLogicalOperators": ImpactLogicalOperators,
    "ImpactInt": ImpactInt,
    "ImpactFloat": ImpactFloat,
+    "ImpactBoolean": ImpactBoolean,
    "ImpactValueSender": ImpactValueSender,
    "ImpactValueReceiver": ImpactValueReceiver,
    "ImpactImageInfo": ImpactImageInfo,
@@ -285,6 +269,7 @@ NODE_CLASS_MAPPINGS = {
    "StringListToString": StringListToString,
    "WildcardPromptFromString": WildcardPromptFromString,
    "ImpactExecutionOrderController": ImpactExecutionOrderController,
+    "ImpactListBridge": ImpactListBridge,

    "RemoveNoiseMask": RemoveNoiseMask,

@@ -318,8 +303,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "ImpactSimpleDetectorSEGS_for_AD": "Simple Detector for AnimateDiff (SEGS)",
    "ImpactSimpleDetectorSEGS": "Simple Detector (SEGS)",
    "ImpactSimpleDetectorSEGSPipe": "Simple Detector (SEGS/pipe)",
-    "ImpactControlNetApplySEGS": "ControlNetApply (SEGS)",
-    "ImpactControlNetApplyAdvancedSEGS": "ControlNetApplyAdvanced (SEGS)",
+    "ImpactControlNetApplySEGS": "ControlNetApply (SEGS) - DEPRECATED",
+    "ImpactControlNetApplyAdvancedSEGS": "ControlNetApply (SEGS)",
    "ImpactIPAdapterApplySEGS": "IPAdapterApply (SEGS)",

    "BboxDetectorCombined_v2": "BBOX Detector (combined)",
@@ -335,6 +320,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "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)",
@@ -358,6 +345,7 @@ 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/on Pixel Space)",
@@ -380,6 +368,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "ImpactSEGSToMaskBatch": "SEGS to Mask Batch",
    "ImpactSEGSPicker": "Picker (SEGS)",
    "ImpactMakeTileSEGS": "Make Tile SEGS",
+    "ImpactSEGSMerge": "SEGS Merge",

    "ImpactDecomposeSEGS": "Decompose (SEGS)",
    "ImpactAssembleSEGS": "Assemble (SEGS)",
@@ -403,6 +392,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "ImpactSwitch": "Switch (Any)",
    "ImpactInversedSwitch": "Inversed Switch (Any)",
    "ImpactExecutionOrderController": "Execution Order Controller",
+    "ImpactListBridge": "List Bridge",

    "MasksToMaskList": "Mask Batch to Mask List",
    "MaskListToMaskBatch": "Mask List to Mask Batch",
@@ -413,6 +403,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "ImpactMakeImageBatch": "Make Image Batch",
    "ImpactMakeMaskList": "Make Mask List",
    "ImpactMakeMaskBatch": "Make Mask Batch",
+    "ImpactMakeAnyList": "Make List (Any)",

    "ImpactStringSelector": "String Selector",
    "StringListToString": "String List to String",
@@ -472,19 +463,6 @@ 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
@@ -5,7 +5,6 @@ import subprocess
 import threading
 import locale
 import traceback
-import re


 if sys.argv[0] == 'install.py':
@@ -13,14 +12,11 @@ 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.environ.get('COMFYUI_PATH')
 if comfy_path is None:
-    print(f"\n[bold yellow]WARN: The `COMFYUI_PATH` environment variable is not set. Assuming `{os.path.dirname(__file__)}/../../` as the ComfyUI path.[/bold yellow]", file=sys.stderr)
+    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')
@@ -33,7 +29,7 @@ if model_path is None:

    if model_path is None:
        model_path = os.path.abspath(os.path.join(comfy_path, 'models'))
-    print(f"\n[bold yellow]WARN: The `COMFYUI_MODEL_PATH` environment variable is not set. Assuming `{model_path}` as the ComfyUI path.[/bold yellow]", file=sys.stderr)
+    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)
@@ -71,219 +67,39 @@ def process_wrap(cmd_str, cwd=None, handler=None, env=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
    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']
-        pip_upgrade = [sys.executable, '-s', '-m', 'pip', 'install', '-U']
-        mim_install = [sys.executable, '-s', '-m', 'mim', 'install']
-    else:
-        pip_install = [sys.executable, '-m', 'pip', 'install']
-        pip_upgrade = [sys.executable, '-m', 'pip', 'install', '-U']
-        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 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:
-            from cv2 import setNumThreads
-        except Exception:
-            try:
-                is_open_cv_installed = False
-
-                # upgrade if opencv is installed already
-                if is_installed('opencv-python'):
-                    process_wrap(pip_upgrade + ['opencv-python'])
-                    is_open_cv_installed = True
-
-                if is_installed('opencv-python-headless'):
-                    process_wrap(pip_upgrade + ['opencv-python-headless'])
-                    is_open_cv_installed = True
-
-                if is_installed('opencv-contrib-python'):
-                    process_wrap(pip_upgrade + ['opencv-contrib-python'])
-                    is_open_cv_installed = True
-
-                if is_installed('opencv-contrib-python-headless'):
-                    process_wrap(pip_upgrade + ['opencv-contrib-python-headless'])
-                    is_open_cv_installed = True
-
-                # if opencv is not installed install `opencv-python-headless`
-                if not is_open_cv_installed:
-                    process_wrap(pip_install + ['opencv-python-headless'])
-            except:
-                print(f"[ERROR] ComfyUI-Impact-Pack: failed to install 'opencv-python'. Please, install manually.")
-
-    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():
-        subpack_install_script = os.path.join(subpack_path, "install.py")
-
-        print(f"### ComfyUI-Impact-Pack: Updating subpack")
-        try:
-            import git
-        except Exception:
-            if not is_installed('GitPython'):
-                process_wrap(pip_install + ['GitPython'])
-
-        ensure_subpack()  # The installation of the subpack must take place before ensure_pip. cv2 triggers a permission error.
-
        new_env = os.environ.copy()
        new_env["COMFYUI_PATH"] = comfy_path
        new_env["COMFYUI_MODEL_PATH"] = model_path

-        if os.path.exists(subpack_install_script):
-            process_wrap([sys.executable, 'install.py'], cwd=subpack_path, env=new_env)
-            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()
-
        # Download model
        print("### ComfyUI-Impact-Pack: Check basic models")
-        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})")
@@ -291,6 +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()

@@ -222,6 +222,31 @@ api.addEventListener("executed", progressExecuteHandler);

 app.registerExtension({
 	name: "Comfy.Impack",
+
+    commands: [
+      {
+        id: 'refresh-impact-wildcard',
+        label: 'Impact: Refresh Wildcard',
+        function: async () => {
+        	await api.fetchApi('/impact/wildcards/refresh');
+        	await load_wildcards();
+        	app.extensionManager.toast.add({
+				severity: 'info',
+				summary: 'Refreshed!',
+				detail: 'Impact Wildcard List is refreshed!!',
+				life: 3000
+			});
+        }
+      }
+    ],
+
+    menuCommands: [
+      {
+        path: ['Edit'],
+        commands: ['refresh-impact-wildcard']
+      }
+    ],
+
 	loadedGraphNode(node, app) {
 		if (node.comfyClass == "MaskPainter") {
 			input_dirty[node.id + ""] = true;
@@ -340,7 +365,11 @@ app.registerExtension({
 					// connect input
 					if(this.inputs[0].type == '*'){
 						const node = app.graph.getNodeById(link_info.origin_id);
-						let origin_type = node.outputs[link_info.origin_slot].type;
+						let origin_type = node.outputs[link_info.origin_slot]?.type;
+
+						if(origin_type==undefined) {
+							return; // fallback
+						}

 						if(origin_type == '*') {
 							this.disconnectInput(link_info.target_slot);
@@ -353,7 +382,7 @@ app.registerExtension({
 						}

 						this.outputs[0].type = origin_type;
-						this.outputs[0].name = origin_type;
+						this.outputs[0].name = 'output1';
 					}

 					return;
@@ -383,7 +412,7 @@ app.registerExtension({
 				}

 				let select_slot = this.inputs.find(x => x.name == "select");
-				if(this.widgets) {
+				if(this.widgets?.length) {
 					this.widgets[0].options.max = select_slot?this.outputs.length-1:this.outputs.length;
 					this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
 					if(this.widgets[0].options.max > 0 && this.widgets[0].value == 0)
@@ -394,7 +423,7 @@ app.registerExtension({

 		if (nodeData.name === 'ImpactMakeImageList' || nodeData.name === 'ImpactMakeImageBatch' ||
 		    nodeData.name === 'ImpactMakeMaskList' || nodeData.name === 'ImpactMakeMaskBatch' ||
-			nodeData.name === 'CombineRegionalPrompts' ||
+			nodeData.name === 'ImpactMakeAnyList' || nodeData.name === 'CombineRegionalPrompts' ||
 			nodeData.name === 'ImpactCombineConditionings' || nodeData.name === 'ImpactConcatConditionings' ||
 			nodeData.name === 'ImpactSEGSConcat' ||
 			nodeData.name === 'ImpactSwitch' || nodeData.name === 'LatentSwitch' || nodeData.name == 'SEGSSwitch') {
@@ -411,6 +440,10 @@ app.registerExtension({
 				input_name = "mask";
 				break;

+			case 'ImpactMakeAnyList':
+				input_name = "value";
+				break;
+
 			case 'ImpactSEGSConcat':
 				input_name = "segs";
 				break;
@@ -479,8 +512,12 @@ app.registerExtension({

 					if(this.inputs[0].type == '*'){
 						const node = app.graph.getNodeById(link_info.origin_id);
-						let origin_type = node.outputs[link_info.origin_slot].type;
-
+						let origin_type = node.outputs[link_info.origin_slot]?.type;
+						if(link_info.target_slot == 0 && this.inputs.length > 1) {
+								origin_type = this.inputs[1].type;
+								node.connect(link_info.origin_slot, node.id, 'input1');
+						}
+						
 						if(origin_type == '*') {
 							this.disconnectInput(link_info.target_slot);
 							return;
@@ -533,7 +570,7 @@ app.registerExtension({
 						this.addInput(`${input_name}${slot_i}`, this.outputs[0].type);
 				}

-				if(this.widgets) {
+				if(this.widgets?.length) {
 					this.widgets[0].options.max = select_slot?this.inputs.length-1:this.inputs.length;
 					this.widgets[0].value = Math.min(this.widgets[0].value, this.widgets[0].options.max);
 					if(this.widgets[0].options.max > 0 && this.widgets[0].value == 0)
@@ -579,17 +616,17 @@ app.registerExtension({
 		}

 		if(node.comfyClass == "ImpactSEGSLabelFilter" || node.comfyClass == "SEGSLabelFilterDetailerHookProvider") {
+			node.widgets[0].callback = (value, canvas, node, pos, e) => {
+				if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
+					node.widgets[1].value += ", "
+
+				node.widgets[1].value += value;
+				if(node.widgets_values)
+					node.widgets_values[1] = node.widgets[1].value;
+			}
+
 			Object.defineProperty(node.widgets[0], "value", {
 				set: (value) => {
-						const stackTrace = new Error().stack;
-						if(stackTrace.includes('inner_value_change')) {
-							if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
-								node.widgets[1].value += ", "
-
-							node.widgets[1].value += value;
-							node.widgets_values[1] = node.widgets[1].value;
-						}
-
 						node._value = value;
 					},
 				get: () => {
@@ -659,18 +696,18 @@ app.registerExtension({
 					break;
 			}

+            node.widgets[combo_id+1].callback = (value, canvas, node, pos, e) => {
+                    if(node.widgets[tbox_id].value != '')
+                        node.widgets[tbox_id].value += ', '
+
+                    node.widgets[tbox_id].value += node._wildcard_value;
+            }
+
 			Object.defineProperty(node.widgets[combo_id+1], "value", {
 				set: (value) => {
-						const stackTrace = new Error().stack;
-						if(stackTrace.includes('inner_value_change')) {
-							if(value != "Select the Wildcard to add to the text") {
-								if(node.widgets[tbox_id].value != '')
-									node.widgets[tbox_id].value += ', '
-
-								node.widgets[tbox_id].value += value;
-							}
-						}
-					},
+                    if (value !== "Select the Wildcard to add to the text")
+                        node._wildcard_value = value;
+                },
 				get: () => { return "Select the Wildcard to add to the text"; }
 			});

@@ -682,24 +719,22 @@ app.registerExtension({
 			});

 			if(has_lora) {
+				node.widgets[combo_id].callback = (value, canvas, node, pos, e) => {
+					let lora_name = node._value;
+					if(lora_name.endsWith('.safetensors')) {
+						lora_name = lora_name.slice(0, -12);
+					}
+
+					node.widgets[tbox_id].value += `<lora:${lora_name}>`;
+					if(node.widgets_values) {
+						node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
+					}
+				}
+
 				Object.defineProperty(node.widgets[combo_id], "value", {
 					set: (value) => {
-							const stackTrace = new Error().stack;
-							if(stackTrace.includes('inner_value_change')) {
-								if(value != "Select the LoRA to add to the text") {
-									let lora_name = value;
-									if (lora_name.endsWith('.safetensors')) {
-										lora_name = lora_name.slice(0, -12);
-									}
-
-									node.widgets[tbox_id].value += `<lora:${lora_name}>`;
-									if(node.widgets_values) {
-										node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
-									}
-								}
-							}
-
-							node._value = value;
+							if (value !== "Select the LoRA to add to the text")
+								node._value = value;
 						},

 					get: () => { return "Select the LoRA to add to the text"; }
@@ -262,7 +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);
@@ -353,13 +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";

@@ -473,8 +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);
 		}
@@ -508,8 +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) {
@@ -1,20 +0,0 @@
-import { ComfyApp, app } from "../../scripts/app.js";
-import { api } from "../../scripts/api.js";
-
-let refresh_btn = document.getElementById('comfy-refresh-button');
-let refresh_btn2 = document.querySelector('button[title="Refresh widgets in nodes to find new models or files"]');
-
-let orig = refresh_btn.onclick;
-
-if(refresh_btn) {
-    refresh_btn.onclick = function() {
-        orig();
-        api.fetchApi('/impact/wildcards/refresh');
-    };
-}
-
-if(refresh_btn2) {
-    refresh_btn2?.addEventListener('click', function() {
-        api.fetchApi('/impact/wildcards/refresh');
-    });
-}
@@ -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;
+}
@@ -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;
+}
@@ -27,7 +27,7 @@ class SEGSDetailerForAnimateDiff:
                     "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",),
+                     "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": {
@@ -60,7 +60,7 @@ class SEGSDetailerForAnimateDiff:
        new_segs = []
        cnet_image_list = []

-        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+        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]:
@@ -94,13 +94,18 @@ class SEGSDetailerForAnimateDiff:
                for condition, details in negative
            ]

-            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)
+            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)

@@ -143,7 +148,7 @@ class DetailerForEachPipeForAnimateDiff:
                      "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", ),
+                      "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": {
@@ -20,7 +20,8 @@ class PreviewBridge:
                    "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."})
+                    "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"},
                }
@@ -75,7 +76,7 @@ class PreviewBridge:

        return image, mask.unsqueeze(0), ui_item

-    def doit(self, images, image, unique_id, block=False, prompt=None, extra_pnginfo=None):
+    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:
@@ -88,10 +89,25 @@ class PreviewBridge:
            pixels, mask, path_item = PreviewBridge.load_image(image)
            image = [path_item]
        else:
-            res = nodes.PreviewImage().save_images(images, filename_prefix="PreviewBridge/PB-", prompt=prompt, extra_pnginfo=extra_pnginfo)
+            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
-            mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")

            path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', image2[0]['filename'])
            core.set_previewbridge_image(unique_id, path, image2[0])
@@ -103,7 +119,7 @@ class PreviewBridge:

        is_empty_mask = torch.all(mask == 0)

-        if block and is_empty_mask and core.is_execution_model_version_supported:
+        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:
@@ -112,6 +128,9 @@ class PreviewBridge:
        else:
            result = pixels, mask

+        if not is_empty_mask:
+            core.preview_bridge_last_mask_cache[unique_id] = mask
+
        return {
            "ui": {"images": image},
            "result": result,
@@ -167,6 +186,9 @@ def decode_latent(latent, preview_method, vae_opt=None):
    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()
@@ -192,11 +214,13 @@ class PreviewBridgeLatent:
                                        "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."})
+                    "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"},
                }
@@ -252,9 +276,15 @@ class PreviewBridgeLatent:

        return image, mask, ui_item

-    def doit(self, latent, image, preview_method, vae_opt=None, block=False, unique_id=None, prompt=None, extra_pnginfo=None):
+    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]
-        preview_method_channels = 16 if 'SD3' in preview_method or 'SC-Prior' in preview_method or 'FLUX.1' in preview_method or 'TAEF1' == preview_method else 4
+
+        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.")
@@ -311,13 +341,28 @@ class PreviewBridgeLatent:
                                'type': 'temp',
                            }]

-                is_empty_mask = torch.all(mask == 1)
+                is_empty_mask = False
            else:
-                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)
+                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 = True
+            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])
@@ -327,7 +372,7 @@ class PreviewBridgeLatent:

            res_latent = latent

-        if block and is_empty_mask and core.is_execution_model_version_supported:
+        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:
@@ -336,6 +381,9 @@ class PreviewBridgeLatent:
        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,
@@ -1,10 +1,10 @@
 import configparser
 import os

-version_code = [7, 5, 1]
+version_code = [8, 4, 1]
 version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')

-dependency_version = 22
+dependency_version = 24

 my_path = os.path.dirname(__file__)
 old_config_path = os.path.join(my_path, "impact-pack.ini")
@@ -11,6 +11,7 @@ from impact.utils import *
 from collections import namedtuple
 import numpy as np
 from skimage.measure import label
+from PIL import ImageOps

 import nodes
 import comfy_extras.nodes_upscale_model as model_upscale
@@ -24,6 +25,8 @@ from comfy import model_management
 from impact import utils
 from impact import impact_sampling
 from concurrent.futures import ThreadPoolExecutor
+import inspect
+

 try:
    from comfy_extras import nodes_differential_diffusion
@@ -39,10 +42,13 @@ SEG = namedtuple("SEG",
 pb_id_cnt = time.time()
 preview_bridge_image_id_map = {}
 preview_bridge_image_name_map = {}
+
 preview_bridge_cache = {}
+preview_bridge_last_mask_cache = {}
+
 current_prompt = None

-SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]']
+SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]']


 def is_execution_model_version_supported():
@@ -64,6 +70,13 @@ def set_previewbridge_image(node_id, file, item):
    pb_id = f"${node_id}-{pb_id_cnt}"
    preview_bridge_image_id_map[pb_id] = (file, item)
    preview_bridge_image_name_map[node_id, file] = (pb_id, item)
+    if os.path.isfile(file):
+        i = Image.open(file)
+        i = ImageOps.exif_transpose(i)
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
+            preview_bridge_last_mask_cache[node_id] = mask.unsqueeze(0)
    pb_id_cnt += 1

    return pb_id
@@ -231,7 +244,8 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
                   detailer_hook=None,
                   refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None,
                   refiner_negative=None, control_net_wrapper=None, cycle=1,
-                   inpaint_model=False, noise_mask_feather=0, scheduler_func=None):
+                   inpaint_model=False, noise_mask_feather=0, scheduler_func=None,
+                   vae_tiled_encode=False, vae_tiled_decode=False):

    if noise_mask is not None:
        noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
@@ -314,9 +328,14 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max

    # prepare mask
    if noise_mask is not None and inpaint_model:
-        positive, negative, latent_image = nodes.InpaintModelConditioning().encode(positive, negative, upscaled_image, vae, noise_mask)
+        imc_encode = nodes.InpaintModelConditioning().encode
+        if 'noise_mask' in inspect.signature(imc_encode).parameters:
+            positive, negative, latent_image = imc_encode(positive, negative, upscaled_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, upscaled_image, vae, noise_mask)
    else:
-        latent_image = to_latent_image(upscaled_image, vae)
+        latent_image = to_latent_image(upscaled_image, vae, vae_tiled_encode=vae_tiled_encode)
        if noise_mask is not None:
            latent_image['noise_mask'] = noise_mask

@@ -351,12 +370,18 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
        refined_latent = detailer_hook.pre_decode(refined_latent)

    # non-latent downscale - latent downscale cause bad quality
-    try:
-        # try to decode image normally
-        refined_image = vae.decode(refined_latent['samples'])
-    except Exception as e:
-        #usually an out-of-memory exception from the decode, so try a tiled approach
-        refined_image = vae.decode_tiled(refined_latent["samples"], tile_x=64, tile_y=64, )
+    start = time.time()
+    if vae_tiled_decode:
+        (refined_image,) = nodes.VAEDecodeTiled().decode(vae, refined_latent, 512) # using default settings
+        print(f"[Impact Pack] vae decoded (tiled) in {time.time() - start:.1f}s")
+    else:
+        try:
+            refined_image = vae.decode(refined_latent['samples'])
+        except Exception as e:
+            # usually an out-of-memory exception from the decode, so try a tiled approach
+            print(f"[Impact Pack] failed after {time.time() - start:.1f}s, doing vae.decode_tiled 64...")
+            refined_image = vae.decode_tiled(refined_latent["samples"], tile_x=64, tile_y=64, )
+        print(f"[Impact Pack] vae decoded in {time.time() - start:.1f}s")

    if detailer_hook is not None:
        refined_image = detailer_hook.post_decode(refined_image)
@@ -1349,9 +1374,14 @@ def segs_to_masklist(segs):
    return masks


-def vae_decode(vae, samples, use_tile, hook, tile_size=512):
+def vae_decode(vae, samples, use_tile, hook, tile_size=512, overlap=64):
    if use_tile:
-        pixels = nodes.VAEDecodeTiled().decode(vae, samples, tile_size)[0]
+        decoder = nodes.VAEDecodeTiled()
+        if 'overlap' in inspect.signature(decoder.decode).parameters:
+            pixels = decoder.decode(vae, samples, tile_size, overlap=overlap)[0]
+        else:
+            print(f"[Impact Pack] Your ComfyUI is outdated.")
+            pixels = decoder.decode(vae, samples, tile_size)[0]
    else:
        pixels = nodes.VAEDecode().decode(vae, samples)[0]

@@ -1361,9 +1391,14 @@ def vae_decode(vae, samples, use_tile, hook, tile_size=512):
    return pixels


-def vae_encode(vae, pixels, use_tile, hook, tile_size=512):
+def vae_encode(vae, pixels, use_tile, hook, tile_size=512, overlap=64):
    if use_tile:
-        samples = nodes.VAEEncodeTiled().encode(vae, pixels, tile_size)[0]
+        encoder = nodes.VAEEncodeTiled()
+        if 'overlap' in inspect.signature(encoder.encode).parameters:
+            samples = encoder.encode(vae, pixels, tile_size, overlap=overlap)[0]
+        else:
+            print(f"[Impact Pack] Your ComfyUI is outdated.")
+            samples = encoder.encode(vae, pixels, tile_size)[0]
    else:
        samples = nodes.VAEEncode().encode(vae, pixels)[0]

@@ -1373,12 +1408,12 @@ def vae_encode(vae, pixels, use_tile, hook, tile_size=512):
    return samples


-def latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
-    return latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
+def latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    return latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]


-def latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
-    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
+def latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)

    if save_temp_prefix is not None:
        nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
@@ -1389,15 +1424,15 @@ def latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_t
    if hook is not None:
        pixels = hook.post_upscale(pixels)

-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
+    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels


-def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
-    return latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
+def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    return latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]


-def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
-    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
+def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)

    if save_temp_prefix is not None:
        nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
@@ -1410,15 +1445,15 @@ def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use
    if hook is not None:
        pixels = hook.post_upscale(pixels)

-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
+    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels


-def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
-    return latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
+def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    return latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]


-def latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
-    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
+def latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)

    if save_temp_prefix is not None:
        nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
@@ -1441,16 +1476,16 @@ def latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upsca
    if hook is not None:
        pixels = hook.post_upscale(pixels)

-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
+    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels


 def latent_upscale_on_pixel_space_with_model(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False,
-                                             tile_size=512, save_temp_prefix=None, hook=None):
-    return latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
+                                             tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    return latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]

 def latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False,
-                                              tile_size=512, save_temp_prefix=None, hook=None):
-    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
+                                              tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
+    pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)

    if save_temp_prefix is not None:
        nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
@@ -1477,7 +1512,7 @@ def latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_mod
    if hook is not None:
        pixels = hook.post_upscale(pixels)

-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
+    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels


 class TwoSamplersForMaskUpscaler:
@@ -1647,8 +1682,14 @@ class PixelKSampleUpscaler:
                preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
                # might add capacity to set pyrUp_iters later, not needed for now though
                preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
-                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
-                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive,
+                                                                                      negative=negative,
+                                                                                      control_net=self.tile_cnet,
+                                                                                      image=preprocessed,
+                                                                                      strength=self.tile_cnet_strength,
+                                                                                      start_percent=0,
+                                                                                      end_percent=1.0,
+                                                                                      vae=self.vae)

        refined_latent = impact_sampling.impact_sample(model, seed, steps, cfg, sampler_name, scheduler,
                                                       positive, negative, upscaled_latent, denoise, scheduler_func=self.scheduler_func)
@@ -1680,6 +1721,9 @@ class PixelKSampleUpscaler:
                self.hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
                                      upscaled_latent, denoise)

+        if 'noise_mask' in samples:
+            upscaled_latent['noise_mask'] = samples['noise_mask']
+
        refined_latent = self.sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise, upscaled_images)
        return refined_latent

@@ -1709,6 +1753,9 @@ class PixelKSampleUpscaler:
                self.hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
                                      upscaled_latent, denoise)

+        if 'noise_mask' in samples:
+            upscaled_latent['noise_mask'] = samples['noise_mask']
+
        refined_latent = self.sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise, upscaled_images)
        return refined_latent

@@ -1819,13 +1866,14 @@ class ControlNetWrapper:

 class ControlNetAdvancedWrapper:
    def __init__(self, control_net, strength, start_percent, end_percent, preprocessor, prev_control_net=None,
-                 original_size=None, crop_region=None, control_image=None):
+                 original_size=None, crop_region=None, control_image=None, vae=None):
        self.control_net = control_net
        self.strength = strength
        self.preprocessor = preprocessor
        self.prev_control_net = prev_control_net
        self.start_percent = start_percent
        self.end_percent = end_percent
+        self.vae = vae

        if original_size is not None and crop_region is not None and control_image is not None:
            self.control_image = utils.tensor_resize(control_image, original_size[1], original_size[0])
@@ -1866,7 +1914,17 @@ class ControlNetAdvancedWrapper:
                                              "To use 'ControlNetAdvancedWrapper' for AnimateDiff, 'ComfyUI-Advanced-ControlNet' extension is required.")
                raise Exception("'ACN_AdvancedControlNetApply' node isn't installed.")
        else:
-            positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, self.control_net, cnet_image, self.strength, self.start_percent, self.end_percent)
+            if self.vae is not None:
+                apply_controlnet = nodes.ControlNetApplyAdvanced().apply_controlnet
+                signature = inspect.signature(apply_controlnet)
+
+                if 'vae' in signature.parameters:
+                    positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, self.control_net, cnet_image, self.strength, self.start_percent, self.end_percent, vae=self.vae)
+                else:
+                    print(f"[Impact Pack] ERROR: The ComfyUI version is outdated. VAE cannot be used in ApplyControlNet.")
+                    raise Exception("[Impact Pack] ERROR: The ComfyUI version is outdated. VAE cannot be used in ApplyControlNet.")
+            else:
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, self.control_net, cnet_image, self.strength, self.start_percent, self.end_percent)

        return positive, negative, cnet_image_list

@@ -1902,7 +1960,7 @@ class PixelTiledKSampleUpscaler:
    def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative,
                 denoise,
                 tile_width, tile_height, tiling_strategy,
-                 upscale_model_opt=None, hook_opt=None, tile_cnet_opt=None, tile_size=512, tile_cnet_strength=1.0):
+                 upscale_model_opt=None, hook_opt=None, tile_cnet_opt=None, tile_size=512, tile_cnet_strength=1.0, overlap=64):
        self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
        self.vae = vae
        self.tile_params = tile_width, tile_height, tiling_strategy
@@ -1912,6 +1970,7 @@ class PixelTiledKSampleUpscaler:
        self.tile_size = tile_size
        self.is_tiled = True
        self.tile_cnet_strength = tile_cnet_strength
+        self.overlap = overlap

    def tiled_ksample(self, latent, images):
        if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS:
@@ -1934,8 +1993,14 @@ class PixelTiledKSampleUpscaler:
                preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
                # might add capacity to set pyrUp_iters later, not needed for now though
                preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
-                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
-                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive,
+                                                                                      negative=negative,
+                                                                                      control_net=self.tile_cnet,
+                                                                                      image=preprocessed,
+                                                                                      strength=self.tile_cnet_strength,
+                                                                                      start_percent=0, end_percent=1.0,
+                                                                                      vae=self.vae)

        return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name,
                                      scheduler, positive, negative, latent, denoise)[0]
@@ -28,6 +28,7 @@ import base64
 import impact.wildcards as wildcards
 from . import hooks
 from . import utils
+import inspect


 try:
@@ -191,7 +192,7 @@ class DetailerForEach:
        return {"required": {
                    "image": ("IMAGE", ),
                    "segs": ("SEGS", ),
-                    "model": ("MODEL",),
+                    "model": ("MODEL", {"tooltip": "If the `ImpactDummyInput` is connected to the model, the inference stage is skipped."}),
                    "clip": ("CLIP",),
                    "vae": ("VAE",),
                    "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
@@ -217,6 +218,8 @@ class DetailerForEach:
                    "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                    "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                    "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                   }
                }

@@ -225,11 +228,15 @@ class DetailerForEach:

    CATEGORY = "ImpactPack/Detailer"

+    @staticmethod
+    def get_core_module():
+        return core
+
    @staticmethod
    def do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler,
                  positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard_opt=None, detailer_hook=None,
                  refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None,
-                  cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+                  cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):

        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: DetailerForEach 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.')
@@ -265,7 +272,7 @@ class DetailerForEach:
        else:
            ordered_segs = segs[1]

-        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+        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 i, seg in enumerate(ordered_segs):
@@ -293,13 +300,16 @@ class DetailerForEach:

            seg_seed = seed + i if seg_seed is None else seg_seed

-            cropped_positive = [
-                [condition, {
-                    k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
-                    for k, v in details.items()
-                }]
-                for condition, details in positive
-            ]
+            if not isinstance(positive, str):
+                cropped_positive = [
+                    [condition, {
+                        k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
+                        for k, v in details.items()
+                    }]
+                    for condition, details in positive
+                ]
+            else:
+                cropped_positive = positive

            if not isinstance(negative, str):
                cropped_negative = [
@@ -319,16 +329,22 @@ class DetailerForEach:
            if wildcard_item and wildcard_item.strip() == '[STOP]':
                break

-            enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for_bbox, max_size,
-                                                            seg.bbox, seg_seed, steps, cfg, sampler_name, scheduler,
-                                                            cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
-                                                            wildcard_opt=wildcard_item, wildcard_opt_concat_mode=wildcard_concat_mode,
-                                                            detailer_hook=detailer_hook,
-                                                            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,
-                                                            cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
-                                                            scheduler_func=scheduler_func_opt)
+            orig_cropped_image = cropped_image.clone()
+            if not (isinstance(model, str) and model == "DUMMY"):
+                enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for_bbox, max_size,
+                                                                seg.bbox, seg_seed, steps, cfg, sampler_name, scheduler,
+                                                                cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
+                                                                wildcard_opt=wildcard_item, wildcard_opt_concat_mode=wildcard_concat_mode,
+                                                                detailer_hook=detailer_hook,
+                                                                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,
+                                                                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
+                                                                scheduler_func=scheduler_func_opt, vae_tiled_encode=tiled_encode,
+                                                                vae_tiled_decode=tiled_decode)
+            else:
+                enhanced_image = cropped_image
+                cnet_pils = None

            if cnet_pils is not None:
                cnet_pil_list.extend(cnet_pils)
@@ -338,7 +354,7 @@ class DetailerForEach:
                # use image paste
                image = image.cpu()
                enhanced_image = enhanced_image.cpu()
-                tensor_paste(image, enhanced_image, (seg.crop_region[0], seg.crop_region[1]), mask)
+                tensor_paste(image, enhanced_image, (seg.crop_region[0], seg.crop_region[1]), mask)  # this code affecting to `cropped_image`.
                enhanced_list.append(enhanced_image)

                if detailer_hook is not None:
@@ -356,7 +372,7 @@ class DetailerForEach:
            else:
                new_seg_image = None

-            cropped_list.append(cropped_image)
+            cropped_list.append(orig_cropped_image) # NOTE: Don't use `cropped_image`

            new_seg = SEG(new_seg_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
            new_segs.append(new_seg)
@@ -371,13 +387,15 @@ class DetailerForEach:

    def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
             scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, cycle=1,
-             detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None,
+             tiled_encode=False, tiled_decode=False):

        enhanced_img, *_ = \
            DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps,
                                      cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
                                      force_inpaint, wildcard, detailer_hook,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, 
+                                      scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)

        return (enhanced_img, )

@@ -400,7 +418,7 @@ class DetailerForEachPipe:
                      "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
                      "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
                      "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                      "basic_pipe": ("BASIC_PIPE", ),
+                      "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
                      "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                      "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),

@@ -412,6 +430,8 @@ class DetailerForEachPipe:
                      "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                      "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                      "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                      "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                      "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                     }
                }

@@ -425,7 +445,8 @@ class DetailerForEachPipe:
    def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
             denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard,
             refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
-             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None,
+             tiled_encode=False, tiled_decode=False):

        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: DetailerForEach 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.')
@@ -443,7 +464,8 @@ class DetailerForEachPipe:
                                      force_inpaint, wildcard, detailer_hook,
                                      refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                      refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
+                                      tiled_encode=tiled_encode, tiled_decode=tiled_decode)

        # set fallback image
        if len(cnet_pil_list) == 0:
@@ -457,7 +479,7 @@ class FaceDetailer:
    def INPUT_TYPES(s):
        return {"required": {
                     "image": ("IMAGE", ),
-                     "model": ("MODEL",),
+                     "model": ("MODEL", {"tooltip": "If the `ImpactDummyInput` is connected to the model, the inference stage is skipped."}),
                     "clip": ("CLIP",),
                     "vae": ("VAE",),
                     "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
@@ -500,6 +522,8 @@ class FaceDetailer:
                    "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                    "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                    "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                }}

    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE")
@@ -517,7 +541,7 @@ class FaceDetailer:
                     sam_mask_hint_use_negative, drop_size,
                     bbox_detector, segm_detector=None, sam_model_opt=None, wildcard_opt=None, detailer_hook=None,
                     refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, cycle=1,
-                     inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+                     inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):

        # make default prompt as 'face' if empty prompt for CLIPSeg
        bbox_detector.setAux('face')
@@ -549,7 +573,8 @@ class FaceDetailer:
                                          refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                          refiner_clip=refiner_clip, refiner_positive=refiner_positive,
                                          refiner_negative=refiner_negative,
-                                          cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                          cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
+                                          scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)
        else:
            enhanced_img = image
            cropped_enhanced = []
@@ -575,7 +600,8 @@ class FaceDetailer:
             bbox_threshold, bbox_dilation, bbox_crop_factor,
             sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
             sam_mask_hint_use_negative, drop_size, bbox_detector, wildcard, cycle=1,
-             sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0,
+             scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):

        result_img = None
        result_mask = None
@@ -593,7 +619,8 @@ class FaceDetailer:
                bbox_threshold, bbox_dilation, bbox_crop_factor,
                sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
                sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector_opt, sam_model_opt, wildcard, detailer_hook,
-                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
+                tiled_encode=tiled_encode, tiled_decode=tiled_decode)

            result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
            result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
@@ -981,6 +1008,7 @@ class PixelTiledKSampleUpscalerProvider:
                        "pk_hook_opt": ("PK_HOOK", ),
                        "tile_cnet_opt": ("CONTROL_NET", ),
                        "tile_cnet_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
                    }
                }

@@ -990,11 +1018,11 @@ class PixelTiledKSampleUpscalerProvider:
    CATEGORY = "ImpactPack/Upscale"

    def doit(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, tile_width, tile_height, tiling_strategy, upscale_model_opt=None,
-             pk_hook_opt=None, tile_cnet_opt=None, tile_cnet_strength=1.0):
+             pk_hook_opt=None, tile_cnet_opt=None, tile_cnet_strength=1.0, overlap=64):
        if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS:
            upscaler = core.PixelTiledKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
                                                      tile_width, tile_height, tiling_strategy, upscale_model_opt, pk_hook_opt, tile_cnet_opt,
-                                                      tile_size=max(tile_width, tile_height), tile_cnet_strength=tile_cnet_strength)
+                                                      tile_size=max(tile_width, tile_height), tile_cnet_strength=tile_cnet_strength, overlap=overlap)
            return (upscaler, )
        else:
            utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_TiledKSampler',
@@ -1249,6 +1277,7 @@ class IterativeLatentUpscale:
            upscale_factor_unit = max(0, (upscale_factor - 1.0) / steps)

        current_latent = samples
+        noise_mask = current_latent.get('noise_mask')
        scale = 1

        for i in range(steps-1):
@@ -1263,6 +1292,8 @@ class IterativeLatentUpscale:
            print(f"IterativeLatentUpscale[{i+1}/{steps}]: {new_w:.1f}x{new_h:.1f} (scale:{scale:.2f}) ")
            step_info = i, steps
            current_latent = upscaler.upscale_shape(step_info, current_latent, new_w, new_h, temp_prefix)
+            if noise_mask is not None:
+                current_latent['noise_mask'] = noise_mask

        if scale < upscale_factor:
            new_w = w*upscale_factor
@@ -1274,7 +1305,7 @@ class IterativeLatentUpscale:

        core.update_node_status(unique_id, "", None)

-        return (current_latent, upscaler.vae)
+        return current_latent, upscaler.vae


 class IterativeImageUpscale:
@@ -1304,7 +1335,11 @@ class IterativeImageUpscale:

        core.update_node_status(unique_id, "VAEEncode (first)", 0)
        if upscaler.is_tiled:
-            latent = nodes.VAEEncodeTiled().encode(vae, pixels, upscaler.tile_size)[0]
+            encoder = nodes.VAEEncodeTiled()
+            if 'overlap' in inspect.signature(encoder.encode).parameters:
+                latent = encoder.encode(vae, pixels, upscaler.tile_size, overlap=upscaler.overlap)[0]
+            else:
+                latent = encoder.encode(vae, pixels, upscaler.tile_size)[0]
        else:
            latent = nodes.VAEEncode().encode(vae, pixels)[0]

@@ -1326,7 +1361,7 @@ class FaceDetailerPipe:
    def INPUT_TYPES(s):
        return {"required": {
                    "image": ("IMAGE", ),
-                    "detailer_pipe": ("DETAILER_PIPE",),
+                    "detailer_pipe": ("DETAILER_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the detailer_pipe, the inference stage is skipped."}),
                    "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}),
@@ -1360,6 +1395,8 @@ class FaceDetailerPipe:
                    "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                    "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                    "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                   }
                }

@@ -1374,7 +1411,8 @@ class FaceDetailerPipe:
             denoise, feather, noise_mask, force_inpaint, bbox_threshold, bbox_dilation, bbox_crop_factor,
             sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion,
             sam_mask_hint_threshold, sam_mask_hint_use_negative, drop_size, refiner_ratio=None,
-             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None,
+             tiled_encode=False, tiled_decode=False):

        result_img = None
        result_mask = None
@@ -1397,7 +1435,8 @@ class FaceDetailerPipe:
                sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector, sam_model_opt, wildcard, detailer_hook,
                refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
-                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
+                tiled_encode=tiled_encode, tiled_decode=tiled_decode)

            result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
            result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
@@ -1531,7 +1570,7 @@ class DetailerForEachTest(DetailerForEach):

    def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
             scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, detailer_hook=None,
-             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):

        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: DetailerForEach 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.')
@@ -1540,7 +1579,8 @@ class DetailerForEachTest(DetailerForEach):
            DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps,
                                      cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
                                      force_inpaint, wildcard, detailer_hook,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, 
+                                      scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)

        # set fallback image
        if len(cropped) == 0:
@@ -1569,7 +1609,8 @@ class DetailerForEachTestPipe(DetailerForEachPipe):

    def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
             denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard, cycle=1,
-             refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0,
+             scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):

        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: DetailerForEach 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.')
@@ -1588,7 +1629,8 @@ class DetailerForEachTestPipe(DetailerForEachPipe):
                                      refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                      refiner_clip=refiner_clip, refiner_positive=refiner_positive,
                                      refiner_negative=refiner_negative,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
+                                      scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)

        # set fallback image
        if len(cropped) == 0:
@@ -1826,6 +1868,135 @@ def get_file_item(base_type, path):
           }


+class MaskRectArea:
+    # Creates a rectangle mask using percentage.
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+            },
+            "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"}
+        }
+
+    RETURN_TYPES = ("MASK",)
+    
+    CATEGORY = "ImpactPack/Operation"
+    FUNCTION = "create_mask"
+
+    def create_mask(self, extra_pnginfo, unique_id, **kwargs):
+        # search for node
+        node_found = False
+        for node in extra_pnginfo["workflow"]["nodes"]:
+            if node["id"] == int(unique_id):
+                min_x = node["properties"].get("x", 0) / 100
+                min_y = node["properties"].get("y", 0) / 100
+                width = node["properties"].get("w", 0) / 100
+                height = node["properties"].get("h", 0) / 100
+                blur_radius = node["properties"].get("blur_radius", 0)
+                node_found = True
+                break
+                
+        if not node_found:
+            raise ValueError(f"No node found with unique_id {unique_id}.")
+                
+        # Create a mask with standard resolution (e.g., 512x512)
+        resolution = 512
+        mask = torch.zeros((resolution, resolution))
+
+        # Calculate pixel coordinates
+        min_x_px = int(min_x * resolution)
+        min_y_px = int(min_y * resolution)
+        max_x_px = int((min_x + width) * resolution)
+        max_y_px = int((min_y + height) * resolution)
+
+        # Draw the rectangle on the mask
+        mask[min_y_px:max_y_px, min_x_px:max_x_px] = 1
+
+        # Apply blur if the radii are greater than 0
+        if blur_radius > 0:
+            dx = blur_radius * 2 + 1
+            dy = blur_radius * 2 + 1
+
+            # Convert the mask to a format compatible with OpenCV (numpy array)
+            mask_np = mask.cpu().numpy().astype("float32")
+
+            # Apply Gaussian Blur
+            blurred_mask = cv2.GaussianBlur(mask_np, (dx, dy), 0)
+
+            # Convert back to tensor
+            mask = torch.from_numpy(blurred_mask)
+
+        # Return the mask as a tensor with an additional channel
+        return (mask.unsqueeze(0),)
+
+
+class MaskRectAreaAdvanced:
+    # Creates a rectangle mask using pixels relative to image size.
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+            },
+            "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"}
+        }
+
+    RETURN_TYPES = ("MASK",)
+    
+    CATEGORY = "ImpactPack/Operation"
+    FUNCTION = "create_mask_advanced"
+
+    def create_mask_advanced(self, extra_pnginfo, unique_id, **kwargs):
+        # search for node
+        node_found = False
+        for node in extra_pnginfo["workflow"]["nodes"]:
+            if node["id"] == int(unique_id):
+                min_x = node["properties"]["x"]
+                min_y = node["properties"]["y"]
+                width = node["properties"]["w"]
+                height = node["properties"]["h"]
+                image_width = node["properties"]["width"]
+                image_height = node["properties"]["height"]
+                blur_radius = node["properties"]["blur_radius"]
+                node_found = True
+                break
+                
+        if not node_found:
+            raise ValueError(f"No node found with unique_id {unique_id}.")
+
+        # Calculate maximum coordinates
+        max_x = min_x + width
+        max_y = min_y + height
+
+        # Create a mask with the image dimensions
+        mask = torch.zeros((image_height, image_width))
+
+        # Draw the rectangle on the mask
+        mask[int(min_y):int(max_y), int(min_x):int(max_x)] = 1
+
+        # Apply blur if the radii are greater than 0
+        if blur_radius > 0:
+            dx = blur_radius * 2 + 1
+            dy = blur_radius * 2 + 1
+
+            # Convert the mask to a format compatible with OpenCV (numpy array)
+            mask_np = mask.cpu().numpy().astype("float32")
+
+            # Apply Gaussian Blur
+            blurred_mask = cv2.GaussianBlur(mask_np, (dx, dy), 0)
+
+            # Convert back to tensor
+            mask = torch.from_numpy(blurred_mask)
+
+        # Return the mask as a tensor with an additional channel
+        return (mask.unsqueeze(0),)
+
+
 class ImageReceiver:
    @classmethod
    def INPUT_TYPES(s):
@@ -2023,7 +2194,12 @@ class LatentSender(nodes.SaveLatent):
                             "samples": ("LATENT", ),
                             "filename_prefix": ("STRING", {"default": "latents/LatentSender"}),
                             "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
-                             "preview_method": (["Latent2RGB-SDXL", "Latent2RGB-SD15", "TAESDXL", "TAESD15"],)
+                             "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"],)
                             },
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                }
@@ -2065,14 +2241,33 @@ class LatentSender(nodes.SaveLatent):
        if preview_method == "Latent2RGB-SD15":
            latent_format = latent_formats.SD15()
            method = LatentPreviewMethod.Latent2RGB
-        elif preview_method == "TAESD15":
+        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] LatentSender: '{preview_method}' is unsupported preview method.")
            latent_format = latent_formats.SD15()
-            method = LatentPreviewMethod.TAESD
-        elif preview_method == "TAESDXL":
-            latent_format = latent_formats.SDXL()
-            method = LatentPreviewMethod.TAESD
-        else:  # preview_method == "Latent2RGB-SDXL"
-            latent_format = latent_formats.SDXL()
            method = LatentPreviewMethod.Latent2RGB

        previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method)
@@ -2146,16 +2341,19 @@ class ImpactWildcardProcessor:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
-                        "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
-                        "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
-                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed"}),
-                        "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                        "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
+                        "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardProcessor' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
+                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\nFixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
+                        "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}),
                        "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
                    },
                }

    CATEGORY = "ImpactPack/Prompt"

+    DESCRIPTION = ("The 'ImpactWildcardProcessor' processes text prompts written in wildcard syntax and outputs the processed text prompt.\n\n"
+                   "TIP: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'Fixed'.")
+
    RETURN_TYPES = ("STRING", )
    FUNCTION = "doit"

@@ -2174,17 +2372,22 @@ class ImpactWildcardEncode:
        return {"required": {
                        "model": ("MODEL",),
                        "clip": ("CLIP",),
-                        "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
-                        "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
-                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed"}),
+                        "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
+                        "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardEncode' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
+                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
+                                                                                                                       "Fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
                        "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"], ),
-                        "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                        "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}),
                    },
                }

    CATEGORY = "ImpactPack/Prompt"

+    DESCRIPTION = ("The 'ImpactWildcardEncode' node processes text prompts written in wildcard syntax and outputs them as conditioning. It also supports LoRA syntax, with the applied LoRA reflected in the model's output.\n\n"
+                   "TIP1: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'Fixed'.\n"
+                   "TIP2: If the 'Inspire Pack' is installed, LBW(LoRA Block Weight) syntax can also be applied.")
+
    RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING", "STRING")
    RETURN_NAMES = ("model", "clip", "conditioning", "populated_text")
    FUNCTION = "doit"
@@ -2209,7 +2412,7 @@ class ImpactSchedulerAdapter:
    def INPUT_TYPES(s):
        return {"required": {
            "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"defaultInput": True, }),
-            "extra_scheduler": (['None', 'AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]'],),
+            "extra_scheduler": (['None', 'AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]'],),
        }}

    CATEGORY = "ImpactPack/Util"
@@ -27,6 +27,8 @@ def calculate_sigmas(model, sampler, scheduler, steps):
        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]
    else:
        sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps)

@@ -22,37 +22,7 @@ import comfy
 from io import BytesIO
 import random
 from server import PromptServer
-
-
-@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)
+import logging


 sam_predictor = None
@@ -110,7 +80,7 @@ async def sam_prepare(request):

        model_name = os.path.join(impact_pack.model_path, "sams", model_name)

-        print(f"[INFO] ComfyUI-Impact-Pack: Loading SAM model '{impact_pack.model_path}'")
+        logging.info(f"[Impact Pack] Loading SAM model '{impact_pack.model_path}'")

        filename, image_dir = folder_paths.annotated_filepath(data["filename"])

@@ -126,7 +96,7 @@ async def sam_prepare(request):
        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_name, filename,))
        thread.start()

-        print(f"[INFO] ComfyUI-Impact-Pack: SAM model loaded. ")
+        logging.info("[Impact Pack] SAM model loaded. ")
    return web.Response(status=200)


@@ -138,7 +108,7 @@ 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")


@PromptServer.instance.routes.post("/sam/detect")
@@ -346,6 +316,8 @@ 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:
@@ -353,20 +325,24 @@ def onprompt_for_switch(json_data):

        cls = v['class_type']
        if cls == 'ImpactInversedSwitch':
+            # 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:
-                        print(f"\n##### ##### #####\n[WARN] {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")
+                        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' 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']:
@@ -375,10 +351,14 @@ 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 if 'select_on_prompt' is selected.\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']
@@ -402,6 +382,11 @@ 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]}"
@@ -418,6 +403,11 @@ def onprompt_for_switch(json_data):
        for kk in disable_targets:
            del v['inputs'][kk]

+    # 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):
    detected_pickers = set()

@@ -440,9 +430,14 @@ 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']
@@ -496,7 +491,7 @@ def onprompt_populate_wildcards(json_data):
                            if not isinstance(input_seed, int):
                                continue
                        else:
-                            print(f"[Impact Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
+                            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
@@ -559,7 +554,7 @@ def onprompt(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

@@ -272,6 +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):
@@ -673,7 +691,7 @@ class ImpactControlBridge:
    def doit(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None):
        global error_skip_flag

-        if core.is_execution_model_version_supported:
+        if core.is_execution_model_version_supported():
            from comfy_execution.graph import ExecutionBlocker
        else:
            print("[Impact Pack] ImpactControlBridge: ComfyUI is outdated. The 'Stop' behavior cannot function properly.")
@@ -748,6 +766,29 @@ class ImpactExecutionOrderController:
        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


@@ -1,5 +1,7 @@
 import folder_paths
 import impact.wildcards
+from impact.utils import any_typ
+

 class ToDetailerPipe:
    @classmethod
@@ -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):
@@ -38,7 +38,7 @@ class SEGSDetailer:
                     "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
                     "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
                     "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                     "basic_pipe": ("BASIC_PIPE",),
+                     "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}),
                     "batch_size": ("INT", {"default": 1, "min": 1, "max": 100}),

@@ -76,7 +76,7 @@ class SEGSDetailer:
        new_segs = []
        cnet_pil_list = []

-        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+        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 i in range(batch_size):
@@ -113,13 +113,17 @@ class SEGSDetailer:
                    for condition, details in negative
                ]

-                enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for, max_size,
-                                                                seg.bbox, seed, steps, cfg, sampler_name, scheduler,
-                                                                cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
-                                                                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, cycle=cycle,
-                                                                inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
+                if not (isinstance(model, str) and model == "DUMMY"):
+                    enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for, max_size,
+                                                                    seg.bbox, seed, steps, cfg, sampler_name, scheduler,
+                                                                    cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
+                                                                    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, cycle=cycle,
+                                                                    inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
+                else:
+                    enhanced_image = cropped_image
+                    cnet_pils = None

                if cnet_pils is not None:
                    cnet_pil_list.extend(cnet_pils)
@@ -704,6 +708,68 @@ class SEGSToMaskBatch:
        return (mask_batch,)


+class SEGSMerge:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                     "segs": ("SEGS", ),
+                     },
+                }
+
+    RETURN_TYPES = ("SEGS",)
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    DESCRIPTION = "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."
+
+    def doit(self, segs):
+        crop_left = sys.maxsize
+        crop_right = 0
+        crop_top = sys.maxsize
+        crop_bottom = 0
+
+        bbox_left = sys.maxsize
+        bbox_right = 0
+        bbox_top = sys.maxsize
+        bbox_bottom = 0
+
+        min_confidence = 1.0
+
+        for seg in segs[1]:
+            cx1 = seg.crop_region[0]
+            cy1 = seg.crop_region[1]
+            cx2 = seg.crop_region[2]
+            cy2 = seg.crop_region[3]
+
+            bx1 = seg.bbox[0]
+            by1 = seg.bbox[1]
+            bx2 = seg.bbox[2]
+            by2 = seg.bbox[3]
+
+            crop_left = min(crop_left, cx1)
+            crop_top = min(crop_top, cy1)
+            crop_right = max(crop_right, cx2)
+            crop_bottom = max(crop_bottom, cy2)
+
+            bbox_left = min(bbox_left, bx1)
+            bbox_top = min(bbox_top, by1)
+            bbox_right = max(bbox_right, bx2)
+            bbox_bottom = max(bbox_bottom, by2)
+
+            min_confidence = min(min_confidence, seg.confidence)
+        
+        combined_mask = core.segs_to_combined_mask(segs)
+        cropped_mask = combined_mask[crop_top:crop_bottom, crop_left:crop_right]
+        cropped_mask = cropped_mask.unsqueeze(0)
+
+        crop_region = [crop_left, crop_top, crop_right, crop_bottom]
+        bbox = [bbox_left, bbox_top, bbox_right, bbox_bottom]
+
+        seg = SEG(None, cropped_mask, min_confidence, crop_region, bbox, 'merged', None)
+        return ((segs[0], [seg]),)
+        
+
 class SEGSConcat:
    @classmethod
    def INPUT_TYPES(s):
@@ -834,7 +900,7 @@ class From_SEG_ELT_bbox:
    CATEGORY = "ImpactPack/Util"

    def doit(self, bbox):
-        return bbox
+        return [int(c) for c in bbox]


 class From_SEG_ELT_crop_region:
@@ -1039,10 +1105,10 @@ class SEG_ELT_BBOX_ScaleBy:
        x1, y1, x2, y2 = x1-cx1, y1-cy1, x2-cx1, y2-cy1
        h, w = mask.shape

-        x1 = min(w-1, max(0, x1))
-        x2 = min(w-1, max(0, x2))
-        y1 = min(h-1, max(0, y1))
-        y2 = min(h-1, max(0, y2))
+        x1 = int(min(w-1, max(0, x1)))
+        x2 = int(min(w-1, max(0, x2)))
+        y1 = int(min(h-1, max(0, y1)))
+        y2 = int(min(h-1, max(0, y2)))

        mask_cropped = mask.copy()
        mask_cropped[:, :x1] = 0  # zero fill left side
@@ -1300,6 +1366,8 @@ class ControlNetApplySEGS:
    RETURN_TYPES = ("SEGS",)
    FUNCTION = "doit"

+    DEPRECATED = True
+
    CATEGORY = "ImpactPack/Util"

    @staticmethod
@@ -1327,7 +1395,8 @@ class ControlNetApplyAdvancedSEGS:
                    },
                "optional": {
                    "segs_preprocessor": ("SEGS_PREPROCESSOR",),
-                    "control_image": ("IMAGE",)
+                    "control_image": ("IMAGE",),
+                    "vae": ("VAE",)
                    }
                }

@@ -1337,13 +1406,13 @@ class ControlNetApplyAdvancedSEGS:
    CATEGORY = "ImpactPack/Util"

    @staticmethod
-    def doit(segs, control_net, strength, start_percent, end_percent, segs_preprocessor=None, control_image=None):
+    def doit(segs, control_net, strength, start_percent, end_percent, segs_preprocessor=None, control_image=None, vae=None):
        new_segs = []

        for seg in segs[1]:
            control_net_wrapper = core.ControlNetAdvancedWrapper(control_net, strength, start_percent, end_percent, segs_preprocessor,
                                                                 seg.control_net_wrapper, original_size=segs[0], crop_region=seg.crop_region,
-                                                                 control_image=control_image)
+                                                                 control_image=control_image, vae=vae)
            new_seg = SEG(seg.cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, control_net_wrapper)
            new_segs.append(new_seg)

@@ -1413,8 +1482,6 @@ class SEGSPicker:

    RETURN_TYPES = ("SEGS", )

-    OUTPUT_NODE = True
-
    FUNCTION = "doit"

    CATEGORY = "ImpactPack/Util"
@@ -106,7 +106,12 @@ def img2img_segs(image, model, clip, vae, seed, steps, cfg, sampler_name, schedu

    # prepare mask
    if noise_mask is not None and inpaint_model:
-        positive, negative, latent_image = nodes.InpaintModelConditioning().encode(positive, negative, image, vae, noise_mask)
+        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:
@@ -17,9 +17,16 @@ class GeneralSwitch:
        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' and stack[3].function == 'add_node':
-                for x in range(2, 200):
-                    dyn_inputs[f"input{x}"] = (any_typ, {"lazy": True})
+            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"}),
@@ -45,7 +52,10 @@ class GeneralSwitch:

        print(f"SELECTED: {input_name}")

-        return [input_name]
+        if input_name in kwargs:
+            return [input_name]
+        else:
+            return []

    @staticmethod
    def doit(*args, **kwargs):
@@ -163,7 +173,7 @@ class GeneralInversedSwitch:
    CATEGORY = "ImpactPack/Util"

    def doit(self, select, prompt, unique_id, input, **kwargs):
-        if core.is_execution_model_version_supported:
+        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.")
@@ -181,7 +191,7 @@ class GeneralInversedSwitch:
        for i in range(0, cnt + 1):
            if select == i+1:
                res.append(input)
-            elif core.is_execution_model_version_supported:
+            elif core.is_execution_model_version_supported():
                res.append(ExecutionBlocker(None))
            else:
                res.append(None)
@@ -366,7 +376,7 @@ class ImageListToImageBatch:

    def doit(self, images):
        if len(images) <= 1:
-            return (images,)
+            return (images[0],)
        else:
            image1 = images[0]
            for image2 in images[1:]:
@@ -392,6 +402,30 @@ class ImageBatchToImageList:
        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):
@@ -470,7 +504,7 @@ class MakeMaskBatch:
    def doit(self, **kwargs):
        mask1 = kwargs['mask1']
        del kwargs['mask1']
-        masks = [utils.make_3d_mask(value) for value in kwargs.values()]
+        masks = [make_3d_mask(value) for value in kwargs.values()]

        if len(masks) == 0:
            return (mask1,)
@@ -492,6 +526,9 @@ class ReencodeLatent:
                        "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"
@@ -499,14 +536,22 @@ class ReencodeLatent:
    RETURN_TYPES = ("LATENT", )
    FUNCTION = "doit"

-    def doit(self, samples, tile_mode, input_vae, output_vae, tile_size=512):
+    def doit(self, samples, tile_mode, input_vae, output_vae, tile_size=512, overlap=64):
        if tile_mode in ["Both", "Decode(input) only"]:
-            pixels = nodes.VAEDecodeTiled().decode(input_vae, samples, tile_size)[0]
+            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"]:
-            return nodes.VAEEncodeTiled().encode(output_vae, pixels, tile_size)
+            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)

@@ -7,6 +7,7 @@ import nodes
 from . import config
 from PIL import Image
 import comfy
+import time


 class TensorBatchBuilder:
@@ -501,15 +502,21 @@ def crop_image(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, :]

-    vae_encode = nodes.VAEEncode()
+    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")

-    return vae_encode.encode(vae, pixels)[0]
+    return encoded


 def empty_pil_tensor(w=64, h=64):
@@ -12,7 +12,7 @@ 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)
+RE_WildCardQuantifier = re.compile(r"(?P<quantifier>\d+)#__(?P<keyword>[\w.\-+/*\\]+?)__", re.IGNORECASE)
 wildcard_lock = threading.Lock()
 wildcard_dict = {}

@@ -58,11 +58,11 @@ def read_wildcard_dict(wildcard_path):
                try:
                    with open(file_path, 'r', encoding="ISO-8859-1") as f:
                        lines = f.read().splitlines()
-                        wildcard_dict[key] = lines
+                        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
+                        wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
            elif file.endswith('.yaml'):
                file_path = os.path.join(root, file)

@@ -121,7 +121,7 @@ def process(text, seed=None):
            select_sep = ' '
            range_pattern = r'(\d+)(-(\d+))?'
            range_pattern2 = r'-(\d+)'
-            wildcard_pattern = r"__([\w.\-+/*\\]+)__"
+            wildcard_pattern = r"__([\w.\-+/*\\]+?)__"

            if len(multi_select_pattern) > 1:
                r = re.match(range_pattern, options[0])
@@ -150,7 +150,7 @@ def process(text, seed=None):
                        matches = re.findall(wildcard_pattern, multi_select_pattern[1])
                        if len(options) == 1 and matches:
                            # count$$<single wildcard>
-                            options = local_wildcard_dict.get(matches[0])
+                            options = get_wildcard_options(multi_select_pattern[1])
                        else:
                            # count$$opt1|opt2|...
                            options[0] = multi_select_pattern[1]
@@ -199,8 +199,36 @@ 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):
-        pattern = r"__([\w.\-+/*\\]+)__"
+        pattern = r"__([\w.\-+/*\\]+?)__"
        matches = re.findall(pattern, string)

        replacements_found = False
@@ -259,7 +287,7 @@ 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):
@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-impact-pack"
-description = "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
-version = "7.5.1"
+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 = "8.4.1"
 license = { file = "LICENSE.txt" }
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]

@@ -3,7 +3,7 @@ scikit-image
 piexif
 transformers
 opencv-python-headless
-GitPython
 scipy>=1.11.4
 numpy<2
-dill
+dill
+matplotlib
Author	SHA1	Message	Date
Dr.Lt.Data	d8738eee2f	hotfix: front - robustness fix https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/886#issuecomment-2600716281	2025-01-19 18:08:59 +09:00
Dr.Lt.Data	c397c68ca3	version marker	2025-01-19 03:17:25 +09:00
Alex Butler	0e0722ec08	Add tiled vae encoding/decoding toggle to detailer nodes (#883 ) * detailers: support optional tiled vae encoding & decoding * Remove start encoding/decoding logging	2025-01-19 03:16:56 +09:00
Dr.Lt.Data	70d0540895	improved: tooltips	2025-01-14 00:47:27 +09:00
Dr.Lt.Data	7330577a0f	version marker	2025-01-10 00:49:38 +09:00
Symbiomatrix	b1d760291f	Dummy shortcircuit. (#880 )	2025-01-10 00:49:06 +09:00
Dr.Lt.Data	12e838a320	improved: refresh wildcard Now refresh feature is moved to menu item. `Edit > Impact: Refresh Wildcard`	2025-01-06 22:59:33 +09:00
Dr.Lt.Data	8e8621df49	update .gitignore	2024-12-30 02:32:46 +09:00
Dr.Lt.Data	cdb7b4d3b0	fix: compatibility patch for VAEEncodeTiled's overlap https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/863#issuecomment-2560861500	2024-12-25 16:14:51 +09:00
Dr.Lt.Data	21eecb0c03	fix: install script - folder_paths error	2024-12-22 18:40:09 +09:00
Dr.Lt.Data	9402ecf4f9	refactor: use ControlNetApplyAdvanced instead of legacy ControlNetApply	2024-12-21 18:21:58 +09:00
Dr.Lt.Data	c21b361e2a	fix: VAEEncodeTiled - overlap compatibility patch https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/863	2024-12-21 17:41:24 +09:00
Dr.Lt.Data	8f04714145	fixed: inversed_switch - If the `select` is pointing to an out-of-range output, remove the input connection instead of the output. https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/851#issuecomment-2543353847	2024-12-19 23:29:34 +09:00
Dr.Lt.Data	a311f9278d	fixed: sam detector - invalid position https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/853	2024-12-19 23:06:49 +09:00
Dr.Lt.Data	0ec21f1a4b	version marker	2024-12-19 22:03:11 +09:00
Andrés Zsögön	40a3df3acd	Added Mask Rect Area and Mask Rect Area (Advanced) nodes (#861 ) Added nodes for simple rectangular masks of arbitrary size and position with built-in preview canvas. Closes issue #856.	2024-12-19 22:02:40 +09:00
Dr.Lt.Data	9ba92862c4	update README.md	2024-12-17 11:03:02 +09:00
Dr.Lt.Data	2c2e148205	update README.md	2024-12-17 10:58:17 +09:00
Dr.Lt.Data	ff9c30787c	Robustly remove legacy subpack directories.	2024-12-11 23:24:00 +09:00
Dr.Lt.Data	64f709741a	The Impact Subpack must now be installed separately.	2024-12-11 21:56:33 +09:00
Dr.Lt.Data	455e993354	Merge branch 'Main' into refactor/subpack	2024-12-11 21:54:51 +09:00
Dr.Lt.Data	ce23d436fc	improve: switch - disconnect output connection instead of input connection if select_on_prompt	2024-12-11 05:25:01 +09:00
Dr.Lt.Data	5f630466fb	fixed: block if empty slot is selected and select_on_prompt https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/846	2024-12-11 03:36:12 +09:00
Dr.Lt.Data	81045cc845	fixed: remove from lazy input if empty slot is selected. https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/846	2024-12-11 03:25:29 +09:00
Ikko Eltociear Ashimine	20f2cac6f0	docs: update README.md (#844 ) minor fix	2024-12-06 05:10:09 +09:00
Dr.Lt.Data	348c3dcb6b	improve: PreviewBridgeLatent - add Latent2RGB-LTXV improve: LatentSender - add multiple latent formats	2024-12-01 02:02:05 +09:00
Dr.Lt.Data	1ec3037613	FIXED: wildcard, lora selector bug from https://github.com/ltdrdata/ComfyUI-Impact-Pack/pull/838/files) FIXED: SAM Detector https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/836	2024-11-30 22:48:00 +09:00
filtered	d4136bc955	Prefer widget.callback over stack trace check (#838 ) * Prefer widget.callback over stack trace check Update to LiteGraph included a rename of a local function. This value was being checked to prevent unwanted value sets. This has been replaced with widget callback(), which for combo boxes is always called when a value is clicked, regardless of any change. * nit * Fix error caused by bug in litegraph Known issue w/widget.callback and setter/getter.	2024-11-30 21:47:22 +09:00
Dr.Lt.Data	c9b3aecd1a	fix: install script exception handling for torchvision.download_url	2024-11-26 18:33:27 +09:00
Dr.Lt.Data	a2bb6f7c91	feat: `LTXV[default]` scheduler is added	2024-11-26 01:16:57 +09:00
Dr.Lt.Data	e1e408d8c9	improve: add description to wildcard nodes	2024-11-22 21:37:23 +09:00
Jonathan Nogueira	f7686845c6	Update core.py - save mask when edited (#817 ) * Update core.py - save mask when edited In order to facilitate the `restore_mask` functionality better, one needs to save the mask to the cache when the mask is edited. * Update core.py - add missing unsqueeze(0) mask cache was missing unsqueeze function, giving the resulting mask the wrong size.	2024-11-22 13:41:55 +09:00
FennelFetish	37f465d4a9	Fixes call to make_3d_mask in MakeMaskBatch (#823 )	2024-11-22 13:28:50 +09:00
Dr.Lt.Data	48b4254e81	fix: compatibility patch for updated ComfyUI https://github.com/comfyanonymous/ComfyUI/commit/156a28786be9ba6352061090096462bfb1b485bb https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/821	2024-11-20 09:46:42 +09:00
Dr.Lt.Data	24de5a846b	hotfix: failing of ultralytics due to security policy	2024-11-09 19:46:33 +09:00
Dr.Lt.Data	6fe85ed6bf	version marker	2024-11-08 22:02:22 +09:00
H.D.Tài	216f4660e1	Typecast possible float values in SEG_ELT to int (#811 )	2024-11-08 22:01:28 +09:00
Dr.Lt.Data	314b676cc3	FIX: compatibility patch latest ComfyUI update. https://github.com/comfyanonymous/ComfyUI/commit/b49616f9511c57c8d54c4032e305d72352ac4ff5 https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/812	2024-11-08 21:58:06 +09:00
Dr.Lt.Data	cd89590a7d	FEAT: `List Bridge` node is added.	2024-11-07 23:33:59 +09:00
Vedat Baday	dc70f40eff	perf: cache pip installed packages (#803 )	2024-11-03 04:13:37 +09:00
Dr.Lt.Data	66bf101341	Merge branch 'Main' into refactor/subpack	2024-11-02 05:39:28 +09:00
Vedat Baday	825296c3e0	fix: install manual (#794 )	2024-11-02 05:39:08 +09:00
Dr.Lt.Data	5c0535942a	Merge branch 'Main' into refactor/subpack	2024-11-01 02:31:49 +09:00
Dr.Lt.Data	bf0e86a10d	FIXED: ImageListToImageBatch - make sure output is not list when item size is less than 2 https://github.com/comfyanonymous/ComfyUI/issues/5039	2024-11-01 02:28:15 +09:00
Dr.Lt.Data	6be257254e	Separate Impact Pack and Impact Subpack	2024-11-01 02:25:14 +09:00
Dr.Lt.Data	727295b52e	fix: wildcard bugs - cannot remove comment - cannot populate <single-wildcard-pattern> if `{count$$<single-wildcard-pattern>}` https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/789 https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/788	2024-10-24 00:25:57 +09:00
Dr.Lt.Data	6c23f7691d	fixed: wildcard - invalid regex syntax https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/790	2024-10-22 12:20:59 +09:00
Dr.Lt.Data	6248f31402	improved: lora in wildcard - Allows omitting leading zero when specifying floating-point values less than 1. e.g. .1 instead of 0.1 https://github.com/ltdrdata/ComfyUI-Inspire-Pack/issues/178	2024-10-21 12:44:00 +09:00
Dr.Lt.Data	cb0b652703	REFACTOR: GeneralSwitch - better implementation	2024-10-16 21:42:41 +09:00
Dr.Lt.Data	9c4fa5fb27	FIXED: switch any <-> inverse switch disconnection issue https://github.com/ltdrdata/ComfyUI-extension-tutorials/issues/58#issuecomment-2415330114	2024-10-16 20:27:33 +09:00
Dr.Lt.Data	759b4d0dc9	fix: invalid version check code https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/782	2024-10-15 23:34:25 +09:00
Dr.Lt.Data	a65fd56b7f	add install-manual.py for manual installation https://github.com/ltdrdata/ComfyUI-Impact-Pack/pull/773 https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/689	2024-10-13 19:28:20 +09:00
Dr.Lt.Data	47d981eca2	feat: preview bridge - `restore_mask` feature is added	2024-10-13 19:23:51 +09:00
Dr.Lt.Data	b2f382776f	FIXED: ControlNetApplySEGS - compatiblity patch - https://github.com/ltdrdata/ComfyUI/commit/7a415f47a90915d755767c29e9f5bcc157fedefe - Make existing ControlNetApply (SEGS) deprecated - Rename `ControlNetApplyAdvanced (SEGS)` to `ControlNetApply (SEGS)` https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/764	2024-10-02 01:43:33 +09:00
Dr.Lt.Data	0e4e439d39	feat: SEGS Merge	2024-09-27 23:17:35 +09:00
RyuukeisyouandRyuukeisyou	18d25a29a0	add ImpactBoolean (#759 ) Co-authored-by: Ryuukeisyou <jingxiang.liu@live.com>	2024-09-27 22:55:46 +09:00
Dr.Lt.Data	2b724e5ed2	version marker	2024-09-25 11:03:07 +09:00
Dr.Lt.Data	96cc242f78	FIXED: torch.load issue https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/754	2024-09-25 10:39:14 +09:00
Dr.Lt.Data	28267da071	fix: new front compatibilty issue https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/755	2024-09-25 09:36:06 +09:00
Dr.Lt.Data	86d7f73981	update README and version marker	2024-09-24 22:02:32 +09:00
mijuku233	f428182ddf	feat: MakeAnyList and AnyPipeToBasic (#753 )	2024-09-24 21:53:23 +09:00
Dr.Lt.Data	a43dae373e	remove useless code	2024-09-24 01:47:46 +09:00
Dr.Lt.Data	1087f2ee06	Restored the default model download feature during the installation step.	2024-09-22 07:30:15 +09:00
Dr.Lt.Data	841a245cd4	FIXED: Anomaly where cropped becomes identical to cropped_refined https://github.com/ltdrdata/ComfyUI-extension-tutorials/issues/28	2024-09-22 07:18:39 +09:00
Dr.Lt.Data	aecc925630	update README	2024-09-21 19:32:05 +09:00
Dr.Lt.Data	b5abf19a1b	remove automatic installation feature	2024-09-21 19:27:06 +09:00
Dr.Lt.Data	9775a98f03	improve: IterativeUpscale - support noise_mask https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/746	2024-09-21 01:33:32 +09:00
Kristian Helk	ac288bbcb9	Make a small fix in wildcard replacement (#743 ) Fixes wildcard replacement when multiple wildcards are separated by a symbol that is included in the regex (e.g. "__custom_wildcard__-__another_custom_wildcard__")	2024-09-16 12:43:38 +09:00
Dr.Lt.Data	fd69570977	Modified: SEGSPicker - it would be better if it were not an output node. https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/742	2024-09-14 15:19:10 +09:00